“Do one thing every day that scares you” – Eleanor Roosevelt

I remember the first time I consciously stepped out of my comfort zone.

I was twelve. It was a Friday night. I sat at a grand piano in an auditorium filled with parents, teachers, and a small group of students from across the region, selected to perform pieces we had worked on over a season of competitions.

The room was quiet with anticipation.

I placed my hands on the keys — and in a split second of shock, realized I did not know where to begin. This was a piece I knew well. But at that moment, my mind went blank. I started. Wrong note. Stopped. Tried again. Wrong note.

The silence shifted. I heard sighs. Murmurs. Coughs growing louder. The audience grew uncomfortable. After several attempts, I stood up and walked off the stage — stunned by the embarrassment of what had just happened.

Backstage, my teacher — an unassuming woman often underestimated for her quirks — pulled me aside.

“You have two choices,” she said calmly. “You can leave now and go home. By Monday, most people will have forgotten. Or, at the end of the program, you can go back on stage and play the piece the way we both know you can. That will be much harder. And you need to be certain you can do it.”

She paused. “Either way — you won’t forget the decision.”

I sat in the auditorium for the next two hours, wrestling with that choice — knowing how easy it would be to walk away. At the end of the evening, the director announced one final performance. He called my name. I walked back into the bright lights, closed my eyes, and began to play.

This time, the music flowed.

What I remember most is not the standing ovation. It was catching my father’s eye — a quiet acknowledgment that something had shifted. A few months later, he passed away unexpectedly.

That night was the first — and the last — time he saw me take a meaningful step outside my comfort zone. It marked the beginning of a pattern that would dramatically change the course of my life.

A Pattern of Stepping Forward

After my father’s passing, I made a decision he had always encouraged — to leave the small farming community where I had grown up and enroll in a much larger high school farther from home. It was my first real step into a world that felt significantly bigger than the one I knew.

Over time, the choices became less obvious — and more consequential.

Right after graduating in finance, I chose a path that none of my peers did. I joined a smaller Deloitte office when every other graduate pursued more conventional options. That decision led to an opportunity I never could have planned — a short-term secondment to Deloitte’s Boston office that ultimately became permanent.

Stepping into the pharmaceuticals industry at AstraZeneca presented opportunities for growth and set of a new cascade of uncomfortable decisions to make. I took the role at AZ without any life sciences background, so while the language of numbers was familiar, that of drug development, novel targets, programs and their numerous naming conventions, was all a vast unknown. Each new challenge carried the same sensation I felt backstage that night — the tightening in the chest, the pull toward safety, the awareness that I could remain where things were predictable or take a leap of faith into something I had not done before. And yet each time I dove into the unknown I also had the same relief on the back end of it, and the accumulated knowledge that I could do hard things and be impactful.

Over the next decade, I made more radical jumps in a variety of healthcare roles, including an opportunity to step away from healthcare. That latter experience really taught me how important it was to me to focus on developing therapies for patients and it quickly drew me back into industry.

But one of the biggest leaps was moving into biotech. I was not the obvious candidate. I had spent over a decade in big pharma. I did not know what a cap table was, or what those letters in “Series A, B, or C” financing really meant. I fumbled my way through early conversations about value inflection points. Ask anyone who knows me, and they’ll tell you how I live by my value inflection maps now – how the tables have turned!

I was, in many ways, starting over. And yet, that decision became the beginning of the most demanding — and ultimately most meaningful — chapter of my career. Over the next fifteen years, I worked alongside teams operating at an exceptional level — building companies, navigating successes and setbacks, and contributing, in small ways, to the development of therapies for patients.

Like all biotech journeys, there were moments that did not go as planned. A company that didn’t succeed. Decisions that were made for me, not by me. Moves that stretched my family as much as they stretched me. But each of those moments reinforced the same lesson: Growth rarely comes from staying where you are comfortable.

Why This Matters in Biotech

It is perhaps no surprise that I feel at home in biotech. This is an industry where being comfortable being uncomfortable is not optional — it is essential.

We advance programs with incomplete data.
We invest capital at risk.
We make decisions in markets we do not control.

The most important choices are often made before certainty arrives. The instinct is to wait — for more data, more clarity, more confidence.

Sometimes waiting is discipline. But often, it is hesitation because it is downright uncomfortable to make important decisions when you don’t have all the pieces in hand. That hesitation often leads to costly delays or worse, in an ever-shifting landscape where others operate faster, with fewer regulations, simply being left behind.

It is in many respects fitting that one of the key lessons I learned from my dad, perhaps not fully understanding it at the time, was to boldly make tough decisions and to do so quickly. He always spoke about the time lost on analysis/paralysis and if said decision wasn’t the right one, to fix it quickly, or to learn from it and move on.

It sounds overly simple, but over time pattern recognition sets in and allows us to take the right risks, at the right time, for the right reasons. We have all heard some variation of the phrase “luck favors the prepared mind,” and it has been attributed to esteemed luck-makers including Seneca, Louis Pasteur, Thomas Jefferson, and most recently Ina Garten

As I reflect on decisions made, what might look like luck is often the result of repeatedly being willing to step into situations where you are stretched, exposed, and forced to grow. Our industry creates those moments constantly.

We can do hard things together

I was reminded of this recently in a vastly different setting.

I had the privilege of joining Luke Timmerman and a group of biotech executives and investors to climb Mount Kilimanjaro in support of the Damon Runyon Cancer Research Foundation.

It was inspiring — and demanding.

In my line of work, many things can be negotiated. Altitude is not one of them. The air thins whether you are ready or not. Each step becomes heavier. Your body reminds you that you are operating well outside its comfort zone.

But the dynamic changes when a group commits to doing something challenging, together. We moved deliberately, led by Eric Murphy and supported by an extraordinary Tanzanian team. We relied on one another, shared perspectives and stories, and carried the harder moments together — often with humor.

No one summits Kilimanjaro comfortably or alone. And no one builds a biotech company that way either. Progress — in both — requires endurance, trust, and a willingness to keep moving forward when things get really uncomfortable. This climb exemplified what makes biotech so special and unique – doing hard things together, as a team.

What I Would Tell My Younger Self — and Those Early in Their Careers

Looking back, a few lessons stand out, some which I hope you will recognize or find helpful.

Don’t wait to feel ready.
Most of the decisions that shape your life will come before you feel fully prepared.

Choose the path that stretches you.
The less obvious path is often the one that creates the most opportunity. Ask yourself – what is the worst that can happen if I make this choice?

Not every uncomfortable decision will feel right — and that is okay.
Some of the most important clarity comes from discovering what doesn’t fit and what you never want to do again.

Take the risk anyway.
The downside is often smaller than it feels in the moment — and the upside is rarely visible in advance.  “Feel the fear and do it anyway” – Susan Jeffers

Pick the best team.
Find mentors, teammates, and communities who will help you take those steps. This is a topic close to heart and one I will come back to in a future post.

Coming Full Circle

When I think back to that twelve-year-old backstage, it is easy to see it as a small moment. It wasn’t. It was the first time I chose to step forward before I felt ready.

What I didn’t know then was that this decision would shape how I approached each one that followed. Over time, it became a pattern. And that pattern became a way of operating.

One that led me across continents, into roles I was not prepared for, through decisions that affected not just my career — but my family — and ultimately led me into biotech, an industry that demands exactly this mindset.

In biotech, as in life, the most important decisions rarely come with clarity. They come with uncertainty. And the question is not whether those moments will come.

It is whether you recognize them when they do — and step forward anyway.

The post Do One Thing Every Day That Scares You appeared first on LifeSciVC.

      

Related Stories

• Twenty Years In Early Stage Biotech VC (Part 2): People
• A Delicate Balance: Building NewCos with Jacks of All Trades and Utility Players
• A Molecular Biologist’s Advice For Life

 

As Jonathan Larson beautifully explores in “Seasons of Love” from Rent, it’s almost impossible to find the right variables to capture life experiences. Yet frequently in the diligence process, we are trying to understand whether indications like ADA-SCID or CGD have “enough” treatable patients or indications like Heme B or CF have “enough” remaining unmet need given existing therapies to garner venture investment in the early R&D process.

In this post, we will walk through how one integrates key aspects of the patient population and demographic, as well as the patient experience into assessment of an opportunity, and show how defining clinical unmet need goes hand in hand with developing your clinical development plan, regulatory strategy, and ultimately market access and reimbursement strategy. And, finally, we’ll answer the question if there is a peak sales number required for an Atlas investment (spoiler alert: no!). This piece will approach these questions through a rare disease lens, although similar principles can be applied to answering whether a prevalent but therapeutically crowded indication has enough “unmet need” to support an “n-th” therapeutic option.

I want to be clear that one can could never truly reduce a patient’s experience of their disease or the burden of a disease on a population to a number. Why then does it appear to be that we are trying to do just that? The people of the biopharma ecosystem are working to improve patients’ lives and outcomes. However, we are ultimately in an industry that requires large aliquots of capital to move life-changing science forward to patients. A recent analysis from the London School of Economics estimates the median research and development cost per approved product is $1.1B¹. The capital intensity of the biopharma industry makes financial incentives a necessary aspect to create a sustainable flow of innovation for patients².

At Atlas, we aim to “do well by doing good”, in other words, build and back companies that create medicines with positive impact, and let the financial returns follow. Therefore, understanding just how much we can do for patients becomes paramount! People are sometimes surprised to find that we are not routinely building discounted cash flow revenue models for programs that we’re evaluating. Frankly, we know it is impossible to prognosticate perfectly, especially at the earliest stages of development, and aim to avoid overconfidence and false precision. Many drugs have proven even the best Wall Street analysts (let alone early-stage investors) wrong by underperforming or overperforming their sales estimates.

A decade ago, ATTR-CM was thought to be a rare disease with perhaps 200K patients worldwide (Ionis Corporate Materials, 2015), but today estimates place the market at a size of 200K patients in the U.S. alone (and >500K globally). The emergence of novel, effective therapies can have profound impacts on clinical diagnosis practices and expand the market. On the other hand, there were strong hopes for Casgevy from VRTX/CRSP given transformative efficacy results. One analyst in 2020 predicted ~$400M sales in 2025. However, actual sales of Casgevy in 2025 ended up at a much more modest $115.8M, despite higher pricing than anticipated, potentially due to complexities of delivering the medicine and perhaps a different risk-benefit calculus from patients and physicians than originally anticipated.

Overall, we proceed with caution and do our best to understand the patient journey through care, and what opportunity exists for improving that care to help guide early investment and development decisions.

Beyond indication selection: hitting the bullseye with your target patient population

Highly efficacious, blockbuster drugs often treat only single digit percentages of patients with a given disease. Although market penetration is typically higher in rare disease, defining the phenotype of patient that will be the ideal candidate for your therapy is still key.

One way to begin to refine your target patient population is by imagining Figure 1 of your future NEJM paper declaring clinical proof of concept. I like to call this productive daydreaming! Figure 1 shares the demographic background of the patients of the trial, often divided by placebo and various treatment groups, and including such rows as time since diagnosis, number of prior lines of therapy, or baseline biomarker values or clinical scores. A non-exhaustive list of questions to consider is included below.

• What segment of the population can you treat?
  • Will the risk-benefit of your potential therapy allow it to address patients with mild, moderate, and/or severe disease?
  • Can you identify patients that may respond to your therapy by any of the following
    • Genetics
    • Biomarkers
    • Clinical Symptoms
    • Demographics
  • Are there frequent comorbidities or complications of the disease that might preclude treatment with your therapy?
  • Are there preferences around frequency and route of administration that might preclude patients from seeking out your therapy (ranging from needlephobia to requirement for an invasive procedure like ICV administration)?
• Where in the patient journey would your medicine fit in?
  • How long since patients have been diagnosed would they start treatment?
  • What is the existing standard of care?
    • How many lines of therapy exist?
    • What is first line treatment and what percentage fail and move on to second line? Third? Etc.
      • What percentage of patients benefit from the therapy?
      • How much do patients that respond benefit?
      • Are there significant side effects or toxicities that are burden to patients? What percentage of patients experience those?
      • Do patients have significant issues accessing the current standard of care?
    • What line of therapy would your work come into market as?
    • Are there competitors in clinical trial that will hit the market in the coming few years? How will that change the standard of care?
  • Are there patients who have progressed to a point where your mechanism may not be efficacious?
• Can you actually reach your target patient population?
  • What portions of patients are even connected to the medical system for their disease or have frequent touch points with a medical provider?
  • Where are these addressable patients geographically located? Estimates of patient numbers often break-out US vs. ex-US patient numbers due to current drug pricing dynamics.
    • What is the breakdown of insurance coverage in your target patient population?
  • How large of a salesforce might be needed to reach the population?
    • Is patient care concentrated in academic or tertiary care centers or, on the other hand, would primary care providers be the main prescriber base?

There are many potential ways to visualize this target population with one commercial-grade example below from recently acquired Atlas portfolio company Day One Biopharma. This figure shows, on the left, the specific pLGG patients addressable first-line, and, on the right, utilizes progression rates to help define the prevalent 2L+ pLGG patients that Ojemda is currently approved for. In this way, they are capturing both the current addressable market and potential expansion market in one slide.

Figure 1. Visualizing your target patient population (Source: Day One Biopharma, March 2026)

Quantifying the Burden of Disease

Once you have identified your target patient population, the next step is to better understand the burden of disease. There are established quantitative frameworks, particularly Quality Adjusted Life Years (QALYs), Disability Adjusted Life Years (DALYs), and equal value Life Year (evLY), that Health Technology Assessment (HTA) agencies such as NICE and ICER often use as a standardized assessment of the cost effectiveness of a potential therapy. Although useful for cross therapy and indication comparisons, like any metric that aims to be broadly applicable, QALYs and associated measures are by nature reductive and do not fully capture what quality means for a given patient population, the waxing and waning nature of some illnesses, and spillover burden outside the patient.

Even for patients that share disease pathophysiology at the cellular level—an altered nucleotide or amino acid, perhaps—the physical, mental, and social effects of their disease is unique. The following questions are a starting point to better understand a patient’s healthcare experiences and defining the healthcare costs associated with living with a particular illness:

• How often are patients interacting with the healthcare system
  • How many doctor’s visits do patients have per year?
    • Which specialists do they see and how frequently do they see each?
  • How often are patients getting laboratory tests or imaging to monitor their disease
  • How many urgent care or emergency department visits occur per year
  • How often are patients hospitalized?
  • How long is a typical hospitalization? Is a stay at a rehabilitation center required after?
  • What are the most common medical complications of a disease?
    • A similar, but separate question: What are patients’ most feared complication of the disease?
  • What is a patient’s life like outside of doctor’s visits and hospitalizations?
    • How frequently are patients experiencing symptoms of their disease?
    • What are the most frequent symptoms?
    • What symptom is most burdensome?
    • What is the impact on a patient’s ability to perform ADLs/IADLs?
    • What other aspects of life are limited by disease (work, hobbies, socializing, eating, going to the bathroom, etc.)?
    • Are there caregiver needs associated with this illness?
      • How much do those needs typically cost?
      • Is there lost productivity for caregivers, including time-off work required?
      • Are there mental health impacts on caregivers?
    • How many medications are they taking on average?
      • What frequency and route of administration are these medications?
      • Do treatments come with monitoring burdens, including laboratory testing?
    • What can you learn from previous work done in the indication?
      • How well is the standard of care addressing patient’s needs?
      • Have there been regulatory submissions or HTA work in your chosen indication?
        • What can you learn from those, including variables used to quantify burden of disease or areas that matter most to regulators and HTA organizations?

One particularly concrete and valuable example of putting in the work to understand your target indication comes from Atlas portfolio company Diagonal Therapeutics, which is advancing an ALK1 activating antibody for Hereditary Hemorrhagic Telangiectasia (HHT), a genetic bleeding disorder with no approved therapies and limited clinical development in the space to date. Diagonal collaborated with one of the leading key opinion leaders and patient advocacy organizations in the field to utilize the Komodo Healthcare Map claims database to quantify the mean patient per year healthcare costs for those living with HHT. This work was published in the American Journal of Hematology and SVP, Finance and Corporate Development, Eric Duhaime, was the senior author on the analysis. They found that for patients that have anemia and receive hematological support, such as iron infusions and/or red blood cell transfusions, annual healthcare costs were $40,000, greater than the average healthcare costs incurred by patients with sickle cell disease or muscular dystrophy and just under the $41,680 of healthcare costs incurred by patients with cystic fibrosis. Since there was limited biopharma interaction with the HHT population in the past, grounding the amount of healthcare costs incurred to diseases that the industry is more familiar with, like SCD and CF, puts a spotlight on the severity of the unmet need in the space.

Figure 2. Graphical Abstract from Al-Samkari et al 2025 Am J Hematology

The importance of engaging patients, patient advocacy groups and physicians in this work early cannot be understated. This has been written about powerfully by many in the ecosystem including Ivana Magovcevic-Liebisch (former CEO of Vigil Neuroscience), Josh Brumm (former CEO of Dyne Therapeutics), and JC Gutierrez-Ramos (former CEO of Synlogic Therapeutics). Collaboration can and should go beyond KOL conversations or a Patient Impact Day, and longitudinal engagements with both physicians (on a Clinical Advisory Board or as investigators), and with patients perhaps through a natural history study (whether prospective, retrospective, or registry-based) are incredibly valuable. Natural history data is critical for understanding disease trajectory and for identifying the clinical milestones that matter most. A key part of development in a new indication may be collaborating with patients to define the very endpoints that may help the FDA ultimately decide if a drug helps patients “feel, function, or survive”^3,4. Many rare diseases do not have a standardized scoring system à la EASI (atopic dermatitis) or SLEDAI (lupus), and establishing and validating these disease specific scoring systems can often happen almost in parallel with running the clinical program.

Thinking even further down the road, there is often a tremendous amount of market shaping work required when launching into a previously untreated indication, such as physician and patient education, introduction of novel diagnostics, or shifting established specialist referral patterns. This time-intensive and costly work is often put off by biotech teams as something that experienced large pharma commercial teams can take care of post-acquisition. However, upfront investment of time and money into building the case for unmet need may be a worthy investment, especially when you are working in an area where the commercial opportunity is not obvious.

How does unmet need factor into your thinking as a investor?

As a physician (particularly pediatrician) with a penchant for working to solve rare disease, I frequently come across entrepreneurs that ask what is the peak sales number that you need to see to consider investment? Or what’s the lowest prevalence disease that you’d consider in indication selection? Although you may hear about peak revenue cut-offs that pharma sets for potential acquisition targets, at least at Atlas, there is no hard and fast number that we use to consider an opportunity commercially viable. These estimates and numbers do not exist in a vacuum and instead one must consider the context around a potential program to better understand the investment opportunity.

Although stating the obvious, the metric that looms large in decision making for a venture capitalist is potential returns, and returns are made up of not only potential exit value, but also capital required to realize that exit value and probability of success. Smaller potential market sizes may still be viable if the capital required to get a drug approved and to patients or to a key value inflection point is relatively small and well-understood biology or precedented mechanisms increase the probability of success.

At the end of the day, generating numbers to describe patient’s unmet need is an important aspect of the process of developing a therapy. If you’ll humor some theater analogies once again, it’s shining a spotlight on patient’s experiences. However, it’s also important to not anchor on any given number, but to do our best to take in the whole stage, and consider the broader context before setting off on the sometimes dramatic and twisting journey that is drug discovery.

Acknowledgetments: Many thanks to Eric Duhaime, Alex Harding, Niranajan Kameswaran, and Essra Ridha for their time and feedback on this piece and many other projects!

Footnotes

1. Bruce last wrote about this topic over a decade ago in 2014 where the Tufts Center for the Study of Drug Development named a price tag of $2.6B invested to drive one new approved drug. Although this seems unfathomably large, it held up to even a certain ex-McKinsey consultant’s scrutiny.
2. I would be remiss in not acknowledging that the financial reality of the biopharma industry leads to patients with ultra-rare disease being underserved. When the number of patients with a given disease is so small, even regulatory incentives do not meaningfully shift the financial calculus and there can be limited allocation of resources to advancing novel therapies for those indications. There is great work ongoing to ensure that patients with ultra-rare disease can have access to life changing innovations (including by an organization I work with the N-of-1 Collaborative) and recent developments have reignited my hope that regulatory innovation by groups, including the US FDA and UK MHRA, will soon allow for sustainable funding of therapeutic development for patient populations down to a size of n=1.
3. For instance, Medivation and academics collaborated on a study to understand which phenotypic traits caregivers of patients with Fragile X disease care most about. They found that controlling behavior and caring for oneself were ranked the highest and published their findings in the Journal of Developmental Behavioral Pediatrics. You may spot Atlas EIR, Judy Dunn, among the authors!
4. The FDA actually has a series of guidance documents on Patient-Focused Drug Development and an ongoing effort in the Rare Disease Endpoint Advancement Pilot Program which we recommend for a deeper dive into these topics.

The post How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need appeared first on LifeSciVC.

      

Related Stories

• Huntington’s Disease and The Triad of Therapeutic Conviction
• Twenty Years In Early Stage Biotech VC (Part 1)
• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective

 

Few neurodegenerative diseases have clearer genetic causation than Huntington’s disease (HD). And yet for decades, translating that genetic clarity into desperately needed effective therapies has proven frustratingly difficult.

This critical gap sets the backdrop for the recent public controversy surrounding uniQure and its investigational gene therapy, AMT-130. AMT-130 is an AAV5-delivered miRNA gene therapy for Huntington’s disease, administered via direct stereotactic injection into a patient’s striatum. It works by lowering huntingtin protein levels in the brain.

According to uniQure, the company reached agreement with FDA on a pivotal study design that would compare treated patients to subjects from a natural history dataset. By this measure, their trial achieved some successes, most notably an apparent slowing of disease progression. However, uncertainty about the extent of clinical benefit remains for reasons such as the small sample size (12 patients on the high dose reached the 36 month endpoint), heterogeneity across clinical and biomarker endpoints and the potential for persistent differences between the treated patients and natural history controls. The agency’s recommendation, for now, seems unambiguous: conduct a prospective, randomized, sham-controlled trial.

This controversy highlights the vulnerability of drug developers to shifts in regulatory policies during the long timelines of clinical trials. It also raises questions about what it means to conduct rigorous, ethical, and feasible clinical trials in people with HD and other rare, severe diseases.

Lost in the regulatory headlines, however, may be a more important story. A broader trend of accelerating progress in drug discovery and development for HD is unfolding, and it holds promise well beyond the fate of any single therapy.

Indeed, HD may be approaching a drug discovery inflection point. The field today sits at the convergence of three forces rarely aligned in neuroscience: human genetic validation of therapeutic targets, mechanistic insight from cutting-edge single-cell biology, and a rapidly expanding set of therapeutic modalities capable of reaching those targets.

Together these form what might be called a triad of therapeutic conviction:

• Human genetics nominates the targets
• Single-cell biology explains why those targets matter at the cellular level
• Advances in modalities enable us to actually drug them

Each pillar of this triad has been strengthened for HD over the past five years, and, critically, they reinforce one another.

Pillar 1: Human Genetic Validation of Targets

When HTT was identified as the causal gene for HD in 1993, it marked one of the earliest examples of a neurodegenerative disorder traced to a single gene mutation. Expansion of a CAG trinucleotide repeat in HTT beyond approximately 36 repeats causes HD with near-complete penetrance.

This level of genetic clarity contrasts sharply with diseases like Alzheimer’s, where decades of debate have surrounded the amyloid hypothesis, or Parkinson’s disease, where genetic subtypes such as LRRK2 or GBA account for only a fraction of cases.

But the genetic story in HD has progressed far beyond the causal mutation itself. Over the past decade, the Genetic Modifiers of HD (GeM-HD) Consortium has conducted large genome-wide association studies that have reshaped our understanding of disease progression and, in doing so, nominated a compelling set of therapeutic targets.

These studies identified more than twenty independent modifier signals that alter HD age of onset independently of inherited CAG repeat length. Strikingly, the strongest signals converge on the DNA mismatch repair pathway, which governs somatic expansion of CAG repeats within neurons.

Several of the key modifier loci harbor DNA maintenance genes, including FAN1, MLH1, MSH3, PMS1, PMS2, and LIG1. Among these, three have emerged as particularly compelling therapeutic targets.

