Welcome to The Seed! We use this newsletter to explore the rapidly evolving world of early-stage drug development and our observations as biotech VCs. Subscribe below to receive new posts by email:

Subscribe now

As several of our portfolio companies near clinical trials, conversations have naturally turned to a critical strategic decision: stay in the US or go overseas? While my background is deeply rooted in preclinical development, supporting our founders through this transition required a rigorous look at the shifting global clinical landscape. Over the past few months, I’ve mapped the long-term implications of taking early trials offshore. Watching this migration play out has made me realize that geographical flexibility often accrues a hidden cost, a concept I call translational debt: the hidden, downstream cost of prioritizing fast, localized trials over rigorous, scalable data. The following essay breaks down those realities and introduces the frameworks I now use to help our teams weigh the true trade-offs of each approach.

Looking at the biotech landscape since the start of the macroeconomic contraction of 2022, it’s no secret that the burden of proof required to underwrite an asset has shifted dramatically. A few years ago, a compelling preclinical data package was often enough to secure a robust Series A. Today, the risk tolerance of the market has fundamentally changed. Investors are demanding significantly more de-risking before they will step in and, in many cases, they expect First-in-Human (FIH) data before the true heavy-lifting rounds are even priced.

In my opinion, this elevated bar isn’t driven by general VC risk aversion; it seems to be shaped by a changing competitive baseline. Over the last few years, we’ve seen a growing trend of investors and pharma companies in-licensing assets from China and South Korea that already possess human data. They spin up a NewCo, drop in an asset that has already survived early clinical testing, and bankroll the US trials. If you are a US-based preclinical biotech startup today looking to raise your Series A, you are no longer just competing against other domestic founders for capital. You are competing against international assets that have already crossed the clinical threshold.

When I speak with founders navigating this environment, faced with a shrinking runway and an exceptionally high bar for their next fundraise, most are doing what any resilient team does under constraint: adapting to survive. Going offshore to generate fast, capital-efficient FIH data isn’t necessarily their ideal commercial strategy; it’s an existential stepping stone.

The scale of this migration becomes clear when you know where to look. You won’t see it by tracking raw global trial registries, which domestic academic studies obscure. Instead, look at the clinical hubs absorbing U.S. demand. Take Australia. According to data from Bellberry, the country’s largest independent reviewer of human research, nearly two-thirds of all early-phase clinical trials there are sponsored by overseas companies, with the United States driving the lion’s share of the investment.

This offshore flight has become pronounced enough that US regulators are trying to intervene. During the recent PDUFA VIII negotiations, the FDA proposed an “America First” fee structure, attempting to implement a multi-million-dollar fee differential to incentivize companies to anchor their Phase I trials domestically. But industry pushback highlighted a stark reality, leading to a negotiation impasse: a back-end fee incentive at the time of FDA approval is irrelevant to a seed-stage founder trying to survive the next 18 months.

The government now appears to recognize this structural flaw. In the FY27 budget proposal released this past April, the administration shifted tactics from financial incentives to regulatory pacing. They proposed a new expedited IND pathway to accelerate early studies. Rather than forcing startups through a 30-day centralized review and requiring exhaustive animal toxicology models before dosing a single human, the proposed pathway shifts the US closer to a notification-based system. It is designed to let founders bypass duplicative preclinical data packages by utilizing New Approach Methodologies (NAMs), like organoids or AI models, to clear the Phase I safety bar faster. The accompanying legislative text acknowledges the friction driving this migration, noting the new pathway targets smaller biotechs that lack capital and have been forced to move trials abroad because US timelines are lengthier and costlier compared to China or Australia.

The FDA has already begun laying the groundwork for this shift, launching its Real-Time Clinical Trials (RTCT) pilot program in late April to stream trial safety signals to regulators continuously via the cloud. However, the resignation of FDA Commissioner Marty Makary in mid-May 2026 introduced significant uncertainty over whether the reshuffled agency will champion these reforms with the same urgency.

