PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING OF LOSARTAN FOR THE PREDICTION OF DRUG-DRUG- AND DRUG-GENE-INTERACTIONS - PAGE Meeting (Population Approach Group Europe)

Veronika Alberg ¹, Fatima Zahra Marok ¹, Charlotte Maria Ursula Dette ¹, Dominik Selzer ¹, Thorsten Lehr ¹

1 Saarland University (Saarbrücken, Germany)

Introduction: According to the World Health Organization, losartan is considered an Essential Medicine and ranks among the ten most frequently prescribed drugs in the United States.[1, 2] It is widely used for the treatment of hypertension and diabetic nephropathy.[3] Losartan is also indicated for the therapy of heart failure and for the prevention of cardiovascular events in high-risk patients.[3]
Losartan undergoes extensive hepatic metabolism, primarily via cytochrome P450 (CYP) 2C9 and, to a lesser extent, CYP3A4, to form its pharmacologically active metabolite EXP3174.[4] Additional metabolic pathways include glucuronidation mediated by uridine 5′-diphospho-glucuronosyltransferases (UGTs) and further oxidative metabolism via CYP3A4 and CYP2C9.[4] Moreover, losartan is a substrate of the efflux transporter P-glycoprotein (P-gp) and the hepatic uptake transporter organic anion transporting polypeptide (OATP) 1B1.[5, 6] Genetic polymorphisms in CYP2C9 significantly affect the metabolic conversion of losartan to EXP3174, leading to genotype-dependent variability in systemic exposure.[7] Co-medication with inhibitors or inducers of metabolizing enzymes and transporters additionally influence the disposition of losartan and EXP3174. [8-13]
Therefore, a physiologically based pharmacokinetic (PBPK) parent-metabolite model of losartan and EXP3174 was developed to mechanistically investigate the impact of drug-drug interactions (DDIs) and drug-gene interactions (DGIs) on their pharmacokinetics.
Objectives:
• To develop a whole-body parent-metabolite PBPK model of losartan and its metabolite EXP3174
• To predict DDIs involving CYP3A4, CYP2C9, P-gp and OATP1B1
• To predict DGIs involving CYP2C9 polymorphism
Methods: A PBPK model was developed using PK-Sim® (Version 12.2, Open Systems Pharmacology Suite).[14] Physicochemical properties and absorption, distribution, metabolism and excretion (ADME) parameters were compiled from published literature. Plasma concentration-time profiles were divided into a training dataset for model development and a test dataset for external evaluation.
Model performance was evaluated by comparing predicted and observed plasma concentration-time profiles and areas under the plasma concentration-time curve between the first and last concentration measurement (AUClast) and maximum plasma concentrations (Cmax) in goodness-of-fit plots. DDI and DGI prediction accuracy was assessed by comparing predicted and observed AUClast and Cmax ratios using the success criteria proposed by Guest et al, which apply stricter requirements than the standard two fold deviation.[15]
Results: The whole-body PBPK model of losartan and EXP3174 was developed using 58 plasma concentration-time profiles covering a dosing range of 25-200 mg. Predicted AUClast and Cmax values showed good model performance, with 48/58 AUClast and 44/58 Cmax predictions within two-fold of the corresponding observed values.
Published PBPK models of grapefruit juice, rifampicin, cimetidine, erythromycin, fluconazole, and itraconazole were integrated with the losartan parent-metabolite model to evaluate DDIs.[16-22] Overall, DDI predictions were consistent with observed data, with 13/16 predicted DDI AUClast ratios and 13/16 predicted Cmax ratios within the Guest limits.[15]
The impact of CYP2C9 genetic polymorphisms on losartan metabolism was also incorporated. DGI predictions were within Guest limits, for 7/16 predicted DGI AUClast ratios and 8/16 predicted Cmax ratios.[15]
Conclusions: A whole-body PBPK model of losartan and its active metabolite EXP3174 was developed. The model incorporates CYP2C9-mediated DGI effects, accurately describing their influence on EXP3174 formation. In addition, the PBPK framework successfully captured multiple CYP-mediated DDIs with losartan as victim drug.

References:
[1] World Health Organization. WHO Model List of Essential Medicines, 24th ed. 2025.
https://www.who.int/publications/i/item/B09474

[2] Kane SP. ClinCalc DrugStats Database. Version 2024.
https://clincalc.com/DrugStats

[3] Merck & Co.. Cozaar® (losartan) Prescribing Information. 2018.
https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/020386s062lbl.pdf

[4] ClinPGx. Losartan Pathway – Pharmacokinetics.
https://www.clinpgx.org/pathway/PA164713428

[5] Soldner A, et al. Br J Pharmacol. 2000;129:1235–43. doi:10.1038/sj.bjp.0703150

[6] Flynn CA. Univ Kansas; 2011.

[7] Yasar Ü, et al. Clin Pharmacol Ther. 2002;71:89–98. doi:10.1067/mcp.2002.121216

[8] Kaukonen KM, et al. Eur J Clin Pharmacol. 1998;53:445–49. doi:10.1007/s002280050405

[9] Kazierad DJ, et al. Clin Pharmacol Ther. 1997;62:417–25. doi:10.1016/S0009-9236(97)90120-X

[10] Zaidenstein R, et al. Ther Drug Monit. 2001;23:369–73. doi:10.1097/00007691-200108000-00008

[11] Fischer TL, et al. Clin Pharmacol Ther. 2002;72:238–46. doi:10.1067/mcp.2002.127945

[12] Goldberg MR, et al. Eur J Clin Pharmacol. 1995;49. doi:10.1007/BF00192369

[13] Williamson KM, et al. Clin Pharmacol Ther. 1998;63:316–23. doi:10.1016/S0009-9236(98)90163-1

[14] Lippert J, et al. CPT Pharmacometrics Syst Pharmacol. 2019;8:878–82. doi:10.1002/psp4.12473

[15] Guest EJ, et al. Drug Metab Dispos. 2011;39:170–73. doi:10.1124/dmd.110.036103

[16] Fuhr LM, et al. Clin Pharmacol Ther. 2023;114:470–82. doi:10.1002/cpt.2968

[17] Hanke N, et al. CPT Pharmacometrics Syst Pharmacol. 2018;7:647–59. doi:10.1002/psp4.12343

[18] Türk D, et al. Clin Pharmacokinet. 2019;58:1595–607. doi:10.1007/s40262-019-00777-x

[19] Britz H, et al. CPT Pharmacometrics Syst Pharmacol. 2019;8:296–307. doi:10.1002/psp4.12397

[20] Hanke N, et al. Clin Pharmacokinet. 2020;59:1419–31. doi:10.1007/s40262-020-00896-w

[21] Open Systems Pharmacology. Erythromycin PBPK Model.
https://github.com/Open-Systems-Pharmacology/Erythromycin-Model

[22] Open Systems Pharmacology. Fluconazole PBPK Model.
https://github.com/Open-Systems-Pharmacology/Fluconazole-Model

Reference: PAGE 34 (2026) Abstr 12246 [www.page-meeting.org/?abstract=12246]

Poster: Drug/Disease Modelling - Absorption & PBPK