MSH3 encodes a mismatch repair protein that, counterintuitively, promotes somatic CAG repeat expansion. Genetic variants associated with reduced MSH3 activity delay disease onset and slow motor decline. Human population genetics suggests that partial reduction of MSH3 activity is tolerated, supporting the possibility of a therapeutic window for MSH3 lowering.

FAN1, located at the most statistically significant modifier locus, encodes a nuclease that protects against repeat expansion, directly antagonizing the pro-expansion activity of the MSH3–MutL complex.

PMS1, another component of the MutL complex, collaborates with MSH3 to drive repeat expansion.

These represent targets nominated not by animal models or pathway speculation, but by the genomes of thousands of HD patients, and they reveal the molecular levers that accelerate or slow disease progression in humans.

Pillar 2: Mechanistic Insight from Single-Cell Biology

If human genetics tells us what drives HD, recent single-cell biology has begun to reveal how.

The concept that somatic CAG repeat expansion might drive HD pathology has deep roots. In 2000, Kennedy and Shelbourne demonstrated in knock-in mouse models that a subset of striatal cells acquired somatic expansions up to three times larger than the inherited allele, and that this instability was age-dependent and highly tissue-specific. The critical link to disease severity came in 2009, when Swami et al. showed that individuals with a greater burden of large somatic expansions in cortical DNA experienced earlier disease onset than predicted by their inherited repeat length alone. These observations, combined with the GeM-HD Consortium’s identification of DNA mismatch repair genes as the strongest modifiers of HD progression, built a compelling case that somatic expansion is not merely a byproduct of disease but a central driver of it.

What had been missing, however, was direct visualization of this process at the single-cell level in human tissue—a gap that recent advances have now begun to fill.

A recent landmark study from Steve McCarroll’s lab at the Broad Institute (Cell, January 2025) developed methods to simultaneously measure CAG repeat length and genome-wide RNA expression in individual cells from postmortem human brain tissue.

In striatal projection neurons (SPNs)—the cell population most vulnerable in HD—inherited CAG repeats of roughly 40–45 were observed to expand somatically to 100–500 or more repeats over the course of a lifetime. By contrast, expansion occurred far less frequently in striatal interneurons or glial cells.

This cell-type specificity offers a compelling explanation for the long-standing mystery of selective neuronal vulnerability in HD.

Perhaps the most important observation was the emergence of a functional threshold. SPNs with CAG repeat lengths below roughly 150 appeared largely normal. But once repeat lengths exceeded this level, neurons rapidly lost their molecular identity. They first shed positive neuronal markers, then activated stress and apoptosis pathways, and ultimately disappeared from the tissue.

These results connect human genetics directly to cellular pathology. Modifier genes in DNA repair regulate the expansion process occurring in the very neurons that die in HD.

For drug development, the implications are profound. They suggest that somatic expansion is a central driver of neuronal vulnerability, explain why reducing MSH3 or PMS1 activity may be protective, and imply a therapeutic window: intervene before neurons cross the critical repeat threshold and disease progression may be slowed.

Pillar 3: Expanding Modalities for Target Pharmacology

With genetically validated targets and mechanistic clarity about disease biology, the focus turns to translation: can we drug these targets?

Here the HD field has benefited from a rapid diversification of therapeutic modalities. And each modality can be deployed against multiple targets, creating a rich matrix of complementary approaches.

Gene Therapy: One-Time Intervention

Gene therapy offers the potential for a single intervention capable of durably altering disease biology. The most advanced program targeting HTT is AMT-130 from uniQure discussed above. Regardless of the ultimate regulatory fate of AMT-130, the program provides an important proof-of-concept that has attracted additional entrants.

Gene therapy is also being applied to MSH3, where lowering the gene may halt the somatic expansion process itself. Latus Bio is developing an AAV-delivered microRNA targeting MSH3 using a proprietary capsid designed for efficient CNS delivery.

Nucleic Acid Therapeutics: ASOs and siRNA

Nucleic acid therapeutics represent one of the most pervasive modalities  in HD. Multiple approaches are in clinical testing to directly silence the HTT transcript. Both antisense oligonucleotides (e.g. Wave, Roche/Ionis) and siRNAs (e.g. Alnylam, Atalanta) are being developed for this purpose. Because HTT may have an important biological role, some silencing approaches have been designed to selectively target the mutant allele. Alternatively, approaches to target CAG repeats directly (e.g. Vico) could selectively silence mutant HTT and potentially address other diseases caused by repeat expansions (such as spinal cerebellar ataxias).

Beyond HTT suppression, nucleic acid approaches are also targeting modifier genes. For example, Atalanta Therapeutics is developing divalent siRNAs designed to reduce MSH3, a strategy that blocks the somatic expansion process upstream of neuronal degeneration.

An alternative strategy is being pursued by Harness Therapeutics, which aims to increase expression of FAN1, enhancing a protective nuclease that counteracts repeat expansion.

Small Molecules: Oral and Scalable

Small molecules offer the possibility of oral, brain-penetrant therapies, avoiding intrathecal injections or neurosurgery.

In the HTT-lowering category, PTC518 (votoplam), a splicing modulator licensed by Novartis in a deal potentially worth over $1B including milestones, has advanced rapidly into late-stage development. Skyhawk Therapeutics’ SKY-0515 represents another HTT splicing-modulator approach and is currently being evaluated in a global Phase 2 study.

Small-molecule approaches are also emerging against modifiers of somatic expansion. Rgenta Therapeutics has presented preclinical work targeting PMS1, while LoQus23 Therapeutics is developing LQT-23, an allosteric inhibitor of the MSH3/MutSβ complex designed to directly block the expansion machinery.

A Portfolio of Approaches

Gene therapy, nucleic acids, and small molecules are each being deployed against HTT, MSH3, PMS1, and FAN1. Indeed, therapies combining more than one of these pathways (e.g. HTT and MSH3) are already in development. This diversification represents a meaningful shift from where the field stood even a few years ago.

The Convergence: Why Now?

What makes the current moment distinctive is not any single advance in isolation. It is the convergence of advances across genetics, mechanistic biology, and therapeutic technology. This alignment is rare in neuroscience.

Similar dynamics are beginning to appear in other neurodegenerative diseases. For example, in ALS and Frontotemporal Dementia, UNC13A is a target validated by human genetics to exacerbate disease progression. Two landmark studies in Nature implicated improper splicing of UNC13A mRNA as a pathologic mechanism, and splice-modulating antisense oligonucleotides are now in development. Identifying additional targets with this profile remains a critical goal for translational neuroscience. (Disclosure: I am the CEO of Trace Neuroscience, a biotech company developing medicines to restore UNC13A in people with neurodegenerative diseases).

From Setback to Setup

The regulatory debate surrounding uniQure’s AMT-130 will undoubtedly shape how treatments for HD navigate approval pathways going forward. But it would be a mistake to let that debate obscure the broader trajectory.

HD has never had this many scientifically grounded therapeutic programs advancing simultaneously across targets and modalities. At this pace, the next five years will produce an extraordinary wave of data from clinical trials targeting HTT, somatic expansion pathways, and protective modifiers.

For the HD community—patients, researchers, and the biotech ecosystem alike—the future will not be defined by any single regulatory decision. It will be defined by whether this scientific convergence finally translates into the first disease-modifying therapies for HD.

The post Huntington’s Disease and The Triad of Therapeutic Conviction appeared first on LifeSciVC.

      

Related Stories

• How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need
• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective
• Dual Wielding in I&I: A Pivotal Year Ahead

 

At the start of 2026, we had a flurry of biotech IPOs in late January and early February.  But as we finish the first quarter of 2026, only one more biotech has so far announced its plans to go public (“flipped its S1”).  The biotech ecosystem feels a lot like the ships stacked up at the Strait of Hormuz: the macro backdrop with conflict in the Middle East, oil crisis, and panic in the bond markets have conspired to slow things down.

But even in great IPO years there are weekly waves of activity – peaks and troughs – due to the nature of the IPO process itself.  Take a look at this graph of weekly biotech IPO numbers from 2014 to 2020, including over 400 offerings, that Claude helped me generate:

As is clear, there are some periods where no real activity happens:

• End of Feb thru mid-March: Prior year Q3 financials go stale and new annual audits are required for the prior full year before you can be ready to go public.
• July 4^th and end of summer/Labor Day: buyside investors are busy doing other things, like playing golf in the Hamptons, Cape Cod, and the islands.
• Thanksgiving week and the end of the year: holiday partying typically takes precedence, for teams and investors.

Interestingly, there are some real temporal spikes in activity as well:

• Late Jan and early Feb: the first annual crop of IPOs that comes after the JPM Healthcare Conference.
• Late June: the most active weeks of the year historically! As an aside, I once had two IPOs price on the same night in 2018… a crazy day.
• Steady Sept-Nov: relatively active period before the year end

One of the biggest reasons for the dynamics of this cadence is the IPO process itself – which I’ll attempt to demystify here.

Before committing to entering the public markets, most private companies have ongoing discussions with investment bankers for years about the IPO opportunity (evaluating it against other funding routes, including Reverse Mergers, SPACs, additional crossover rounds, etc). These dialogues occur at a steady clip for most clinical-stage companies, and often earlier.

During this period, companies select what bankers to partner with for their IPO.  The most lucrative underwriting role is to be the Lead Left Bookrunner – the primary underwriter that leads the overall IPO process.  Additional banks can be Co-lead Bookrunners, as well as Manager/Co-managers, with different roles related to assisting the book-building, distribution, and future research.  Lead left typically gets 40%+ of the fees, 30% for the Co-lead, and 10-20% for the co-managers.

There are lots of great banks to use for all these roles, and Atlas has worked with all of them at some point. My typical advice to management teams, within some guardrails for the different underwriting roles, is they should pick bankers that they would like to be in a foxhole with for their IPOs – deep, trust-based, and authentic relationships are critical.

But the “clock” for a biotech IPO doesn’t really start until the Organizational Meeting or “Org Meeting” – when the syndicate of underwriting bankers meets with the management team for multiple hours to get the formal elements of the process started. The company shares a detailed overview of the story to get all the bankers on the same page; the bankers present a detailed calendar of all the things that have to get done; and the lawyers go through the legal elements of a new listing…

This is the type of IPO calendar that gets shared at an Org Meeting:

The most important aspect of this process: it takes 16-20 weeks from Org Mtg to pricing an IPO.  It’s hard to go faster than this, given the needed time for S1 drafting, confidential filing and SEC review periods.  Once an S1 is public, there’s a ~15 day waiting period before you can price an offering.

So that 4-5 month process becomes the gatekeeper of the industry’s IPO cadence.  Now look back at the weekly spikes in the first figure.  For a late January IPO, those companies did their Org Mtgs back right around Labor Day, a natural time for planning for the following year.  The June burst of IPO activity is for Org Mtgs that happened in the few weeks after the industry kicks off at JPM, another natural time for teams to reflect on their capital formation plans for the year.

This temporal delay from committing down the path to actually pricing an IPO is why the market doesn’t just flip on when the markets get frothier.  In the last half of 2025, when the biotech markets ($XBI) started to rip, we still didn’t see many IPOs in large part because of this temporal dynamic.

Another important reflection on the IPO process is that the timeline can be much longer.  The JOBS Act allows for confidential filings to occur, which enables companies to get ready but not declare their intentions publicly.  I’ve blogged on its impact in the past, including how fundamentally it changed the IPO process.  With this confidential filing approach, when companies, and the markets, are ready to go, they can flip their S1 public and price in 3-4 weeks. However, to sit in this confidential limbo, companies need to maintain their financials through the different time windows– which is why some of the weekly dynamics still occur (especially the draught of IPOs in late Feb and early March every year as companies get their annual financials refreshed by the auditors).

Here at the end of March 2026, with the chaos emanating from the Iran conflict and the frenetic ripple effects in the stock market, many biotechs considering an IPO are sitting on their hands right now.  But I expect things to get busy again with the likely (and hoped for) de-escalation in the Middle East this spring…  I fully expect the crop of biotech IPO to hit its stride in the 2Q, especially June, once again this year.

While I’m on the IPO topic, I’ll close out with some other truisms:

• Insider investors need to at least conceptually “cover the book” – which means the full amount of the proceeds the IPO plans to raise are covered by the allocation demand of the existing investors. This is largely just perception and allocations never happen this way, but driving scarcity around allocations creates more demand in a virtuous sentiment loop. Coming into your roadshow with a covered book is now fairly standard (and expected) messaging for an IPO.
• Over-subscribed rounds perform better in the after-market. A 5x over-subscribed IPO book is generally weaker than a 10x+ book.  But quality allocation coverage is the key part, not the “junk” in the book.  Quality is typically the big long-only and specialist fund orders in the book who are likely to be long-term buyers of the stock.
• Mis-pricing goes in both directions: a big drop on the first day is really bad, as the stock “broke issue” right away. This creates a negative headwind on sentiment for the name that takes time to address.  The opposite is less bad, but still not great and reflects mis-pricing, too: a huge “pop” in the stock on opening day means the existing shareholders took far more dilution than they needed to. The latter is a champagne problem, perhaps, but still not great for existing owners.
• High IPO pre-money valuations generally perform worse in the after-market. While these deals are frequently celebrated, they set big expectations.  With that often comes high cash burns and large future capital needs, which weigh on stocks over time.  And, of course, the need to advance a pipeline that grows into the high valuation…
• Bankers and buyside investors often talk about price relative to “step-ups” over the last private round, rather than building up the valuation from intrinsic value. These days it’s not atypical to be in the 1.2-1.6x range.  If the Series C crossover round, by example, was done at a $500M post-money valuation, then a ~1.4x step-up means the IPO should be at $700M pre-money – without at regard for the degree of value creation since that round was closed. The logic of step-ups (rather than intrinsic value) is a real albatross heading into an IPO pricing discussion, in my opinion.
• Lastly, bankers get 7% of the offering size as fees regardless of the after-market performance. Wish there was a way to incentivize (carrot and stick) pricing dynamics with banks, but there’s currently not.  That said, good bankers and their ECM colleagues build their long-term reputations on successful pricing dynamics and partnering with companies over serial rounds of financings (IPOs and FOPOs like PIPES, RDs, etc).  The best IPOs are ones that pop 20-30% on their debut, reflecting a good sense for the market’s receptivity to the story and the illiquidity premium being removed as one goes from private to public.

Tons more perspectives on IPOs, but I’ll leave the rest for another day.

Bets on the 2026 number of biotech IPOs?  If the Iran conflict de-escalates, I think we’ll see 25-35 by year end.

The post Process-Driven Cadence: Biotech IPOs Demystified appeared first on LifeSciVC.

      

Related Stories

• Atlas Venture 2025 Year In Review
• Biotech Venture Creation: The Benefits Of Scarcity
• Atlas Venture 2024 Year In Review

 

Of the recurring themes that tend to circulate in the drug development discourse,  China is a uniquely fascinating and polarizing subject. Perspectives vary tremendously, ranging from those who view China as an expansive opportunity of which we are just beginning to scratch the surface, to those who see China as the single largest single threat to the health and viability of the US and European biotechnology industries.

In this blog, I’d like to offer a perspective from the lens of a CEO of US small biotech company, in particular regarding early-stage clinical development in China.  This builds on a number of themes covered in Arthur Tzianabos’ recent blog, Ex-US Clinical Trials: Tribulations, Preparations, and Expectations.

The dramatic shifts in biomedical innovation in China that have occurred over the past few years are no secret: China significantly outpaced the US by 17% in 2024 in Nature’s Index of leading scientific publications, while 49% of global BD&L deals in 2025 were transacted on Chinese-originated assets. Moreover, numerous recent notable transactions (Roche/Qyuns, GSK/Rapt, AZ/CSPC) included assets from China as key centerpieces, highlighting the quality and innovative nature of molecules emerging from the country.

On the one hand, the perception of China as a threat to the conventional biotech model is easy to understand – in terms of developing innovative therapies, they are – relatively speaking – a newer player in town. With these recent successes in hand, there’s no reason to doubt that many more differentiated, novel, and even first-in-class therapies may emerge from the territory and play the role of direct competitor to US-or European-originated drugs. Sitting in the large pharma or venture capital ‘buyer’ seat, molecules developed in China often have substantial human “proof of signal” datasets available well in advance of ex-China competitors.

But I’d raise that even as a small biotech, the framing of China solely as a threat is incredibly limiting – looked at from a different angle, China offers tremendous opportunity across myriad  stages of the drug development process. While US/EU companies have long relied on China for chemistry, biology support, etc., opportunities across a broader stretch of the drug development cycle remain untapped, including early clinical trial enrollment and execution in China.  Rather than undermining the global biotech industry, Peter Kolchinsky, a Managing Partner at RA Capital Management, offered the perspective that China could be a source for innovation and achieving human proof of concept, while other stages of drug development, such as the conducting of global registrational trials and local manufacturing, ultimately ensure the protection of global industry and drug access.

As the model for working with China expands beyond contract research and CDMO relationships, it important to remember that conducting synthetic chemistry in China is dramatically different to executing a clinical trial within China and within a Chinese patient population. As is the case whenever any experimental drug is dosed to anyone, ethical and risk/reward balance is paramount, and a China-focused clinical development strategy should similarly keep this top of mind.

There is an opportunity to develop a long-term, sustainable plan to bring new agents to the Chinese population, whether directly by the sponsor or via a relationship with an established Chinese partner.  Big picture, at the core of our industry is a desire to improve the lives of people on a global scale by bringing forward drugs to the patients who need them. A strategy that opens the door to development within China works not only to benefit the drug developers, but the very same global patient population we are collectively working to benefit.

At a surface level, from a clinical development standpoint, China offers very clear and straightforward opportunities, including speed of patient enrollment and cost efficiencies, but a series of challenges lie below the surface:

For those in a small biotech without a direct link to China, the hurdles to operating within the geography are that much more significant. With the resource constraints inherent to small biotech, a necessary priority is quality clinical trial management absent large on-the-ground infrastructure. While small biotech companies are used to contracting with global CROs to conduct clinical studies in countries where they do not have a physical presence, this type of arm’s length model is unlikely to work well in China.  Despite the large accessible patient population, the competition from local Chinese biotech companies is intense, leaving small foreign biotech companies are at a significant disadvantage.  Not only do western companies face heightened scrutiny, the potential PR ramifications of adverse events associated with a clinical study conducted by a foreign sponsor mean that not all Tier 1 hospitals in China are readily taking on these risks.

There’s no doubt that the environment in China is evolving rapidly and remains highly dynamic.  The regulatory environment, for example, for bringing innovative therapies to patients in China has become dramatically easier and faster recently as the Chinese government has sought to stimulate biomedical innovation through smart regulatory reform.  Of note, the IIT (Investigator-Initiated Trial) framework has been a significant accelerant for cell and gene therapies, as well as the new 30 working day regulatory review for innovative drugs that meet certain criteria.

Against this backdrop of regulatory innovation, the FDA is experiencing unprecedented turbulence amidst a high degree of personnel turnover, including Vinay Prasad’s (second) departure from FDA, as well as overall regulatory uncertainty (take Moderna’s efforts to gain approval for their flu vaccine as just one recent example). Additionally, the new harmonized European approval process (CTIS) and UK Health Authority timelines have deteriorated over recent years.

With these moving pieces in mind, it seems to me the question has turned on its head… Can high growth biotech afford not to be enrolling patients in China… And if not, how should we as small, non-Chinese companies be thinking about executing in China? Two existing models come to mind:

• Establishing a local presence – although still relatively rare in small biotech, we have started to see a handful of companies establish wholly-owned subsidiaries in China (Candid Therapeutics, as one example). These companies are building local teams with the requisite CRO project management, medical monitoring, and physician engagement to succeed.
• Co-development – a number of companies have executed deals with local Chinese biopharma companies granting them access to Chinese clinical development infrastructure in exchange for cash and co-development & commercialization rights within China.

While the first local presence approach offers some clear advantages in terms of close oversight and controlled management, there are fundamental challenges to this path as well. For one, there’s a real time and monetary cost to building out a team and infrastructure in a wholly new territory, particularly one so far removed physically from a company’s headquarters. Moreover, finding strong, local talent remains a huge barrier, with the vast majority of the top-tier candidates employed at established Chinese biopharmas or CROs.

Investors such as RA Capital and Hillhouse Investment Management are tackling this challenge head-on on behalf of their portfolio companies. RA Capital, in partnership with Qiming Venture Partners, has formed a joint venture called Swiftbridge, which is providing development infrastructure within China for its portfolio companies. Hillhouse has constructed a similar entity for a number of its companies, alongside a strategy to consolidate clinical development infrastructure in China through its roll-up acquisition of Emerald, Wuxi Clinical and Wuxi MedKey.

The second co-development approach solves a number of the access and know-how issues, but stakeholders have mixed perspectives on the value/cost associated with Chinese product rights. At present, particularly in the current political climate of escalating rhetoric emerging from Washington, these rights may be of limited current value, but the reality of the size of the Chinese patient pools, means that there is a fundamental risk to ceding such rights and potential future profits so early in the development cycle.  In small biotech companies, we are used to weighing significant short vs long-term tradeoffs like this: is three-fold faster enrollment, and critical human data a year sooner, worth it?  In many cases it will be, but it is important to recognize that finding the right Chinese co-development partner is a long, complex and highly time-consuming endeavor (12+ months for many deals).

Stepping back, neither approach offers a simple model for a small non-Chinese biotech company to bring new drug candidates to China, nor do we yet have the benefit of a well-trodden path that can be followed.  There is however a massive unmet need of innovative companies looking to do exactly this and I am certain that we are going to see multiple ‘first movers’ over the next 12 months.

If you agree with me, I’d like to hear your ideas and work with you on building solutions that can benefit the entire ecosystem so patients across all geographies, including China, can benefit from the innovation that we are all spearheading.

The post Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective appeared first on LifeSciVC.

      

Related Stories

• Ex-US Clinical Trials: Tribulations, Preparations, and Expectations
• Why Owning the Learning Loop Matters More Than Owning the Lab
• How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need

 

As biotech companies tackle the escalating time and cost of clinical trials, the decision to conduct early-stage, proof-of-concept studies outside the U.S. is becoming more common. While this approach is often faster and sometimes cheaper, it should not be the sole driver. Instead, there should be a thoughtful, deliberate set of strategic discussions within the company and its board that considers many factors and requires a careful benefit/risk assessment.

Here, I want to highlight some of the considerations and challenges (expected and unexpected) for those of you who are contemplating this endeavor.  As Bruce Booth noted in his 20 year reflections last fall, “the single most important value creation activity in biotech is excellent clinical trial execution ”─so getting this right is critical.

Conducting clinical trials abroad will impact every function in the organization. Some things can be anticipated, such as managing different time zones, language barriers, diverse patient populations, materials and drug supply, and supply chains. However, it is the unexpected issues that can have the greatest impact on the success of the trial and in some cases the entire company. This harkens me back to the old mantra, “Don’t let the trial fail the drug,” a truth that may be even more fitting when conducting clinical trials in other regions of the world.

Here, I have collected insights from my own experiences and from a few of my biotech colleagues to provide additional perspectives. Deciding which examples to include was not easy. Capturing the ones that can be anticipated but may be underestimated, as well as the unexpected and costly ones, should serve as good reminders and a heads-up for all of us.

Speed and Cost of Trials

The cost of ex-US clinical trials is generally considered to be much less, somewhere on the order of 30-50% cheaper (though not 70-80% as some might think). I have yet to meet a company that conducted trials outside the U.S. and correctly budgeted for travel and other ancillary costs. The frequency with which employees will need to visit clinical sites, key vendors or manufacturing plants can be surprising. Unforeseen situations will arise and the unanticipated, last-minute flights and extended stays are expensive. Additionally, a company may need to provide standard of care (SOC) medicines that are often not available in other countries, and/or offer post-treatment therapies, both of which drive up clinical trial costs. Establishing an in-country presence, whether it is someone ‘parked’ there or hired as your own company representative, may seem like a luxury. However, it is a highly recommended strategy that ultimately can save time and money. When running trials in the EU, companies also need to sponsor a qualified person to ensure the quality of materials and facilities meet GMP standards. Local regulators will examine materials and want either compendial materials or locally sourced ones. A company will need to know and prepare for this ahead of submitting the required investigational product paperwork. Since most clinical plans evolve later on, it is critical to cast a wide net in the process.

