The high failure rate of drugs in human trials after passing animal tests stems from a fundamental biological reality: a "mouse is not a small human." This "structural mismatch" is especially severe for modern, human-specific therapies like CAR-T and RNA, rendering animal models poor proxies.

Current drug development heavily relies on animal testing. However, significant biological differences mean we may be filtering out effective human medicines that fail in animal models, creating a hidden opportunity cost for medical breakthroughs.

The push away from animal models is a technical necessity, not just an ethical one. Advanced therapeutics like T-cell engagers and multispecific antibodies depend on human-specific biological pathways. These mechanisms are not accurately reproduced in animal models, rendering them ineffective for testing these new drug classes.

Despite the buzz, a clinical development expert cautions that AI's impact in drug development is limited. The primary bottleneck isn't the algorithms but the lack of sufficient, high-quality human biological data that can be translated into reliable predictions, as animal models often fail to provide it.

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 NIH will no longer award funding to new grant proposals that rely exclusively on animal models. This policy forces a shift towards New Approach Methodologies (NAMs), such as organoids and organ-on-chips, serving as a major catalyst for innovation and adoption in the preclinical testing space.

The FDA is eliminating mandatory animal testing because it's often misleading—90% of drugs passing animal studies fail in humans. The agency is embracing modern alternatives like computational modeling and organ-on-a-chip technology to get faster, more accurate safety data.

The CNS biotech ecosystem has incredible momentum from new tools like advanced imaging, genetics, and AI. However, progress is stalled because the industry still uses outdated development frameworks, such as decades-old clinical trial designs and over-reliance on flawed animal models that fail to recapitulate human disease.