While these proposed reforms signal a welcome shift in regulatory thinking, policy is ultimately a lagging indicator of ecosystem strain. For a management team staring down a runway of less than a year today, the promise of a faster US framework in 2027 simply doesn’t offer relief. Until the dust settles under Acting Commissioner Kyle Diamantas, founders cannot pause their burn rate waiting for enacted laws and new operational guidance. They must navigate the constraints exactly as they exist right now.

Until the domestic baseline officially changes, other countries offer highly efficient decentralized alternatives. Australia, for example, utilizes a Clinical Trial Notification (CTN) scheme that requires no IND and allows startups to dose their first patient in under 70 days. Paired with a 43.5% R&D tax rebate, this pathway can cut early clinical costs almost in half. This framework acts as a vital release valve for founders fighting to reach their Series A. But getting human data offshore is not a monolith. To understand the mechanics of this shift, I find it helpful to view the goal of an offshore FIH trial through one of three distinct strategic lenses:

Strategy 1: The investor-enabling bridge

In this scenario, the startup runs a Phase I or Investigator-Initiated Trial (IIT) overseas purely as a financial catalyst. The data is ‘investor-enabling’ rather than purely regulatory. Founders following this strategy aren’t necessarily trying to skip US trials; they intend to use the Series A capital they raise to perform whatever bridging IND-enabling studies the FDA demands before kicking off a traditional clinical pathway in the US. Here, the offshore data is simply the cost of admission to the venture market.

Strategy 2: The regulatory leapfrog (skipping to Phase II or dose-expansion)

This approach requires a heavier lift. The goal is to generate offshore human data rigorous enough that the FDA will accept it as foundational. If executed correctly, the company can import the foreign data, open a US IND, and bypass redundant early domestic safety testing. They do this either by jumping directly into a US Phase II trial, or leapfrogging the slow, sub-therapeutic dose-escalation cohorts of a Phase I to initiate US sites at therapeutically relevant doses. This can save years of time and millions of dollars of capital.

A higher-stakes variation of this path is the accelerated catalyst. The company aims to leverage overseas efficacy data to access an expedited US regulatory pathway, such as an RMAT (Regenerative Medicine Advanced Therapy) or Breakthrough Therapy designation. In the best-case scenario, this vaults them straight into pivotal Phase III trials or acts as an immediate trigger for an acquisition by a pharma buyer eager to ingest a promising asset early.

But as the regulatory climate shifts, a hidden catch has emerged for founders pursuing this accelerated route.

Startups operating under capital constraints naturally default to designing their early FIH trials as simple, single-arm, dose-escalation studies. This is perfectly fine for the initial cohorts where the sole objective is evaluating safety and establishing a maximum tolerated dose. The trap is sprung when founders try to extend that cheap, single-arm design into their dose-expansion cohorts to hunt for early efficacy signals. When evaluating domestic data for areas of high unmet need, the FDA has historically shown some flexibility; however, when founders rely entirely on foreign data to petition for an expedited pathway, the burden of proof shifts dramatically.

If you approach the agency with efficacy data from a single-arm offshore Phase Ib expansion, you are essentially asking regulators to evaluate an equation with two unanchored variables simultaneously: an unproven molecule and an uncalibrated patient baseline. Without a concurrent control arm, regulators (and downstream pharma buyers diligencing the asset) cannot confidently determine if an impressive response rate is actually driven by the drug, or if it is simply a byproduct of differing local genetics, regional healthcare dynamics, or less advanced earlier lines of therapy.

To execute the accelerated catalyst strategy today, founders must be highly intentional about where their trial phases transition. While the initial dose-escalation can remain lean and uncontrolled, the subsequent dose-expansion cohorts must graduate in complexity. Founders increasingly must introduce a randomized control arm that mirrors the current US Standard of Care (or satisfy randomized dose-optimization mandates) directly into their offshore expansion phase.