Tax incentives for conducting clinical trials in certain territories can be very attractive.  Understanding the details of these is important because they can impact a company’s cash runway and change fundraising plans. Several companies have recounted experiencing ‘tax breaks by two offices.’ This relates to a particular region of the world where one business office distributed the money from tax incentives while another ensured all requirements were met. It became clear that the offices operated independently and didn’t always communicate the same information. As a result, the money received was far less than anticipated, or a significant portion of the money needed to be returned.

These are just a few of the cost considerations when deciding to conduct clinical trials outside the U.S., however, there are other important factors that might influence this decision.

Patient Populations

Studying a more geographically diverse patient population in a clinical trial offers broader safety and efficacy evaluation as well as an opportunity to establish relationships with a global network of regulators and key opinion leaders. This also helps pave the way for broader commercialization beyond the U.S.  Accessing patients might be easier, faster, and less expensive in some places. Accounting for ethnic, genetic, cultural, lifestyle differences, pre-existing conditions and environmental exposures is also required. The “all medicine is local” aspect to this can become apparent during screening and enrollment. In one particular example, it wasn’t until a company had designed the protocol, selected a clinical research organization (CRO), and identified clinical trial sites, that it learned about a genetic mutation native to the area at reasonable background levels that it could predispose healthy participants to screen-fail at a much higher rate. Fortunately, the trial hadn’t started, but it required a protocol amendment, ensuring that a local lab could perform a non-standard test, and increasing the number of patients to be screened.

Holding frequent and detailed meetings with CRO leadership and the teams assigned to your study, clinical investigators and trial site coordinators before a protocol is finalized is critical to avoid unexpected issues like this. It could have saved another company from learning too late that the assay it developed to measure an enzyme involved in fat metabolism did not perform well in a local population whose diet preference was eating sausages at every meal. Companies need to understand how local populations live, receive care, and how those factors might influence trial outcomes.

The Practice of Medicine and Clinical Endpoints

As a condition of approval, FDA typically requires that companies demonstrate that the clinical data generated ex-US is representative of the patient population and the practice of medicine in the U.S.  While healthcare is managed and reimbursed differently throughout the world, the details are often underappreciated. Depending on the region, health records might be unavailable, incomplete, or unreliable, particularly where care is not tightly monitored. Assessing health and compliance with SOC can be challenging.

Relying solely on CROs to ensure protocols are being followed and all aspects of a trial are well managed is one of the biggest mistakes companies can make. While CROs and trial sites are responsible for overseeing compliance, one company running an ex-US trial in a region where compliance is particularly challenging decided to administer a test to determine whether study participants were taking the SOC medicine during the trial screening period. This would help determine patients who are likely to be compliant during the study and minimize the risk of enrolling the wrong patients. Companies might also pay for crossover treatments, especially for Phase 1 trials, thus providing SOC treatment for a full year post study.

To complement CRO efforts, some companies choose to have ‘boots on the ground’ for key trial events such as first dose(s), especially for first-in-human (FIH) studies. It is expensive but often yields the greatest ROI. As issues arise, extended travel can’t be avoided. It drains employees and organizations when people are away for weeks or a month at a time, particularly for emerging biotechs who are stretched thin and people wear multiple hats. The best advice is to identify potential needs ahead of time. This alleviates pressure, maintains strong relationships inside the company and with ex-US partners, and minimizes clinical trial risks longer term.

Building trust with clinical trial partners in certain foreign countries can be harder and take longer. It is also not something that should be left solely to the CRO. Getting to know the people, cultural norms and communication preferences helps to bridge the distance. It can take months to develop strong enough relationships in some countries compared with others where Zoom meetings are enough to garner the trust needed to get clinical trials up and running.

Establishing clinically meaningful endpoints may vary across regions. There can be different measurement ranges that reflect clinical practice and these complicate data analyses. Understanding exactly how endpoints will be measured and patient outcomes will be recorded is essential for trial success. Language barriers, treatment regimens, diagnostics capabilities ─including imaging and videos, and lifestyle differences can all lead to inconsistent interpretations and assessments. One biotech company with global trials involving over 7,500 patients and ~600 clinical trial sites had to align on big-ticket items with multiple regulatory agencies, clinical sites, and ethics committees across regions. Country-specific amendments were expected and accepted.

Recently, FDA has asked at least two companies for a placebo arm requiring some form of an invasive surgical procedure for their pivotal trials. In each case, these are in rare disease patient populations that require delivery of drugs to the central nervous system.  In one case (with which I am familiar), several European countries deemed this approach unethical, which required additional placebo patients to be added to the study to perform a less invasive surgery.  The lack of alignment between regulatory agencies continues to be a major challenge.

Legal and Regulatory Considerations

As noted above, regulatory requirements vary by country and region, but a clinical development strategy should be global from the outset. Aligning study design and protocols with appropriate regulatory expectations and operational realities is critical to avoid delays, cost overruns, and complicated data interpretations. Regulatory filings outside the U.S. are often less extensive than full investigational new drug  (IND) submissions and review timelines may be shorter, however early engagement with the FDA is important. Even when initial clinical proof-of-concept data will be generated ex-US, conducting a pre-IND meeting before completing GLP/GMP toxicology studies helps ensure the nonclinical package aligns with their feedback and meets FDA expectations. Unfortunately, some companies have encountered significant delays presenting positive ex-US clinical data to the FDA and learned afterwards that additional toxicology work would be required.

International trials must also comply with country-specific legal standards governing data privacy, confidentiality, and cross-border data transfer. In the EU, data protection regulations differ significantly from HIPAA requirements in the U.S. and require companies to appoint a Data Protection Officer to oversee compliance. Understanding the requirements and incorporating them into clinical trial applications and site selection decisions can prevent delays, penalties, and incurring unexpected costs later on.

Manufacturing and Supply Chain Logistics

It is considered good practice for Chemistry, Manufacturing and Controls (CMC) company personnel to be present for drug production runs. While this requires additional time and expense, experienced operators believe this is non-negotiable.  At my company, Lifordi Immunotherapeutics, I had a senior member of my CMC team observe our first production runs in Scotland. Due to a number of issues, my colleague spent several late nights eating dinner from an all-night gas station food market but was thankfully able to manage through the process before it became a major problem for us.

Supply chains and shipping logistics for ex-US trials are complex. It can take a village ─or large network of reliable vendors ─to achieve success. In one instance, a clinical site near a war-torn territory necessitated that materials be flown to a neighboring country and driven across the border to the clinical trial site. Patient samples needed to take a similar route for proper analyses. Maintaining temperatures for drug product, sample storage, and delivery was a top priority. Companies routinely manage these logistics by keeping ahead of things such as new labelling requirements ─a situation that is known to cause delays and incur further expense. Today, rapidly changing import/export rules and imposed tariffs are further complicating supply logistics. New regulations often impact regulatory filings and while the magnitude of changes can be difficult to predict, the potential time and cost to comply must be budget.

Destinations for Ex-US Clinical Trials

Governments, pharma companies, and other industry partners continue to invest in building critical infrastructure overseas including training and creating substantial talent pools. For many years, conducting clinical trials outside the U.S. included locations in Europe, Australia, Japan, and Latin America. Today, Asia-Pacific is the fastest growing region with major investments in China made by pharma companies including a $15B investment by AstraZeneca and a ~$1B investment by Pfizer ─both through 2030, and more recently, a $484M investment in South Korea over the next five years by Roche.

China has increased its relative share of clinical trials by 57% from 2019 to 2023, and in 2024 it was the third most utilized region mainly due to its large patient population demanding new therapies, technological capabilities, cost-effective solutions, and favorable regulatory policies.

While this increase was mostly driven by the explosion of clinical work from China-based companies, trial recruitment in China can also be 5-10 times faster than it is in the U.S. and averages one patient per month for some indications. For these reasons, China is increasingly a topic of discussion by Western companies for FIH studies.

Currently, most US companies are doing FIH trials in Australia (including Lifordi), New Zealand, Belgium or the Netherlands.  Timelines remain rapid in these countries. For healthy participant studies, it takes approximately 2-3 months from final protocol and investigator’s brochure through to dosing first patient. SAD/MAD studies can be completed with 4-week follow-up within about 6 months. Healthy participant data is also readily accepted by the FDA.

Conclusion

As companies weigh the risks and benefits of conducting clinical trials outside the U.S., rapid data generation and potential cost-savings often rise to the top. Other substantial advantages include access to diverse populations and broader access to innovative medicines for patients. Yet these benefits come with expected and unexpected challenges. Companies that invest time and resources to develop local knowledge and build strong relationships are far better positioned to achieve success and avoid costly setbacks. As the old mantra reminds us, careful planning and execution is key—because no company wants the trial to fail the drug.

The post Ex-US Clinical Trials: Tribulations, Preparations, and Expectations appeared first on LifeSciVC.

      

Related Stories

• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective
• Chaos is a Ladder: Predictions for 2026
• CMC: A ‘Secret Sauce’ of Biotech Success

 

What if the biggest obstacle to the next generation of I&I therapeutics isn’t finding better targets — but accepting that one target was never going to be enough? It’s a question the industry spent twenty years not asking. The Humira playbook was too lucrative, the growth too easy, and the incentives too misaligned to seriously reckon with biological reality. Now, with the single-target era approaching exhaustion and an efficacy ceiling that fifty antibodies have failed to crack, biopharma has finally embraced multi-drug and multi-target interventions in its quest to reset the bar. 2026 promises to be a defining year for these efforts, and therefore a defining year for the future of I&I.

How’d we get here in the first place? The story starts in 1998 with the approvals of Enbrel and Remicade, the first-ever I&I biologics. The two drugs’ early success catalyzed a “biologics boom” that would define the modern era of immunology. By 2002, both surpassed $1 billion in annual revenue, becoming the first autoimmune-indicated biologics to achieve “blockbuster” status. That same year, Humira was approved for the treatment of Rheumatoid Arthritis (just barely, on December 31st – and for my biotech trivia enthusiasts, that’s only happened twice since). Humira’s approval, the first for a fully human monoclonal antibody, was rightfully celebrated as a major scientific milestone. Wall Street, meanwhile, wasn’t nearly as enthusiastic. Analysts viewed the anti-TNF class as crowded, questioned Humira’s ability to differentiate versus the chimeric antibody Remicade (despite the former’s pre-filled syringe for convenient, Sub-Q, at-home dosing – brings to mind a few oft-criticized players in I&I today, but I’ll leave it there), and criticized Abbott for recklessly overpaying in its $6.9 billion deal for the drug. Abbott itself, despite emphasizing Humira’s multi-indication potential to justify the price tag, was tepid in its forecasts, pegging RA peak sales at just $1 billion.¹

23 years, 11 indications, and $240 billion of revenue later, Humira has proven as important a milestone for the biopharma business model as it was for science. Along the way, Humira became the archetype “pipeline-in-a-product” and dogmatized the business model as a core strategy across the industry. Today’s best-selling I&I biologics – Dupixent, Skyrizi, Stelara, Cosentyx, etc. – all followed this blueprint en route to multi-blockbuster status. Throughout the same 23-year period, an explosion of novel mechanisms and protein engineering technologies has fueled the I&I space through the approval of nearly 50 antibodies that collectively modulate more than 15 distinct targets spanning the full breadth of canonical immune pathways.² Collectively, these medicines have reached tens of millions of patients, and many have redefined standard-of-care in clinical practice.

The cumulative innovation in I&I has been so substantial that, for a subset of indications, some argue we’ve reached “endgame” status. In Psoriasis, for instance, Bimzelx’s 60%+ PASI-100 sets a daunting benchmark. Among the majority, however, unmet need remains. Novel mechanisms have improved patient outcomes by enabling class switching and multi-line intervention but have largely failed to deliver step-change efficacy. In most indications, particularly those characterized by greater severity and heterogeneity, the clinical high-water mark has stubbornly plateaued. In Inflammatory Bowel Disease (IBD), the bar has scarcely budged from ~25-30% Clinical Remission (all benchmarks based on placebo-adjusted, induction period endpoints). In Rheumatoid Arthritis (RA), ACR50 has plateaued at ~30-40%. In Axial Spondyloarthritis (AxSpa), ~30-35% ASAS40, in Hidradenitis Suppurativa (HS), ~40-45% HiSCR50, and so on… Or, more succinctly, the “Efficacy Ceiling.”

Underlying biology helps explain this ceiling, to a point. Autoimmune diseases are not monolithic entities driven by single pathways. Rather, they are complex ecosystems of dysregulated cytokines, aberrant cell populations, and tissue-specific factors that vary across patients and even within the same patient over time. Blocking one pathway can provide meaningful benefit but rarely addresses the full constellation of disease-driving mechanisms. After many millions of years in a co-evolutionary arms race, redundancy is a feature, not a bug.

Where biology’s explanation stops, market structure and commercial incentives take over. For the last two decades, biologics in I&I have enjoyed a rising tide – in a set of markets with very low biologics penetration historically, the primary growth lever was simply expanding access to the class. When “good enough” is lower risk and highly lucrative, the rational economic actor naturally allocates fewer resources toward high-risk, paradigm-shifting projects. Today, however, the incentives have shifted – biologic penetration has plateaued at ~60% in the largest indications, biosimilar adoption is finally gaining momentum, and fierce competition amongst an ever-growing arsenal of options has fragmented the branded market. The remaining I&I white space sits behind the glass ceiling, and much of biopharma has resolved to break through.

Most of those players have since coalesced around biologics combinations and/or multi-specific antibodies as their preferred strategy. Look no further than Big Pharma commentary during this year’s JP Morgan Healthcare Conference. J&J, Sanofi, and UCB all highlighted co-formulations and/or multi-specifics as a core pillar of their I&I strategy. Abbvie positioned Skyrizi as the anchor asset for I&I combinations, and projected sustained long-term growth for the franchise. Regeneron unveiled an IL-4 x IL-13 bispecific at the center of its Dupixent LCM strategy, notable given leadership’s frequent disparagement of “me-better” assets (including, with as much earnest zeal as ever, during this very same presentation).

Moreover, the industry pipeline reflects those comments – in a late 2025 analysis of >100 clinical-stage Big Pharma I&I assets, ~70% were biologics, and within this subset, ~25% were combinations or bispecifics.³ Not everyone is sold, however. Equally interesting are those still on the sidelines, a list that includes some of the largest I&I franchises today, such as Novartis, Roche, Eli Lilly, and AstraZeneca.

So, who’s right? 2026 should reveal much of the answer. In contrast to a smattering of one-off datapoints in recent years, this year’s dense calendar of clinical catalysts should finally provide a sufficient corpus of data to enable a more grounded debate as whether combos and multi-specifics are the next chapter in I&I or merely an addendum to an already crowded therapeutic narrative. To contextualize this inflection point, one must understand how we got here, and what we’ve learned along the way.

Target & Modality Selection – Insights from the Bench to Bedside

Not all combinations are created equal. History is littered with examples of biologically rational combinations that failed to translate, most attributable to underestimated toxicity overlap or overestimated mechanistic orthogonality. The rationale for supporting a target combination should be built from a foundation of first principles: have the targets / pathways been shown to contribute independently to disease pathophysiology? Is there evidence of non-redundancy? Can mechanistic synergy be demonstrated preclinically?

For clinically validated mechanisms, an integrated analysis of basic biology, human genetics, preclinical models, and clinical data tends to spit out clear answers to these questions; and in my view, the answers are often “no.” If the explosion of cytokine target combinations across biopharma pipelines is any indication, that will be a controversial take, but I think it’s important to keep the bar high, because that signal is much harder to see through the biological complexity and heterogeneity of real-world patients.

From there, practical translational considerations can be overlaid to inform modality selection. As important as the targets themselves are the tools employed to modulate them. The relative virtues of the two dominant strategies in I&I – antibody co-formulations and bispecific antibodies – are hotly debated, often pitted against each other in a zero-sum, winner-take-all competition. More likely, in my view, is that both ultimately find a place in the armamentarium, with their respective territories defined by those sets of targets for which each is uniquely positioned. For example, the relative abundance and localization of the two targets are critical variables. Traditional bispecific antibody formats may be well-suited for a combination of two soluble targets typically observed at comparable concentrations in the serum, whereas a bispecific may underperform its co-formulated monoclonal constituents for a combination in which one soluble target’s concentration exceeds the other’s by several orders of magnitude, or one in which the TMDD of a membrane-bound target precludes saturation of its soluble counterpart.

Of course, this choice also has downstream implications for discovery and development. On average, bispecifics are harder to discover (incremental degrees of freedom necessitate careful engineering and multiparametric coordination), harder to enhance (Fc mods like YTE perform inconsistently), and harder to make (light chain mispairing reduces yield and necessitates sophisticated purification processes). Conversely, antibody co-formulations are much harder to develop – a consequence of statutory requirements to demonstrate the contribution of each component.

J&J’s Phase 2b DUET studies in IBD illustrate this dynamic perfectly. To comply with the above, each trial has six treatment arms: placebo, each monotherapy, and three doses of the combination. To maintain power, they enrolled ~575 and ~700 patients, more than twice the size of an average single-agent Phase 2 in IBD historically; and even now, at any clinical remission delta below 20%, the trials are less than 80% powered for dual superiority.⁴ Naturally, enrollment scope cascades through timelines – both DUET trials enrolled in ~22 months, ~6-9 months longer than historical benchmarks – and cost – at least twice that of historical benchmarks, conservatively. For more granular look at cost, keep an eye on Spyre’s P&L in 2026 and beyond. The Phase 2 SKYLINE platform trial looks a lot like DUET-UC, and those costs will become increasingly visible as the placebo-controlled portion begins enrolling later this year.

The best target combinations are those for which scientific rationale and translational tractability are airtight – when genetics, mechanistic biology, preclinical models, and interventional pharmacology all converge on orthogonal, non-redundant pathways that are imminently druggable with well-understood tools. Today, this framework also benefits from the efforts of early pioneering efforts to bring antibody combinations and multi-specifics to I&I patients. Though many failed, and most of those that haven’t remain early and immature, valuable translational insights have already emerged from a decidedly mixed set of early clinical outcomes.

You might be surprised (I was) to learn that randomized, controlled trials of antibody combinations in I&I date as far back as the early 2000s. Between 2004 and 2007, results were published from three multicenter, 100-plus patient RCTs testing combinations of Enbrel (anti-TNF), Kineret (anti-IL-1Rα), and Orencia (CLTA4-Ig) for RA. Across the board, it was worst case outcome – the combinations not only failed to demonstrate superior efficacy, they were also associated with 2-3x higher rates of SAEs and serious infections.⁵ Not a great start, but the data points weren’t universally negative – around the same time, a smaller RCT testing the addition of Tysabri (anti-α4 integrin) to a steady-state regimen of Remicade (anti-TNF) in Crohn’s patients failing to achieve remission with monotherapy. Most efficacy measures numerically favored the combination, particularly among high-risk patients; and importantly, no incremental safety risk was observed.⁶ Despite that, one small, inconclusive win stood little chance against the new narrative. The prevailing view was so negative that ACR went as far as updating treatment guidelines with an explicit recommendation against dual biologic therapy in RA based on unfavorable benefit-risk.⁷ Although experimentation from the backwater would continue to emerge through case reports and single-arm studies, the biologic combination hypothesis had largely been put on ice.

The concept of biologic combinations and multi-targeting emerged again in the mid-2010s with the advent of bispecifics in I&I. In much the same way as the trials above, these pioneering efforts universally fell short while teaching the field important lessons along the way. Two programs, Covagen’s COVA322 and AbbVie’s ABT-122, entered the clinic around the same time. Both targeted TNF and IL-17A, informed by preclinical validation and observations of elevated IL-17 and Th17 cells in TNF-treated patients. The former failed too quickly to test the hypothesis. The latter was evaluated in separate RA and Psoriatic Arthritis (PsA) Phase 2s, head-to-head against Humira. In neither case did ABT-122 outperform Humira. Clean attribution of that outcome, however, proved challenging. In particular, high rates of neutralizing ADAs were thought to have precluded a true test of the biological hypothesis.⁸ A subsequent post hoc exposure-response analysis across both trials determined that, at comparable molar exposures, there was no differentiation in efficacy, and therefore no detectable contribution of IL-17 suppression.⁹

In the aftermath of ABT-122, the bispecific hypothesis largely sat dormant in I&I, despite significant advancement in other therapeutic areas. It wasn’t until Bimekizumab (anti-IL-17A+F), now Bimzelx, established definitive proof-of-concept for dual cytokine neutralization in 2018-19 that the concept of multi-specificity was revisited en masse.

Shortly after Bimekizumab blew open the door for multi-specifics, J&J’s VEGA trial readout did the same for antibody combinations. VEGA evaluated the combination of anti-IL-23 Guselkumab (Tremfya) and anti-TNF-α Golimumab (Simponi) vs. each monotherapy in predominantly biologic-naïve Ulcerative Colitis patients. This combination, in addition to leveraging the trust and reliability of IBD’s two grizzled veterans, capitalized on a clean and compelling biological hypothesis. IL-23 had increasingly been implicated in the development of anti-TNF resistance, and a host of preclinical models had shown clear mechanistic synergy of intervening downstream via TNF and upstream via IL-23 simultaneously. First announced in 2022, VEGA finally notched a win for antibody combinations in I&I. At week 12, the combination achieved 83% clinical response, 8-12% higher than the monotherapy arms. More impressively, the combination achieved clinical remission of 47%, 22-23% higher than the monotherapy arms.¹⁰ Unlike first-generation strategies, the combination possessed comparable safety profile to the monotherapies. The ceiling had been broken, emphatically and safely.

Momentum only built further from there. In 2023, provocative early Ph1b Asthma data from Sanofi’s Lunsekimig (IL-13 x TSLP) established a compelling first proof point in Th2-driven disease. Later that year and into 2024, J&J’s acquisition of two peri-clinical bispecific assets for a cumulative $2B drove sentiment to a fever pitch.^11,12 Companies were started (probably too many), billions of dollars traded hands in collaborations and licensing agreements (mostly to China), and the cytokine bingo card was soon full. The die had been cast.

2025 marked the first year with multiple, meaningful multi-specific readouts, a representation of the maturation of this broader theme. It was also a reality check, with several of the assets that had fueled the industry’s excitement failing to live up to expectations. First, the full Phase 1b Asthma dataset for Sanofi’s Lunsekimig (IL-13 x TSLP) revealed a much more equivocal package than assumed. The previously disclosed and highly impressive FeNO signal was, in fact, the outlier. The drug failed to achieve broader anti-inflammatory effects such as deeper eosinophil suppression or improvements in lung function, raising doubts about its ability to raise the bar in its ongoing Asthma Phase 2.¹³

Subsequently, J&J terminated a Phase 2b Atopic Dermatitis trial evaluating JNJ-4939 (IL-4Rα x IL-31) after an interim analysis failed to meet the internal threshold for efficacy (likely superiority versus Dupixent).¹⁴ With how well-understood the itch-scratch dynamics are in AD, IL-4Rα and IL-31 were generally considered among the most rational, low risk target pairs. J&J’s willingness to spend $1.25B to acquire the asset in the first place exemplifies that. There’s much more to learn in the fullness of time, but the membrane x soluble target pair compatibility may have played a role. At the very least, this outcome was a timely reminder that the consensus low-hanging fruit probably isn’t as easy to pick as it seems.

On the other hand, the space also enjoyed a few notable successes. UCB’s Galvokimig (IL-13 x IL-17A/F) produced one of the more striking proof-of-concept data points in 2025. Intended to suppress both the Th2 pathway (via IL-13) and Th17 pathway (via IL-17A/F), Galvokimig aims to address a broader and more heterogenous AD population. Initial Phase 2a data presented at EADV 2025 garnered a lot of interest, for good reason – at 12 weeks, Galvokimig achieved 53% and 43% placebo-adjusted EASI75 and EASI90, respectively.¹⁵ The latter compares favorably Dupixent’s Phase 2b performance and suggests the mechanism may be accomplishing exactly what it intended, but such a small trial should not be overinterpreted.

Sanofi’s Brivekimig (TNF-α x OX40L) grabbed headlines at the same conference. The rationale underlying Brivekimig is the simultaneous modulation of two distinct but complementary inflammatory nodes – TNF drives acute tissue damage and immune cell recruitment, while OX40L-OX40 signaling promotes effector T-cell survival and proliferation that sustains chronic inflammation. The initial data from an HS Phase 2a impressed, achieving 29% and 32% placebo-adjusted HiSCR50 and 75, respectively, comparable to those achieved by Bimzelx in a similar setting.¹⁶ Again, caveats abound – small trial, biologic-naïve patients, high placebo response – but a strong start, nonetheless.

