The pharmaceutical industry's focus on rare diseases has intensified, with 57% of all novel drugs approved in 2025 designated as orphan treatments. This is a continued increase from prior years, indicating a strategic shift towards smaller patient populations with high unmet needs, as exemplified by three different drugs for Hereditary Angioedema (HAE) being approved within ten weeks.

Drug developers often operate under a hyper-conservative perception of FDA requirements, avoiding novel approaches even when regulators might encourage them. This anticipatory compliance, driven by risk aversion, becomes a greater constraint than the regulations themselves, slowing down innovation and increasing costs.

The fear of toxicity pushes many companies to pursue the same few well-validated targets, leading to an average of nine assets per target. This hyper-competition not only crowds the market but, more importantly, leaves vast patient populations without effective options because their diseases lack these "popular" targets.

The Orphan Drug Act successfully incentivized R&D for rare diseases. A similar policy framework is needed for common, age-related diseases. Despite their massive potential markets, these indications suffer from extremely high failure rates and costs. A new incentive structure could de-risk development and align commercial goals with the enormous societal need for longevity.

Successful drug launches require nailing three fundamentals. Common failures include: misjudging the patient population (epidemiology), failing to secure reimbursement and patient access, and lacking clear differentiation against the established "gold standard" treatment in physicians' minds.

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.

With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.

The fastest, cheapest path to drug approval involves showing a small survival benefit in terminally ill patients. This economic reality disincentivizes the longer, more complex trials required for early-stage treatments that could offer a cure.

Despite major scientific advances, the key metrics of drug R&D—a ~13-year timeline, 90-95% clinical failure rate, and billion-dollar costs—have remained unchanged for two decades. This profound lack of productivity improvement creates the urgent need for a systematic, AI-driven overhaul.