A Digital Twin of the Angiotensin II Receptor Blocker Losartan: Physiologically Based Modeling of Blood Pressure Regulation

TL;DR

This study creates a digital model of losartan's effects on blood pressure, showing how factors like dose and genetic differences influence its effectiveness.

Contribution

A novel PBPK/PD digital twin model of losartan and its metabolite E3174 is developed to explain variability in drug response.

Findings

The model accurately predicts losartan's PK/PD behavior, including dose-dependent receptor blockade and effects of hepatic impairment.

Genetic variability in CYP2C9 significantly impacts losartan metabolism and blood pressure reduction.

ABCB1 genetic variability has minimal effect on losartan's systemic exposure and blood pressure effects.

Abstract

Background/Objectives: Losartan, an angiotensin II receptor blocker (ARB) used to treat hypertension and heart failure, shows significant variability in pharmacokinetics (PK) and pharmacodynamics (PD) among individuals. Methods: In this study, we developed a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of losartan and its active metabolite, E3174, using curated data from 25 clinical trials. The model mechanistically describes the processes of absorption, hepatic metabolism, renal and fecal excretion, and pharmacodynamic blood pressure regulation. Simulation studies examined the effects of dose, hepatic and renal impairment, and genetic polymorphisms in cytochrome p450 2C9 (CYP2C9) and P-glycoprotein 1, also known as multidrug resistance protein 1 (MDR1) or ATP-binding cassette sub-family B member 1 (ABCB1), on the model. Results: The model successfully reproduced key PK/PD observations, including dose-dependent receptor blockade, attenuated responses with hepatic impairment, modest enhancement with renal impairment, and substantial variability in E3174 formation dependent on CYP2C9; the effects of ABCB1 were minimal. Specifically, dose dependency simulations confirmed the saturable nature of CYP2C9 metabolism, predicting a decreasing E3174-to-losartan ratio and a stronger, sustained suppression of blood pressure and aldosterone at higher doses. Hepatic impairment was predicted to lead to elevated losartan plasma concentrations (increased AUC) and attenuated metabolite formation, confirming the clinical need for dose reduction. Renal impairment simulations predicted stable losartan AUC but showed an overestimation of E3174 accumulation compared to observed data, where E3174 exposure remained stable. Genetic variability (CYP2C9) was the major determinant of response, with simulations confirming that reduced-function alleles lead to a 1.6- to 3-fold increase in losartan AUC and diminished blood pressure reduction. ABCB1 variability resulted in only minor modulation of systemic exposure and blood pressure effects. Conclusions: This mechanistic digital twin framework provides a quantitative basis for understanding variability in losartan therapy and supports its application in individualized dosing strategies.

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