The 2026 Calendar – Key Catalysts & Potential Learnings

Against this backdrop, 2026 is shaping up to be a defining year for the combination / multi-specific thesis in I&I. There are several key readouts we’ll be watching closely:

J&J’s JNJ-78934804 (IL-23 x TNF-a) DUET-CD & DUET-UC – the largest, most robust test of the combination thesis to date. Building on VEGA’s proof-of-concept, the long-awaited DUET readouts will determine if a similar benefit can be achieved in a refractory population, and with a much more patient-friendly fixed-dose co-formulation. Many were hopeful to see these data last year, but J&J has remained cagey on timing. With both trials reaching their Primary Completion Dates in May 2025, data are almost certainly imminent.

AbbVie’s Skyrizi Combo Platform Trial (IL-23 x a4b7 & IL-23 x IL-1A/B) – relative to the attention DUET receives, the Skyrizi Platform trial is massively underappreciated. It’s a DUET-esque trial – 500 patients, biologic refractory – likely to provide a robust read on two of the most important target pairs in IBD. One of these, IL-23 x a4b7, will have much broader implications, particularly for the fate of Spyre’s pipeline.

Apogee’s APG279 (IL-13 + OX40L) in Atopic Dermatitis – first of all, any Phase 1b that’s set-up to demonstrate superiority over King Dupixent is a must-watch. That said, within the Th2 category, APG279 also stands out as one of the only co-formulation efforts amongst a sea of multi-specifics.

UCB’s Donzakimig (IL-13 x IL-22) in Atopic Dermatitis – beyond the buzz of Galvokimig, UCB’s bispecific portfolio includes Donzakimig, which pairs IL-13 with IL-22, a cytokine involved in skin barrier integrity. IL-22 hasn’t been a popular target for multispecifics, despite independent proof-of-concept for anti-IL-22 antibodies in AD and clear pathway orthogonality. Phase 2a AD data later this year will put this hypothesis to the test for the first time, and that’s always worth watching.

Sanofi’s Lunsekimig (IL-13 x TSLP) in Asthma & Atopic Dermatitis – on the back of Sanofi’s creative disclosure choices for the Phase 1 Asthma data, data from a large, multi-dose Phase 2b are sure to separate signal from noise. The latter is of less interest given TSLP’s lackluster data in AD historically.

Pfizer’s Tilrekimig (IL-4 x IL-13 x TSLP) & PF-07264660 (IL-4 x IL-13 x IL-33) in Asthma & Atopic Dermatitis – Pfizer’s “trispecifics” have been a topic of intrigue following Tilrekimig’s progression into the second stage of the ongoing Phase 2 platform trial in AD. Pfizer has thus far declined to disclose any of the data supporting this decision, but they learned enough to move Tilrekimig into a separate Phase 2b in Asthma. 2026 should bring clarity.

Others – Bambusa’s BBT001 (IL-4Rα x IL-31) in Atopic Dermatitis (a first clean second shot on goal for a failed pair of targets), Aclaris’ ATI-052 (IL-4Ra x TSLP) in Asthma and AD (additional insight into the performance of membrane-bound x soluble bispecific constructs), and Zura’s Tibulizumab (IL-17A x BAFF) in HS (one of few programs testing parallel T and B cell modulation).

Through the I&I Looking Glass – Fad or Future?

2025’s mixed track record injected a healthy dose of humility into what had become an increasingly consensus bull case. The setbacks and successes alike reinforce the importance of target selection, yet remind us that biology is unpredictable and attrition is inevitable.

While I suspect the list of truly synergistic, efficacy ceiling-breaking target pairs is shorter than most expect, I firmly believe combinations and multi-specifics will be a defining theme of the next decade in I&I. Those that truly raise the bar will reshape treatment paradigms, redefine efficacy expectations, and create significant value for patients and investors alike.

The broader Atlas team has explored these dynamics equally deeply. The fund’s recently announced investment in Caldera reflects conviction in this broader theme, the TL1A x IL-23 hypothesis specifically, and the capacity of an extraordinary, experienced team to out-execute in a hyper-competitive space.

2026 will not provide all the answers, but it will be defining. For the first time, the data will be dense enough, the trials large enough, and the target diversity broad enough to move beyond anecdote and toward empiricism. The next chapter of I&I is being written as we speak – stay tuned.

References

1. Abbott Laboratories. Annual Report 2002: Form 10-K for the Fiscal Year Ended December 31, 2002. U.S. Securities and Exchange Commission, 19 Feb. 2003.
2. The Antibody Society.YAbS: Database for Therapeutic Antibodies.
3. Stifel Biotechnology Equity Research. 2025 I&I Strategic Pipeline Deep Dive: Mapping Out Key Areas of Focus and White Space Opportunities. Stifel, 10 Sept. 2025.
4. European Union Clinical Trials Register. A Phase 2b Randomized, Double-blind, Active-and Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Efficacy and Safety of Induction and Maintenance Combination Therapy with Guselkumab and Golimumab in Participants with Moderately to Severely Active Ulcerative Colitis (EudraCT No. 2021-005528-39).
5. Furer, Victoria, and Ori Elkayam. “Dual Biologic Therapy in Patients with Rheumatoid Arthritis and Psoriatic Arthritis.” Rambam Maimonides Medical Journal, vol. 14, no. 2, 30 Apr. 2023.
6. Sands, Bruce E., et al.“Safety and Tolerability of Concurrent Natalizumab Treatment for Patients with Crohn’s Disease Not in Remission While Receiving Infliximab.” Inflammatory Bowel Diseases, vol. 13, no. 2, 2007, pp. 2–11.
7. Singh, Jasvinder A., et al.“2012 Update of the 2008 American College of Rheumatology (ACR) Recommendations for the Use of Disease-Modifying Anti-Rheumatic Drugs and Biologics in the Treatment of Rheumatoid Arthritis (RA).” Arthritis Care & Research (Hoboken), vol. 64, no. 5, May 2012, pp. 625–639.
8. Genovese, Mark C et al. “ABT-122 in Patients With Rheumatoid Arthritis With an Inadequate Response to Methotrexate: A Randomized, Double-Blind Study.” Arthritis & rheumatology vol. 70,11 (2018).
9. Khatri, Amit et al. “Exposure-response analyses demonstrate no evidence of interleukin 17A contribution to efficacy of ABT-122 in rheumatoid or psoriatic arthritis.” Rheumatology (2019).
10. Feagan, Brian G., et al.“Guselkumab Plus Golimumab Combination Therapy Versus Guselkumab or Golimumab Monotherapy in Patients with Ulcerative Colitis (VEGA): A Randomised, Double-Blind, Controlled, Phase 2, Proof-of-Concept Trial.” The Lancet Gastroenterology & Hepatology.
11. Johnson & Johnson. “Johnson & Johnson Strengthens Pipeline to Lead in Atopic Dermatitis with the Completion of the Acquisition of Yellow Jersey Therapeutics, Gaining Ownership of NM26.”  23 Nov. 2023.
12. Johnson & Johnson. “Johnson & Johnson to Acquire Proteologix Inc. to Lead in Atopic Dermatitis Treatment.” 9 Aug. 2023.
13. Deiteren, Annemie, et al.“A Proof-of-Mechanism Trial in Asthma with Lunsekimig, a Bispecific NANOBODY Molecule.” European Respiratory Journal, vol. 65, no. 4, 2025.
14. Mahatole, Siddhi. “Johnson & Johnson Halts Mid-Stage Trial of Experimental Eczema Drug.” Reuters, 26 Dec. 2025.
15. UCB. Galvokimig EADV Data Presentation. UCB, 29 Sept. 2025.
16. Sanofi. EADV: Sanofi’s Brivekimg Achieved Positive Results in Hidradenitis Suppurative in Phase 2a Study. 17 Sep. 2025.

The post Dual Wielding in I&I: A Pivotal Year Ahead appeared first on LifeSciVC.

      

Related Stories

• How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need
• Huntington’s Disease and The Triad of Therapeutic Conviction
• CMC: A ‘Secret Sauce’ of Biotech Success

 

In early 2009, when global markets were bottoming out, you can imagine what it felt like walking the halls of the JPM Healthcare Conference. Biotech felt fragile. Capital was scarce, pipelines were thin, and more than a few investors were quietly asking whether the venture model in drug discovery even worked anymore.

That was also the moment when a small group of firms, Atlas Venture included, were sketching a different kind of biotech on whiteboards with lawyers and scientists. Not a fully integrated pharma. Not a lab-heavy roll up. But a focused, small-molecule company built around chemistry, leading computational tools (Schrödinger in our case), and a virtual operating model that could survive volatility and still produce real drugs.

Nimbus was born out of that era.

We did not set out to build the biggest labs. In fact, Nimbus has never had its own labs. We set out to build the fastest learning engine.

Sixteen years later, Nimbus has sold drugs to Gilead (here) and Takeda (here), partnered programs with Genentech (Roche) (here), Celgene (BMS) (here), and most recently with Lilly (again) (here, here), and built what we believe is one of the most consistent small-molecule engines in biotech. We did that by owning the Design–Make–Test–Analyze loop, using global partners to execute at scale without ever giving up control of what mattered.

Today, DMTA at Nimbus is concrete and central to the business. We design molecules on computers. We make them through CRO partners. We test them in proprietary, bespoke biological screening cascades built for each target. We analyze the data and feed it directly back into the next design cycle using integrated AI and physics-based approaches.

That lived experience is why a recent essay on geopolitical risk in biotech sparked conversations about whether Western pharma may have inadvertently funded a competing biotech ecosystem by treating preclinical biology as a commodity.

The piece argues that outsourcing execution inevitably leads to outsourcing learning, leaving Western biotech’s licensing assets from foreign ecosystems that their own capital, years before, helped train.

Our experience suggests another path is possible. Nimbus continues to make compounds that are fully ours: the molecules, the data, and the learning.

The IP sits in the molecule, assigned cleanly to a designated subsidiary. More importantly, the learning loop lives inside the company, giving us repeatability and conviction.

It is also worth acknowledging how much the landscape has changed. Chinese biotechs and TechBio companies have made real progress, particularly in medicinal chemistry efficiency and the rapid advancement of me-too clinical candidates. Timelines for these “follow-on” programs have compressed, execution has improved, and competition has increased.

But that progress has been uneven. Generic medicinal chemistry and early clinical execution are increasingly commoditized. What remains difficult, and far less transferable, is preclinical biology and the novel chemical equity to crack it open: identifying high-value targets, building disease conviction, generating unique chemical tools, and designing experiments that separate signal from noise before patients and capital are put at risk. This is where many of these models remain weakest today.

Nimbus has always invested there. As execution becomes more intensely competitive, biological insight becomes the differentiator.

DMTA Is Not New. Compression Is.

The best small-molecule teams have always worked in loops. Form a hypothesis. Make a molecule. Test it. Analyze the data. Decide what to do next. That loop has always existed inside strong core teams, from Merck in its small molecule heyday of the 1980-1990s to today’s innovative “smart chemistry” firms like Nimbus and others.

What AI and physics-based methods have changed is not the existence of the loop, but its speed and resolution. They let you explore more chemical space, filter more aggressively, and arrive at better experiments faster. Nimbus has been integrating generative design, free-energy perturbation (FEP), Absorption, Distribution, Metabolism, and Excretion (ADME) and pharmacokinetics (PK), prediction, and structure-based modeling into daily decision making for years.

While we never had a ‘platform’ the loop was always the engine – our engine. AI now compresses it.

Asset-Light Does Not Mean Learning-Light

There is an important distinction between outsourcing execution and outsourcing learning. The concern that companies might be training their competitors is valid when the learning loop itself moves offshore.

Shared infrastructure doesn’t inherently weaken drug design capabilities. This is exactly what cloud computing did for software. No startup built its own data center. Yet the winners still owned their code, their architecture, and their product decisions. Shared infrastructure did not weaken those companies. It strengthened them by allowing talent and judgment to concentrate where they mattered most.

Nimbus fits cleanly into that logic.

Not every startup needs to build a full-stack lab. What matters is maintaining control of the learning loop. CROs and CDMOs are execution layers. Nimbus owns the hypotheses, the molecular designs, the prioritization logic, the data integration, and the go or no-go decisions.

The learning stays inside the company. That distinction matters.

Rational Capital Structures, Intentional Strategy

Asset-light models emerged partly from venture financing and governance realities. That is true. But for Nimbus, the virtual model was not simply a response to capital constraint. It was an intentional strategy that allowed us to survive multiple capital cycles, preserve optionality, compound learning over long horizons, and partner repeatedly from a position of strength.

Because we were not anchored to fixed infrastructure or a single domain of expertise, we could adapt organically to the most compelling science, whether in immunology, oncology, cardiometabolic disease, or liver disease. The absence of in-house labs was not a limitation. It created freedom to pursue programs opportunistically and strategically, guided by data rather than structure.

Sixteen years of outcomes suggest this can be a successful approach when executed with discipline.

 Sovereignty Lives in Decisions

There is a real and important question about sovereignty and resilience at the industry level. But startups are not meant to be sovereign infrastructures. Building and maintaining domestic capacity at scale for long-term industrial resilience is a national welfare problem, typically carried by governments, strategics, or shared platforms.

Nimbus’s model fits that reality. We do not aim to be a sovereign infrastructure. We aim to be a sovereign decision engine.

That is exactly the role startups should play. Focus on judgment, learning, and conviction, while leveraging shared execution capacity to move faster and stay flexible.

Coming Full Circle

Biology is not semiconductors. It is messy, much more complex, and deeply human. Value accrues in layers. Execution and data generation sit at the base. Hypothesis generation, molecular design, and the integration of weak signals into coherent decisions sit at the top.

Nimbus has always invested at the top of that stack.

As I reflect on the JPM Healthcare Conference now in 2026, nearly sixteen years after those early conversations, what struck me most was how familiar the dialogue still felt. At meetings, dinners, and side conversations, the focus was not on infrastructure or geopolitics.

It was on the quality of the drugs. The molecules. Whether they would matter for patients.

That is where our IP lives. That is where our sovereignty lives. Nimbus was built for that world long before it was fashionable to describe it that way.

Many thanks for the contributions and editorial advice from Cindy Fung (Nimbus head of corporate affairs), Peter Tummino (President, Research & Development), and Bruce Booth, (Partner, Atlas Venture).

The post Why Owning the Learning Loop Matters More Than Owning the Lab appeared first on LifeSciVC.

      

Related Stories

• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective
• How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need
• Huntington’s Disease and The Triad of Therapeutic Conviction

 

I’m not a resolutions person but I’ve always loved the start of the year – there is something about turning the page on a new chapter that is refreshing. This year especially feels like a fresh start, after the weird year that was 2025. These past 12 months are probably best described as a constant sensation of vertigo… we thought biotech may benefit from a “pro-business” administration in January, then “Liberation Day” chaos ensued when it felt like the broader markets were headed for a recession, then from late summer to end of year we saw a brilliant recovery in public biotech stocks, driven by positive data flows, exuberant M&A, and improved macro tailwinds (e.g., rate cuts). While some macro threats to the ecosystem (most notably tariffs and onshoring demands) seem more benign than in Spring 2025, many others remain to be fully pressure-tested in 2026.

To add some levity to the start of the year, and ahead of next week’s JPM conference, I am sharing my top predictions for 2026. While I’m sure I could opine for days, I restricted these to a lucky number (13…). If I see you in SF next week, I welcome debate on what I got wrong!

Science & Innovation

• What can’t the incretins do?! GLP1s will show striking evidence in 1-2 indications beyond the cardiometabolic TA. I’ve written about it before, but the incretin class will likely be one of the most impactful medicines of the 21^st century. As nicely expounded in this recent Nature review, the breadth of indications now or soon to be addressed by GLP1s is staggering. Some of these indications (knee OA, for example) are intuitive – if one loses substantial weight, it makes sense that mechanical stress on the knees will decrease and therefore pain and function scores will improve (and by similar logic it’s also not surprising that retatrutide, in delivering substantially higher weight loss in knee OA patients vs. semaglutide, delivered larger improvements from baseline on these scales). But there are indications of potential relevance on the horizon that are not strictly cardiometabolic, and for which the CNS and anti-inflammatory impacts of GLP1s has potential to shine through. Looking at pipelines offers clues – Lilly’s novel GLP1/GIP agonist brenipatide, for example, is already in Phase 3 studies for alcohol use disorder (here, here) and a Phase 2 trial for smoking cessation as well as Phase 2 trials in bipolar disorder and asthma. Unlike in Alzheimer’s disease (where oral sema recently failed to impact clinical dementia rating scales), there is strong mechanistic evidence for GLP1s and impact on neurocircuitry and inflammation that may rationalize future efficacy in these new indications. I believe at least one of these novel indications will read out positively in 2026, suggesting a path to differentiated treatments for these patients and commercial growth opportunities for the broader incretin class.
• Large strides will be made in advancing therapies for precision kidney targeting. 2026 will see several key readouts for small molecules in genetically defined kidney diseases, spearheaded by Vertex but also including several biotechs. In a devastating set of indications broadly referred to as APOL1-mediated kidney disease (AMKD), Vertex’s inaxaplin (APOL1 inhibitor) is currently in an adaptive Phase 2/3 study set to read out an interim analysis later this year. Maze is also developing a small molecule APOL1 inhibitor MZE829, with topline data in 1H 2026 from their Phase 2 proof-of-concept study. If successful, these programs could be blockbusters in an important segment of kidney disease. In another genetically defined kidney disease, autosomal dominant polycystic kidney disease (ADPKD), Vertex is developing a small molecule corrector VX-407 for a subset of patients with certain PKD1 variants. While that proof-of-concept study may not read out until 2027, other progress is being made towards developing corrector chemistry for ADPKD, including from Renasant Bio.

Additionally, while earlier-stage, strides are being made in precision delivery of therapeutic payloads to the kidney. While some oligo therapeutics like farabursen (RGLS-8429) have historically relied on chemistry for preferential kidney delivery, for many payloads there is benefit in targeted delivery. Two companies in particular are pioneering novel kidney-selective delivery approaches – Judo Bio and Borealis Bio. While clinical readouts on these targeted oligo delivery strategies are unlikely as early as 2026, we will undoubtedly see progress made towards potent and selective kidney delivery this year.

Regulatory & Commercial

• 2026 will see the first (accelerated) approval of a blood-brain barrier (BBB) shuttle technology by the FDA. Denali’s DNL310 (tividenofusp alfa) leverages a transferrin receptor (TfR1) binding arm to transport an enzyme (iduronate 2-sulfatase) across the BBB and into the CNS for Hunter Syndrome. The PDUFA target action date for potential accelerated approval is April 2026, and I am optimistic about an approval even with all the noise coming out of *certain FDA leadership* on open-label studies. The Phase 1/2 data was recently published in NEJM, and while most of the data are not new, collectively it solidifies the impressive results in an underserved patient population. The performance, compared to marketed Elaprase, at this point is uncontested. While certain aspects of the TPP (frequent and long infusions, high levels of infusion reactions, some anemia, etc.) may be less than perfect, the program represents a huge step forward in treatment for Hunter Syndrome as well as for CNS delivery of large molecules. Whether or not DNL310 is successful in AA approval this Spring, there are a host of additional TfR1-based BBB crossing technologies in the pipeline, most notably additional programs from Denali, Roche’s trontinemab, and AbbVie’s ABBV-1758 (via Aliada acquisition).
• If you build it, will they come? We will see critical late-stage development traction around 2 additional indications previously written off by (most) Pharma and investors. As an industry, predicting future blockbuster markets is not our strong suit. Very few expected the Rezdiffra sales trajectory in MASH despite being the first effective drug approved in 4 decades in the indication. Before Madrigal started commercializing Rezdiffra, common bear refrains included “improvement in MASH resolution is modest rather than transformational,” the GLP1s will make MASH obsolete,” “payers will require biopsies – it will be a slow sales ramp,” “how will a biotech commercialize in MASH?”). And yet a year after launch it is on track for nearly $1B in sales, with high growth forecasted through 2031. 2025 saw a banner year in MASH acquisitions too, with 3 deals for FGF21 programs totaling >$10B in total deal value. So, clearly MASH is a compelling blockbuster indication, it just took an incredible sales ramp for a strong product in Rezdiffra to prove it.

As another example, until the recent enthusiasm for OX2R agonists in hypersomnias, the space was seen as challenging (“it’s a 10-year patient odyssey to get diagnosed – the addressable population is too small to matter,” “oxybates and stimulants are generic – switching hurdles are high.”). Even in an indication as “obvious” today as obesity, before 2021 it was widely frowned upon to be an investor in the indication, given common (but misplaced) refrains of limited and fragmented market, difficulty associated with primary care providers as a key callpoint, stigma against patients, and the like.

Is the (often ill-conceived) pushback a symptom of groupthink? Is it a cumulative lack of creativity? A fear of being non-consensus and wrong? We have all been crowding into the same small set of 30-40 “safe” indications with clear comparables and well-understood, precedented sales ramps. Future growth (especially of the scale required to offset LOE cliffs – more on that below) will need to come from new, large TAM indications where exponential growth is feasible.

Capital Markets

• 25-30 biotech companies will IPO this year. Compared to <10 IPOs in 2025, I expect there to be a substantial increase in companies going public, especially in the first 6 months of the year. The volume will still be measured compared to the highs of 2020-2021 (when there were 75-85 biotech IPOs annually), but will reflect a more favorable market dynamic.
• 2 biotech stocks will have +300% single-day stock swings following positive data. By my (and ChatGPT’s) count, only a few stocks had 300%+ single-day stock swings following positive data[1]: Abivax, ProKidney, and Capricor. Generally there are dozens of stocks that trade exceedingly well upon positive trial readouts, increasing by 50-100%+ following a readout. But for huge swings, investors need to be non-consensus and right. While +300% may be a higher bar in 2026 with markets overall in a healthier position than they were for much of 2025, I do think we will see at least a couple massive upswings.

M&A

• 2026 will continue the M&A streak, with >25 deals over $1B in value. 2025 was a banner year for M&A, especially larger deals – 32 acquisitions topped $1B in total value, and 8 of these topped $5B. Drivers of this increased activity included lower perceived FTC risk under the new administration as well as pressure to fill future revenue gaps driven by loss of exclusivity (LOE). But when are pipelines satiated? For how long will patent cliffs continue to drive inorganic growth? To get a sense for just how much 2025 M&A chipped away at threatened revenue due to LOE, I evaluated M&A spend (focused on acquisitions of >$100M and not including other BD like licensing deals) compared to projected Pharma revenue loss over the 5-year time period 2026-2030. As shown in Figure 1 attached at the bottom, some Pharma with imminent and substantial revenue loss are spending aggressively to fill pipelines. Merck, for example, targeted both commercial products (via Verona) as well as late-stage pipelines (via Cidara) to fill the gap largely driven by upcoming Keytruda biosimilar entry. Others (like Pfizer) focused on competitive acquisitions of potential blockbusters in large hot categories (e.g., Metsera). Several Pharma spent materially, not necessarily to fill near-term revenue gaps but rather those anticipated in the 2030s (e.g., JNJ, Sanofi, Novartis). Finally, some Pharmas with imminent LOE threats did not spend much in 2025, likely both a function of cash (and debt) capacity as well as lack of true pipeline synergies in potential acquisition targets. Perhaps some of these Pharma become more aggressive buyers in 2026?
• We will see another 2 bidding wars this year. The Pfizer / Novo bidding war for Metsera and then subsequent Alkermes / Lundbeck bidding war for Avadel at their best can be described as rationally competitive and at their worst, unhinged. In both cases the first bidder ultimately won the deal, albeit at a higher price (to the benefit of target’s shareholders). As Pharmas acquire to rapidly compete in emerging hot spaces (see above re: new market commercial builds), we are likely to see at least a couple more bidding wars. I’ll grab the popcorn…

Macro

• China will soon transition into an innovation economy. Most of the competition coming out of China in 2025 was of the me-better variety, but we are starting to see more innovative first-in-class approaches. I’ve been surprised by the protectionist sentiment in response to China’s rise – it seems to me the one way to assure China’s dominance is to become isolationist here in the U.S. and attempt to restrict global collaboration. I hope the case study of China – a country-wide focus on implementing massive step-changes in efficiency across scientific, manufacturing, and regulatory verticals – can be an inspiration to other nations rather than something we fear. Collectively, my wish is that 2026 brings less unproductive grand-standing and quibbling over overseas competition and more collaborative action to improve our own innovation ecosystems locally.
• We will be able to meaningfully quantify FDA dysfunction in 2026 – we will start to see more systemic delays in drug approvals and trial initiations. “Nothing changes instantaneously: in a gradually heating bathtub you’d be boiled to death before you knew it.” – Margaret Atwood

I hate to say it, but I predict we start to see cracks in the regulatory system in 2026, including more frequent and impactful delays in PDUFA dates and IND feedback (latter of which will be harder to track), as well as more instances of senior leaders over-riding their teams on critical decisions. What’s happening at the FDA right now is extremely concerning – 90% of senior leaders in place a year ago are no longer at the agency (source), and it’s likely that 25-35% of the agency’s rank and file has turned over (both voluntary and involuntary). I commend those still at the FDA who are getting work done and maintaining critical review timelines. But I have to ask – how long can we expect top performers to last in what appears to be an increasingly toxic workplace that doesn’t seem to place universal value on scientific evidence-based decision-making? I certainly hope my fears are over-stated, but I don’t think we can continue to assume the status quo will hold. What happens if we lose a critical amount of competent team members and it takes 2-5 years to rebuild? Can we afford that kind of hit to our drug development productivity?

