From The Trenches

By Aimee Raleigh, Principal at Atlas Venture, as part of the From The Trenches feature of LifeSciVC

Drug discovery and development is a long, and often fraught, journey – it typically takes more than a decade to progress from idea to approved drug, and less than 10% of drugs that enter the clinic succeed in achieving commercialization. Creating new drugs for patients is certainly not for the faint of heart. Behind every story of a drug approved there are countless tales of challenges, whether it was early technology that failed to scale, early compounds that proved toxic in IND-enabling studies, lack of clinical efficacy that wasn’t predicted by preclinical models, and many more. The road to success is never straightforward and often takes many years to establish traction. As an industry we must be steadfast even in the face of extreme doubt or hardship. This is part of what makes us great.

However, there are times when refusing to stop and re-evaluate is to our disadvantage, especially if our (and our teams’) bandwidth can be better spent pursuing other potential therapies. I realize this moment in time is especially jarring to be discussing failure, when so many of our core institutions for novel thesis generation (NIH), drug development and approval (FDA), and the basic tenets of our biotech economy are in flux. But I believe the lessons on “failing fast” apply in any circumstance, and now more than ever are crucial as we think about ensuring enduring success for our ecosystem.

How do we define failure and success? How can we prospectively set up frameworks for decision-making to keep us honest when multiple experiments read out with gray or negative data? When do we collectively decide to shut down a program? Every team goes through periods of doubt, and the decision of when to stop and when to persevere is unique to each circumstance. That said, there are learnings we can draw from cumulative wisdom. For today’s post I am honored to have collected feedback from colleagues who have encountered the question of “should we stop” before, and have handled it with integrity and thoughtfulness:

• Alex Harding has previously written about the decision to stop, whether in shutting down seed-stage newco Apneo Therapeutics (here) or more generally as it applies to the public markets (here)
• Sam Truex spoke about the decision to shut down Quench Therapeutics after failing to identify tractable chemical matter against gasdermin D (here)
• Abbas Kazimi has written about “failing fast” when it comes to pipeline program management at Nimbus Therapeutics (here, here)

Whether during an exploratory newco build, after a large Series A financing, or for individual programs within a company’s pipeline, there are a set of frameworks that can help teams parse through difficult decisions. While we won’t be diving into any company-specific examples in this piece, I will share cumulative learnings and recommendations from Alex, Sam, and Abbas (and myself!) for how to approach decision-making in situations where the path forward is muddied.

Set up expectations before kicking off a new company or program

Failure is inherently defined as the inverse of success, but so often we don’t take the time to put pen to paper on what success looks like for an individual program or company. It’s essential to establish a rigorous evaluation mindset early in new company builds, and to continuously apply that mindset as objectively as possible. It takes time and thought, but before any amount of money is invested in a new idea, consider mapping out the program(s) in detail, including the first 1-3 indications with highest mechanistic rationale. In building the target product profile (TPP) for these, consider what makes the mechanism uniquely suited to address unmet needs in a given disease, and objectively map out what would be base, low, and high cases for the program in the context of the competitive landscape (see Fig. 1 for an exemplary framework).

As it’s often hard to dive headfirst into complexity, Sam recommends starting with the extreme cases – what does a mediocre or poor profile look like in this indication, and how does that compare to a stellar profile? From there, you can figure out the achievable but still differentiated middle ground, the “base case.” Once the theoretical profiles are established, you can then cross-check internal and external data over time to ensure the base case remains competitive and valuable. Make sure to always stay conscientious of changing relative benchmarks, as you wouldn’t want to put your head down in discovery for 5 years only to realize the landscape for your prioritized indication has completely changed and the bar is much higher than you originally thought. It’s important to maintain discipline over conviction when evaluating your programs against competitors – maybe you can justify moving forward with a program that hits the low vs. base case on one parameter, but for all others the program should meet or exceed the base case. To remain objective, it’s helpful to consistently pressure-test your view of the attractiveness of your program not only with insiders (team, board, investors), but also with external perspectives (e.g., key opinion leaders).

