The U.S. Department of Health and Human Services announced SURPASS, a new program designed to cut clinical trial timelines, reduce costs, and shrink patient burden by using computational models, real-time data analysis, and automation. It's paired with three complementary projects to fix infrastructure problems like site capacity and fragmented data access.

• Current clinical trials take over a decade, cost up to $2 billion, and fail more than 90% of the time — SURPASS aims to compress timelines and lower costs through adaptive trial designs and continuous data analysis
• The program includes three technical areas: a "phaseless design engine" using digital twins to simulate outcomes before launch, a "continuous inference engine" for real-time trial adjustments, and automation tools to speed site activation and data handling
• Three companion projects tackle ecosystem barriers: STACK expands trial sites through AI-driven site activation, COMMONS builds national data infrastructure with privacy protections, and CINCH lets patients contribute and access their own real-world data

Enveda, which uses AI to find drug candidates in plants and microbes rather than synthesizing them, just doubled its valuation to $2 billion with a Series E round. The startup has moved beyond discovery into human trials—testing drugs for skin conditions and weight loss maintenance.

• Enveda raised $311 million at a $2 billion valuation, doubling its value in 12 months
• The company has multiple drugs in clinical trials, including treatments for severe skin conditions and post-GLP-1 weight loss
• No AI-discovered drug has received FDA approval yet, but Enveda is part of a wave of biotech companies pushing candidates into human testing

China is running seven clinical trials using chemogenetics, a technique that lets doctors control specific neurons with designer drugs, but the current approach uses clozapine at ultra-low doses instead of truly inert compounds, raising questions about precision and safety. The technology is sound for treating epilepsy and Parkinson's disease, though next-generation versions could be significantly better.

• Seven ongoing trials in China are testing chemogenetics for epilepsy, Parkinson's disease, and pain by injecting modified genes into the brain that make neurons respond to clozapine taken orally—affecting roughly 10 patients so far.
• Clozapine is used at 1% of the antipsychotic dose in Parkinson's trials and similarly low in epilepsy trials, minimizing off-target effects, but it's still not truly inert and requires long-term monitoring for rare idiosyncratic reactions like agranulocytosis.
• Current trials use hM4Di, which is only 2 amino acids different from the native human M4 receptor, reducing immune rejection risk, but genuinely inert alternatives like DCZ exist and could improve precision in future versions—they just haven't been approved for human use yet.

The article argues that AI’s success in molecular design won’t cure most diseases without a deeper understanding of human biology and disease mechanisms. It calls for large-scale, causal biological measurements and better links between cellular data and clinical outcomes before AI can deliver truly transformative medicines.

• AI drug discovery has focused on molecule design, but the real bottleneck is upstream: most diseases lack a validated biological target to hit.
• 90% of clinical trials fail because they target the wrong biological mechanism, not because molecule design is too slow.
• Genuinely new drug targets have dropped from ~100 in 2015 to ~30 in 2024, while 38 targets each now have 50+ drug programs piling onto the same well-known locks.
• Fixing this requires massive causal/perturbational biology data (billions more measured cell states), not just better AI models, since current cell atlases barely scratch the surface.

The FDA plans to allow drug approvals based on a single clinical trial instead of the traditional two. Commissioner Marty Makary stated that this change aims to streamline the approval process while maintaining safety and efficacy standards. Some exceptions will still apply, where two trials may be required.

• FDA will now generally accept a single clinical trial for drug approval instead of the traditional two
• Two trials will still be required in certain (unspecified) cases
• Makary argues a well-designed, controlled single trial can match the statistical power of two trials
• Many drugmakers were already submitting only one pivotal trial, so the policy formalizes existing practice