Problem & Opportunity
Clinical R&D is extremely risky and expensive: Only a small fraction of drug programs succeed. Citeline data show just ~6.7% of new drugs entering Phase I ultimately gain approval. Attrition is especially steep in early phases – only ~28% of programs clear Phase II – but remains high even in Phase III (roughly 55% of Phase III trials succeed). In other words, nearly half of Phase III trials still fail, often for lack of efficacy. Each failure represents massive sunk costs, with total capitalized cost per approved drug at ~$880M. Every failed trial – especially a late-stage one – can mean hundreds of millions of dollars lost, along with collapsed pipelines and writedowns.
Attrition & Cost
Attrition rates: Phase II success ≈28%, Phase III ≈55% (so ~45% Phase III failure). Cumulatively, only about 1-in-10 candidates ever reach patients.
Costs of failures: Including trial failures raises average development costs by ~3× (from ~$172M to ~$516M). When capital costs are added, per-drug R&D approaches ~$880M. Each failed pivotal trial can mean hundreds of millions to over a billion dollars lost.
Gaps in Current Evaluation Practices
Manual due diligence: Reviewing patents, trials, contracts etc. by hand is slow and limited in scope. Analysts routinely report “disconnects” between presentations and underlying data that are hard to catch without extensive effort.
Lack of predictive rigor: Traditional analysis seldom uses predictive models to estimate trial success probabilities. Investors often rely on heuristic benchmarks and expert opinion, rather than quantitative forecasts. Poor signals and subtle risks can be missed, and assets may be overvalued or advanced too far before failure.
Fragmented data: Information is scattered across publications, registries, internal databases, etc. Integrating it manually is inefficient. Without automated tools, nuanced insights remain hidden.
Growing Demand for AI-Driven Tools
Industry momentum: Many biopharma firms are piloting AI/ML for trial planning, patient selection, and risk monitoring. Deloitte projects that AI investments could generate up to ~11% of revenue in value for pharma R&D, underscoring big expectations.
Regulatory tailwinds: The sharp rise in AI-equipped drug applications (170 in 2023) shows growing comfort with analytics. This official signal encourages companies and investors to adopt decision-support tools.
Empirical benefits: Predictive analytics uncover “subtle signals” of success or risk. Adopting AI/ML lets experts focus on strategy rather than data sifting, improving trial design and patient matching.
Large Market Opportunity
AI in Clinical Trials: Projected at ~$2.6 billion in 2025, growing to $22.4 billion by 2034 (≈27% CAGR).
Clinical Trial Analytics Services: Estimated at $6.8 billion in 2025, rising to $12.0 billion by 2030 (≈12% CAGR).
Healthcare Predictive Analytics: Forecast to surge from $14.6 billion in 2023 to $67.3 billion by 2030.
In summary: Predictive analytics tools that can reliably forecast trial outcomes and stratify risk are tapping a multi-billion-dollar opportunity. In a field where one accurate prediction can save or earn hundreds of millions, sophisticated decision-support represents a highly compelling value proposition.