Breakthrough drugs aren't always driven by novel biological targets. Major successes like Humira or GLP-1s often succeeded through a superior modality (a humanized antibody) or a contrarian bet on a market (obesity). This shows that business and technical execution can be more critical than being the first to discover a biological mechanism.

Despite its first-mover advantage, Novo Nordisk lost its lead in the weight-loss drug market by failing to recognize its consumer-driven nature. While it planned a traditional pharma launch, competitor Eli Lilly adopted a direct-to-consumer model, treating the drug like an e-commerce product and capturing the market.

Alzheimer's can be understood as a vascular disease rooted in nitric oxide deficiency. This decline impairs blood flow, glucose uptake, and inflammation regulation in the brain. Therefore, strategies to restore nitric oxide address the physiological root causes of the disease, not just the symptoms like plaque buildup.

While AI can accelerate the ideation phase of drug discovery, the primary bottleneck remains the slow, expensive, and human-dependent clinical trial process. We are already "drowning in good ideas," so generating more with AI doesn't solve the fundamental constraint of testing them.

Investing in clinical studies is not just for product validation; it's a powerful marketing strategy. It allows you to make scientifically-backed claims in ads that competitors cannot legally replicate, creating a significant and sustainable competitive advantage.

Eli Lilly ran the fastest-accrued Alzheimer's study in history by going direct-to-patient. This model, using televisits and centralized diagnostics, is highly effective for preventative medicines where motivated patients can be recruited online.

In high-stakes fields like medtech, the "fail fast" startup mantra is irresponsible. The goal should be to "learn fast" instead—maximizing learning cycles internally through research and simulation to de-risk products before they have real-world consequences for patient safety.

The bottleneck for AI in drug development isn't the sophistication of the models but the absence of large-scale, high-quality biological data sets. Without comprehensive data on how drugs interact within complex human systems, even the best AI models cannot make accurate predictions.

The median $40,000 cost per trial enrollee is high because pharma companies essentially run a parallel, premium healthcare system for participants. They pay for all care and level it up globally to standardize the experiment.