How to prepare for due diligence, and why transparency wins
In criminal law, the accused is presumed innocent until proven guilty. In pharmaceutical due diligence, the opposite applies: your asset is presumed to have problems until you demonstrate otherwise. This may feel adversarial, but understanding it is the first step toward navigating DD successfully.
The companies that close deals faster and on better terms are those that embrace this reality rather than resist it. They prepare as if every question will be asked - because it will be. They disclose issues before they're discovered - because discovery without disclosure destroys trust. They treat DD not as an obstacle to be survived but as an opportunity to demonstrate that they understand their own program deeply enough to merit partnership.
The "guilty until proven innocent" standard stems from structural realities that companies cannot change - only accommodate.
Consider the payoff structure: if an investor misses a flaw and the deal closes, they may lose their entire investment when the problem surfaces. If they identify a flaw and walk away from a good deal, they lose only the opportunity cost - and there will be other deals. This asymmetry makes aggressive skepticism rational. Most experienced investors have been burned by something they missed; few regret being too careful.
You know your program intimately. The investor has weeks - sometimes days - to understand it. They cannot possibly identify every issue through their own investigation. They depend on your disclosures being complete and accurate, and they know you have every incentive to present your program favorably. The rational response is to assume you've omitted something important until they've verified you haven't.
Approximately 90% of drugs entering clinical development fail to reach approval.1 This is not a pessimistic assumption - it is empirical reality. When the base rate of failure is this high, skepticism is not cynicism; it's calibration. The investor who assumes your program will succeed is not being generous - they're being statistically naive.
The implication for companies: Complaining about aggressive DD is complaining about rational behavior. The investor asking hard questions is doing their job. The one who doesn't ask is either inexperienced or not seriously considering the deal.
Not all DD findings are equal. Experienced investors distinguish between issues that require explanation (most findings), issues that require mitigation (some findings), and issues that kill the deal outright (rare but decisive). Understanding this hierarchy helps companies prioritize their preparation - and recognize that most issues, properly disclosed, are manageable.
In our experience, the vast majority of DD findings fall into the "normal" category - issues that need to be explained and understood but don't fundamentally threaten the deal. Even "serious concerns" are typically addressable through negotiation, deal structure, or mitigation commitments. True deal-killers are rare, and they almost always involve either data integrity or undisclosed material information.
Data integrity failures. If DD reveals that clinical data has been manipulated, selectively reported, or generated under conditions that undermine its reliability, the deal is dead. This is not about isolated discrepancies - it's about patterns that suggest the data cannot be trusted. Once an investor concludes they cannot trust the data, nothing else matters. There is no mitigant for a credibility collapse.
Undisclosed material risks. An FDA clinical hold is survivable if disclosed upfront with a remediation plan. The same hold discovered during DD - after the company presented a rosy picture - transforms a manageable regulatory issue into evidence of either incompetence or deception. The distinction matters: investors can partner with companies that have problems; they cannot partner with companies that hide them.
Fundamental viability questions. Some findings reveal that the core thesis is broken: freedom-to-operate opinions that expose blocking IP, undisclosed non-GLP toxicology findings, or regulatory feedback indicating the proposed pathway is not viable. These findings don't require mitigation - they require a different deal or no deal.
Beyond scientific and regulatory issues, financial DD can reveal problems that derail transactions. Clinical trial accruals are frequently under-reported until reconciled against vendor contracts and site budgets. Burn rate projections often assume favorable scenarios without stress-testing. Delayed vendor invoicing can mask systematic under-accrual rather than simple timing differences. These findings rarely kill deals outright, but they affect valuation and deal structure - and they signal how carefully the company manages its operations.
Below the deal-killer threshold, there's a category of findings that don't terminate discussions but significantly affect terms. These typically involve quantifiable risks that can be priced or mitigated but represent real value destruction if unaddressed.
| Domain | Deal Killer | Serious but Manageable |
|---|---|---|
| Clinical Data | Evidence of data manipulation; selective endpoint reporting; GCP violations | Protocol deviations with documented rationale; missing secondary endpoints; site quality variations |
| CMC | Process cannot be reproduced; undocumented manufacturing changes; no analytical method validation | Sole-source API supplier; scale-up not yet demonstrated; stability data gaps |
| Supply Chain | No commercial supply strategy; launch timeline assumes clinical supply can serve market | CDMO capacity constraints; technology transfer incomplete; secondary supplier not qualified |
| Regulatory | Undisclosed clinical hold; warning letter on relevant facility; pathway not viable | Open FDA questions requiring response; conditional approval dependencies; label negotiation risk |
| IP | Freedom-to-operate blocked; patent invalidity likely; undisclosed prior art | Narrow claims requiring design-around; expiry timing issues; licensing dependencies |
| Preclinical | Lead data not reproducible; target validation from single lab; key findings cannot be replicated | No backup compounds; early tox signals not yet characterized; unclear path to human dosing |
Stage matters. Many findings that are serious concerns at Phase 3 are routine at preclinical - sole-source API supply, incomplete tox packages, unvalidated commercial assays. Conversely, preclinical DD focuses heavily on things that matter less later: reproducibility of academic findings, target validation depth, and whether the foundational science is real. The severity spectrum shifts with stage.
