Across the pharmaceutical and biotech industries, failure has become routine. Particularly in clinical trials, the statistics are stark: over 86% of drug candidates that enter clinical testing fail to receive regulatory approval, according to a comprehensive study published in Biostatistics (Wong, Siah & Lo, 2019). Despite this, the cycle of failure repeats itself — often with familiar strategies, similar endpoints, and recycled assumptions. Why?
The answer isn’t scientific. It’s structural. Beneath the surface of scientific endeavor lie incentives, and they are profoundly misaligned.
Pharma: Innovation is Risk, and Risk Threatens Revenue
Large pharmaceutical companies are not inherently innovation-averse, but they are structurally risk-averse, and for good reason. The cost to develop a single new drug has been estimated at $2.6 billion, when factoring in out-of-pocket expenses and risk-adjusted capital (DiMasi et al., 2016). With blockbuster drugs generating billions in annual revenue, the priority becomes protecting these assets, not jeopardizing them.
This risk aversion manifests in R&D strategy. New molecular entities (NMEs) that break therapeutic paradigms face steep internal hurdles, while “me-too” drugs — modified versions of existing treatments — are often prioritized because they are more likely to succeed and extend market share. A 2020 Nature Reviews Drug Discovery analysis showed that incremental innovation accounts for the majority of newly approved drugs annually.
Moreover, the regulatory landscape reinforces conservative behavior. Phase III trial failures not only cost hundreds of millions of dollars but can also impact stock prices and investor confidence. In this environment, trial designs are optimized for likelihood of approval, not necessarily for breakthrough outcomes.
So, while pharma holds the infrastructure and capital to scale innovation, it often defaults to safe bets, not because it lacks vision, but because its survival depends on managing downside risk.
Biotech: Selling Complexity to Raise Capital
Biotech, by contrast, is driven by a different engine: capital acquisition. The average biotech startup requires years of negative cash flow before reaching any commercialization stage. During that time, survival depends on the ability to attract ongoing rounds of investment, often on the promise of future breakthroughs rather than tangible outcomes.
This environment incentivizes narrative complexity. A compelling, technically dense, and futuristic vision can justify valuations in the hundreds of millions, even if core mechanisms remain unvalidated. Scientific opacity, often wrapped in layers of bioinformatics, platform terminology, and omics jargon, can shield unproven claims from rigorous investor scrutiny. As a result, biotech is not always rewarded for truth, but for theater.
For instance, platform biotechs that promise pan-disease solutions — from CRISPR to AI-driven drug design — have frequently received funding and IPOs with limited preclinical evidence. In a 2023 analysis by Evaluate Vantage, it was noted that nearly 40% of biotech IPOs between 2020 and 2022 had no clinical-stage assets, relying instead on platform potential and investor enthusiasm.
While this doesn’t negate the groundbreaking work happening across the sector, it underscores a broader trend: complexity sells and it sells better when the underlying risks remain hard to decipher.
The Cost of Misalignment
This divergence in incentives — revenue protection in pharma and capital storytelling in biotech — has created a landscape where scientific failure is not a crisis, but a recurring operational cost. And the price is paid in missed opportunity.
Clinical trials too often lack biological grounding, relying on historical endpoints and assumptions rather than real-world relevance. Biomarkers are selected without sufficient reproducibility testing. Hypotheses are recycled rather than re-examined. A 2022 JAMA paper found that more than 50% of late-stage oncology trials still use surrogate endpoints not directly tied to overall survival, despite decades of criticism.
Yet these failures are rarely disruptive. Within pharma, a failed trial is written off and the next iteration begins. Within biotech, a failed program is rebranded, pivoted, or sold and the fundraising continues.
This isn’t a science problem. It’s an incentive problem.
What Needs to Change
To escape the loop, the industry needs a shift, not in its science, but in what it rewards:
- In pharma, this means building structures that de-risk innovation without requiring it to fit legacy molds. Public-private partnerships, flexible regulatory pathways (like FDA’s Breakthrough Therapy designation), and real-world data integration can help pharma balance risk with relevance.
- In biotech, clarity must replace obfuscation. Investors need better tools — scientific diligence frameworks, reproducibility scoring, and standardized data transparency — to differentiate between truly promising science and well-marketed opacity.
- Across the board, biological relevance must be prioritized. Trial designs should start with a grounded understanding of disease mechanisms, not historical templates. Biomarkers must be validated across populations, not cherry-picked from idealized cohorts. And patient impact should become a primary KPI, not just share price or milestone delivery.
Conclusion: Same Game, Different Rules
Pharma and biotech operate within the same biomedical ecosystem, but they play two very different games. Pharma plays it safe, guarding known territory. Biotech sells the unknown, cloaked in complexity. Both models have, in their own way, drifted from the core goal of biomedical science: to understand disease and improve lives.
Innovation cannot thrive where incentives reward stagnation. Until risk, rigor, and reward are realigned — across investors, regulators, and executives — we will continue funding the same failed experiments, at immense cost to patients, credibility, and progress.
References:
- Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics, 20(2), 273–286.
- DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33.
- Mullard, A. (2020). 2020 FDA drug approvals. Nature Reviews Drug Discovery, 20(2), 85–90.
- Evaluate Vantage (2023). Biotech IPO Trends Report.
- Gyawali, B., et al. (2022). Surrogate Endpoints in Oncology Trials: A JAMA Oncology Analysis. JAMA Oncology, 8(4), 564–566.