What can we do as an industry? (1) Continue to be vigilant – track delays and U-turn decisions by the agency. (2) Call your representatives – it may not feel like much of an impact but it matters, especially with midterms coming up. (3) Applaud agency actions where they are science-based and innovative (e.g., those that promote increased transparency in decision-making or those that offer potential to rationally streamline path to approval for critical medicines). There is high potential for a productive path forward, but we as an industry need to speak up when decisions are likely to cause undue harm to patients and our biotech economy.

• A broader recession could threaten the biotech recovery. 2025 witnessed exuberant markets even despite broader macro tailwinds. I won’t attempt to take a stance on whether or not we are in an AI-driven tech bubble, but it’s hard to imagine we will go much longer without some type of reset (and not just a short-lived March 2020 or April 2025 flavor of reset). While I am certainly not rooting for a broader recession, if / when one does occur, it will likely impact markets broadly. We are now just starting to see generalists tiptoe back into the sector – perhaps biotech will be viewed as a haven if tech hits a bumpy patch. More likely though, we may see another period of leaner times including both company and investor attrition. While not obvious in the moment, every cycle (bull or bear) has an end, and there are always opportunities even in the downward slope.

Early-stage biotech VC and company creation

How does all the above translate to the small but mighty portion of the VC universe that is early-stage biotech (and specifically company creation VC)?

• The top 25% of private, early-stage deals will be competitive and finance at valuation premiums. Select high-quality, high-conviction deals are hot again, even at earlier stage (late preclinical to early, pre-PoC clinical). Investors shouldn’t assume they can hang around the hoop and wait for a round to come together. For the top tier of deals in 2026, syndication is likely to be competitive and rapid.
• Seed and Series A companies formed in 2025-2026 will stay stealthy for much longer. Given macro pressures (especially the speed at which “fast follower” approaches are emerging), I expect up to ~35% of new companies formed in 2025-2026 to remain in stealth for much longer than was historically common (3-12 mos.). Some companies may never officially publicly “launch” if exited via acquisition over the next few years. Don’t let the veneer of a quiet news stream from early-stage biotech VCs phase you – many firms have been extraordinarily active recently, but are not publicizing newcos in order to maintain competitive advantage for non-obvious theses.

Closing Thoughts

If 2025 taught us anything, it’s that the highs are exuberantly high and the lows are devastatingly low, but either sentiment is transient. Turbulence always leads to pockets of opportunity, and some of the most successful biotechs have been built in the challenging of times. It’s difficult when in the moment (of a bubble or a meltdown) to keep a level head, but I’m hoping for more measured decision-making in the new year. The one prediction I know to be true is that I will be wrong more often than right – look forward to checking in at the end of 2026 to reflect on just how badly I misread the vibes for 2026

Footnote [1]: Only includes biotechs that were up ≥300% at close (vs. intra-day) and those with market cap >$100M at time of news release

The post Chaos is a Ladder: Predictions for 2026 appeared first on LifeSciVC.

      

Related Stories

• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective
• Atlas Venture 2024 Year In Review
• Ex-US Clinical Trials: Tribulations, Preparations, and Expectations

 

I recently joined Inventiva Pharma, a late-stage company developing a therapeutic for metabolic disease and MASH, a field that is both scientifically complex and once again visible. It is an interesting moment to return to late-stage work after several years in early-stage biotech. Early-stage life is oriented toward possibility and invention. Late-stage life is oriented toward consequence and delivery, and that difference becomes apparent almost immediately.

In earlier essays I described the capability stack, the collection of technologies and tools beneath a therapeutic, and the organizational stack, the system that must carry those tools into practice. The widening space between them is new. The capability stack is compounding faster than the organizational stack can absorb. They no longer move together. Their separation is where the hard problems live, where time reverses its logic and each day brings less room to explore and more urgency to decide. When I describe these as hard problems, I mean something specific. Early-stage development is demanding, but its demands are exploratory as much as procedural: documenting biology, mapping uncertainty, designing early trials, understanding exposure, and building an initial picture of behavior. The work is structured around learning. The system expects uncertainty and tolerates failure because its purpose is knowledge accumulation. Even setbacks add information.

Late-stage development is different, and returning to it makes that difference clear. What once felt open now feels bounded by commitments already made. Systems that once made the work seem effortless now reveal their limits. Late-stage development rewards precision and continuity. The cadence is set not by what teams hope to discover but by what must hold to bring a therapy to patients. Much of what matters is quiet and rarely noticed until it is tested. Early-stage environments can absorb iteration. Late-stage environments tolerate very little of it. The problems become hard because at some point you must assume you understand enough biology to scale. The phase 3 program must work because the phase 2 signal held. The safety profile must remain stable because the aggregate data suggest it will. Entire medical affairs and commercial plans begin on foundations that feel familiar, but organizational sediment can obscure how fragile those foundations are. Commitments expand. Timelines narrow. Consequences sharpen. Planning shifts from kilograms for trials to tons for global supply. The questions shift from whether the biology is plausible to whether the system can withstand the scrutiny of regulators, health systems, and payers across regions. Scientific uncertainty becomes operational and financial.

That is the point at which the work becomes hard in the way I mean it. You are no longer learning. You are committing.

In my earlier essay on biotech’s strategic infrastructure moment, I argued that value was no longer defined solely by the molecule but by the system that surrounded it. The geopolitical shifts of the past several years made this easier to see. Supply chain fragility, manufacturing sovereignty, and emerging federal infrastructure programs placed deployment on equal footing with discovery. Early-stage groups responded by building new layers. The capability stack thickened. What I did not fully appreciate then, but feel now, is how slowly the organizational stack adapts in late-stage life. Processes harden for reasons that are legitimate — regulatory expectations, patient safety, data integrity, commercial rigor — but those same reasons create friction when new capability tries to enter the system. A better tool does not immediately translate to a better outcome. Late-stage systems must absorb the cost of change before they benefit from it.

It is easy to dismiss this as conservatism, but I believe that much of it is structural. A later-stage organization is shaped by commitments already made. It holds inspection timelines, quality milestones, reimbursement strategies, evidence plans, and global sequencing decisions that cannot be rewritten because a new capability has appeared. The early-stage instinct is to improve the toolset. The late-stage reality is that improvement is only valuable if the system can tolerate the change.

What is new is the velocity gap. Early-stage teams build with assumptions about how the system will respond downstream — rapid CMC transitions, flexible manufacturing, resilient supply chains, and trial infrastructure that can pivot as needed. These assumptions rarely survive into late stage. What remains is obligation. And obligation without context is where systems fail. Late-stage organizations confront regulatory shifts, payer expectations, regional constraints, and timelines that compress rather than flex. Stated differently, early stage imagines what could be. Late-stage bears what must hold.

This difference in accountability resonates with me in ways that have been unexpectedly sharp and even a little painful. Before biotech I spent years in academic anesthesia and then in private practice as part of a twenty-eight-physician group responsible for the daily execution of care. The rhythm was precise and unyielding. Cases had to run on time. Surgeons needed to return to their offices to see patients. Every encounter carried both clinical and financial implications. At the same time, I served as the chief medical quality officer for the hospital system, responsible for moving the institution toward evidence-based practice, pay-for-performance expectations, and reimbursement tied to outcomes. The challenges in that environment mirror the challenges here: systems that depend on routine, coordination, and consequence.

But there is a critical difference. In medicine the work continues regardless of how difficult the day or week has been. Operating rooms open again. More patients arrive. The system stretches because the clinical imperative demands it. Late-stage drug development carries a different form of consequence. A difficult day or week may begin with the same type of event — a safety signal or a quality issue — but the effects do not dissipate in the same way. In clinical care the system resets. In drug development the event can redefine the program, and in late-stage work it often does, because the timeline does not forgive. That difference helped me see more clearly why the gap between the capability stack and the organizational stack matters so much now.

I have found that the most useful models for this work come from fields where timing, clarity, and consequence converge. As I wrote previously, special operations and medicine operate this way, and their principles translate more directly than anything I have seen in management literature. Business theory often reaches similar conclusions, but from a greater distance. In practice the rule is simple: authority should move to the person with the clearest context. It is a functional requirement. The statistician inside the data flow of an ongoing study. The pharmacovigilance lead who senses a pattern before it appears in the signaling data. The regional medical lead who understands why a label phrase will block access in one country and not another. On many days those are the people who should lead the decision. This only works when the organization has done the slow work of building shared context.

This is where the stack disconnect becomes an organizational problem rather than a technical one.

Early-stage capabilities often promise acceleration, and early-stage teams expect downstream systems to absorb advances with the same agility. Assumptions accumulate quietly: manufacturing can pivot without penalty; quality systems can absorb new data flows; regulatory pathways will accommodate new methods; medical affairs will translate emerging complexity into something clinicians can use. Each assumption is reasonable on its own. Together they form an implicit model of a system that can stretch indefinitely. Late-stage organizations do not have that latitude. They carry the accumulated commitments of the program. They cannot accept acceleration as a free parameter. Every gain in speed demands assurance that the system will remain stable under inspection, reproducible in manufacturing, defensible to regulators, and credible to payers. This requires a clear articulation of where the system can stretch and where it cannot. Without that framework, claims of acceleration become noise.

The real work shows up in decisions that cannot be abstracted away. When to file an NDA and how much data is enough. Whether to dual-source drug substance or accept the risk of a single supplier. How far to extend long-term safety exposure, not only as a scientific or ethical matter but as a regulatory and reimbursement requirement. These are not theoretical tradeoffs. They commit an organization to a specific course of action. They determine whether a therapeutic can reach patients and whether the company can return value to the long line of inventors, operators, and investors who have already committed time and capital. They define what the system must now sustain.

So what to do about it.

The gap will not close by asking late-stage teams to behave like early-stage groups or by assuming new capability will carry itself into practice. Most of the work lies in creating deliberate connections between the two stacks so expectations and constraints are visible at the same time. One answer is to create explicit handshakes between the stacks. Someone has to translate a capability into operational terms and surface its consequences early. A small integration group working beside a pivotal program can remove months of delay by capturing the cost of adoption before it becomes a crisis. When implications are visible early, much of the cynicism that accompanies late change begins to recede. This is also where familiar business vocabulary tends to surface — change management, alignment frameworks, integration models. The labels are secondary. The function is what matters: make consequences visible early enough that the system can absorb them without destabilizing itself.

Narrative is another form of handshake. Early-stage groups speak the language of capability and potential. Late-stage groups speak the language of credibility and durability. If a capability cannot be expressed in late-stage terms, it is not ready. It remains aspiration rather than practice. A common example is a pharmacovigilance vendor promising faster triage or improved signal detection. In early-stage settings that may sound sufficient. In late-stage settings it is not. The questions become concrete: Can they meet inspection standards across regions. Can they maintain validated audit trails. Can their system remain consistent across thousands of cases when a label is under review. Can safety leadership explain the basis of a signal if part of the logic is automated. If those translations do not exist, the capability is not ready.

Medical Affairs often becomes the definitive handshake here. Their work is sometimes reduced to perjoratives — messaging, unprompted recognition, field alignment — but those are partial descriptions of a much harder job. They set the external frame: how clinicians, thought leaders, healthcare systems, and payers will understand the therapy, what evidence will matter, and where the claims must hold. If the narrative remains rooted in capability and potential, the organization will fail at the point of consequence because none of the external stakeholders operate in that register. Medical Affairs must translate innovation into a story that is scientifically coherent, clinically relevant, and durable under challenge. When the capability stack outruns the organizational stack, this becomes defining work.

It is easy to treat the gap I’m describing as a failure of intent and to assign blame to policy shifts, risk committees, or cultures shaped by different tempos. Each plays a role, but the gap, as I stated above, is structural. Late-stage constraints exist because obligations are real. Patients, regulators, and payers are not theoretical. The system carries commitments that cannot be undone simply because a new capability has appeared or because acceleration seems desirable.

The challenge is to help the organization absorb new capability without destabilizing itself. That is not a matter of speed. It is a matter of design, and the work does not resolve neatly. Early stage opens into possibility while late stage narrows into consequence, and the space between them now carries most of the real difficulty. These problems are not solved by accelerating one side or restraining the other. They are solved by making the system explicit: where it can stretch, where it cannot, and what each decision will displace downstream. Organizations that learn to operate with this level of clarity will carry programs across the gap. Those that do not will continue to mistake speed for progress. The space between the stacks is no longer a transition. It is the operating environment for modern late-stage biotech.

The post Back to Late Stage: Where the Hard Problems Live appeared first on LifeSciVC.

      

Related Stories

• How (and why) do you measure a year in the life? A Practical Guide to Defining Clinical Unmet Need
• Huntington’s Disease and The Triad of Therapeutic Conviction
• Balancing the Risks and Rewards of Drug Development in China: A Small Biotech CEO Perspective

 

I often hear biotech leaders refer to their company’s technology and approach in terms of the “secret sauce” or “how the sausage gets made.”  I have probably used these phrases myself over the years.  But here is an interesting question…I wonder how often these words are said with the company’s Chemistry, Manufacturing and Controls (CMC) in mind?

CMC is usually on the critical path and increasingly a major cause of Complete Response Letters (CRLs). Of the >270 CRLs released by the FDA in recent months, more than 50% of the rejections cited CMC issues and resulted in significant delays, as Bruce Booth highlighted in Atlas Venture’s Year in Review 2025.  Perhaps this is not surprising given the difficulties in developing and producing some of the more complex medicines we have today.  With the regulatory requirements for advancing research grade material to clinical supply and then moving to developing and manufacturing pivotal trial supply, it seems that it’s one gigantic leap after the next.

CMC represents a significant expense and is often the highest risk since a major part of the spend precedes clinical data.  So why don’t we talk about CMC more across our companies and in Boardrooms?  Why don’t we share what we’re learning along ‘the CMC way’ so that we can help others adopt and cultivate the right mindset at each step, thereby achieving better outcomes for patients?

Here, I will piggyback on the irrepressible Mike Gilman’s most recent blog on LifeSciVC.  Mike described a fundamental truth about science:  “There are standards – the right way to do science – and they are non-negotiable.”  Back in my graduate school days in the Department of Microbiology at the University of New Hampshire, I met a guy early in my first year who was in the Biochemistry Department (one floor below us) named Dave Nichols.  We hit it off immediately as we shared a similar view on scientific rigor and the quality of the draft beer at the local bar in downtown Durham where we often went to grab dinner (and a beer) after a long day in the lab and before heading back to finish up with experiments later into the evening.  That was 40 years ago…so how does this relate to the topic of CMC you might ask?

Post graduate school, I spent 15 years heading up a lab at Harvard Medical School where it turned out that manufacturing was key to the success and quality of the research we did to establish a new paradigm in immunology:  that polysaccharides isolated from the surface of certain bacteria could activate and convert naïve T cells to become T regulatory cells.  This concept was considered heresy in immunology circles as textbooks taught us for decades that sugars from bacteria are T cell-independent antigens.  The argument from critics and reviewers of our work was that there must be some protein contaminant in our manufacturing preps that was responsible for this finding.  Well, it took a while, but we put that argument to rest through rigorous science and strict quality control measures that we employed in our processes.  This body of work ended up being published in top-tier journals and the technology was ultimately licensed to pharma.

So, when I joined Shire in 2005, you might say that I had an ‘enhanced’ view of the importance of CMC and manufacturing compared to most academics at the time.  The importance of this capability only became more evident when programs I worked on in Discovery reached the development candidate stage and needed to be transferred over to the Process Development (PD) group within the Technical Operations function.  And guess who was a key person in that group at the time?  My old friend Dave Nichols.  This transition from Research to PD had historically been a tough one at the company.  But Dave and I worked together to make this process more seamless and efficient over the years.

Later on at Shire when I headed up Program and Alliance Management, I was responsible for leading diligence on potential acquisition targets to bolster our pipeline.  Dave was often part of those teams, and we saw many great companies with promising drugs, but we would often pass because companies were just not prepared on the CMC side for moving these drugs into the clinic or progressing them to pivotal trials.  It became very clear that smaller companies (and even some large ones) did not realize the many advantages of incorporating early CMC effort into their workstreams.  A proactive, integrative mindset for development, quality, process, and product understanding was not a priority.

The critical role that CMC plays was crystallized for all of us in 2008 when the FDA asked Shire to ramp up supply of VPRIV, an enzyme replacement therapy for Type 1 Gaucher disease, to help with a CMC crisis that a major competitor and, by extension, their patients were facing. Shire was able to meet that demand as the leaders in our company always kept CMC front and center.  I learned a lot from this experience, and it is why I insist on having a CMC mindset for companies that I run, advise, or evaluate.

In the summer of 2023, I reconnected with Dave over lunch after he had just finished his run at Magenta, an antibody drug conjugate (ADC) company focused on oncology indications.  I discussed with him the concept of a new company that I was forming with ARCH Venture Partners, Atlas Venture, and 5AM Ventures as investors.  Lifordi Immunotherapeutics (as the company was named) endeavored to take the ADC concept in a different direction.  I told him that we were going to lead the way by leveraging the success of ADCs in oncology and bringing them into the immunology and inflammation (I&I) space.  Lifordi’s lead program was a novel ADC conjugated to a steroid designed to target immune cells, and I asked for his help.  His eyes lit up at the thought of this approach for ADCs.  But in Dave-like fashion his first question was, “Have you started the long-lead CMC activities yet?  Because if you haven’t, you better move your ass.”   That’s when I immediately asked, “Hey, what do you think about getting the band back together for this?”  Luckily for Lifordi, Dave agreed to be my first hire, initially as a consultant, and we started to get CMC moving ‘at risk’ since it was ahead of getting Lifordi officially started.  Thankfully, my investors saw the wisdom in this approach and funded the work with a note in advance of closing the $70M Series A.  And from there, we have not looked back as CMC has never been on the critical path.  Lifordi started a Phase 1 clinical trial of our lead ADC program, LFD-200, in Rheumatoid Arthritis in October that is enrolling and dosing healthy participants.

Smart investors and pharma companies make similar decisions when they evaluate biotech assets with a CMC lens.  In fact, this is a key reason why Sanofi Ventures just invested in Lifordi as part of our recent financing.  They recognized the value of an ADC-based approach for I&I, as well as the potential of LFD-200 based on a strong preclinical and non-clinical data package.  Sanofi also agreed that investing in CMC earlier rather than later is prudent, especially when it can shave 12-18 months off the timeline to a potential Phase 2 study.  It’s hard to argue that this is not a good use of proceeds.

Under Dave, our CMC team works very closely with multiple CDMOs that they have known and trust to produce each of the three components of our ADCs and another to conjugate them in a final product. Even though ours is a complex process, CMC remains ahead of schedule. Importantly, our research team continues to work hand-in-hand with CMC on building the pipeline and vetting new molecules upfront. If CMC can’t make it, repeat it, and deliver it with an eye toward scaling it, the molecule is put aside and significant savings in time and costs are realized.  Developing a cell-based mechanism of action assay and building off an industry-precedent setting subcutaneous formulation for our DAR 8 ADC generated by our Research team in New Hampshire, CMC has established a stable, pure, and scalable ADC in LFD-200 that is critical to our success.

What I have learned is that if you underestimate the time or complexity of CMC, Executive teams can:

• Rely too heavily on CDMOs

Do not assume that the development and manufacturing can run on autopilot. Trusting the CDMO to know what they are doing all the time can lead to performing work that isn’t required, and this can happen at each stage of development.  It may also result in cost over-runs that typically go unnoticed when there isn’t enough depth of in-house expertise and experience to know that you are being over-charged.  For years CDMOs have thrived outside the U.S. despite the added complexities related to storage, shipping logistics, customs challenges, and time zone snafus.  This is fortunately shifting now that pharma companies have committed billions of dollars to training people and building more manufacturing facilities in the U.S.

• Under hire technical expertise or mismanage the timing or number of FTEs

Do not assume that any technical person or any type of technical expert should be able to oversee CMC.  That is a big mistake and it’s usually too late when you realize it, e.g., when the FDA expresses concern, trials are delayed, material is lost, and costs escalate.  This is when management teams often react by building in-house expertise.  Demonstrating safety to the FDA and other regulatory authorities requires careful measures and a deep understanding of the robustness, purity, reproducibility, and quality of your product, among other things.  ALL this needs to be clearly presented at the time of the regulatory filing.  I remember a regulatory filing where CMC totaled 300 pages out of approximately 900 pages.  It took approximately four months to complete the document, which for the most part was written by in-house scientific and technical experts who were intimately involved in the process development and manufacturing of the drug.  Just imagine the time and cost that all of that work would have amounted to if it were fully outsourced.

• Fail to question, understand, or devote time to regular CMC updates

CEOs, Board members, or investors rarely, if ever, have a background in CMC or have risen up through the ranks from a development/manufacturing career.  As a result, fewer questions are asked or a misconception that CMC isn’t an important part of the business can develop.  The impact of CMC-related decisions on time and money may be sorely underestimated.  For example, it is not uncommon for CMC updates at Management Team and Board meetings to last as little as 3-5 minutes. Consider then that 25-40% or more of a financing may be devoted to CMC/ manufacturing, and yet <1% of the meeting time might be spent on discussing it at the highest levels of the organization.

It’s never too early to start CMC work and it’s definitely too late if you are just talking about it.  CMC needs to be a tie that binds teams and cross-functional efforts by integrating a development and production mindset into early scale development for a much smoother drug development path.  While we all understand and appreciate that clinical data moves the investment needle, preclinical and non-clinical data are ways to gaze into the “crystal ball” to gauge potential trial readouts.  Importantly, it also supports some ‘at-risk’ investment in CMC.  Recognizing the difficulty that comes with committing precious capital to CMC, especially early on and in  ‘risk-off’ environments, I always think that the risk in NOT supporting CMC is likely to be far worse.

What I’ve learned about CMC can be summed up nicely:

When you can, build it.

When you can’t, buy it.

In either case, control it.

And in all cases, fund it.

CMC deserves to be on our radar for good reasons.  If we wait until bad news focuses our attention on it, it’s too late.  Now that CMC/manufacturing is coming back to the U.S., it is a good time to talk about the power of ‘the CMC continuum’ and to bring it to bear throughout all phases of drug development.  We should be sharing more about the ‘secret sauce’ that an integrated CMC mindset yields in our companies, where we find that thoughtful CMC planning and practices turn good ideas into leads, development candidates into clinical assets and commercial products into medicines that can improve patient’s lives.

The post CMC: A ‘Secret Sauce’ of Biotech Success appeared first on LifeSciVC.

     

It’s been a hard one to pin down for our “Year In Review” – but we gave it a shot. In the past month, we’ve held our 2025 Atlas Venture Retreat with industry leaders and our Annual General Meeting for our Limited Partners (our investors). It was a privilege to once again kick off each of these meetings.

Here’s the link to this year’s presentation: Atlas’ 2025 Year In Review.

As I’ve noted in past years, listening at 1.25x+ speed is definitely recommended, and moves through the nearly 50-minute presentation slightly faster.

For those interested, here’s a library of past Year In Review presentations (2024, 2023, 2022, 2021, 2020, 2019, 2018).