"Proving innocence" in DD is not about persuading investors you have no problems - every program has problems. It's about demonstrating three things: that you know what your problems are, that you've thought seriously about their implications, and that you have credible plans to address them. The company that acknowledges limitations with clear-eyed analysis earns more credibility than the one that claims perfection.
The instinct to bury bad news is understandable but counterproductive. Investors will find the issues - that's what DD is for. When they find something you didn't disclose, they wonder what else you're hiding. When they find something you disclosed upfront, they credit your transparency and move to evaluating the substance.
What if you find something unfixable? Not every issue can be pivoted away from. If internal DD reveals a fundamental problem - a blocking patent you can't design around, a competitive asset that's clearly superior, a regulatory pathway that's closed - you have options. Disclose it early and seek investors who are comfortable with that specific risk profile. Reframe the opportunity (different indication, different geography, platform value vs. lead asset). Or, in some cases, recognize that the honest answer is to preserve capital rather than pursue a compromised opportunity. This might mean pursuing a platform-value transaction rather than lead-asset pricing, seeking acqui-hire interest, or returning capital to investors rather than spending it on a thesis that no longer holds. Better to know before you've spent years and millions than to discover it in a partner's DD.
DD teams will compare your management presentations against source materials: clinical study reports, FDA correspondence, raw data. When the investor presentation says "strong efficacy signal" but the clinical study report shows a p-value of 0.048 with multiple secondary endpoints that missed, credibility erodes. When the regulatory strategy slide shows a clear pathway but FDA meeting minutes reveal unresolved questions, skepticism deepens.
The discipline: ensure your presentation materials can withstand comparison to primary sources. Every claim in your deck should be traceable to underlying documentation that supports exactly what you've claimed - not an optimistic interpretation of ambiguous data. If you're not confident a claim will hold up, soften the language or add appropriate caveats. The goal is not to be pessimistic but to be accurate.
Before entering DD, conduct a systematic pre-mortem: assume the deal will fail, then work backward to identify why. What would a skeptical reviewer find most concerning? Where are the gaps in your data package? Which assumptions are you making that an outsider might not share?
Investors ask questions because they don't have complete information. If you've anticipated the question, you can provide a complete answer immediately rather than scrambling to pull documentation.
A practical test: Take your data room. Give it to someone who doesn't know your program - a consultant, an advisor, a board member - and ask them to find the three most concerning issues. If their list matches yours, you're prepared. If they find something you hadn't considered, you have work to do.
In DD, documentation is not bureaucracy - it is evidence. When an investor asks "how do you know your manufacturing process is under control?", they are not looking for reassurance. They are looking for documentation: batch records, deviation reports, change control logs, stability data. The company that can produce primary source documents earns credibility; the company that offers verbal explanations invites skepticism.
Key documentation principles:
Completeness over perfection. A complete set of regulatory correspondence - including the difficult exchanges - is more valuable than a curated selection of favorable letters. Investors assume you're showing them the best version; they want to see the full picture.
Primary sources over summaries. Clinical study reports are more credible than management presentations. Raw data with statistical analysis plans are more credible than summary tables. When investors can verify your claims against source documents, they trust your other claims more readily.
Contemporaneous records over reconstructions. A deviation report written at the time of the event is evidence. An explanation reconstructed for DD is advocacy. Investors know the difference.
DD is not purely a document review - it is an assessment of whether investors can work with this team. How management responds to difficult questions reveals as much as the answers themselves. Defensiveness, blame-shifting, or inability to acknowledge uncertainty are warning signs independent of the underlying facts.
The teams that navigate DD successfully share common characteristics: they answer the question asked rather than the question they wish they'd been asked; they distinguish between what they know, what they believe, and what they're guessing; and they treat DD as a collaborative process of establishing shared understanding rather than an adversarial contest to be won.