Strategy 3: Run everything overseas and then apply for a US NDA/BLA

This is the most extreme version of the offshore strategy. In this scenario, a company bypasses the US clinical ecosystem entirely, electing to run Phase I through Phase III trials in foreign jurisdictions. The strategic draw is undeniable: leveraging large patient populations in decentralized, lower-cost healthcare systems can theoretically yield a complete registrational data package in a fraction of the time and capital required by a domestic program. Once the pivotal data is in hand, the company packages the offshore dataset and submits it directly to the FDA for final commercial approval (a New Drug Application or Biologics License Application).

While the financial logic can make sense, this pathway carries an existential level of binary risk. By deferring deep US regulatory engagement until the very end of a multi-year clinical journey, a company is likely to accumulate lots of variation in demographics, clinical baselines, and standards of care. They are betting the entire asset (and maybe the company) on the assumption that the FDA will view their foreign clinical ecology as perfectly generalizable to the US population. Unless the disease epidemiology explicitly forces your hand (e.g., targeting an indication that is highly endemic overseas but exceptionally rare in the US, making domestic trials practically impossible), arriving at the FDA’s doorstep with a Phase III package devoid of American demographics or US standard-of-care parity is a huge gamble.

However, any one of the three approaches I’ve outlined above can be successful, if aligned with the startup’s capitalization, indication strategy and the broader market dynamics. But friction arises when there is a mismatch between the game a founder thinks they are playing and the underlying architecture of their trial.

This mismatch between strategy and trial design is exactly where translational debt comes due. Much like technical debt in software engineering, translational debt is incurred when companies choose a fast, localized solution over a rigorous, scalable one. In this context, the debt surfaces when a founder thinks that they are operating and budgeting for the second lens (leapfrogging to Phase II) but are actually only designing the trial and collecting sufficient data to execute the first strategy (collecting human data to secure another financing). In the best case, this mismatch might yield clinical data that looks phenomenal on a board update and successfully unlocks the next funding round, but it risks fracturing the moment the FDA (or a downstream pharma acquirer) stress-tests its underlying validity.

The downside case (which I believe is much more likely) is that the management team told downstream investors that their offshore data would unlock a US Phase II. If they now come back to their potential investors and say, ‘Well, we have the data, but we’d like you to underwrite some bridging studies to satisfy the FDA...are you ready to invest?’ That may be a challenging sell.

To make this more concrete, let’s look at how translational debt actually came due in the market across three recent outcomes:

Case study: MEI pharma and the unanchored efficacy trap

Imagine a startup runs a fast, single-arm, open-label trial in Australia, fully intending for the data to serve as a Phase II leapfrog. The results look fantastic, and they take it to the FDA, only for the agency to push back. While the FDA has long demanded rigorous, randomized dose-optimization and control arms for most non-oncology therapeutic areas, recent mandates like Project Optimus and Project FrontRunner have closed the final loopholes for single-arm studies in oncology. Across the board, the agency is doubling down, ruling that without a concurrent control arm pegged to the US Standard of Care, or without randomized dose-optimization, the efficacy data is unanchored.

Now for a real world example, let’s look at MEI Pharma and their lymphoma drug, Zandelisib. MEI ran a fast, open-label, single-arm Phase II trial (the TIDAL study) intended to serve as a leapfrog for an FDA accelerated approval pathway (the higher stakes version of strategy 2). The clinical data was objectively great, demonstrating a 70.3% objective response rate. Yet, the FDA abruptly rejected the submission pathway. Regulators ruled that without a randomized control arm, the single-arm efficacy data was unanchored and could not be safely evaluated against existing treatments. Instead of a fast-track approval, the FDA demanded a fully randomized Phase III trial before moving forward. MEI and its commercial partner simply didn’t have the runway or risk tolerance to absorb the years of delay and massive capital required to spin up a brand new randomized trial. They were forced to completely abandon the drug’s development outside of Japan.