Special thanks again this year to my Atlas colleagues Aimee Raleigh and Kristen Margeson, who were instrumental in helping me pull it together, and to Edward Goin, our graphic designer.

I’d also like to thank all of our friends in the industry who helped provide data supporting this talk, including BCG, Cambridge Associates, Cantor, Centerview, Citi, Cowen, Evercore, Goldman Sachs, IQVIA, Jefferies, JP Morgan, Leerink, LEK, McKinsey, Morgan Stanley, Pitchbook, Radford, Raymond James, SVB, Stifel, and others (listed in alphabetical order).

The post Atlas Venture 2025 Year In Review appeared first on LifeSciVC.

     

Hospitals were never meant to be familiar to me, yet for over twenty years MD Anderson became a place I could navigate by heart: the cafeteria where families gather between updates, the hidden cafés where you grab a moment to breathe, the lobbies where hope and fear sit quietly beside each other. I know the hallways that lead to conversations no family ever wants to have, and I know the small relief of stepping outside to feel the humid Houston air after hours at a loved one’s bedside.

This October, my mother passed away at home after years with breast cancer in the same room my father passed away seven years earlier after his MDS turned into AML. Becoming “orphaned to cancer” is a phrase I never thought would apply to me, yet I have realized how many people live with versions of the same story.

While there are many hard moments in the journey for the families of cancer patients, the hardest moments are the last conversations — the moments when their oncologist has nothing left to give.

“We have run out of options, and the tumors grow as they remain untreated. It’s best to start giving [them] comfort.”

That finality stays with you. And it’s also what fuels me — why I, and so many of us, work in drug discovery and development, and why we stay committed to it. 

Biotech’s Quiet Engine: Determination in a Cynical World

 People outside biotech see the cost of a drug.
People inside biotech see the absence of one.

I’ve lived my entire adult life in bio-pharma — forming companies, advising founders, and now leading one. And what continues to strike me is how far the public narrative sits from our lived reality. Outsiders see pricing, margins, and mega deals. Insiders see the absence of a therapy that should have existed. What they also miss is that many people in this field carry their own losses where many of us have sat in hospital rooms wishing for an option that wasn’t there.

Most people in biotech didn’t choose it for predictability; they chose it because they want the next family in that room to hear a different sentence. That motivation shows up quietly, usually in the effort to understand imperfect data, in the patience to test whether a signal is real, and in the willingness to stop or redirect a program when the evidence demands it.

Grief can sharpen your focus, but rigor moves the science forward.

Personal experience can shape purpose, but it cannot dictate decisions. One responder matters profoundly, as I understand that now in a deeper way, but one datapoint cannot drive a program as well. We owe patients the discipline of evidence, the humility of uncertainty, and the willingness to stop when the data asks us to. The evidence eventually tells us whether a therapy has the strength to reach approval and truly change lives.

 What This Means for Building Companies

Progress happens when personal motivation and data-driven judgment work together.

What truly builds a biotech isn’t jargon or frameworks. Capital efficiency. Portfolio discipline. Clear go/no-go gates. These ideas matter and they help keep teams focused. But, none of them work without something more basic: people who care enough to keep going when the work gets confusing or slow or frustrating.

Great teams carry resilience into failure, creativity into constraint, and urgency into every small decision because they know a patient, someday, will rely on them. We remember that behind every mechanism, every model, every IND package, there are real patients and families waiting for better answers than the ones my family was given. That mindset is what turns a research project into a real therapeutic effort.

Where Nimbus Fits In with My Mission

At Nimbus, science is our vehicle. Purpose is our driver.

I have often wondered whether my personal experience with cancer would pull me toward one part of our pipeline at Nimbus more than another. Instead, I found something broader: a vision driven by a simple belief that patients need therapeutic options, wherever it shows up. Our programs span oncology, immunology, and metabolic diseases. Each therapeutic space looks different on the surface and have their own unique challenge to develop, but they all start from the same place: something is missing for these patients. Every debate, every paused molecule, every long data meeting carries an unspoken truth: these choices will matter to real families. And eventually, the pharma partners — the shepherds, as we call them inside our office walls — who we believe will deliver our agents to patients most effectively and make the most meaningful impact.

Losing my parents has not hardened me, but instead brought me clarity. Experiencing death strips away ego and reminds us that time is our most precious currency and turns for me company-building from a strategic pursuit into an act of service. We don’t get to choose which diseases touch our families, friends, and loved ones, but we do get to choose how we respond. For me, the response is simple:

Honor their memory by building therapies that give other families more options.

Push the science forward with discipline and conviction, and stay grateful for the privilege of doing work that can change a life.

To continue supporting this mission, we created The Kazimi Family Endowment for the Institute for Data Science in Oncology (IDSO) at MD Anderson. It is one small way of turning grief into contribution and keeping our parents’ legacy alive in the work ahead. A memorial page for my mother can be found here. 

The post Death, Grief, and the Drive to Build Better Therapies appeared first on LifeSciVC.

     

I’m far from the first person to point out the prolonged market challenges that have depressed the biotech fundraising environment over the past few years. While we’ve recently seemed glimmers of optimism in the form of positive data read-outs, sizeable M&A transactions, and increased clarity on topics like drug pricing and tariffs, we remain far removed from the (all too unsustainable) boom of five years ago.

A limited access to capital places undeniable strain on nearly every stakeholder in the sector. From a corporate perspective specifically, as a leader looking to build a company for long-term success, the strain is particularly acute. In a field like drug discovery, innovation simply can’t happen overnight – it takes years to bring forward and develop new medicines, and these years come with an unavoidable capital cost.

In navigating these current waters, I’ve found myself reflecting on some of my past experiences – be it the downturn of the early 2000s or the 2008 recession – for not only the lessons learned, but for the perspective gained by having to guide a ship through a difficult course.

Each of these downturns felt existential at the time, with many jobs and companies lost in the absence of capital. And yet with hindsight, each of these cycles also created the fertile soil from which new leaders emerged, sharpened strategies took root, and truly innovative companies began to grow.

And this dynamic is worth sitting with: as painful and seemingly endless as a market contraction can feel, they inherently necessitate clarity of vision and decisive focus for companies.

Forged in Scarcity: The Nimbus Story

Looking back to 2009, during my days as a member of the founding team of Nimbus Therapeutics, we were deeply inspired by the promising findings of our early computational work, yet faced the uphill climb of establishing our footprint in an era in which skepticism around computational drug discovery abounded. At the time, one colleague joked “computational chemistry can tell you why a molecule didn’t work two years after you shut down the project.”

Capital was incredibly hard to come by in this climate. We ground our way through countless meetings with VCs who thought we were crazy. “We’ll get back to you on Tuesday … ” was oft the response (and when we didn’t hear back for months, it was clear the VC hadn’t specified which Tuesday).  Despite these frequent closed doors and dead ends, we triaged the feedback, focused our vision, and sharpened our story. Amidst all the negativity, we never lost the conviction that Schrödinger’s 20-year innovation powering the Nimbus team/business model had something very special to offer when directed the right way.

Despite tremendous headwinds, we were able to secure key investments from non-traditional pockets of capital, such as from Bill Gates, that anchored and ultimately enabled our first proper Series A financing. During this time, I especially appreciated the active engagement of our founders (Bruce Booth and Ramy Farid) as it took all hands on deck to navigate through this specific downturn.

Looking back at the early days at Nimbus, I still remember the pain, but also can plainly see how the enforced discipline made the company stronger and laid the groundwork for the company’s ongoing success. A clear and decisive early strategy was a key ingredient in what has ultimately allowed Nimbus to successfully demonstrate how computational chemistry can indeed be used prospectively to design breakthrough medicines.

Today’s Environment: Parallels to the Past

Fast forward to today, and the industry is living through another prolonged correction.  Following the pandemic boom, the tide rapidly receded and the persisting hangover is severe. I feel this acutely while running an early-stage private company, as a large proportion of capital has shifted to later-stage clinical stories or, in the case of generalists, away from the sector all but entirely.

The contours feel familiar – skepticism, scarcity, triage – but the duration may be even longer this time. The implication for founders and CEOs is clear: discipline isn’t optional. Clarity isn’t a nice-to-have. The bar for capital efficiency and differentiated science has permanently risen.

Principles for Leading Through Scarcity

Looking across these cycles and reflecting on my own journey, a few themes stand out for those leaders navigating the current environment.

1. Hardship Clarifies Strategy
   When capital is abundant, it’s easy to pursue too many ideas at once. In contrast, scarcity forces prioritization. It demands that leaders answer the hard questions: What is essential to our mission? Which bets truly differentiate us? Steve Jobs is famously quoted as saying, “Deciding what not to do is as important as deciding what to do.” At HotSpot, a close examination of our pipeline has led to the challenging decision to partner our oncology programs. Despite encouraging early clinical and pre-clinical data, we recognized that a focused internal deployment of capital on our immunology pipeline opens the door to realize distinct synergies – of talent, platform, and innovation – within this one area, which is all the more valuable in a scare capital environment.
2. Execution Discipline Creates Credibility
   In a bull market, momentum stories can raise capital on vision alone. Non-specialist investors that lack the patience for life-sciences value creation flood into the sector. As one buy-side investor put it, “tourist investors were investing in cartoon companies.”  In our current environment, generalist investors drawn solely to big visions and exciting stories have largely exited, and the overwhelming focus is on product stories with clearly defined upcoming milestones. In this environment, investors and pharma require execution, not froth. Companies who show they can thoughtfully deploy runway to deliver on milestones are the ones who create financing opportunities, whether that be through capital raising or partnerships.
3. Culture Sustains Endurance
   Downturns are hardest on teams, testing the leadership and culture of a company. Transparency, intellectual honesty, and a shared sense of purpose are the shock absorbers that get a company through lean times. In my experience, the companies that endure are not just the ones with capital in the bank, but the ones with cultures resilient enough to keep believing in the mission.

The Upsides of a Downturn

None of this is to downplay the pain – and potential permanent costs – of the current market. Over just the past two years, 156 public biotech companies have ceased to exist, representing a 16% contraction – such a large number of shuttered companies and shelved companies undoubtedly carries unknowable costs. And yet we can also view this contraction through a different lens – a lot of bloat has been culled, leaving focus, discipline, and innovation in its wake.

The companies that survive this period will skew toward those with genuinely differentiated science, thoughtful capital strategies, and resilient teams. And these are the precise ingredients that enable the industry to advance real medicines to patients. Moreover, past experience suggests that these companies will be rewarded. The post-contraction IPO market has historically been extremely robust, in large part due to the pent-up demand from many high quality companies that effectively weathered the storm of the contraction and now sit in queue.

While each market downturn I’ve lived through causes deep pessimism and anxiety, it’d be inaccurate to deny the lens of hindsight, wherein each has also cleared the path for a new wave of innovation and for a generation of leaders who were forged in adversity. That’s the quiet promise embedded in today’s challenges: clarity, discipline, and resilience are being cultivated in real time.

The post A CEO’s Perspective on Navigating Choppy Market Waters appeared first on LifeSciVC.

     

My thesis advisor, Mike Chamberlin, passed away last week. I owe much of who I am as a scientist to him. As anyone who worked in his lab would surely agree, he was not an easy guy to work for. I don’t think that was a strategy; it’s just who he was. And it took me many years to fully appreciate why and how that helped me – even though it certainly didn’t feel that way in real time.

I still remember my first group meeting presentation. I was a first-year rotation student and hadn’t even joined the lab yet. I don’t remember exactly what the experiment was, but it involved purifying ribosomes from T4-infected E. coli. The critical step was a long spin in the ultracentrifuge, which for some reason I ran at room temperature (and who knows how warm it actually got in there at 40K RPM). Maybe I didn’t know any better. Maybe I couldn’t figure out how to set the temperature and said eff it, I’m just gonna do this. In any case, as I presented the experiment to the group, I received my first piece of blunt MJC feedback: “Gilman, that’s the stupidest experiment I’ve ever heard.” At which point, everyone else in the group piled on. Literally over 45 years ago, and it’s still crystalline in my memory – which retains little else these days.

That’s what it was like in the Chamberlin lab. And, honestly, it was great.

Mike was a golden boy in the ascendant age of protein biochemistry and enzymology. He trained in the Stanford Biochemistry department, which was the beating heart of the discipline, dominated by Arthur Kornberg, famous for many things but notably for the aphorism, “Don’t waste clean thoughts on dirty enzymes.” That was absolutely Mike’s ethos. Every reagent in every experiment is purified, characterized, controlled, tested, and dated. Your pipettes are calibrated, your stopwatch is wound, your water bath is precisely adjusted, and your mind is clear.

We spent days in the cold room purifying RNA polymerase at ridiculous scale and in the hot lab synthesizing and purifying alpha-labeled rCTP from 100 millicuries of inorganic ³²P, because the store-bought sh*t wasn’t good enough. We purified phage DNA for transcription templates, which spun off a lucrative bartering business in the early days of cloning, as we would also isolate and purify T4 ligase and polynucleotide kinase, which we’d swap for restriction enzymes purified in other labs in the building. The principal transcription template in the lab was T7 DNA, a by-product of which was purification and characterization of the phage-encoded RNA polymerase, which, unlike the E. coli enzyme, was a stable single-subunit monster that could transcribe DNA into RNA like nobody’s business. Ironically, T7 RNA polymerase may be the reagent we’ve spent the most money on at Arrakis. I probably purified a million dollars worth in graduate school. Circle of life.

Over his career, Mike tackled pretty much every step in what we came to call the transcription cycle for bacterial RNA polymerase. How RNAP locates promoters, settles in, melts the DNA duplex, initiates transcription, elongates the RNA, checks its work along the way, recognizes termination sequences, releases and recycles. The lab workhorse was Mike’s famous “Method One” assay, a precisely choreographed and synchronized single cycle of binding, initiation, elongation, and termination, each step of which could be measured in the assay. It was a triumph of reductionism, revealing and concretizing the critical under-the-hood workings that form the foundation of gene expression.

What I came to understand about Mike with time, especially as I moved out into the world to see how other people practiced science, is that his bluntness, his impatience, and his obstreperous manner reflected a simple belief that guided his research and teaching philosophy: That there are standards – the right way to do science – and that they are non-negotiable. If you fell short, he was gonna tell you. Because that’s his job. And the scientific enterprise is owed no less. Once you understood that, it stopped being personal. You didn’t let him down, you let science down. That’s also why the lab culture was to rigorously and relentlessly critique one another’s experimental design, data interpretation, hypotheses. It was never personal – in fact, this was the tightest-knit, most mutually supportive team I’ve worked with. We were all committed to getting to the truth. What was really happening down there at the molecular level?

My own scientific mind is not as sharp as it once was, but the deep grooves that remain – that to this day, even when I’m clueless about the underlying science or technical method, I can still usually spot the missing control – that’s all Mike and the people he surrounded himself with. Mike taught me that science is a search for what is real and true, not what you wish to be so. And that the standard of proof is – and should always be – unassailably high.

Rest in power, MJC.

(The picture below with Mike and his wife and fellow faculty member Caroline Kane was taken in Berkeley in 2019).

The post Everything I Know I Learned In Graduate School appeared first on LifeSciVC.

     

For twenty years, to deliver on that, I’ve embraced what I call the “first principle” of early stage biotech investing: if we can positively impact the lives of patients by discovering and developing an innovative new medicine, the system will reward that risk-taking with superlative investment returns.”  I stand by this today, even with the turmoil and uncertainty raised from the policy chaos on drug pricing.

Beyond great science and wonderful people, the third big ingredient required to deliver on this first principle is capital – which brings us to the business of biotech. This is the third post in my blog trifecta reflecting on the last two decades.

In this section, I’ll cover a range of topics around corporate development (like BD and fundraising), board governance, and investor syndication.  This only scratches the surface of these topics… thankfully, there are over 400 prior blogs on this site for those that want to dig into these and other topics!

Here are a few business-related observations.

Corporate Development

• The cliché is true: BD deals are never done until the ink is dry. I’ve seen too many deals fall apart at literally the 11^th hour.  11:59 in fact.  Press releases agreed, champagne on ice… only to have a wonderful pharma “partner of choice” change their mind. Getting that phone call is a gut punch. It’s so painful, and often hurts the morale of executive teams and damages the cohesion of a Board… the blame game ensues out of the deal wreckage and there are usually casualties. You really can’t count your chickens until they’re hatched. The other axiom is BD is that “time kills deals” which is also certainly true. Don’t let minor non-critical, non-business issues drag on too long. Lawyers get paid to redline and “add value” by fighting to win every point during contracting, but it can come at the cost of time. BD fatigue is real and partners can and do walk because of it. If there’s an impasse in the deal dialogue (either at term sheet or in contracting), don’t let it fester – escalate it quickly to the right senior business principal (often the Head of R&D or BD, or in some cases the CEO) to get it resolved. Don’t be shy about doing that (see Opener vs. Closer CEO point in the People blog).  Where needed, leverage your investors’ relationships to help – I’ve made that call to senior Pharma executives countless times to facilitate getting a deal done.
• Companies are bought not sold – but with caveats. The gravity of Big Pharma’s balance sheets is too big for most emerging biotechs to escape over the long run, which is why M&A is a fairly common exit, at some point, for those firms developing or commercializing high impact new medicines. But putting a “for sale” sign up in front of an emerging biotech is generally not a great way fetch an attractive acquisition offer. Instead, planning for the long-term and executing on the business plan is frequently what attracts M&A interest. But, obviously, you can put yourself into the position of being an acquisition target by proactively engaging Pharma around BD discussions. Bankers will frequently acknowledge that 80% of M&A deals start as partnering/licensing discussions, and then flip into M&A through the deal dialogue. If you didn’t engage in any BD discussions at all, M&A likely wouldn’t happen (especially not for earlier stage companies) – so there is a subtle balance in most deals. Sure, you aren’t selling yourself, but you are helping potential buyers appreciate the great things you have going on in your pipeline. Add into the mix a good set of strategic deal advisors and the boundary between getting bought vs getting sold blurs even more.
• Going public really isn’t for everyone. Being CEO of a company that successfully priced an IPO has for years seemed like the brass ring to grab on the biotech carousel. It makes for a great career milestone, so everyone seems to want to go public. The bell-ringing NASDAQ photo with the confetti is awesome. There are, of course, legitimate reasons to go public – the primary one being to access the “lower” cost of capital funding from the huge pools of public equity investors, including both generalists and healthcare specialists alike. It’s almost always easier and faster to raise money as a public company. Rather than a six-month endoscopic dataroom-dive in the private markets, as a public company a good data catalyst can trigger an overnight offering, which can fill up the coffers nicely. But it’s not always easier, and in tough times it can be brutal to be public. As a “going concern” you need one year of cash to be public (versus the all too common situation of running it down to weeks of cash in the private setting). It’s also expensive: banker fees, D&O insurance, legal and audit costs, quarterly reporting, etc… It’s not unusual to burn $50M over the first few years as just the cost of becoming/being a public company. Being public can be a total drag: countless IR meetings, annoying investors, analyst management, short sellers to deal with, and so many other headaches. All of the public market focus can easily distract a leadership team from strategy and execution. Not to mention having your entire organization staring at a stock price every day (and your Board… I’m guilty!). It can feel like every move is somehow a measure of their success (or failure). It’s not, obviously. But the truth is one should only go public if you really can’t fund yourself as a private company through the important data inflection points. I’ve heard plenty of regrets from CEOs who got beaten up in the public markets, who went out too early… and I’ve shared those regrets as an underwater investor on several painful occasions.  Stay private as long as you can continue to access capital.
• Raising venture capital funding is never easy. Or maybe better said: raising capital at a “good” price is never easy. Our definition of what “good” is depends on where we are in the financial cycle. When times are tough and money is tight, it’s common to hear the refrain: “flat is the new up” so we’re just happy to get the money. I’ve seen multiple time periods like this since 2005. But in more bubblicious go-go times, “good” begins to require a healthy step-up in value or the fundraise will disappoint. Getting that step-up might not be easy. I’ve seen this euphoric part of the cycle a number of times, too, in particular in the pandemic bubble. But regardless of where we are in the cycle, one of the best ways to make a private biotech fundraise “easier” is to focus on the highest probability potential investors. Simply put, these are the ones that previously bought into a deal that looks like yours and made money from it. For instance, if they’ve never done an investment in a single asset biotech, you probably won’t be their first. In contrast, if they made money off of a deal like that recently, they will be looking to do it again. In general, investors like to draw lines from limited data points, sometimes just one point. This worked, it will work again. Or the opposite. This type of pattern recognition behavior is very evident in raising capital, and focusing on this during a fundraise is helpful prioritization. I’ve seen teams spin their wheels chasing low probability folks; most of the time, it’s just not worth it. The other reality affecting the pain or ease of fundraising: it’s a relationship business and connections matter. If no one in your current syndicate or team knows anyone very well at a potential investor’s firm, it’s not a high probability conversation. Relationships build trust. Trust makes fundraising easier.
• Investment fatigue is real and can lead to loss of equipoise. Investments that become particularly long in the tooth can grind down the patience of even long-term investors, causing value-destroying decisions. Sometimes this fatigue is just a case of needing more electrolytes and stamina – convincing yourself to stay in because the thesis is still intact and it can still deliver positive returns. I think about Prestwick in 2008, where we sold it for a little more than our money back right after finally getting Xenazine approved, in part because the syndicate was tired after three CRLs from the FDA. We left a ton a value on the table. But other times the fatigue means it’s time to pack up and go home, and not throwing good money after bad. I’ve walked from several, and it’s painful after a decade of investing. Undoubtedly it’s very hard to walk away from a big loss rather than chasing it, but you have to fold up on occasion. Further complicating the decision, sunk costs shouldn’t matter in theory, until they do (in a closed end fun) based on marginal returns. Unfortunately, it’s hard to make this call and whether or not you made the right or wrong bet is only known with hindsight. To me, the key as a long term investor is to know you’re fatigued, be candid with your partnership, and try to get a fresh look at whether you should be more bullish (or even more bearish). And CEOs should try to spot a fatigued insider and offer to spend more time with their team, if salvageable.

