Integrating population heterogeneity in drug research and early development: a proactive strategy

Population heterogeneity in disease biology, drug exposure and treatment response is often addressed only after development has progressed, limiting generalizability and increasing late-stage risk. We propose an anticipate-verify-influence framework to integrate clinically relevant variability from research through development. The approach combines diverse human-derived models, real-world data, model-informed drug development and AI/ML to identify drivers of heterogeneity, test their clinical relevance early and translate validated findings into trial design, dosing and patient selection. Rather than pursuing demographic representation alone, the framework emphasizes mechanistic and quantitative understanding of intrinsic and extrinsic determinants of response. Earlier characterization of meaningful heterogeneity could improve development decisions, trial representativeness and evidence generation for patients who are frequently underrepresented.