Case study: Lilly and demographic disconnect

Now let’s examine a case of optimizing for Strategy 3 (running everything overseas to jump straight to an NDA/BLA). In 2022, Lilly attempted to secure US approval for a cancer drug called Sintilimab using clinical data generated entirely in China. The logic was obvious: utilizing a decentralized regulatory framework was cost-effective. The drug actually worked, and the clinical data was pristine. Yet, the FDA’s Oncologic Drugs Advisory Committee (ODAC) voted 14-1 against approval. In their review, they indicated that the trial failed to reflect US demographic diversity or compare the drug against the current US standard of care. Lilly and Innovent spent years and millions of dollars generating data that the FDA ultimately deemed inapplicable, resulting in a formal Complete Response Letter. The geographic shortcut invalidated the result.

Case study: Verve Therapeutics and pristine provenance

Contrast the two above examples with Verve Therapeutics, who demonstrated how to successfully execute the baseline version of strategy 2, the safety/IND leapfrog. When Verve launched the First-in-Human trial for VERVE-101, a pioneering in vivo base editing medicine, they initiated it in New Zealand and the UK, later expanding into Australia. Their goal wasn’t to run the entire trial offshore forever, nor were they simply looking for a cheap data catalyst for investors. Instead, they intended to execute a dose-escalation leapfrog. By running the tedious, high-risk ‘micro-dose’ safety cohorts overseas, they hoped to import that foundational safety data to the FDA and add US sites mid-stream at therapeutically relevant doses.

When Verve applied for US IND clearance to bring the trial domestic, the FDA initially placed them on a clinical hold. At first glance, this might look like a failure of the offshore strategy, but the nuance is critical. The FDA didn’t issue the hold because of where the trial was taking place; they issued it because of the unprecedented nature of the technology. As the first therapy of its kind, regulators were hyper-cautious, demanding more data to rule out theoretical risks like off-target genetic effects or unintended germline editing.

Crucially, to help answer these safety questions, the FDA asked to review the interim human data Verve was actively generating overseas. This was the ultimate stress test of translational debt. If Verve had optimized purely for cheap data in a low-tier jurisdiction, the FDA would have never trusted those results to clear such a huge safety hurdle. Because they had intentionally chosen high-tier jurisdictions with deep institutional muscle memory for FDA-interoperable Good Clinical Practice (GCP), their foreign data possessed unquestionable integrity. The agency reviewed the New Zealand and UK data, found it highly credible, lifted the hold, and cleared the path for US enrollment.

By ensuring their offshore data was rigorous enough for the FDA to accept as native, Verve executed the leapfrog. They didn’t have to start over with redundant early safety testing on US soil; instead, they bypassed the slow, sub-therapeutic dose-escalation phase, jumping straight into later dose cohorts and successfully adding US sites.

It is worth acknowledging that Verve voluntarily paused VERVE-101 in April 2024 after the trial accurately detected a dose-limiting liver toxicity related to the drug’s lipid nanoparticle delivery system, seamlessly pivoting to their backup asset, VERVE-102. Rather than detracting from their offshore strategy, this actually validated the operational rigor of their trial design. Their offshore clinical infrastructure captured the safety signal with such high fidelity that it reliably supported a fast, data-driven pipeline pivot, rather than leaving the company guessing if the toxicity was a biological reality or an artifact of poor trial operations. Verve successfully executed the exact same playbook again, clearing a US IND for VERVE-102 in March 2025 using offshore dose-escalation data from the UK, Canada, Australia, and New Zealand. That pristine offshore execution ultimately culminated in Eli Lilly acquiring Verve for over $1 billion in mid-2025, the ultimate market validation that downstream buyers will underwrite assets with unquestionable data integrity.

Why did Verve’s data bridge the gap while Lilly’s stalled? Perhaps because moving overseas isn’t just a change in geography; it involves altering an interconnected system. Lilly failed because they accumulated demographic and standard of care debt. Verve succeeded because they ensured they didn’t have any operational or compliance debt.

Take the clinical baseline, for instance. A patient population’s health is inextricably linked to their local healthcare environment. If your drug looks highly effective against a foreign standard of care, but the US standard of care is far more advanced, pharma cannot confidently extrapolate your Phase I data. You may have survived the trial, but you risk losing the ultimate buyer. Beyond the clinical baseline lies the operational code of the trial itself. Bridging early clinical data back to the US demands flawless traceability and an unbroken chain of custody. Verve succeeded because trial sites in Australia and the UK possess deep institutional muscle memory for FDA-grade Good Clinical Practice (GCP).

