Novo Nordisk ran a nearly 4,000-patient Phase 3 Alzheimer's trial despite publicly stating it had a low probability of success. This strategy consumes valuable patient resources, raising ethical questions about whether a smaller, definitive Phase 2 study would have been a more responsible approach for the broader research ecosystem.

Many groundbreaking scientific discoveries never reach patients because they fail to attract capital or secure a commercial partnership. This "translation death" highlights that business development, not just R&D, is a critical bottleneck in delivering therapies to patients.

The abrupt failure of Arena Bioworks, a well-funded institute designed to spin off biotechs, highlights the current market's preference for de-risked clinical assets. Investors are shying away from long-timeline, platform-based models that require significant capital before generating clinical data, even those with elite scientific backing.

Voyager Therapeutics can't afford massive, long-term clinical trials. Instead, it selects programs where it can use tools like imaging and fluid biomarkers to quickly and efficiently confirm a drug is working as intended. This strategy allows for early de-risking before committing massive capital.

The "time is lives" mantra also applies to the companies themselves. For single-asset biotechs with short financial runways, trial delays can bankrupt the company before the drug has a chance. "Time to first patient" is a critical business milestone, not just a clinical one.

The process of testing drugs in humans—clinical development—is a massive, under-studied bottleneck, accounting for 70% of drug development costs. Despite its importance, there is surprisingly little public knowledge, academic research, or even basic documentation on how to improve this crucial stage.

As large pharmaceutical companies shift focus to acquiring clinically validated assets, a gap has emerged in early-stage development. Smaller and mid-sized pharmas, unable to compete on price for late-stage assets, are now incentivized to take on more risk and partner earlier, driving innovation.

The company intentionally makes its early research "harder in the short term" by using complex, long-term animal models. This counterintuitive strategy is designed to generate highly predictive data early, thereby reducing the massive financial risk and high failure rate of the later-stage clinical trials.

Contrary to popular belief, AI's role in drug discovery is marginal. Martin Shkreli argues the main hurdle is the billion-dollar, multi-year process of human clinical trials, an area where AI has little impact. The chemistry itself is a relatively solvable problem for experts.