The best clinical development plans I've seen this year weren't written by regulatory strategists. They weren't produced by commercial teams running expensive competitive benchmarking exercises. They came from translational scientists who happened to have access to competitive intelligence data. Almost by accident.
I've now watched this play out at multiple biotechs, and it has genuinely changed how I think about who should be consuming competitive intelligence in drug development and when.
Here's what happened. Two biotechs I've been working with recently, one a rare disease company and the other in oncology, gave their translational teams access to competitive landscape data. Trial registries. Approved drug labels. Mechanism of action maps. Published clinical results. The kind of data that typically lives in a strategy or business development silo, gets surfaced in quarterly board decks, and rarely makes it anywhere near the bench.
What these translational scientists did with it was remarkable. Without being asked, they started building what amounted to clinical development plans. They mapped competitor endpoints against their own biomarker strategies. They spotted gaps in existing approved labels that their asset could credibly fill. They benchmarked their preclinical efficacy signals against published Phase II data from competitors and started proposing differentiated patient selection strategies.
One team produced a target product profile so sharp that the regulatory consultant brought in three months later said it was better than most TPPs he'd seen from dedicated regulatory affairs teams.
This shouldn't be surprising. But somehow it is.
According to the Sedulo Group's 2024 survey of 109 CI professionals across 90 life sciences companies, 64% of companies engage external CI vendors during the earliest development stages, from pre-clinical through Phase II [1]. The industry is slowly waking up to the fact that competitive intelligence belongs well beyond the commercial function. And yet most biotechs still aren't routing that intelligence to the people who can do the most with it. Their translational teams.
Think about what a translational scientist actually does. They sit at the most consequential junction in drug development, right in the gap between preclinical promise and clinical reality. They understand the mechanism of action at a molecular level. They know the biomarker landscape. They grasp the pharmacology and the disease heterogeneity in ways that most strategy teams simply don't.
They're trained to think in terms of "what evidence would it take to prove this works in humans?"
Now hand them a dataset showing every competitor's trial design, every endpoint selected, every comparator chosen. They don't just read that data. They metabolise it. The questions start almost immediately. Why did competitor X choose that primary endpoint, is it the right one for our mechanism? Their label only covers second-line use, what would it take for us to go first-line? And then the big one that keeps coming up: they powered their study for progression-free survival, but if our drug's real advantage is on patient-reported outcomes, maybe we should be designing around that instead.
I'll be blunt. This is the kind of strategic thinking that typically requires a cross-functional war room, a few consultants, and a six-figure competitive assessment project. Translational scientists, given the right data, do it instinctively. They already have the scientific substrate to make sense of it. As Brynne et al. noted in the Journal of Translational Medicine, enabling cross-compound comparisons through integrated data visualisation can identify poor candidates early and catalyse cross-functional collaboration through a common data language [2]. Competitive intelligence is that common language.
The counterargument, of course, is that translational teams should focus on the science and leave strategy to the strategists. Tidy in theory. Expensive in practice.
Roughly 90% of drugs entering clinical trials fail [3]. Let that number sit for a moment. Of those that make it to Phase III, 54% still fail, and 57% of those failures are attributed to inadequate efficacy [4]. Not because the molecule didn't work. Because the trial was designed to measure the wrong thing, in the wrong population, against the wrong comparator. These are strategic failures masquerading as scientific ones. The Aducanumab story is a textbook example of what happens when evidence gaps across stakeholders go unaddressed.
The cost sits somewhere between $800 million and $1.4 billion per failed programme [5]. For a small biotech, that's an extinction event.
The rare disease space makes this dynamic even more acute. The competitive landscape in clinical development for rare diseases has become increasingly crowded, and as Johari and Sukenik wrote in Pharmaphorum, competitive intelligence in rare disease must now move "beyond reporting past events to actively interpreting early scientific and regulatory signals" [6]. The rare disease biotech I mentioned earlier? Their translational team found, through competitor label analysis, that every approved therapy in their indication carried a boxed warning related to a specific safety signal. Their molecule didn't have that liability. That insight was buried in drug labels that nobody on the development team had previously bothered to read. It became the centrepiece of their differentiation strategy and the foundation of their TPP.