Supply chain considerations

Translational debt isn’t just clinical, it’s also physical. Beyond the trial baseline lies the reality of supply chain debt. Navigating early trials offshore often tempts founders to also offshore their Chemistry, Manufacturing, and Controls (CMC) to cut costs. But I think we need to update our mental models around how downstream buyers evaluate this.

The BIOSECURE Act was officially enacted on December 18, 2025, as Section 851 of the National Defense Authorization Act (NDAA), complete with a formalized Office of Management and Budget (OMB) blacklist tied directly to the Department of Defense’s 1260H list. This law has shifted buyer psychology. Pharma isn’t just sensitive to geopolitical risk anymore; their legal departments are operating under strict compliance mandates. In fact, industry data from L.E.K. Consulting shows that 68% of life sciences companies are actively adjusting their operations, imposing new legal requirements and background checks on their partners. When Pharma evaluates a startup today, they aren’t merely reviewing clinical readouts, they are following strict guidance from international law firms like Baker McKenzie to police indirect contract violations. This means tracing Active Pharmaceutical Ingredients (APIs) and Key Starting Materials (KSMs) back to their source. If any node in your manufacturing supply chain touches a designated Biotechnology Company of Concern (BCC), the conversation stalls.

I know some companies are relying on the law’s five-year grandfathering window for existing contracts, hoping it will at least cover their early FIH batches. I have a lot of empathy for this survival instinct, but I worry it reveals a profound time-horizon mismatch. Startups are inherently forced to think in 18-month runway increments; pharma, however, underwrites 15-year commercial lifecycles. If your underlying CMC architecture relies on a restricted vendor, the fact that your Phase I batch is legally grandfathered is irrelevant to an acquirer who needs a resilient, compliant supply chain for the next decade.

When a downstream buyer acquires an asset tied to a restricted overseas CDMO, they cannot simply scale that process. They are legally obligated to rip and replace it. I view this as a hidden tax on the asset, essentially a forced tech transfer penalty.

The severity of this penalty depends heavily on your modality. Ripping and replacing the manufacturing for a traditional small molecule is an expensive headache, but it is ultimately a predictable engineering problem; you are essentially transferring a chemical recipe. For biologics and advanced therapies, however, this forced transition borders on an existential risk. In biomanufacturing, the classic regulatory adage holds true: the product is the process. You aren’t just handing a new facility a recipe; you are transferring living cell lines, proprietary media, and highly calibrated bioreactor conditions.

For these complex modalities, tech transfers can be brutal. They have the potential to cost tens of millions of dollars, delay pivotal trials by quarters or even years, and frequently require exhaustive analytical comparability studies. If even a minor change in a new facility’s equipment alters a protein’s folding, glycosylation, or immunogenicity, those comparability studies will fail—often triggering the FDA to demand brand-new clinical bridging studies just to prove the domestic batch behaves like the offshore one. Acquirers don’t ignore this friction; they subtract that technical risk directly from your term sheet.

So, if rigid, heavily restricted CMC detracts from value, is there a reasonable cost-effective offshoring path forward?

The founders commanding the highest M&A premiums right now are building with what we might call portable CMC. Even for small, first-in-human batches, they design their manufacturing processes to be highly modular, exceptionally documented, and instantly transferable to US or global CDMOs. They don’t view manufacturing as a commodity to be outsourced to the lowest bidder; they treat their manufacturing lineage as intellectual property that can add significant value to their asset(s).

When starved for cash, the temptation to optimize purely for the ‘right to test’ just to keep the science alive is immense. But advancing a discovery from the clinic to the market requires looking beyond the immediate survival instinct. Downstream partners aren’t just arbitrary hurdles; they are the distribution network required to actually reach patients. To confidently scale a therapy, they require data with bulletproof auditability and an unbroken, compliant supply chain. In my opinion, the founders who ultimately win understand the interconnected mechanics of the entire journey, building with the final destination in mind from day one.

