As mechanistic modeling becomes increasingly integrated into drug development, biologically informed approaches to first-in-human (FIH) dose selection are receiving greater regulatory attention.
The FDA‘s newly released draft guidance on Quantitative Systems Pharmacology (QSP)-Based Dose Selection for Minimum Anticipated Biological Effect Level (MABEL) outlines how mechanistic QSP modeling approaches may support biologically informed dose selection.
MABEL is often used to establish a scientifically justified starting dose for FIH studies, particularly for therapies with novel mechanisms of action or elevated safety considerations.
Key regulatory takeaways:
🔹 Application of mechanistic QSP modeling to support MABEL determination and FIH dose selection.
🔹 Integration of pharmacokinetic, pharmacodynamic, target engagement, and systems biology data within a quantitative modeling framework.
🔹 Evaluation of model assumptions, uncertainty, and sensitivity analyses to support decision credibility.
🔹 Use of quantitative simulations to characterize anticipated biological responses across candidate dose levels.
🔹 Recognition of QSP as a complementary component of broader Model-Informed Drug Development (MIDD) strategies.
🔹 Emphasis on fit-for-purpose model development, scientific justification, and regulatory transparency.
Importantly, the guidance reflects continued regulatory interest in the application of mechanistic systems pharmacology modeling to support dose-selection decisions within a broader evidence framework.
At InSilicoMinds, we view this guidance as further evidence of the growing role of mechanistic modeling and model-informed approaches in regulatory decision-making. As these methodologies continue to mature, the focus is increasingly shifting toward demonstrating scientific credibility, transparency, and clear decision impact. 💡 The guidance further reinforces the role of mechanistic biology, QSP modeling, quantitative modeling, and translational science in supporting informed and risk-aware development decisions.