No expensive strategic review required. Just a translational scientist with access to the right data.
Regulators explicitly expect you to understand the competitive landscape. This is baked into the guidance.
The ICH E17 guidance on multi-regional clinical trials requires sponsors to justify their choice of comparator and to account for differences in standard of care across regions [7]. The EMA generally favours active-comparator trial designs. The FDA has historically been more comfortable with placebo-controlled studies. Two very different philosophies. You can't figure out how to thread that needle without knowing what competitors are running, where, and what comparators they've gone with. A translational team, with their understanding of the asset's mechanism, is actually well placed to interpret that kind of intelligence.
And the stakes of getting comparator selection wrong are quantifiable. A data landscaping study can verify comparator assumptions within days. A 2022 study in Clinical Pharmacology & Therapeutics found that leveraging real-world data to inform trial design, including comparator selection and endpoint relevance, could reduce required sample sizes by at least 40% [8]. That's the difference between a programme that hits its next milestone and one that runs out of runway.
IQVIA Biotech has argued that the clinical development plan should be treated as "a living document that evolves with emerging clinical data, regulatory feedback, and shifts in the competitive landscape" [9]. I'd go further. The CDP should be informed by the people closest to the science, not just the people closest to the strategy function. That means giving translational teams the competitive intelligence they need to contribute.
So what should your translational team actually have access to? Here's a practical checklist. Ten categories of competitive intelligence data that, based on what I've seen, turn good translational scientists into accidental strategists.
1. Competitor clinical trial registry data Trial designs, primary and secondary endpoints, comparators, sample sizes, inclusion/exclusion criteria, and estimated completion dates. This is the foundation. If your translational team can't see what competitors are running, they're designing in the dark. Why it matters: Endpoint selection and comparator choice are two of the most consequential decisions in clinical development, and two of the most common reasons trials fail.
2. Approved drug labels in the therapeutic area Full prescribing information, including indications, dosing, contraindications, boxed warnings, and limitations of use. Not the summaries. The actual labels. Why it matters: Labels reveal the precise boundaries of what competitors can and cannot claim. Every limitation is a potential opening for differentiation.
3. Mechanism of action mapping across the competitive set A clear picture of which targets and pathways are being pursued, by whom, and at what stage. Why it matters: Translational scientists understand MOAs better than anyone in the building. Give them the landscape and they'll spot white space, combination rationale, and potential resistance mechanisms that strategic teams might miss entirely.
4. Regulatory approval timelines and pathways Which competitors used accelerated approval, breakthrough therapy designation, or orphan drug pathways? What post-marketing commitments were imposed? Why it matters: The regulatory pathway a competitor took tells you what the agency valued. And what they'll likely expect from you.
5. Published clinical results and conference presentations Peer-reviewed publications, conference abstracts, and poster presentations from competitors, including negative results and trial terminations. Why it matters: Published efficacy and safety data is the benchmark your asset will be measured against, whether formally in a regulatory review or informally in a KOL conversation.
6. Patent and exclusivity landscapes Key patent expiry dates, exclusivity periods, and any patent challenges or settlements. Why it matters: Timing your development programme without understanding the IP landscape is like planning a road trip without checking for roadworks. You might get there, but it'll take longer than it should.
7. Failed programme post-mortems Why did competitors drop assets? Was it safety, efficacy, commercial viability, or strategic reprioritisation? As we've explored in the context of how biotechs can reduce development risk with real-world evidence, understanding why programmes fail is just as valuable as understanding why they succeed. Why it matters: Other people's failures are some of the cheapest data in drug development. A translational scientist can distinguish between "the target doesn't work" and "they designed the trial badly." But only if they can see the data.
8. Treatment guidelines and standard-of-care evolution Current clinical practice guidelines, including any recent updates or ongoing revisions. Why it matters: Your comparator in a trial should reflect how patients are actually being treated today, not how they were treated when your programme started. Guidelines shift. Your team needs to know when they do.
