Best Practices on QSP Model Reporting for Regulatory Use: perspectives from ISoP QSP SIG Working Group

TL;DR

This paper proposes best practices and a flexible reporting framework for QSP models to improve transparency and regulatory review, based on collective experience and aligned with existing PBPK guidance.

Contribution

It introduces a tiered, adaptable reporting framework for QSP models to support regulatory interactions and enhance model credibility.

Findings

Developed a tiered reporting framework for QSP models.

Aligned recommendations with ICH M15 PBPK reporting principles.

Based on collective real-world regulatory experience.

Abstract

Quantitative systems pharmacology (QSP) models are increasingly applied to inform decision making across drug development and to support regulatory interactions within model informed drug development (MIDD). QSP supports a broad range of applications across drug development and can be tailored to specific therapeutic areas, mechanisms of action, and contexts of use (CoU). While this diversity is a core strength of QSP, it also presents challenges for reporting for regulatory use. Despite the growing impact of QSP models, there is currently no established guidance on how QSP analyses should be documented and reported for regulatory purposes. This white paper, developed by the International Society of Pharmacometrics (ISoP) QSP Special Interest Group Working Group on Credibility Assessment of QSP for Regulatory Use, seeks to address this gap by proposing best practices for QSP model reporting in regulatory settings. The recommendations are grounded in collective real world experience from regulatory interactions and are aligned with reporting guidance established for physiologically based pharmacokinetic (PBPK) modeling and reporting principles outlined in ICH M15. Rather than prescribing a rigid, one size fits all template, this work proposes a flexible, tiered reporting framework that accounts for development phase and model impact. The proposed framework is intended to facilitate regulatory review and enhance transparency while accommodating the inherent diversity of QSP modeling.