Over the last two decades, we have impacted decision making on numerous fronts through the application of model-informed drug discovery and development (MIDD) across the whole spectrum of drug development, from bridging animal data to predict human data when clinical studies are unethical, to avoiding Phase III studies, to approving drugs in a population not studied in Phase III trials. The single most important strength of MIDD is its ability to integrate knowledge across the development program thereby improving the efficiency of drug development and probability of success (POS) of new therapeutics. The goal of drug development teams should be to fully integrate modeling and simulation (M&S) end-to-end across programs agnostic of therapeutic areas of interest together with deep therapeutic area (TA) expertise, pharmacology and competitive knowledge to enable MIDD.
Historically, the regulatory agencies have played a key role in advocating for the modernization of drug development. With the recent inclusion of MIDD as one of the goals in PDUFA VI authorization, it is all the more imperative that we unleash the full potential of M&S in decision making. In addition to the traditional impacts of M&S i.e., informing dose, regimen, population, trial design, Go/No Go decisions etc., there is a huge opportunity to leverage the predictive capabilities of novel and advancing technologies for e.g., MBMA, QSP, CTS for the evaluation and incorporation of novel endpoints in Phase 2/3 trials, or providing confirmatory evidence of efficacy/safety in lieu of clinical data for regulatory submissions by conducting integrated analysis of data from across trials and drug classes. There are also untapped benefits of applying AI/ML across both the traditional and advanced technologies to gain efficiencies in decision making. The key feature driving successful application of MIDD is dependent on the strong collaboration and partnership between all stakeholders involved in drug development, including regulatory colleagues and the agencies to improve the POS of candidates that are progressed in the pipeline using a risk-based approach to portfolio investment decisions and thereby enable faster access to new and safe treatments with reduced costs.