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Over the past 20 years, the chorus of voices advocating for greater use of model-informed drug development (MIDD) and trumpeting its success has grown in volume, as regulatory agencies and the pharmaceutical industry have improved collaboration and aligned on a shared goal of greater efficiency and smarter decision making to improve attrition rates. A review of the literature reveals numerous examples of the successful use of modeling and simulation in all stages of drug development, from the selection of starting doses for first-in-human studies to exposure– response analyses and Bayesian adaptive designs for selecting phase III doses, to clinical trial simulation and lifecycle management.1–3 Recently, the US Food and Drug Administration (FDA) initiated a pilot program to facilitate the application of MIDD approaches with the goal of improving clinical trial efficiency, increasing the probability of regulatory success, and optimizing individualization of therapy.4 Opportunities for MIDD include the use of physiologically based pharmacokinetic (PBPK) modeling to address the risk of drug–drug interactions (DDIs), inform dose selection for special populations and pediatrics, and to predict the impact of changes in formulation or the effect of food on drug absorption. PBPK modeling and its application have grown rapidly in recent years, with greater acceptance by both the pharmaceutical industry and regulators for decision making, as well as to inform clinical dosing recommendations. As the FDA’s Office of Clinical Pharmacology (OCP) has reported, the most extensive experience in the application of PBPK is in the prediction of cytochrome P450 (CYP)-mediated DDIs, with many examples of recommendations in product labels reflective of PBPK modeling in lieu of clinical data.5

Clinical Studies for the Sake of Negative Data: The Proof Is in the Pudding