💡 The pharma paradox: why big checks still flow to China: At this point, you might be noticing a tension in the logic. If generating clinical data in China can generate translational debt, why is it that large pharma companies and mega-VCs are writing large checks to in-license Chinese assets? It looks like a contradiction. But watching this play out, I think it’s actually an exercise in capital asymmetry. When pharma in-licenses a Chinese asset, they aren’t expecting a ready-to-file FDA package. They are treating the local clinical data as the ultimate, highly de-risked in-human proof of concept for their own internal diligence. They are buying the underlying biology. Crucially, pharma acquires these assets fully aware they are inheriting translational debt. They possess the balance sheet to absorb the 18-month delay required to run a US bridging study (to satisfy the FDA’s demographic mandates), and they have the capital to execute a multi-million-dollar CMC tech transfer to move manufacturing away from restricted CDMOs to BIOSECURE compliant Western vendors. This is where the scale of the system matters. A cash-constrained pre-Series A startup cannot survive the timeline or the cost of a forced tech transfer and an unbudgeted bridging trial; it presents an existential threat to the runway. Pharma, however, views this friction as a standard cost of doing business. They price it directly into the term sheet, utilizing heavily back-loaded milestones to acquire validated biology at a huge upfront discount. The takeaway here is structural: for a US startup, carrying this degree of translational debt is a system vulnerability. For pharma, it is an arbitrage opportunity.

Field notes on the translational debt audit

While the 2022 rejection of Sintilimab was a highly visible wake-up call, observing the regulatory landscape over the past few years indicates that the FDA’s stance on offshore data applicability has solidified into a structural reality. You can see this translational debt being priced into the market across a spectrum of recent outcomes:

• Glofitamab (Roche / Genentech, 2025): Even companies with immense resources and scale can get caught in the translation gap. The FDA issued a Complete Response Letter (CRL) following an 8-1 ODAC vote against the applicability of the Phase III STARGLO trial. The core friction? The trial was heavily conducted in Asia (nearly 48% of patients) with only 9% US enrollment. The FDA determined the demographic data was too ambiguous to extrapolate safely, highlighting that you cannot out-resource a fundamentally mismatched trial ecology.

• Plinabulin (BeyondSpring, 2021): This is the ultimate cautionary tale for a US-headquartered biotech attempting the geographic shortcut. BeyondSpring ran a pivotal Phase III trial predominantly in China to accelerate enrollment and save capital. The trial met its primary endpoints, but the FDA issued a crushing Complete Response Letter. Regulators ruled that a trial heavily concentrated in Asia was not generalizable to US patients and demanded a second domestic trial. The geographic arbitrage wiped out hundreds of millions of dollars in market capitalization overnight.

• Toripalimab (Coherus / Junshi, 2023): This asset did secure FDA approval, but context is everything. It targets nasopharyngeal carcinoma, a disease endemic in Asia but exceptionally rare in the US. It is the exception that proves the rule: foreign-only data tends to be accepted only when the underlying epidemiology makes it practically impossible for the US to generate the data domestically.

Before locking in a foreign clinical jurisdiction or signing an overseas CDMO contract, I’d encourage you to audit your underlying trial architecture against this reality across three interconnected nodes:

1. The clinical baseline match

• Standard of care parity: The clinical baseline is a moving target dictated by current US guidelines. If an offshore trial compares a novel drug against an older, generic chemotherapy, but the US standard has evolved into an advanced immunotherapy, the resulting efficacy data becomes incredibly difficult to anchor. The trial ideally needs to prove the drug beats what US doctors are currently prescribing.

• Demographic bridging: If early trials are ethnically localized and non-representative of the US population, it naturally introduces friction. Navigating this requires building a statistically sound bridging strategy (often leaning on ICH E5 guidelines) from day one.

