Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making

Area of Science:

• Pharmacokinetics and Pharmacodynamics
• Drug Discovery and Development
• Computational Biology

Background:

• Modeling and simulation (M&S) are increasingly vital tools in pharmaceutical research.
• Integrating M&S early can optimize decision-making throughout the drug development pipeline.
• Industry experience highlights the value of M&S from discovery to clinical trials.

Purpose of the Study:

• To summarize collective industry experience on applying M&S to key decision points.
• To illustrate the utility of M&S in drug design, feasibility, and preclinical-to-clinical extrapolation.
• To provide insights from diverse case studies across multiple pharmaceutical companies.

Main Methods:

• Review of collective author experience in applying M&S.
• High-level overview of M&S applications in pharmaceutical decision-making.
• Detailed case study analysis from leading pharmaceutical companies.

Main Results:

• M&S effectively informs decisions in drug design, such as lead optimization.
• Feasibility analyses are enhanced by M&S, improving project planning.
• Preclinical drug design and preclinical-to-clinical extrapolation are significantly improved using M&S.
• Case studies demonstrate successful M&S implementation at Nektar Therapeutics, Genentech, Novartis, Pfizer, Merck, Takeda, and Amgen.

Conclusions:

• Modeling and simulation are powerful, versatile tools that significantly impact drug discovery and development.
• The strategic application of M&S across various stages, from discovery to clinical trials, enhances efficiency and success rates.
• Cross-company case studies validate the broad applicability and benefits of M&S in pharmaceutical R&D.