Board governance

• Boards shouldn’t just be quarterly cheerleaders. Being supportive of the executive team and appreciative of their effort is certainly important, but Boards should know their role isn’t to just rubber stamp what the CEO wants. Good boards challenge their teams to be better – on overall strategy as well as the plan to execute against it. But Directors also need to do their work – be prepared, be helpful, engage productively outside of Board meetings, among other things (see blog here on high performing boards). Too many Directors (even some “celebrity” Directors!) are simply box-checkers who show up quarterly to pontificate; it’s particularly annoying if they simply cheer the CEO’s greatness all the time, when a more critical view might help the company. I could provide examples, but I’ll protect the innocent. As an aside, this point extends to Scientific Advisory Boards as well; SAB members should challenge and push the team, not just cheerlead. On Boards, the Chair and CEO should work together to ensure the right dynamic is at work in the boardroom – from the quality of the agenda topics and discussion framework, to the engagement level of the Directors themselves.
• Owner-Directors are critical voices in the Board. One of the major problems in many public company boards, big and small, is that long-term Owner-Directors (like VCs or public investors) are often absent. A boardroom full of “independent” Directors without any real skin in the game, clipping their quarterly paycheck and option grants, not rocking the boat since they are largely serving at the discretion of the CEO… is not a recipe for stellar stewardship of current shareholders’ interests. Board composition should certainly evolve over time, but at no point, in my opinion, should a few big Owner-Directors be absent. Early stage investors can rotate out as companies have line of sight to commercialization, but it’s critical to replace them with later stage downstream investors (e.g., Avoro, Baker, BVF, Deerfield, EcoR1, Orbimed, Perceptive etc all serve on Boards of many of their public biotech investments). I’ve worked with many of them over time and they are committed, highly engaged Board members. When you have real ownership stakes in the boardroom, they share an important and often different perspective that’s invaluable to the dialogue.
• Get independent viewpoints on Boards early in the life of a startup. By the same token, independent voices are also important – and valuable to have as early as Series A stage companies. These independents should bring different perspectives (e.g., sitting/former CEOs, mix of R&D vs BD vs Commercial experience, etc) – but they should also be good at challenging teams, and challenging their fellow Board members, including the owner-directors in the room. Independents that just “say yes” to what the CEO wants aren’t helpful, and the same goes for those that just go along with the lead investor in the room. Good “independents” bring real independent thinking and perspective to the discussion, and I’ve worked with many who are exceptional contributors.
• Compensation philosophy and execution is a core responsibility of the Board. In order to benchmark cash and long-term incentive compensation, every company uses compensation consultants. They are almost always hired by the CFO or HR head, and typically behave like that’s who they are working for. But this isn’t the case: Comp consultants work for the Board, and specifically the Compensation Committee (CC) of the Board. Having a clear understanding of this relationship is important, and they should have a direct line of communication to the chair of the CC. The never-ending escalator upwards of cash and stock compensation is in part driven by the Lake Wobegon benchmarking process: every biotech thinks they are above-average or above-median. “Exceptional performance” is actually quite common in biotech, based on my compensation discussions. I hate being Debbie Downer, but median performance is what is common – by mathematical definition. Further, what’s rarely included in the compensation metrics and discussion is the underlying median tenure/experience/value in the role. For instance, should a first-time CEO be at the median for compensation? If we’re intellectually honest, only if the “typical” CEO in the data sample is also a first-time CEO. But, alas, no one wants to be hired below the median. So everything marches upwards. Evergreen option reloads used to be 3% annually after IPOs… then 4%… and during the bubble moved to 5%. Why? Because benchmarking is largely devoid of market context and almost never goes backward. This why the overall compensation math feels like it’s forever rising even as the market cycles cools… Boards generally feel bad pushing back on these topics, and its uncomfortable, but it’s a core responsibility.
• Resource allocation is how strategy gets implemented and must be a key part of good Board governance. Having a detailed understanding of the cash burn is also a core function of the Board, and weighing tradeoffs between what, where, and how much to spend is critical. Scenario planning around future buy-ups (or no-go’s) helps provide resolution on the timeline to key decisions. Fully allocating all the company costs to the programs themselves is also important so that Boards know truly how much is being spent per program. This visibility helps evaluate the tradeoffs: are we spending too much on Program #3 vs our Lead Program? Why is G&A 30% of our spend rather than 15-20%?  In particular, understanding fixed cost infrastructure, like leases (!@#@!) or expansive internal FTE hiring plans, isn’t something to gloss over as a board.  I have recommended detailed financial transparency for Boards in the past (see an old 2012 post here); these data can help Boards understand where the money is going. When markets are hot and the cost of capital is relatively low, companies often get out over their skis… we’ve all been there. But you pay the price later because the Board didn’t push back on over-committing or over-building. As noted in the People blog, RIFs are often the painful hangover of partying too hard in the fast times. In short, though most executives might not like the sense of being “micro-managed”, I’d rather see a Board err on the side of being too deep into the weedy details of a spending plan than not deep enough.

 Investor syndication

• Big syndicates are like herding cats – a big headache. The bigger the syndicate of investors, the harder it is to manage so many elements of running a biotech. For instance, the chore of getting all inside investors on the same page for the next financing or whether or not to do a BD transaction. Big syndicates are also challenging in part because of the greater potential for misalignment: e.g., different liquidation preferences create seniority (vs pari passu), which leads to divergent views on downside protection vs swinging for the fences. Different stock price entry points mean different views on the target exit value. In addition, hiring/firing decisions around key executives, especially the CEO, are more difficult with big syndicates, especially if there are multiple investors on the Board with divergent viewpoints. In most cases, you only get to really unwieldy syndicates if things have taken far longer and cost much more than originally anticipated (e.g., the Series E round might asymptote towards maximal dysfunction). You often need a lot of co-investors to build biotechs, but too many lead or co-lead investors is where the havoc starts. Having been there before, especially in the 2007-2010 timeframe, we try to avoid that outcome. It’s also true that bad co-investor behavior – often revealing itself in these unwieldy syndicates – has populated our “blackball” lists for those VCs not to be invited into future deals.
• Which venture firm certainly matters, but which Partner matters more. VC firms have reputations and styles for how they like to invest. But in a boardroom, you have a person as a Director, not a firm. Whether you’re the CEO, or one of the lead investors, make sure you know (and like) that individual. Different partners at the same firm often have different styles and reputations. Collectively, they all, of course, represent the firm but working with different partners is a different experience. So choose wisely: when raising capital or trying to syndicate a deal, pick the one you “fit” with most and ask them to lead the deal. It’s certainly important that the individual partner or deal sponsor can “represent” their firm in the Boardroom (rather than running back to the “boss” for approval), but in my experience the firm is typically less important day-to-day versus the value of the individual partner – so long as they are with a firm with the ability to invest capital over time. I’ve often sought out specific individual partners, even as they hopscotched around different firms in the past two decades.
• There is no place for free-riders in early stage biotech venture investing. Nearly two decades ago one of my VC mentors (Kyle Lefkoff) told me that you have to be there at the end of the evening in order to hear your number called if you want to win prizes in the biotech raffle. In short, you have to have staying power to be there at the end – by investing in essentially all the future private rounds of the company. This is very different than tech investing, where “seed” funds don’t always participate or follow-on in future rounds. Biotech doesn’t have that dynamic. All existing investors are expected to do their pro rata of the insider portion of those future rounds, especially in tough markets. If a firm doesn’t want to participate (or can’t due to lack of funds), and new external demand is soft, terms like “pay to play” clauses will dramatically reduce their equity position in punishing ways (e.g., 10 to 1 conversion to common). The reason is simple: in tough markets, future investments often bail out past investments, so one can’t expect to free-ride on that new capital (and goodwill). As an investor, we make sure we have adequate reserves to support deals we have conviction in, and we expect the same from our co-investors. There are, of course, special situations that require special accommodations – but in general, a private biotech deal is like Hotel California… you can never leave. We’re all in this adventure together.

And that final note – that deals stick with you longer than you likely imagine at the time of original investment – brings me to the “full circle” moment for this blog post trifecta. Invest in strong science, good people, and be diligent about supporting companies through every stage of discovery and development.

In light of the science, people, and business complexity we all face, I’ve embraced a simple motivational phrase when working with teams: D.F.I.U.  I suspect most of the CEOs I’ve ever worked with have heard this from me over the years… mostly joking, of course. But when (not if) we do stumble and mess something up, let’s hope its from an original mistake that we can learn from!

I’ve loved the past two decades of this journey. It’s been full of great people, incredible science, and exciting business. Hopefully I’ll have better reflections in 2045 if I’m fortunate enough to celebrate by 40^th anniversary at Atlas.

Onward and upward!

The post Twenty Years In Early Stage Biotech VC (Part 3): Business appeared first on LifeSciVC.

     

After a couple decades and scores of investments in the early stage venture business, I’ve been privileged to work with great people and incredible teams.  I’ve also worked with some that were less than stellar. And I’ve tried to avoid working with others whose bad reputations preceded them.

Here are some observations on talent and teams:

• The power of luck! Many biotech executives (and investors!) are really one-hit-wonders who got lucky by being in the right place at the right time, rather than a result of their intrinsic greatness. Nothing wrong with this at all, but it’s good to acknowledge it. They showed up at the right time, with the right asset in a hot space, and managed to deliver an exit – wonderful good fortune. It’s a significant part of the stochastic nature of the business. Of course, “chance favors the prepared mind” and “hard work helps you make your own luck” – these axioms are very true. But given the high false positive rates – both of science and of exits – randomness plays more of a role in our outcomes than most practitioners acknowledge. There are several upshots once you appreciate the role of luck. First, embrace humility. Being humble and appreciating that the “gods of chance” blessed you is important. Second, the “celebrity worship” that happens in biotech (and startup culture in general) should be more tempered. Lots of folks can get lucky, but they shouldn’t drink their own Koolaid. Of course, once an executive has joined the rarefied air of those who have delivered several great biotech wins (or several drug R&D wins), then they’ve earned their “hero” status. Delivering long term batting averages far higher than industry’s typical rate is likely a real signal. That happens with some folks, but more typically luck only really strikes once. On multiple occasions I’ve worked with folks that were lucky in their prior deal; you’re absolutely thrilled to recruit them (yeah, we got a celebrity CEO!), only to realize they really weren’t that good. So be skeptical. Third, great and talented executives can easily be caught on the “wrong side of the dice” for prolonged periods – and that doesn’t make them bad executives. Testing a concept from start to finish can be a multi-year project, so each cycle (each throw of the dice) takes time. They generally get a chip on their shoulder after multiple bad outcomes, but they can still be far smarter and more competent than a Lucky Leprechaun executive – and very much back-able as an investor.
• Life is short… live by the NAR. The NAR is the No-Asshole-Rule. As I wrote in a 2017 blog on culture, “brilliance can’t overcome asshole behavior.” Sadly, we often face some element of the “dark triad” of psychopathy, narcissism, and Machiavellianism.  All of these traits suffer from a toxic empathy deficit of some form. Even if an CxO executive or star scientist has demonstrated an ability to make money for investors (or make successful drugs), if they violate the NAR, I’d rather not work with them. Bad behavior reveals itself in everyday practice: people who treat their staff poorly, or are totally coin-operated for themselves (rather than their teams), or are inappropriately abrasive or condescending. Do I want to hang out with this person for a beer?  If not, it’s a pass. Reference checks are relatively easy here too if you ask the simple question: would you actually work with this person again?  A candid “no” is all you need to hear, and I’ve very much appreciated hearing it on multiple occasions. Early stage venture investing is a long-term business, with only a deal or two a year, so I’m locking in to work with these teams for many years. Doing this with violators of the NAR isn’t part of my program even if they are money-makers. Life is too short indeed.
• By the time you know you need a leadership change, you’re probably late. Hiring and firing a CEO is ultimately the most important job of the Board (and by extension the lead investors). More often than not, the signals that you likely need a CEO change are visible far before you finally make the decision: a lack of traction in telling the “story” externally; a lack of accountability or detailed focus on execution; or an inability to recruit and retain a stellar team, etc… By the time a Board has fully processed those signals, some damage is likely already done. Not always, as some transitions are smooth and CEO skillsets evolve over the lifetime of companies. But usually tradeoffs are being debated in the backroom amongst the board/investors along the way: “we’re on the cusp of a financing so we can’t change the CEO”; “we’re in BD discussions and changing the CEO could jeopardize those”; “we don’t have the cash runway to make a change right now”… And the list goes on. We’ve all made those tradeoffs and choices, shaped by “transitory” status quo biases. Sometimes its ok and works out, but almost never is it great. In the fullness of time, and 20/20 hindsight, there’s usually the clarity of “we should have made the change a year earlier” or some version of that. So if the signals are there, it’s best to address them head-on and “sooner” than one might otherwise default.
• Surround yourself with data-driven truth-seekers. This relates to the several of the Science observations – being skeptical and doing great science – but is important enough to emphasize again. Investors often think they want (and glorify) the hard-nosed entrepreneur who runs through walls; but sometimes science puts immutable wall-studs in the way. If the data say we should kill a program or shut down the company, we love it when the CEO/entrepreneur actually comes and tells us that’s their recommended call. I’ve had CEOs do that in the past: “we saw an on-target tox signal and although we could ask to pull the next tranche, we think we should shut it down.” There are many variants of that – but fundamentally it’s truth-seeking and data-driven. That’s what we want in our portfolio leadership, helping prevent us from throwing good money after bad. Another version of this is the presence or absence of scientific rigor and discipline in the face of weak or confusing data; a truth-seeker might just say “we don’t know why it didn’t work as we hoped” and lay out a clear go/no-go case as a consequence, whereas the hand-waver often says “we sort of expected this” and will rattle off a bunch of vague (unsupported) scientific reasons for why. Hand-wavers are always trying to survive and spin the data via a deep narrative bias; truth-seekers are trying to learn from the data and be disciplined even if it means a “no-go” decision. Those are fundamentally different. This happens both preclinically and clinically. This is why truth-seekers, largely devoid of confirmation bias (as much as that is possible), are definitely the type of executive/entrepreneur you want in your biotech portfolio. The time spent by a truth-seeking executive is the ultimate scarce resource, more scarce than our capital, and they know it (and we appreciate it).
• The best CEOs understand and are aligned with their current shareholders. There are so many conflicts of interest for a biotech CEO and their shareholders: how much to raise and when; how big of an option pool or evergreen issuance should we have; how big should our compensation increase be this year etc… Fundamentally, all of these come down to questions of dilution. Every biotech has to take significant dilution along the long journey of raising capital, and burning it, to advance drugs through to market. On the tech side, founder-CEOs often own huge chunks of their company and so are by definition (intrinsically!) aligned with their current shareholders. In contrast, biotech CEOs typically own in the mid-single-digits, and get reloaded to that “market rate” after every private financing (or annually for public companies). This dynamic creates misalignment: CEOs in biotech know they are getting reloaded so are indifferent to the amount of dilution, both the size of the raise and the price. Great CEOs, in my experience, recognize that misalignment, openly acknowledge it and work to manage the tradeoffs by prioritizing equity capital efficiency. They understand that a CEO represents current owners, not future owners, and so are both disciplined on raising capital and exploring alternatives to equity sales (like when asset dilution through partnerships is a better alternative). Dilution in biotech is like death and taxes in life, it will definitely happen – but a good CEO helps constrain how much of it we face while building a great company.
• Most CEOs are either openers or closers, rarely both. Openers are great at “selling the sizzle” and networking their way into discussions with important investors and decision-makers in pharma; they are great are schmoozing, in a good way, and have superb connectivity in the ecosystem. They are often great and inspiring scientists. Closers have the killer business instinct to get it done; they are relentless in pushing BD or investor interest into term sheets and onto closings. At times, they may appear too aggressive. Closers don’t let issues in deal dialogues fester, as they escalate quickly to CEO-level discussions. Openers often let the play come to them (“if we build it, they will come”); closers go make the play (“always be selling”). Most CEOs are typically one or the other, and either can be successful; but complimenting them with “the other” phenotype on their executive team is often an important element of their success. On rare occasions, a CEO exhibits both profiles – one of the styles of the exothermic catalytic visionary who can transform companies. It’s not easy to figure out who is what phenotype, but years of pattern recognition certainly helps. Great companies are almost always bigger than single individuals, even catalytic ones, so building a leadership team of synergistic skillsets is a major lever for value creation.
• Build lean organizations by encouraging teams to “earn the right” to grow. When resources are aplenty, it’s easy to over-hire teams and over-build infrastructure; these come back to haunt you during the leaner times. RIFs and lease renegotiations are never fun, and are always costly. It’s often best to staff up internal talent for the trough workflow demands, using flexible support for peak demand periods. It’s also best to understand how hiring fits with the biggest risks a biotech really faces: we once had a company hire an entire sales and marketing team a few quarters before its first approval… only to get rejected by the FDA. The restructuring nearly killed the company, and the price correction killed our investment. Companies should earn the right to hire big teams: as risk is discharged and pipelines mature, the company’s fundraising accelerates with the cost of capital going down. Scaling a biotech in this thoughtful capital efficient manner is the way successful companies are most often built; it’s a rare exception where prematurely scaling a discovery story with $200M out of the gate actually generates positive returns. But being too lean can hurt progress, too. That said, there’s an axiom in venture that “indigestion has killed more startups than starvation” – so avoiding over-extension before you’re ready to grow is key.  Importantly, be vigilant about G&A bloat, too. A Chief People Officer often grows into an HR team, whether or not a company needs all that HR support in-house. We used to joke once you’ve hired an HR head in-house, you’re doomed as a startup; obviously that’s not true, but there’s a tiny kernel of truth in it. Other G&A functions are similar: the in-house GC often quietly takes on a few junior folks; finance teams quickly populate their FP&A groups, too. All of these functions have critical roles in biotech, especially in emerging public companies, but none create a real competitive advantage – real advantage comes from making real medicines. So keep the G&A to the barebones required minimum, be tight on growth, and focus any incremental spend on advancing the science and medicine. Being balanced – like a Goldilocks-level of hiring – is obviously the right approach, but sadly only appreciated in hindsight in many cases. I’ve been through far too many RIFs in tough markets to want more of them.
• Fractional leadership in a startup works until it doesn’t. Most of the seed-stage companies incubating in our offices have part-time leadership teams in place for various roles; for example, fractional CFO/accounting folks to track the spending, part-time CBO/BD support, or CMO/clinical consultants helping think about translation. We even have CEO/EIRs splitting their time across different seed-stage concepts at times. All this works for a while, especially as seed-stage risks like validation and reproducibility are being discharged. But running far with fractional leadership means no one is fully immersed in thinking about the startup 24 hrs a day and 7 days a week. This is the other side of earning the right to grow: with that in mind, as soon as “real money” comes in, perhaps with what used to be called a Series A (i.e., $20M+ financing), it’s time to get serious about full-time folks. CEOs need to be in 110%, and the same holds for CSOs and CMOs for early stage companies. It’s true, we’ve had fractional CMOs run successful Phase 1 programs for us in the past, but those are the exception rather than the rule: bring on a CMO as early as you can recruit a great one. Great singular talents hired early will often find enterprise-level leadership themes to contribute to… and an early CMO can drive the clinical execution planning that is so important for value creation (see Science blog). Once you’re ready to scale, get a full-time leadership team locked in – and preferably in person, in the office, unless you’ve truly perfected the virtual model.
• Leadership coaches/advisors are valuable for all types of performers. Running biotech startups, or teams within them, can be a very isolating and lonely job with few people to turn to.  A Board ultimately evaluates the CEO’s performance, so being vulnerable by sharing fears and challenges with them doesn’t feel great for many. Direct reports also don’t really want to sense edgy anxiety from their boss.  So this is where great leadership coaches come in: I fully recommend them for any CEO, includig both high performers and those struggling with the role, first-time CEOs and veterans.  A good coach can be a great sounding board when no one else can play that role well. This concept also extends to having a small group of CEO peers as informal advisors; they provide a shared experience in the markets and can help any CEO grow. I frequently connect my CEO’s to other CEOs that might compliment their styles. Further, this same idea applies to other C-level roles in the company (CSOs, CMOs, etc); having a group of external folks in the same role to share learnings with, and vulnerabilities, helps to improve everyone’s leadership game. Atlas organizes CxO groups across our portfolio to help facilitate these interactions. And all this coaching advice probably also holds for VCs, too – maybe I should get one.
• Build authentic multi-faceted relationships with people in your biotech community. Professional-only relationships are great and important, but can be unidimensional and somewhat transactional at times: the LinkedIn bio network isn’t really very personal or connected. Instead, I’ve derived great value and pleasure from building multi-dimensional work relationships – around family, hobbies, sports, and beyond. I called this “work-life symbiosis” in a prior blog: “find ways of integrating your passions into the workplace, and use them to create community, connectivity, and followership. We spend far too much time at work during our lives to not bring our personal passions into it in a beneficial way.”  To that end, I’ve built deeper biotech social networks around a few passions of mine: running (like Reach The Beach or RunningAtlas), fishing (Biotech Bait & Boats), hiking (the Atlas Hike in the Whites), and others. In my hometown of Wellesley, over the past decade I’ve learned there are over two hundred biopharma execs – and so I host a get together a few times a year to connect over drinks about our kids in the local schools, what’s going on in the town, and of course biotech itself. My Atlas partners also do the same around their hobbies and communities, and as a firm we do this through our annual Atlas Venture Retreat (which my partner JF Formela has spent 30+ years curating and cultivating as a premier relationship-building event). All of these things, and many more, help to build the longitudinal, trusted relationships that help scale our biotech ecosystem. Over time, I’ve become friends with many of the executives I work with. These close personal connections sometimes make it hard to make tough calls about leadership transitions, but it also can open up topics around honest trusted feedback that are otherwise difficult to tackle. As an early stage venture investor in particular, it’s our job to naturally convene the community around us and build deep connectivity – bringing the right talent to the right opportunities in our portfolio.

So many other themes to cover, so little time… at the end of the day, it’s all the talented people we get to work with that make biotech so special… and a fun industry to work in!

Tomorrow we’ll hit the final set of corporate and business observations!

The post Twenty Years In Early Stage Biotech VC (Part 2): People appeared first on LifeSciVC.

     

Like the world, biotech has changed dramatically since 2005. So has Atlas. Over the course of the first nine years after joining, we retooled ourselves from a global multi-office, multi-sector firm into a biotech-only VC based in Cambridge MA.  Simplicity and focus are beautiful.

Since joining Atlas, I’ve worked with scores of biotech companies as an early stage investor and board member. I’ve also co-founded many of those along the way. I’ve had a front row seat on our emerging portfolio. It’s been an incredible ride.

Without a doubt, I love my job. The best part: the people. It’s been a privilege to work with so many wonderful people over the years – both inside of Atlas, in the portfolio, and across the industry.  And, most importantly, the people we are able to help – patients. We fully embrace the double-bottom-line mantra of “doing well by doing good” at Atlas.

It’s also been hard. Venture capital is a risky business. Losses are common. I was told early in my career, perhaps tongue in cheek, that I would have to lose $100M before I’d know what I was doing.  Well, I’ve gotten close to that threshold: including my unrealized positions, I’ve now lost almost $100M on paper. Thankfully, my returns outweigh those losses by a very meaningful margin.

Related to these losses is the reality of making mistakes. I’ve made plenty. But the key is to “only make original mistakes” as an early mentor told me; learn from them, try not to do them again, and share the lessons if possible.

To that end, I’ve done some reflecting on two decades at Atlas and some of the observations I’ve made along the way – insights generated by my biotech pattern recognition receptors over time. I’ve decided to share a few blogs on them.

The first, here, will be on the Science of biotech startups – we are, after all, science-first investors.  I’ll follow up with one on People (talent and teams), and a final one on Business topics (BD, Boards, syndicates). There won’t be any bombshells, and I’ll disguise the innocent, but it was somewhat cathartic to think through a few of these.

