Precision Medicine 3.0: Integrating Genomic Stratification with Adaptive Therapeutic Trajectories

Complex diseases require therapeutic decisions that adapt as the disease evolves over time and in response to treatment. Current precision medicine remains largely centered on selecting an individual drug or predefined regimen from molecular characteristics measured at a particular time. The next evolution of precision medicine is toward adaptive therapeutic trajectories in which treatment decisions change over the course of treatment. Germline genomic information remains constant and contributes baseline susceptibility and pharmacogenomic information. By contrast, somatic alterations, multi-omic profiles, clinical state, and treatment response may change during disease progression and therapy and can inform subsequent treatment decisions. The volume and complexity of longitudinal molecular and clinical data exceed what can be integrated reliably by unaided human cognition. Artificial intelligence (AI) may help integrate these high-dimensional longitudinal data. The scientific challenge is whether AI-derived treatment recommendations are reliable and improve patient outcomes. This model raises a regulatory problem beyond conventional drug approval: how to evaluate a bounded therapeutic system in which patient-specific treatment recommendations change as the patient’s condition evolves.