This logic is a little more straightforward for asset-centric companies, but what if you are developing an early discovery-stage platform? Similarly, it’s encouraged to map out your key 1-3 programs before even running any experiments to validate your platform. In the base case scenario, how can your new technology / modality uniquely enable the treatment of a disease beyond available (commercial and pipeline) therapies today? If you are at a loss for what these differentiated programs should be, it’s probably a sign that the platform concept needs some additional thinking before spending substantial funds to build it out.

Finally, perhaps an obvious point but one that goes unaddressed in many new companies: no one should be more familiar with “bear case” for your program than you. It’s a strength and not a weakness to fully understand the theoretical risks of the mechanism or modality in the context of your lead indication. In fact, it’s safe to assume that you will gain credibility in being aware of the risks and expounding upon (and refuting) them soundly.

Map out potential outcomes before an experimental readout

Of course, the initial theoretical TPP will change over time as you generate data and as the competitive landscape shifts, but it’s important to hold yourself accountable to “what good looks like” as a program progresses. It’s also useful when starting a program in discovery to map the TPP back to a preclinical set of criteria that is required to uphold the “base case” in the clinical profile. Development candidate (DC) criteria (exemplary in Fig. 2) should be established to similarly set up threshold performance specifications for a program that are required to move it forward into IND-enabling studies and then subsequently into the clinic. Every time you receive a new piece of data, consider it in the context of the DC criteria and TPP – how does your estimation of the program stack up with this new data in mind? You may become more bullish that the profile is competitive and exceeding expectations, or you may decide that a certain limitation (e.g., narrow tox margins, lack of compatibility with oral dosing) make the profile untenable for a given indication.

Sam recommends framing up goals (annual, quarterly, and even per-experiment) based on the construct of “what would we like to be able to say about our program if it goes as well as we could imagine, and what will we have proven?” It’s important to lay out expectations for success ahead of a readout, to minimize bias that creeps in after we have seen data and know what is or is not feasible. You will undoubtedly encounter hurdles – experiments that are negative or challenging to interpret. But setting up expectations prospectively allows you and your team to rapidly pivot in a data-driven manner and while keeping the big-picture DC criteria and TPP in mind.

For a platform company, consider approaching every technological fork in the road with the question “does this serve my programs and target indications?” It may be difficult to draw the line between what is scientifically interesting for the platform (continual technological advancement and clever add-ons) vs. what is essential to drive execution towards the clinic for your program. Much like you map out a DC checklist and TPP, consider applying similar logic for each experiment testing your platform:

• What is this experiment testing?
• What does full success look like, and how does it enable my programs?
• If successful, what are the next 1-3 experiments?
• How does this readout enable progress towards the clinic for my programs?

It can be easy to revert to letting the data drive decisions, but in doing so we may stray away from the original goals and the therapeutic program in mind. For platform companies there are nearly infinite degrees of freedom, so there is a balance between what is “good enough” for version 1.0 that enables progress towards clinic and what is best saved for version 2.0+ once early de-risking is achieved.

The DC criteria and TPP can serve as inputs to distinct but related decision frameworks that can be used to continuously evaluate preclinical or clinical readouts against a prospectively defined minimum viable profile (see Fig. 3). For each node in the decision tree, take time to map out expectations for critical readouts – what does the “go-forward” base case scenario include? A company’s strategy is ultimately influenced by these data, so consider implications for financing opportunities, strategic decisions (additional pipeline, indication expansion), and path to the clinic or eventual approval for the key readouts.