So here are a handful of observations on the Science of startups from my vantage point as an early stage VC:

• Science-first opportunism beats top-down strategy for deal selection. Declaring you are going to build an Alzheimer’s company and then boiling the ocean to find the assets or platform to create a NewCo isn’t typically a route to value, despite my roots as a McKinsey consultant. I wasted time on these boiling exercises early in my career. But I quickly found that finding a nucleating asset or foundational insight in a science-first, disease-agnostic manner (via academic papers, a discussion with an entrepreneur, or an asset out-licensing list, for instance) and then building a business framework around it is much more productive. This nucleating asset can prompt aggregation of other related programs, or it can move forward as a focused asset play. Similarly, an initial platform idea may morph into an interesting albeit narrower asset-centric concept too (especially since many platforms aren’t really platforms). Opportunistically following the science may also identify other assets to bring in over time (like we did at Disc Medicine, as an example). Being business model agnostic has been important for us too: platforms/discovery engines and single asset companies create diversification in the portfolio. We shun dogmatic rules around business frameworks (“this is what a Series A should look like”) and have built an eclectic, almost artisanal, portfolio of different business models and wide-ranging scientific substrate. Starting with great science also means that we are willing to engage with a NewCo before any of the management team is in place, as we can help build the talent required to deliver on the thesis via our incubation model.  All this deal-type diversity, underpinned by this science-first mindset, has rewarded us across many capital market cycles.
• Don’t believe what you read in science papers at face value. Be disciplined about academic validation and reproducibility. Lots of published findings are weak observations rather than robust discoveries. If the therapeutic hypothesis can’t be both generalizable and reproducible, it’s unlikely to survive the attrition curve of making things work inside the complexity of the human body.  I’ve written on this topic many times over the years (here, here, and here), and we continue to find irreproducible science endemic. Perhaps the publish-or-perish model of an academic career leads to less stringency on the robustness of the observations that get published. This observation is why we’ve largely adopted the seed-led approach to validate novel science with “wet experiments” as a primary goal (or even as a diligence item) for many of our new startups here at Atlas. I’ve “killed” a bunch of seed-stage startups for the failure to replicate the “transformational finding” of an academic lab. Fortunately, an early kill on “small dollars” helps bend the capital-weighted risk curve of a venture portfolio in your favor.
• Don’t over-value reductionism, but also avoid phenotypic over-enthusiasm. Understanding how a drug engages its target is an important element in designing new medicines, and striving for selectivity is a valuable aspiration. However, taking an overly reductionist view of molecular drug targets is fraught with error as the precision of selectivity is often wrong in a world of promiscuous interactions. Reality is most (small molecule) drugs when dosed in the human body engage multiple targets, in part due to the massive concentration differences across peak to trough on a PK curve. But the converse of being overly-reductionist is even more true: phenotypic effects with unknown targets make for challenging drug discovery. I frequently recall one of Phil Needleman’s rules: phenomenology is different than pharmacology. The former often involves things like “cellular homeostasis”, “oxidation states”, “RNA expression patterns” or other fuzzy cellular observations. The latter (pharmacology) is about truly understanding PK/PD relationships around specific molecular mechanisms. For discoveries from phenotypic screens, target deconvolution is key; but even with all the great tools today, it remains hard to do. In the right settings, it’s ok to take on trying to “find the target” in the context of an investment – and we’ve done this a number of times, including recently. But if you can avoid it and embrace the reductionist view (sometimes illusion) of target identification/validation, it makes the downstream job easier – both for R&D reasons (like lead optimization and candidate selection) and for business reasons (like funding the company!).  Investors and Pharma partners want to know you know the target, even if everyone sort of winks while they say it.
• Rare disease drug development isn’t as “easy” as it seems on paper. Orphan drugs have generally been an exciting area of discovery for biotech over the past couple decades, and over 50% of recent FDA approvals are for “rare” diseases.  But startups often approach these opportunities under the misguided notion that rare means less expensive and shorter timelines. That can certainly be the case, but only if there are solid natural history datasets and a fulsome understanding of the disease course and presentation. And in cases like that, there are often predicate standard of care drugs that require comparative trials, making them longer and more expensive. Doing a placebo-controlled trial (vs open label) in many rare diseases is really quite challenging today (often untenable from a financing perspective, which I’ve learned first-hand).  It’s true that the size of the Phase 3 program (measured in number of patients enrolled) is much smaller than for larger disease settings, but there are lots of other costs in rare settings (including random things like flying in hard-to-find patients for gene therapy procedures, as one example).  Further, because many rare diseases don’t have a comprehensive deep clinical understanding, the “unknown unknowns” can bite you; for example, knowing what the background rate of pulmonary embolisms is in Prader-Willi Syndrome, or suicidality in Huntington’s, or what a brain MRI should look like over the course of a rare neurodegenerative disease… all become important in the clinic with implications for powering, safety interpretation, and others.  I’ve seen experienced all of those, and more, in our rare disease portfolio.
• The single most important value creation activity in biotech is excellent clinical trial execution. Rapid trial enrollment, with the right inclusion/exclusions, with robust and consistently-measured endpoints, and delivered on time (all the way to database lock and top line data) – these are the things that unlock real value from the Science in startups.  After years in discovery, the clinic makes or breaks everything, so never cut corners or shortchange on an important early clinical trial.  It’s oddly funny that the single most important event for an emerging biotech is often the one that is largely outsourced to clinical CROs. We lease lab space, do all these in vitro experiments on our own benches, use expensive machines to augment our discovery DMTA cycles, build vivariums for animal work… but then outsource all of our clinical execution to a CRO?  Clearly not the right answer. Partnering with a CRO is almost always required in light of a small biotech’s organizational limitations, but never delegate the important details to a CRO, or assume things are going to go fine. For early patient trials, in particular, a startup’s Chief Medical Officer should be signing off on every patient enrolled – understanding who is being put on the drug and adjudicating questionable participants. Sending team members to be onsite with both the CRO’s field team and the clinical investigators during the initial doses (and beyond) is smart. Hiring additional clinical research folks is often well worth it. We’ve had patients put on trials that shouldn’t been enrolled, or where the trial participant did stupid things (like running marathons right before blood work), or even where trial sites were fraudulently making up data to get paid. Most CMOs with grey hair have tons of stories like these and more. In addition, don’t skimp on the funding of the trial: when you can afford it, buy more power with a few more patients… rarely a regret. Bottom line is you can’t be all over the details too much in clinical development.
• Novelty is great, but only when it’s unlocking something truly valuable to patients. Taking on novel biology or novel modality risk is only worth doing if it delivers compelling clinical performance. Cool science is not enough. A new first-in-class drug against a genetically-validated target that only performs as well as generic drugs from older classes just isn’t interesting (or shouldn’t be) – despite how sexy the science is. That’s novelty for novelty, rather than novelty for patient value. Further, taking a novel modality and derisking it with a “known validated target” with intense competition just isn’t very compelling (like the Ab-like scaffolds in the mid-2000s all chasing TNF and CD20) – unless you truly believe it will differentiate as a best-in-class asset. I firmly believe new modalities or novel complex therapies need to do truly new things, like making the previously undruggable become a viable target addressing an important medical need.
• Be skeptical, as most things fail, but embrace “a permission to believe”. There are 100s of reasons why a drug could fail, so much so that it’s easy to become cynical in this business. Saying “no” during diligence is the right answer in the majority of cases over a long enough time interval, given attrition rates.  But asking what is really required to say “yes” – that’s the better question. Focusing on the few scenarios for where it could actually “work” as a drug is often important. Obviously, being irrational about its success isn’t helpful, but thoughtfully understanding what you’d need to see at every step of the drug R&D process is key.  What are the lead characteristics or DC criteria (linked to a Target Product Profile grounded in commercial or competitive reality) that give us confidence in moving forward to lead optimization or Phase 1, respectively. What is the clinical profile we expect to see in early studies. Defining these frameworks upfront, with go/no-go scenarios done a priori, can greatly aid in the decision rubric when the time comes – and help keep everyone honest if a drug will make it or not. Confirmation bias and group think often overwhelms healthy skepticism, especially if you don’t articulate the rules of the game beforehand.  Being disciplined is the key.  But there’s a positive side to the discipline too: thinking about the scenarios beforehand means that if the data starts to reveal itself in a truly positive way, it helps teams be ready to accelerate.
• Understand the real risk you are underwriting and the value of discharging it. Science risk is only one of them, and often it’s not the most expensive to discharge; non-technical risks like differentiation risk, financial risk, execution risk, team risk, regulatory risk, or even geopolitical risk are often important. My friend Mike Gilman blogged beautifully on this point years ago, and it’s all still salient today. Competitive risk is often a late stage and expensive risk to discharge (in head-to-head Phase 2b/3 studies) relative to a novel biology risk (often in a small Phase 1b patient study). Differentiation like dosing convenience can often be derisked early with PK data (e.g., this mAb has twice yearly dosing vs a weekly SoC), but proving better efficacy (like deeper response rates) often requires bigger, longer trials. Having to run a big placebo-controlled phase 3 to find out if you’re comparable to a standard of care has a big dollar-weighted risk profile. One real clinical execution risk, often overlooked, is the veracity of the primary endpoint itself: taking a novel unproven metric as your primary can be really risky, and we’ve been burnt on the “cool” novel endpoint in the past (e.g., as I learned using Sitzmarks radiopaque markers as the endpoint for IBS motility circa 2008). And don’t forget about CMC risk, an often overlooked and yet hugely critical element in biotech; a 30-step synthesis that takes 6-9 months to make enough material also adds to the financing risk. The reality is we always are taking a healthy dose of risk in biotech – but accept the risk burden only when you really have to (in order to drive value), and not in other domains of the plan, where it feels like excess or incremental risk without the upside. It’s also key as an investor (or CEO) to make sure you know what risk you are expecting to discharge with the capital (or time) you are investing, and the likely value accretion in doing so.
• Seek the scientific truth, not just “making money”. I firmly believe that in the long run doing quality science and being data-driven unlocks value. If there are questions that can be answered that help you undercover whether you have a real drug or not, then answer them. Do the experiments that tell you if you have a false positive (a drug candidate that is actually not going to make it) – before you put patients at risk. Every once in a while I’ll hear things like “we don’t want to know that answer” – with the fear that the data could kill their potential drug before it gets tested clinically or monetized in a deal. This is almost never the right call – for companies and investors in the long run, and especially for the patients who volunteer in our studies. Do good science, leverage all the tools you can to learn about your drug program, and in the long run value will most likely accrue to you.
• Innovation and optimism are tightly interconnected – and have been ever present in biotech. In every period over the past twenty years, I’ve felt innovation couldn’t be more exciting – and that it was always accelerating. Back in 2005, the Human Genome had only been sequenced a few years before, with ultra-high-throughput sequencing opening up the analysis of genetic variants, and technologies like RNAi and SBDD were shiny new toys with enormous promise. A few years later, breakthroughs in gene and cell therapy (AAV, LV, CART) in both cancer and rare disease reignited those areas. Then the I/O revolution and CRISPR. Today its degraders, bi/tri-specifics, ADCs, RLT, base editing, and so much more. But, in every period, it always seems like we’re the cusp on great advances, with wonderful new medicines just out in front of us, and are optimistic about the impact of science. Today is always more exciting than yesterday, it seems. Of course, novel discoveries take years or even decades to turn into products, and each new concept travels thru the ups and downs of the Gartner hype-hope innovation cycle. But through all this the flame of eternal optimism just keeps burning – part of what makes this innovation business so exciting.

This optimistic note is a great one to end on.  Plenty of other observations about R&D and science in biotech that I could have highlighted, but those are a good hit list.

Next up will be People and talent!

The post Twenty Years In Early Stage Biotech VC (Part 1): Science appeared first on LifeSciVC.

     

Let me tell you a secret that most biotech executives won’t admit at cocktail parties: we’re all making it up as we go along. Sure, we’ve got fancy degrees and impressive resumes, but when you’re working on a novel platform / new modality, trying to drag a molecule from a napkin sketch to a clinical trial, there’s no playbook for this specific instance.

The Evolution Problem Nobody Talks About

Here’s how every successful biotech evolves, without exception:

• Stage 1: Conception: “We have this one brilliant idea!” (Written on actual napkin)
• Stage 2: Toddler:  “Look, it works in mice!” (Preclinical product)
• Stage 3: Adolescence: “Holy cow, we’re in humans!” (Single clinical product)
• Stage 4: Adulthood: “We’re a real company now!” (Multiple products, usually plus a Big Pharma collaboration)

At each stage, companies face the same temptation: let’s get involved in everything. After two decades in healthcare (plus a brief detour through tech), I’ve watched countless startups flame out not because their science was bad, but because they tried to do everything at once. It’s like being at an all-you-can-eat buffet when you’re starving—your eyes are bigger than your organizational stomach. This lack of focus isn’t just inefficient; it’s a company killer.

Three Epiphanies That Helped Shaped My Thinking

Epiphany #1: How to Win by Doing (Almost) Nothing

During my time at Janssen (J&J’s pharmaceutical arm), I witnessed something that would make most MBA professors weep into their optimization models. When J&J acquired companies, they did something radical: absolutely nothing. Well, not nothing—they provided support. But for 4-5 years, they let acquired companies run themselves like teenagers with the car keys. No forced integration. No “synergies.” No death-by-PowerPoint standardization meetings.

The result? Pure chaos, right? – Wrong.

What emerged was beautiful, decentralized decision-making by teams closest to the actual data. Yes, we had redundant systems. Yes, we had higher headcounts. Yes, our IT department had nightmares about non-harmonized databases. But we also had speed—that magical ingredient that separates biotech winners from the also-rans who are still in committee meetings discussing their committee meeting structure.

Epiphany #2: Why I’d Rather Hire the Curious Than the Credentialed

After 15 years in startups, I’ve learned something that would have horrified my younger self: the person with the perfect resume is not often the right hire.

Here’s why: When you’re developing novel biology on novel platforms with novel clinical approaches (notice a theme?), experience becomes almost irrelevant. It’s like hiring someone who’s an expert at chess to play a game that hasn’t been invented yet. We are all learning together. What works best is a team that accounts for people with experience in drug development, while at the same time having individuals with curious minds that are not held back by conventional thinking.

What you need are people who wake up at 3 AM thinking (or in a shower at 7 AM), “But what if we tried it this way?” People whose response to failure is “Fascinating!” rather than “Not my fault!” People with the intellectual curiosity of a toddler and the intensity of someone who just had three espressos. Attitude beats aptitude when you’re inventing the future.

See the following reads at Forbes and the Kindergartener versus CEOs

Epiphany #3: The Netflix Buffer Lesson

Remember dial-up internet? (If you don’t, please pretend you do so I don’t feel ancient.)

Back in graduate school, while studying network engineering, I was exposed to Queuing Theory—basically, the science of how data packets line up to be processed. It’s the difference between seamless Netflix binging and the spinning wheel of death. Here’s the kicker: biotech companies are just biological networks with the same queuing problems. When you have multiple drug candidates competing for limited resources (money, people, equipment, executive attention), you need a routing algorithm. Otherwise, everything gets stuck in buffer hell.

In platform companies with multiple assets, resource allocation isn’t just important—it’s existential. One wrong prioritization decision and your lead program stalls while your backup program burns cash. It’s like juggling, except the balls are worth $50 million each and on fire.

Enter the Focused-Team: Our Secret Weapon

So how do you solve the evolution problem, the people problem, and the resource problem simultaneously? We created something we call Focused-Teams. (Yes, the name is boring. We’re scientists, not poets). It accounts for decentralized decision making, having a cross functional set of curious individuals working closely together questioning each dogma, and finally allocating individual resources to support different teams working at different pace.

• Small, cross-functional teams (Think Ocean’s Eleven, not Ben-Hur’s army)
• Single, laser-focused goal (Not “cure cancer,” but “get compound X through IND-enabling studies by Q3”)
• 80%+ dedication (Not “I’ll squeeze this in between my other seventeen priorities”)
• Hands-on work (Everyone codes/pipettes/analyzes—no pure managers allowed)
• Minimal governance (Asking for permission is optional; asking for forgiveness is rare)
• Fixed timeline and budget (But flexible when reality laughs at your plans)
• Speed (Move fast and iterate)

This isn’t your Big Pharma compound development team where managers manage managers who manage the people doing the actual work. Our Focused Team members are the ones with their sleeves rolled up, making decisions at 7 PM on a Friday because waiting until Monday’s meeting would lose us a week. They meet in a room with a whiteboard (yes, physical presence required—Zoom doesn’t work when you need to read body language and share pizza at midnight), give them resources and a deadline, then get out of their way.

The result? Scientists who joined us to escape Big Pharma bureaucracy get to own their decisions. They fail fast, learn faster, and iterate at startup speed. Their learning trajectory looks like a hockey stick rather than a gentle corporate ladder climb.

The 3-2-1 Strategy: Our Moonshot

Earlier this year, we announced our 3-2-1 strategy:

• 3 programs in the clinic
• 2 tissue types
• 1 platform
• All by 2027

Ambitious? Sure. Impossible? We would have said yes before Focused-Teams. But now we’ve got targeted innovation teams working on platform improvements while separate teams sprint toward IND submissions. No stepping on toes. No resource wars. No death by consensus.

The Plot Twist

Here’s what nobody tells you about organizational innovation: it’s messier than your science. Focused-Teams aren’t perfect. Sometimes they move so fast they trip over each other. Sometimes the lack of governance means someone breaks something. Sometimes the intensity burns people out, if they don’t get a break or take a break. But perfect is the enemy of good and fast. In biotech, god enough and fast wins.

What Would I Tell My Younger Self when working on a novel platform/modality?

1. Stop trying to control everything. Give smart people problems and resources, then disappear (and be available for advice, and support / step in when shit hits the fan).
2. Hire for curiosity over credentials. You need a good mix of individuals that knows what needs to get done as well as question existing dogma
3. Think like a network engineer. Your resources are packets; route them efficiently or watch everything buffer.
4. Create Focus Teams. Small, empowered, fast-moving units beat large, coordinated armies every time.
5. Embrace the chaos. Biotech is jazz, not classical music. Improvisation is a feature, not a bug.

We’re still iterating on this model (and always will be—evolution doesn’t stop). Some days it feels like we’re building the plane while flying it. But that’s the thing about breaking new ground in biotech: there’s no instruction manual because nobody’s been here before. If I’m still writing blogs in a few years (and haven’t been driven completely mad by oligonucleotide chemistry), I’ll let you know if this all worked out. But right now? We’re too busy developing medicines that will help patients.

Certain statements in this blog may constitute “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, as amended. Forward-looking statements include, but are not limited to, express or implied statements regarding expectations, hopes, beliefs, intentions or strategies of Korro Bio, Inc. regarding the future including, without limitation, express or implied statements regarding execution of the 3-2-1 strategy, among others. Forward-looking statements are based on current expectations and assumptions that, while considered reasonable are inherently uncertain. Nothing herein should be regarded as a representation by any person that the forward-looking statements set forth herein will be achieved or that any of the contemplated results of such forward-looking statements will be achieved.

The post Speed Wins: The Focused-Team Formula for Platform Biotechs appeared first on LifeSciVC.

     

Building the right team is a popular topic because it is difficult and a company’s success hinges on it. In a cash-crunched biotech environment like today, it is top of mind because as company builders, we are being asked to “do more with less.”LifeSci VC blogs like Pitfalls of the Scarcity Mindset and How Early is Too Early to Establish a Commercial Function in Biotech? have described the impact of limited resources on hiring decisions and what functions are needed when. The debate about building internal capabilities or using external consultants will rage on. But apart from whether you hire or borrow talent, you should be asking yourself what type of individual do I need to complement the team? Given how long it takes to develop a drug, needs will inevitably change and talent pools will vary. Determining the right mix of talented individuals can make or break a team. Are you continually assessing, attracting, and balancing the Jack of All Trades and Utility Players?

The Jack of All Trades (“Jacks”)

Jacks can do many kinds of work across the different functions within a company. The term was first used in the 1600s when performing manual labor was essential for survival. Today, these multi-talented individuals are still valuable contributors to the workplace and society despite the integration of AI into offices and homes. When discussing the theme of this blog, each time I mentioned, ‘Jack of All Trades’ to someone, they felt compelled to add ‘and Master of None.’ The second half of that phrase evolved over time and unfortunately, it gave Jacks a bit of a bad rap. Sometimes misunderstood, Jacks are responsible for the success of many biotechs since ‘wearing many hats’ is essential for survival. Possessing a broad range of skills, Jacks are flexible, adaptable, and reliable. They are often also great leaders, which I believe stems from their willingness to jump into any situation, bring people together and get the job done.

The ‘Jack’ Advantage

Inside the last few biotech startups I have led, I was fortunate to work alongside the same individual who is a quintessential Jack of All Trades. In fact, he was one of my first hires at Lifordi. Performing many functions and mastering more than one at several of the companies, he has assumed the role of real estate agent, IT guru, marketing and sales rep, and KOL relationship manager, among other responsibilities. He locates the ideal office space and knowingly designs a flexible layout because he knows our needs will change over time. At a prior company, when we outgrew the confines and prices of a Cambridge office, he found a great place for us in Waltham —and this was at a time when few biotechs had even considered taking that leap. He is a fierce lease negotiator, insisting on the most favorable tenant improvements I have ever seen. At a previous company, he found a spot for our new headquarters that could accommodate a full-scale gene therapy manufacturing facility and then managed the build out in record time and on budget. A few years later when we entered into a joint venture to monetize this facility, he reconfigured the space without any major disruptions to operations.

From day one, he works alongside technical experts to develop an efficient IT infrastructure.  At Lifordi, this extends across state lines and country borders. He scouts out the right equipment to buy and finds the best price. He can fix most computer or printer problems, saving countless hours of frustration for employees and maintaining productivity for the company. I have also seen him jump in to design marketing materials, partner with colleagues on the commercial team to identify global prospects and then help maintain strong relationships with doctors and clinics using our technology. At Lifordi, he consulted experts and other biotech companies to establish a new entity in a foreign country. Working closely with legal, regulatory, clinical, research, and CMC colleagues inside and outside the organization, he helps to assess needs and the associated costs, which is integral to our planning and budgeting process. A natural leader and organizational ‘go-to-guy, he has earned the highest level of respect from the team.

An Executive Assistant (EA) who is a Jack of All Trades is a rare and coveted team member. She or he can support the entire Management team, manage the office, and even handle essential Human Resources responsibilities. At Lifordi, our EA Jack of All Trades jumped right in to develop and manage our employee onboarding program, learn how to select and administer the health benefits and 401K plans, and execute the company meetings and outings, among a host of other much needed things. Our Lab Manager showed her Jack of All Trades capability by taking on our global procurement and shipping needs. She has fast become the linchpin in directing complex logistics that are required to deliver a high-quality antibody drug conjugate (ADC) to another country on time for our clinical trial. These are just a few examples of the value of the Jacks. We all need them, but do you have them in your network when building a biotech newco?

Spotting a Jack of All Trades

You can usually identify a Jack of All Trades right away. The examples above may be helpful, but you can also find a potential Jack from resumes. While a good resume should be tailored to the job description and focus on the requirements and accomplishments of the position, you should look for his/her contributions across the company and their achievements inside and outside the organization. Has their work helped the company, stakeholders, or perhaps even the field? A variety of seemingly unrelated personal interests can also be a clue to their innate curiosity, affinity for pushing boundaries, and willingness to jump in and get it done.

The advantages of a Jack of All Trades are clear, but you also need Utility Players to be successful.

The Value of Utility Players

Utility Players are highly skilled, experienced professionals who are expert within a function but can assume multiple roles within that function. As “Masters of Their Domain” they help define goals, drive strategy, and achieve results. They are experts who know all aspects of their function and how to operate efficiently to achieve the desired results.

While you probably never heard of him, the most famous Utility Player in all of baseball is Bert Campaneris, a shortstop for the California Angels who, in 1965 at the age of 23 years, became the first player to play all nine positions in one game (only four others have achieved this). Bert’s story is worth reading if you have the chance, but suffice it to say it’s a thriller, reminiscent of the famous baseball poem Casey at the Bat. The game began with an incredible start and ended with a 5-3 win in 13 innings. Unfortunately, Campaneris wasn’t there to see how it played out because this ‘super utility player’ was headed to the hospital after his inning-ending save as catcher. While Utility Players perform multiple roles within a function, like Campaneris, they shouldn’t play them all and never on the same day.

To illustrate the versatility and value that a good Utility Player brings to the team, consider the Chief Technology Officer (CTO). The CTO Utility Player can be a sinner or savior of biotech companies. Manufacturing is frequently the reason for significant delays and the subject of CRLs. More often than not, they are among the unsung heroes that get new drug applications (NDAs) accepted. He/she is experienced in process development and Good Manufacturing Practice (GMP) clinical and commercial manufacturing, analytical development and quality control, as well as cold chain logistics and management.

At Lifordi, the level of expertise required for this role is heightened by our need to manufacture an antibody drug conjugate (ADC) with a drug to antibody ratio (DAR) of 8. The ADCs’ three components: the drug, monoclonal antibody and linker, must be optimized and manufactured separately and then assembled into an easy-to- administer subcutaneous injection. Understanding the complexity and long-lead time required, it made sense to hire our CTO Utility Player prior to officially founding the company. This enabled him to begin transforming research grade ADCs into reliable, reproducible and scalable products under the GMP conditions required for IND-enabling studies. However, we are lucky that our CTO is a veteran C-suite team player who also understands the broader business enterprise view of the Lifordi mission, which is also extremely valuable.  Today, our ADC drug is vialed, labeled and ready for shipping to clinical sites despite the fact that CMC is usually on the critical path for a clinical study.

Chief Medical Officers (CMO) are another good example of Utility Players. Many people believe a CMO needs to be a domain expert in particular therapeutic area(s) of interest. This is not always the case. With a strong team of external clinical and regulatory advisors, including former heads of medical societies in relevant disease areas, it may be more important to hire an expert in first-in-human (FIH) studies. This is the case at Lifordi where our CMO Utility Player is an expert ‘clinical trialist’ who has led more than a dozen FIH studies. What’s more, he previously worked with the clinical research organization (CRO) and some clinical sites we selected for our global trials. Expert in designing a highly flexible, easy to follow, patient-centered FIH study protocol, and effectively managing global clinical operations as well as placebo rates, he is essential for success.

Finding the Right Mix of Jacks and Utility Players

Establishing just the right mix of Jack of All Trades and Utility Players cannot be underestimated. Further complicating this equation is that the mix will fluctuate. People learn and grow, company needs change, and market conditions will dictate. With the proper motivation, aptitude, guidance and opportunity, Jacks can also move on to become Utility Players. While this temporarily disrupts the delicate balance and challenges the team to divvy up some desperate responsibilities, overall, it is good for them and the company.

Determining the right mix of Jacks and Utility Players largely depends on a company’s business strategy, stage of development, and available resources. However, understanding how to spot them, attract them and the best way to leverage the value they bring is more than half the battle. Striving to maintain a healthy balance of both is how you build a great team that can deliver revolutionary medicines to patients and caregivers, and ultimately, create the greatest value for all stakeholders.

The post A Delicate Balance: Building NewCos with Jacks of All Trades and Utility Players appeared first on LifeSciVC.