• Regulatory interoperability: Are the local ethics committees and regulatory agencies deeply embedded in the International Council for Harmonisation (ICH)? Jurisdictions like Australia, the UK, and Western Europe possess a proven track record of reciprocal data acceptance with the FDA.

2. Trial operations

• Institutional muscle memory: It is one thing for a foreign Contract Research Organization (CRO) to claim Good Clinical Practice (GCP) compliance on a brochure; it is entirely different to live it. Acquirers need the operational assurance that every adverse event was logged seamlessly and the protocol was administered with absolute fidelity.

• Digital ecology (21 CFR Part 11): This is the FDA’s rulebook for electronic records. If offshore sites utilize Electronic Data Capture (EDC) systems that fall outside of 21 CFR Part 11 compliance, the FDA may view the digital data as legally fragile. The information flow must be perfectly compliant from the foreign clinic straight into the US buyer’s data room.

• Sample logistics & geofencing: It is crucial to understand local genetic data privacy laws; in some jurisdictions, legal frameworks can trap patient samples in-country, inadvertently blocking a startup from sharing its own raw data with US regulators.

3. Supply chain topography

• Deep-tier enforcement: Have you mapped the entire supply chain, down to KSMs and APIs, to ensure zero exposure to restricted foreign vendors?

• The grandfathering mismatch: Relying on the BIOSECURE Act’s five-year grandfathering clause for early FIH batches reveals a profound time-horizon mismatch between 18-month startup runways and 15-year pharma lifecycles. Expect buyers to immediately price the cost, time, and friction of a forced tech transfer directly into your term sheet.

• Portable CMC: The most effective antidote to this debt is building modular, highly documented manufacturing processes that a downstream buyer can seamlessly port to a Tier-1 US or allied facility.

Tactical heuristics: sifting through international sites

When you are trying to filter through the marketing noise of offshore clinical sites, I generally suggest three operational heuristics:

• Review the audit history, not the brochure: Every CRO markets themselves as “FDA compliant.” A sharper approach is to ask for their actual FDA audit history. Exploring when a site was last inspected, and whether it resulted in a Form 483 or Warning Letter, offers a much truer signal of operational ground truth.

• Look for embedded infrastructure: The most resilient Phase I data generally stems from dedicated clinical pharmacology units embedded directly within major academic or private research hospitals, rather than standalone sites.

• Audit the PI’s global footprint: Does your Principal Investigator regularly publish in top-tier, US-centric peer-reviewed journals (like NEJM, Lancet, or JCO)? Investigators who frequently navigate global peer review inherently understand the level of rigor Western evaluators expect.

What’s next: from clinical theory to capital strategy

Identifying translational debt is an exercise in strategy; navigating it with twelve months of runway is an exercise in survival. If you are a founder reading this, you are likely looking at these structural constraints and wondering how to put them into practice. Typical questions we hear are:

• How do we bridge the potential cash-flow gap while waiting for Australia’s 43.5% R&D rebate to clear?

• If Tier-1 Western CDMOs won’t return emails for a tiny 50L batch, and the cheapest offshore options are now BIOSECURE-restricted, who occupies the missing middle vendor space that we can actually trust?

• How do we design a Phase I trial that satisfies the FDA’s demographic diversity mandates without completely bankrupting our early clinical budget?

These aren’t just abstract thought experiments; they are the questions keeping talented teams up at night. Translating a discovery into a viable medicine requires balancing that immediate survival instinct with long-term structural rigor. Because the operational answers are bespoke to your specific science, there is no one-size-fits-all playbook or vendor directory.

But how you frame these choices to the market matters immensely. In a future post, I’ll shift the discussion from clinical theory to capital strategy: breaking down how founders should pitch an offshore clinical plan to investors, how to preempt compliance concerns in the data room, and how to turn a capital-efficient global strategy into a valuation premium rather than a discount.

Footnotes

GlobalData Healthcare. “Large pharma drug licensing from China hits high at 28% in 2024.” Pharmaceutical Technology, April 1, 2025. https://www.pharmaceutical-technology.com/analyst-comment/large-pharma-drug-licensing-china-2024/

Subscribe now