Of course, no one is suggesting a single experimental readout should be cause for shutting down a program or company. But what is the right criteria? As Alex puts it, it’s all about balancing curiosity and conviction with the probability of success, and that may look different for a given team, board, or company. History has taught us that in some instances (e.g., ALNY, TRIL, PCYC, LBPH), to give up early would have meant forgoing incredible advances for patients. But those examples are often exceptions to the rule, where relentless persistence has paid off. If you and your team have encountered experiment after experiment of gray data, or have tried to fundraise for more than a year unsuccessfully, or are at a collective loss as to how to progress a program, take a moment of pause. Does the data (internal or external) tell you something different about your program or platform than your ingoing assumption? If so, is there a path forward with a pivot, or is it time to reassess the path forward for this particular thesis?

Ultimately, remember that candor, objectivity, and proactivity are often helpful when murky data emerges. It’s helpful to ground everyone to the same base case so that when anything unexpected arises, stakeholders are best situated to evaluate it objectively and quickly determine the best path forward. Drug discovery and development is a team sport – teams and boards should collaboratively work together on all aspects of expectation-setting as well as analysis and decision once the data emerges.

Appreciate that our cumulative definition of “failure” should be re-framed:

Now that we’ve set up best practices for rigorously evaluating a program or platform’s profile, it’s important to acknowledge all the nuanced reasons why remaining objective is so difficult to do in real life. Setting up expectations and then missing the mark can be existential, Alex mentions, especially for a company focused on a single program or target. Additionally, there may be an asymmetric perception of risks for an operator compared to other stakeholders, where the former may have dedicated many years to this sole endeavor and feel a sense of loss aversion when faced with the potential of shutting it down.

All three of Alex, Sam, and Abbas have emphasized that the industry should consider how we define and speak about success vs. failure. Rigorously developing a hypothesis and approach to testing that hypothesis, regardless of the end result, should be applauded as a success. The only way we can push our understanding of science and medicine forward is to test these hypotheses and disseminate the readouts to the community. Where would the incretin field be if Novo hadn’t persevered in improving the half-life of GLP-1 analogs when original molecules stalled? If Roche hadn’t first failed to slow Alzheimer’s progression with gantenerumab and crenezumab, would they have had the insights to develop trontinemab, a next-gen TfR1 shuttle conjugated to anti-amyloid? Countless new programs are born out of hard-learned failures.

Additionally, it’s important to dissociate any ego or personal identity from the outcome of a well-planned experiment. Just because a program or technology fails to move forward doesn’t imply the people running the program failed. In fact, if they got to a “no-go” quickly, they made a huge contribution to their team’s and the collective industry’s knowledge for a given target or approach. Abbas recommends cheering for yourself and others when you decide to shut a program down just as much as when a program moves forward – ultimately our ecosystem is data-driven, and even negative data can help to advance knowledge and future medicines. At Nimbus, they have a saying: “like the program, love the portfolio.” To let go of one program where the thesis hasn’t panned out frees up bandwidth and resources to execute on programs that do. With this mentality in mind, over a representative time frame from 2016-2022, Nimbus shut down 70% of the programs it was working on for a variety of reasons: scientific, commercial, competitive positioning, etc. In acknowledging when a thesis wasn’t advancing, their team collectively re-deployed energy and talent to those programs that did, undoubtedly driving to the successful TYK2 program (now in the hands of Takeda), WRN inhibitor, and more.

It should be noted that success in “failing fast” is best enabled when company leadership, investors, boards, and other stakeholders are all aligned on the stated mission and expectation for “what good looks like.” This logic is best framed ahead of readouts and when companies have sufficient runway, as nothing breeds poor decisions like a limited runway and lack of backup options.

Every company and situation is unique, so there is no right or wrong answer on when to keep pushing vs. throw in the towel. Hopefully the frameworks here are useful the next time you find yourself staring down that question. Ultimately we each have a fixed amount of time to do some real good for patients – how will you decide to spend it?

Thank you to Sam Truex, Alex Harding, and Abbas Kazimi for generously sharing their perspectives and time for this article. Many thanks also to Akshay Vaishnaw for providing feedback on this post.

