Simple cell viability screens fail to identify powerful drug combinations where each component is ineffective on its own. AI can predict these synergies, but only if trained on mechanistic data that reveals how cells rewire their internal pathways in response to a drug.

The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.

A critical distinction exists between a clinical adverse event (AE) and its impact on a patient's quality of life (QOL). For example, a drop in platelet count is a reportable AE, but the patient may be asymptomatic and feel fine. This highlights the need to look beyond toxicity tables to understand the true patient experience.

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.

Modern, highly sensitive assays often detect high rates of anti-drug antibodies (ADAs). However, the critical question for drug developers isn't the ADA incidence rate itself, but whether that immune response actually impacts drug exposure, efficacy, or overall patient outcome.

Dr. Smith argues that while drugs are essential for acute emergencies like heart attacks or broken bones, they are ill-suited for chronic problems. For long-term issues, focusing on root causes is more effective than continuous symptom management with medication.

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 TRILINX trial revealed Xevinapant's toxicity was so high that it forced reductions in standard, effective treatments like cisplatin and radiation. This compromised the foundational therapy, leading to worse patient outcomes and demonstrating a key risk in adding novel agents to established regimens.

Despite showing massive weight loss, new obesity drugs from Eli Lilly and others have high discontinuation rates due to side effects. This suggests the industry's singular focus on efficacy may be hitting diminishing returns, opening a new competitive front based on better patient tolerance and adherence.