9. Real-world evidence on current treatments Effectiveness data, treatment patterns, adherence rates, and outcomes gaps from real-world sources. Why it matters: RWE reveals the gap between what clinical trials promise and what patients actually experience. That gap is where your value proposition lives.
10. Biomarker and companion diagnostic strategies What biomarker-driven approaches are competitors using for patient selection, stratification, or response monitoring? Why it matters: Translational teams are the natural owners of biomarker strategy. Without competitive context, they're optimising in isolation.
None of this requires turning translational scientists into business strategists. It requires giving them access to data they should have had all along. That's it.
The biotechs getting this right aren't investing in expensive CI departments or running quarterly competitive war rooms. They're using platforms like Inovia Clinical Strategy that put competitive landscape data directly into the hands of the teams doing the science. Drug labels, MOAs, clinical trial intelligence, published evidence, all searchable in a single interface. When a translational scientist can search across competitor drug labels, map mechanisms of action, and cross-reference published clinical results without waiting for someone in strategy to pull a deck together, the strategic thinking happens on its own. No mandate required.
McKinsey estimates that generative AI in clinical development alone could generate $13–25 billion in value across life sciences, with research contributing another $15–28 billion [10]. Big numbers. But the value was never really about the technology. It's about putting the right information in front of the right people at the right time. For translational teams, competitive intelligence is the missing input. The bit that turns good science into good strategy.
The pattern I described at the start of this piece, translational scientists accidentally building CDPs and TPPs, shouldn't be accidental. It should be how things work. The biotechs that figure this out first will develop better drugs, faster, with sharper positioning and fewer of the blind spots that turn promising molecules into expensive failures.
Your translational team is already doing the thinking. Are you giving them the data to do it properly?
[1] Sedulo Group. (2024). "2024 Annual Life Sciences Competitive Intelligence Survey Report." https://sedulogroup.com/life-sciences-ci-survey-report/
[2] Brynne, L., Bresell, A., and Sjögren, N. (2013). "Effective Visualization of Integrated Knowledge and Data to Enable Informed Decisions in Drug Development and Translational Medicine." Journal of Translational Medicine. https://pmc.ncbi.nlm.nih.gov/articles/PMC3842641/
[3] Sun, D., Gao, W., Hu, H., and Zhou, S. (2022). "Why 90% of clinical drug development fails and how to improve it?" Acta Pharmaceutica Sinica B. https://pmc.ncbi.nlm.nih.gov/articles/PMC9293739/
[4] Fogel, D.B. (2018). "Factors Associated with Clinical Trials That Fail and Opportunities for Improving the Likelihood of Success: A Review." Contemporary Clinical Trials Communications. https://pmc.ncbi.nlm.nih.gov/articles/PMC6092479/
[5] Huss, R. (2016). "The High Price of Failed Clinical Trials: Time to Rethink the Model." Clinical Leader. https://www.clinicalleader.com/doc/the-high-price-of-failed-clinical-trials-time-to-rethink-the-model-0001
[6] Johari, L. and Sukenik, S. (2026). "Rare Disease at an Inflection Point: Why the Next Wave Will Be Won on Strategic Insight." Pharmaphorum. https://pharmaphorum.com/rd/rare-disease-inflection-point-why-next-wave-will-be-won-strategic-insight
[7] FDA/ICH. (2017). "E17 General Principles for Planning and Design of Multi-Regional Clinical Trials." https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e17-general-principles-planning-and-design-multi-regional-clinical-trials
[8] Dagenais, S. et al. (2022). "Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design." Clinical Pharmacology & Therapeutics. https://pmc.ncbi.nlm.nih.gov/articles/PMC9299990/
[9] IQVIA Biotech. (2025). "Designing the Path: Strategic Development Planning to Maximize Biotech Investment Appeal." https://www.iqviabiotech.com/blogs/2025/11/designing-the-path-strategic-development-planning-to-maximize-biotech-investment-appeal
[10] McKinsey & Company. (2025). "Generative AI in the pharmaceutical industry: Moving from hype to reality." https://www.mckinsey.com/industries/life-sciences/our-insights/generative-ai-in-the-pharmaceutical-industry-moving-from-hype-to-reality