Model-Integrated Evidence: A Vision to Expand the Role of Modeling in Drug Development and Regulatory Decision-Making

The pharmaceutical industry is undergoing a pivotal transformation driven by advances in computing, deeper biological insight, and evolving regulatory expectations. Despite these innovations, drug development continues to suffer from “Eroom’s Law,” whereby the cost of developing a new medicine roughly doubles every 9 years. To counter this productivity crisis, a paradigm shift is emerging from traditional model-informed drug development (MIDD), in which models primarily support sponsor decisions and strategy toward model-integrated evidence (MIE). In the MIE paradigm, rigorously verified and validated computational models generate “digital evidence” with evidentiary weight comparable to empirical clinical data for regulatory purposes, including approvals, labeling, and lifecycle management. This approach reflects an ethical and economic imperative to maximize the value of every data point and to leverage accumulated knowledge to predict future outcomes. This paper reviews the evolving MIE landscape in light of recent regulatory initiatives, including the Food and Drug Administration (FDA) MIE pilot for generic drugs and the International Council for Harmonisation (ICH) M15 guideline. We will also provide an overview of some of the quantitative methodologies enabling MIE, and present high-impact case studies illustrating recent applications of MIE. Finally, we will propose measures that could be enacted by regulatory agencies, pharmaceutical companies, and academic institutions to further expand the adoption and impact of MIE. Should these measures prove successful, we envision a future in which MIE is treated as primary evidence of medical product safety and effectiveness, thereby accelerating biopharmaceutical research and development and ultimately improving treatment options for patients.