Investor sentiment has fundamentally changed. During the COVID era, investors funded good ideas. Now, they want to de-risk their investments as much as possible, often requiring solid Phase 1 and even compelling Phase 2 data before committing significant capital.

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.

Investors without a scientific background can de-risk biotech portfolios by avoiding early-stage "science projects" (Phase 1-2). Instead, they should focus on companies that have completed Phase 3 trials. This strategy shifts the primary risk from unpredictable scientific development to more analyzable commercial execution.

Don't wait until Phase 3 to think about commercialization. Biotech firms must embed secondary endpoints in Phase 2 trials that capture quality of life and patient journey insights. This data is critical for building a compelling value proposition that resonates with payers and secures market access.

Recent biotech deals are setting new valuation records for companies at specific early stages: preclinical (AbbVie/Capstan, ~$2B), Phase 1 (J&J/Halda, $3B), and pre-Phase 3 (Novartis/Abitivi, $12B). This signals intense demand for de-risked innovation well before late-stage data is available.

Developers often test novel agents in late-line settings because the control arm is weaker, increasing the statistical chance of success. However, this strategy may doom effective immunotherapies by testing them in biologically hostile, resistant tumors, masking their true potential.

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.

While AI is on the verge of cracking preclinical challenges, the biggest problem is the high drug failure rate in human trials. The next wave of innovation will use AI to design molecules for properties that predict human efficacy, addressing the fundamental reason drugs fail late-stage.

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.