Can PBPK Modeling Improve Compound Optimization and Medicinal Chemistry Decision-Making in Complex Chemical Space?

Abstract As drug discovery increasingly explores beyond-rule-of-five compounds and challenging classical small molecules such as zwitterionic, highly lipophilic, and tissue-targeted drugs, pharmacokinetic behavior is more often governed by interactions with physicochemical and biological processes that limit the utility of purely empirical optimization approaches. In this Perspective, we discuss the role of physiologically based pharmacokinetic (PBPK) modeling as a mechanistic framework for understanding complex drug disposition and enabling model-informed decision-making during early stages of drug discovery by integrating early in vivo data, literature-derived compounds, and sensitivity and uncertainty analysis.

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Publication Details

Journal
ACS Medicinal Chemistry Letters
Published
2026-10-08
DOI
https://doi.org/10.1021/acsmedchemlett.6c00425
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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article

Can PBPK Modeling Improve Compound Optimization and Medicinal Chemistry Decision-Making in Complex Chemical Space?

Simone Esposito, David Álvaro Cebrián
ACS Medicinal Chemistry Letters
Computational Drug Discovery Methods
article

Can PBPK Modeling Improve Compound Optimization and Medicinal Chemistry Decision-Making in Complex Chemical Space?

Simone Esposito, David Álvaro Cebrián
article en

Abstract

Abstract As drug discovery increasingly explores beyond-rule-of-five compounds and challenging classical small molecules such as zwitterionic, highly lipophilic, and tissue-targeted drugs, pharmacokinetic behavior is more often governed by interactions with physicochemical and biological processes that limit the utility of purely empirical optimization approaches. In this Perspective, we discuss the role of physiologically based pharmacokinetic (PBPK) modeling as a mechanistic framework for understanding complex drug disposition and enabling model-informed decision-making during early stages of drug discovery by integrating early in vivo data, literature-derived compounds, and sensitivity and uncertainty analysis.

ACS Medicinal Chemistry Letters
Openalex Percentile: Top 13%
Computational Drug Discovery Methods
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