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By Jason Campagna, CMO of Q32 Bio, as part of the From The Trenches feature of LifeSciVC

We’re entering a phase shift in biotech—one that extends beyond molecules, pipelines, or even platforms. Tariff escalations, supply chain fractures, and a reassertion of industrial policy are reshaping the terrain beneath us. The scrutiny and legislative backlash against WuXi AppTec—one of the world’s largest CDMOs—has brought into public view what many in the industry have long navigated quietly, that biotechnology’s foundational reliance on globally integrated supply chains is no longer politically or operationally stable.

The WuXi case is not an outlier—it’s a signal. U.S. policymakers are now treating biotech the way they’ve come to treat semiconductors and energy: as strategic infrastructure. Input costs, regulatory pathways, and even contractual viability are being reinterpreted through the lens of national interest. In this context, “free trade” is no longer a default assumption.

From my perch within the Atlas ecosystem, and through conversations across its portfolio companies, I’m seeing clear signs of transition. Biologics pipelines increasingly feature bispecifics, trispecifics, and ADCs built not only for biological sophistication but for differentiated deployment—exemplified by Pheon Therapeutics, which recently raised $120M to advance next-generation ADCs into the clinic. At the same time, Chinese-origin NMEs are flowing into U.S. portfolios at a striking pace. Hercules, a spinout of Hengrui Pharmaceuticals backed by Atlas and others, raised $400M in one of the largest cross-border biotech launches to date.

And while these capital movements accelerate, state-aligned instruments—such as the Department of Defense’s Office of Strategic Capital—are treating biotechnology with the same strategic framing once reserved for semiconductors or defense platforms. The terrain is shifting, and the signals are becoming harder to ignore. ARPA-H’s decision to locate its Investor Catalyst Hub in Cambridge, Massachusetts further underscores this shift—placing national strategic infrastructure for health innovation directly within one of biotech’s most active ecosystems. It reflects a broader reality: the government is no longer just a funder of innovation. It is becoming a platform builder, aligning capital and geography to accelerate translation and resilience.

Biotech’s Infrastructure Moment

Change is already underway—but our frameworks for value, relevance, and readiness haven’t fully caught up. Biotech has traditionally been framed as a domain of therapeutic innovation: a source of breakthrough medicines, investor returns, and occasionally, public-private collaboration. But that framing is incomplete for the decade ahead. We are shifting from a world that valued therapeutic novelty in isolation to one that prioritizes stability, deployment, and sovereign readiness.

Just as the CHIPS Act reframed semiconductors as infrastructure, and the IRA did the same for energy systems, we are now witnessing the early architecture of what might become the bio-industrial policy era. Agencies like BARDA, ARPA-H, and CEPI are not simply funding science—they are funding capabilities. The core question is shifting: not just what can you discover, but what can you deploy?

We’re already seeing this play out. Nimbus Therapeutics has leveraged modular drug discovery and computational chemistry platforms that align with DARPA- and BARDA-style interests in scalable, rapid-response therapeutic development. Meanwhile, Moderna’s ongoing CEPI partnerships—including pre-pandemic commitments to rapidly produce mRNA vaccines in LMICs—demonstrate how platform readiness, not just innovation, is being treated as public health infrastructure. And through ARPA-H, we’re beginning to see a new funding model that prioritizes translational velocity and technical risk: programs like NITRO (Novel Innovations for Tissue Regeneration and Organoids) or the use of rapid manufacturing hubs are explicitly designed to create deployable biotech toolkits—not just advance single-asset R&D.

These emerging funding models share a common thesis: that the value of a biotech platform lies not only in its novelty, but in its readiness—its ability to scale, deploy, and operate under constraint. But in practice, many of the most promising therapeutic modalities—particularly RNA-based platforms—remain fragile and logistically intensive. This creates a strategic blind spot: we are investing in innovation faster than we are building the systems to reliably deliver it. What’s missing is a capability layer beneath the molecule. A kind of infrastructure that enables biologics to function not just in ideal settings—but in conflict zones, LMICs, and heat-stressed geographies.

