Quantitative Systems Pharmacology: A Regulatory Perspective on Translation

Toward a Consensus on Applying Quantitative Liquid Chromatography‐Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

“…These highlight the relevance of such data to the discipline of clinical pharmacology as the demand for more mechanistic understanding of the handling of drugs in various patient groups and variations in drug action in patients becomes the norm, not only by regulatory agencies but also healthcare providers. There are indications suggesting that such practices should be viewed in their post‐“hype” era and not just based on promise and hope of impact …”

Model‐Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing

“…1 QSP models have been shown to play an important role in addressing key drug development questions, in particular, related to the translation of pharmacological properties from preclinical to clinical and are increasingly part of regulatory submissions. 2 Among the most illustrative examples, physiologically-based pharmacokinetic models, recapitulating absorption, distribution, metabolism, and elimination, are already used in drug development to avoid clinical studies, such as drug-drug interactions for which the outcome can be predicted with good confidence by the model integrating clinically relevant in vitro data. 3 New modeling platforms, also integrating pharmacodynamics and disease properties, are currently under development to tackle questions in areas for which the underlying biological processes are much less understood than for absorption, distribution, metabolism, and elimination, for instance, in the field of cancer immunotherapy.…”

Applications of Quantitative Systems Pharmacology in Model‐Informed Drug Discovery: Perspective on Impact and Opportunities

“…Based on the outcome of the QSP survey, QSP models are rarely or never included in regulatory documents. However, recent data suggest that this is changing, with most regulatory examples occurring in investigational new drug submissions . This is perhaps not too surprising because the mechanistic nature of QSP models lends itself to potential inclusion as part of the supporting knowledge defining the proposed mechanism(s) and its role in the pathophysiology of the disease as well as initial trial design considerations.…”

“…This is perhaps not too surprising because the mechanistic nature of QSP models lends itself to potential inclusion as part of the supporting knowledge defining the proposed mechanism(s) and its role in the pathophysiology of the disease as well as initial trial design considerations. The details of when and how QSP models should be shared with the FDA are still developing and may require different regulatory engagement depending on the intent of the QSP modeling results . One aspirational goal is that, similar to internal documentation, QSP model results could be included in target validation/mechanism of action sections of FDA documents ( in vitro , in vivo , in silico ).…”