Model-Informed Drug Development

Model-Informed Drug Development (MIDD) at the FDA involves the integration of quantitative pharmacology, mathematical modeling, and simulation techniques to inform regulatory decision-making throughout the drug development process. MIDD encompasses a range of approaches, including pharmacokinetic modeling, pharmacodynamic modeling, exposure-response modeling, and physiologically-based pharmacokinetic modeling, among others.

These models leverage data from preclinical studies, early-phase clinical trials, and real-world evidence to predict drug behavior, optimize dosing regimens, and assess the potential risks and benefits of investigational drugs. MIDD facilitates the exploration of complex drug-drug interactions, population variability, and exposure-response relationships, enhancing the understanding of a drug's pharmacokinetics, pharmacodynamics, and safety profile.

The FDA encourages the use of MIDD to support regulatory submissions, including Investigational New Drug (IND) applications, New Drug Applications (NDAs), and Biologics License Applications (BLAs), providing sponsors with opportunities to enhance the efficiency and probability of regulatory success. Through MIDD, sponsors can address regulatory questions, anticipate potential issues, and optimize study designs, leading to more informed regulatory decisions, reduced development timelines, and improved patient outcomes.

The FDA collaborates with sponsors to facilitate the adoption of MIDD approaches, offering guidance documents, workshops, and regulatory consultations to support the integration of modeling and simulation into drug development programs. MIDD plays a crucial role in advancing regulatory science, promoting innovation, and enabling the development of safer and more effective therapies for patients.

AreteVolo offers,

• Modeling and Simulation for FIH dosing selection

• Population PKPD modeling and simulation for dose selection for Phase 2 & Phase 3 studies

• Protocol design where data could be maximally utilized for population PK analysis

• Exposure-response analysis for both efficacy and safety for dose optimization, justifying addition or approval of untested dose when appropriate

• PBPK Modeling to predict the effects of extrinsic/intrinsic factors on PK, request clinical study waiver when appropriate

• Clinical trial simulation to predict clinical outcomes based on various conditions