Think of it as a capability stack: at the top sits the therapeutic innovation itself—the siRNA, mRNA, or biologic payload. Beneath that lies formulation science, packaging design, and delivery hardware. And at the foundation are the supply chain dynamics: how therapies are stored, transported, and accessed across geographies. When any layer is unstable, the entire stack is vulnerable. Today, too much of biotech is built on assumptions that only hold in centralized, well-resourced environments. If we want RNA and advanced biologics to fulfill their global promise, we need to invest in the foundational layers that make them operable under stress.

Stability as a Service: A Missing Layer

RNA-based therapeutics exemplify this new duality. mRNA, siRNA, and related modalities have massive potential, but remain constrained by cold-chain fragility, complex manufacturing, and delivery limitations.

That constraint is an opportunity. What we need is a new operational layer: “Stability as a Service” (SaaS). SaaS, in this context, is the development of modular capabilities that enable nucleic acid therapeutics to be deployed under stress—heat, logistics, time, and geopolitics. These include lyophilized formulations, non-cryogenic delivery systems, and packaging designed for decentralized distribution.

SaaS is not just a formulation problem—it’s a strategic lever:

• Global equity: enabling therapeutic access in LMICs without centralized infrastructure
• Defense readiness: platform vaccines and antivirals for use in conflict or outbreak zones
• Climate resilience: robust delivery in unstable supply chains and temperature-volatile environments

We are approaching a world where the “formulation layer” becomes just as strategic as the API itself.

Three Signals, Three Layers of the Stack

If biotech’s next phase will be shaped not just by what we discover, but by how we deploy it, then the strategic advantage will lie in mastering a new capability stack. Several companies already signal the growing importance of this architecture:

• Comanche Biopharma is developing a novel siRNA therapeutic for preeclampsia, with a delivery profile that may one day function outside of cold-chain environments. While the lead program is still in development, the deeper signal is this: if the team succeeds in building a thermostable, infrastructure-light RNA delivery system, it could represent a foundational shift in how biotech engages with LMICs, fragile states, or climate-stressed geographies. This is the base layer of the stack—deployability, logistics, and readiness under constraint.
• Isomorphic Labs, an Alphabet subsidiary, recently raised $600M to advance its mission of using AI to transform drug discovery. But the true innovation isn’t just computational—it’s architectural. Isomorphic is building a design platform that operates across therapeutic areas, compressing discovery timelines while enabling scalability and iteration. This is the middle layer: a flexible, data-driven operating system that expands the reach and speed of therapeutic development across institutional and national boundaries.
• Tierra Biosciences, a Material Impact portfolio company, offers high-throughput, on-demand protein synthesis via a cloud-based e-commerce model. Their platform reduces friction in biological research and development—especially in decentralized or resource-limited settings. It’s a quiet but radical innovation in how we access biological inputs. Tierra represents a rethinking of biotech’s manufacturing and input infrastructure—part of the base layer that determines who gets to build, iterate, and respond in real time.

Together, these companies signal a broader redefinition of biotech—not just as a generator of novel molecules, but as a builder of resilient, scalable systems. The future may depend not only on discovery—but on the structures we build beneath it.

From Signals to Structure: Two Case Studies

If the companies above suggest where the field is heading, the next examples make the path and direction explicit. These aren’t signals—they are structural commitments. Both Resilience and Laronde have moved beyond singular product ambition and are actively building the infrastructure that could define biotech’s next operating system. Each embodies a distinct philosophy: one focused on manufacturing sovereignty, the other on platform endurance. Taken together, they highlight the emerging belief that biotech’s long-term relevance may depend as much on systems architecture as on scientific novelty.