In our experience across more than 300 DD engagements, certain patterns emerge in the companies that generate enthusiasm rather than merely passing muster. They demonstrate deep understanding of their program's weaknesses as well as its strengths - and they have realistic plans to address them. They've thought through failure modes and have contingency plans. They respond to hard questions with curiosity rather than defensiveness - "that's a good point, here's how we've thought about it" rather than "that's not really a concern." They make the DD team's job easier by anticipating needs rather than treating every request as an imposition.
The goal is not to have no issues - every program has issues. The goal is to demonstrate that you've done the hard thinking, that you're being honest about what you know and don't know, and that you're the kind of team an investor wants to back when things inevitably get difficult.
Technical DD - clinical, regulatory, CMC, IP - gets most of the attention, but commercial DD kills just as many deals. Market size assumptions, competitive positioning, pricing and reimbursement outlook, and the realistic path to adoption all matter. A program with clean data and a clear regulatory pathway can still fail DD if the commercial thesis doesn't hold up. The earlier you stress-test your market assumptions - ideally with real payer and KOL input - the less likely you are to be surprised when investors do the same analysis.
Beyond preparation, the DD process itself requires active management. DD typically takes 4-8 weeks for financing rounds, often 2-4 months for acquisitions or complex partnerships. A few tactical considerations:
Designate a DD coordinator. One person should own the process - tracking requests, coordinating responses, and ensuring consistency. This is usually someone from the business or legal team, not the CEO. The CEO should be available for key calls but shouldn't be chasing document requests.
Respond quickly but carefully. Slow responses signal disorganization or, worse, that you're crafting answers rather than retrieving facts. Aim to acknowledge requests within 24 hours even if the full response takes longer. But don't sacrifice accuracy for speed - a wrong answer is worse than a delayed one.
Match expertise to questions. Technical questions should be answered by technical people. Don't have the CEO explain CMC issues or the CFO describe the mechanism of action. Investors notice when the wrong person is answering.
Prepare for the management meeting. Most DD processes include a live Q&A session with the investment team. Anticipate the hard questions - they'll ask about the issues you're most worried about. Have the right people in the room (CSO for science questions, CMO for clinical, CFO for financials). Answer the question asked, not the question you wish they'd asked. If you don't know something, say so - credibility matters more than appearing to have all the answers.
Track everything. Keep a log of all requests, responses, and follow-ups. This helps you identify patterns (repeated questions about the same issue may signal a concern), ensures nothing falls through cracks, and provides a record if disputes arise later.
After DD findings, expect negotiation. Most DD processes surface issues that affect deal terms. This is normal. Findings typically lead to valuation adjustments, escrow holdbacks for specific risks, milestone-based payments, or representations and warranties. Understanding that DD findings lead to negotiation - not automatic deal death - helps you respond constructively rather than defensively.
The "guilty until proven innocent" framework assumes there is evidence that could, in principle, prove innocence. For programs with historical comparators and well-defined success criteria, this holds. For genuinely novel approaches - first-in-class mechanisms, new modalities, unprecedented regulatory pathways - it may not. When an investor asks "what is the probability of success?" for a program with no clinical precedent, there is no honest quantitative answer.
This distinction matters.2 For quantifiable risks, DD can establish probability bounds based on historical data. For genuine uncertainties, DD can only characterize what is unknown and what questions must be answered before probability estimates become meaningful. (For a deeper treatment, see our companion whitepaper on risk, exposure, and uncertainty in due diligence.)
Companies with genuinely novel programs cannot "prove innocence" through precedent because no precedent exists. Instead, they must demonstrate that they've correctly identified what is uncertain, that they have a credible plan to resolve key uncertainties, and that they've structured their capital around staged learning rather than assuming success. For these programs, DD shifts from assessing probability of success to assessing the team's ability to navigate uncertainty intelligently.
The danger of false precision: When companies assign confident probabilities to genuinely unprecedented programs, sophisticated investors recognize this as a red flag. It suggests either that the company doesn't understand the difference between risk and uncertainty, or that they're manufacturing confidence where none exists.
Rigorous DD can establish whether a company has clean data, controlled manufacturing, viable regulatory strategy, and freedom to operate. It can identify hidden problems and assess whether the team is competent and honest. What DD cannot establish is whether the underlying science will work - no document review can predict whether a novel mechanism will produce clinical benefit or whether regulators will accept an unprecedented evidentiary package. These are irreducible uncertainties that only experimentation can resolve.
The "guilty until proven innocent" rule applies fully to the things DD can assess. For the things it cannot - will the science work? - a different framework is needed: one that acknowledges uncertainty rather than pretending it can be due-diligenced away.