Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis

Area of Science:

• Pharmacology and Toxicology
• Computational Biology
• Genetics

Background:

• Adverse drug reactions (ADRs) are a significant cause of mortality, often mediated by drug-drug interactions (DDIs) and drug-gene interactions (DGIs).
• Physiologically based pharmacokinetic (PBPK) modeling is an established tool for optimizing pharmacotherapy in the context of DDIs and DGIs.
• Extending PBPK modeling to complex drug-drug-gene interaction (DDGI) networks is crucial for advancing precision dosing.

Purpose of the Study:

• To develop and validate a novel PBPK modeling approach for predicting complex DDGI scenarios.
• To establish a whole-body PBPK network for simvastatin incorporating genetic polymorphisms and perpetrator drugs.
• To optimize simvastatin dosing recommendations within a DDGI network using simulations and a decision support system.

Main Methods:

• Construction of a whole-body PBPK network for simvastatin.
• Inclusion of three key genetic polymorphisms (SLCO1B1, ABCG2, CYP3A5) and four interacting drugs (clarithromycin, gemfibrozil, itraconazole, rifampicin).
• Performing extensive network simulations and optimizing 10,368 DDGI scenarios using an exposure marker cost function.

Main Results:

• A comprehensive DDGI network for simvastatin was simulated and optimized.
• Dose recommendations derived from the PBPK model were integrated into a digital decision support system.
• The model, while covering a fraction of potential DDGIs, demonstrates the feasibility of PBPK-guided precision dosing.

Conclusions:

• PBPK modeling can be extended to predict complex DDGI scenarios, offering a pathway to personalized pharmacotherapy.
• The developed digital decision support system provides a practical tool for optimizing simvastatin dosing.
• This work represents a foundational step towards integrating comprehensive PBPK-based precision dosing into routine clinical practice to mitigate ADR risks.