Resilience and the Productization of Capacity

Resilience, backed by ARCH Venture Partners, is perhaps the clearest example of biotech’s infrastructural turn. Rather than focusing on therapeutics, Resilience builds distributed, modular GMP manufacturing capacity as a platform. In doing so, it reframes biomanufacturing as a service layer—akin to AWS for biotech—where scale, speed, and sovereign flexibility become core capabilities. It is a vivid demonstration that infrastructure itself can be a strategic asset, not just a supporting function.

Laronde and the Challenge of Sustained RNA

Flagship Pioneering’s Laronde is developing “endless RNA” (eRNA), a programmable RNA platform with extended durability. But the promise of persistent, systemic expression raises a fundamental question: how do we deliver and control such a molecule across diverse clinical and logistical environments? The technology is bold—but its success may hinge not only on the biology, but on the infrastructure that surrounds it. Laronde illustrates that as RNA therapeutics evolve in sophistication, their dependency on robust deployment systems only intensifies.

Why Now

This isn’t an abstract forecast. Recent policy developments are clarifying the stakes. The next wave of tariffs and trade policy—particularly around China—will implicate APIs, excipients, reagents, and packaging materials essential to biotech manufacturing. Add to this the rising scrutiny around CMO geography, sovereign biomanufacturing, and dual-use technology, and the message becomes clear: biotech is entering the strategic domain once occupied by energy and defense. And with that shift comes opportunity: for access to new pools of capital, for advanced market commitments, for blended public-private partnership models that reward infrastructure alongside innovation.

Why Me

I don’t write this as an academic observer or policy analyst—I write as someone who, like many in this field, has spent years inside the system, leading development efforts while navigating firsthand the intersections of infrastructure, geopolitics, and clinical urgency. These experiences don’t make me unique—but they’ve made the patterns harder to ignore.

Earlier in my career at The Medicines Company (MDCO), I wasn’t just involved in product development—I was part of an effort to build a scalable operating model for how innovation could be embedded into high-acuity health systems. MDCO’s ambition extended beyond launching therapies like Angiomax or Cleviprex. The company sought to solve the diffusion problem in modern therapeutics: how to institutionalize change, not just individual prescriptions. Through strategic frameworks like the “nine-box model,” modular rollout strategies, and global playbooks tailored to hospital behavior, we aimed to make therapeutic adoption as programmable as therapeutic discovery.

Ironically, the company’s name—“The Medicines Development Company”—wasn’t about molecules alone. It reflected a vision for drug development as a platform discipline: repeatable, systems-aware, and built to scale under constraint. That experience reshaped how I see the field today. Formulation, delivery, and integration aren’t downstream of innovation—they are innovation. And whether in antibiotics or RNA, it’s often the infrastructure, not the science, that determines reach. This essay reflects that shift—not from the outside in, but from the inside out.

Conclusion: The Opportunity of Convergence

The infrastructure layer of biotech is no longer secondary. It’s becoming the stage on which scientific, economic, and geopolitical pressures collide. Tariffs, trade restrictions, and the fracturing of long-assumed global supply routes are accelerating—not hypothetically, but right now.

None of this diminishes the core value of biotech’s traditional model: founding and funding therapeutics, advancing human health, and delivering breakthrough science. That work remains essential—and in this regard, Atlas has consistently backed companies that push the frontier of science with rigor, focus, and a deep understanding of what it takes to bring a therapeutic to life. But the context around that excellence is changing.

What does this mean for our industry? For how we build companies in the next decade? For the types of founders, funders, and platforms that will define the next era? These aren’t rhetorical questions—they’re live ones. And they’re thrilling to consider.

This essay has raised the possibility that companies who invest early—into stability, logistics, and delivery—won’t just lead clinically. They’ll be the ones still standing when the context shifts, the ones partners and policymakers turn to when deployment becomes the bottleneck. This isn’t about speculative preparedness. It’s about staying relevant in a world that is reorganizing beneath our feet. And that moment is already here.

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