Khanh Linh Duong1, Thi Tham Bui1, Soyoung Lee1,2,3, Jeremiah D. Momper4, Thi Lien Ngo5, Hwi-Yeol Yun1,2,3, Jung-woo Chae1,2,3
1College of Pharmacy, Chungnam National University, 2Bio-AI Convergence Research Center, Chungnam National University, 3Senior Health Convergence Research Center, Chungnam National University, 4Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California - San Diego, 5Faculty of Pharmacy, PHENIKAA University
Introduction/ Objectives: Anatomical and physiological changes during pregnancy, including alterations in the function of proteins such as plasma proteins or enzymes, can affect the pharmacokinetic/pharmacodynamic (PK/PD) profile, potentially leading to modifications in drug efficacy or safety. To effectively manage conditions like human immunodeficiency virus (HIV) infection in pregnant women, dose optimization for long-term antiviral administration is crucial for preventing prenatal transmission and maintaining treatment efficacy for the mother. Population pharmacokinetic (popPK) modeling is valuable for optimizing drug exposure predictions in pregnant women. Therefore, the objective of our study was to develop a model to predict the PK profile and assess the influence of various covariates on exposure to darunavir (DRV), an antiviral agent recommended for pregnant women with HIV. Methods: Drug concentration data from patients receiving DRV in combination with a PK booster – ritonavir (RTV), were collected from clinical studies reporting PK profiles in pregnant patients with HIV infection. Patient characteristics, all pre- and post-dose concentration data of DRV and/or RTV during the second (T2) and third (T3) trimesters and postpartum, and initial parameter values from previous models of DRV in adult patients were used to develop the model. Nonlinear mixed-effects modeling (NONMEM version 7.5.1. and PsN – Perl-speaks-NONMEM, version 5.5.0) was used to estimate the value of parameters and the coefficient of covariates in the DRV model. For model evaluation, the objective function value (OFV) and Akaike’s information criterion (AIC) were used as performance metrics, and the goodness of fit (GOF) and visual predictive check (VPC) were used as visual diagnostics. Results: The study-reported dosage regimens for pregnant patients aged over 18 years included DRV 800 mg/RTV 100 mg once daily (QD), DRV 600 mg/RTV 100 mg twice daily (BID), and DRV 800 mg/RTV 100 mg BID. A one-compartment pharmacokinetic model with zero-order absorption best described the DRV plasma concentration profile during pregnancy. The Ctrough values decreased between the pregnancy (T2-T3) compared to postpartum periods; the mean ratio range was [0.43 – 0.64] for all regimens. Estimated values of the apparent oral clearance (CL/F) and volume of distribution (V/F) of DRV at steady state were 12.5 L/h and 213 L, with both inter-individual variability values (relative standard error %) of 0.223 (13% and 15%), respectively. No covariates were added to this popPK model. The final model provides good performance visualized by GOF and VPC, with over 85% of the observed concentration distributed within the simulated confidence interval. Conclusions: This work is a collaborative effort between our Pharmacometrics research groups to optimize drug dosing selection during pregnancy by developing a model of drug exposure. Our model confirms that DRV exposure can be altered during pregnancy. Reduced antiviral exposure may increase the risk of virologic failure in the mother and transmission to the child. Therefore, dosage regimen selection should be tailored to individual characteristics and medication adherence to ensure long-term effectiveness. The final validated model will serve as a basis for dose recommendation in clinical practice.
[1] http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm [2] Arab-Alameddine Met al (2014). Population pharmacokinetic modeling and evaluation of different dosage regimens for darunavir and ritonavir in HIV-infected individuals. J Antimicrob Chemother, 69(9), 2489-2498. [3] Bauer RJ (2019). NONMEM tutorial part I: description of commands and options, with simple examples of population analysis. CPT Pharmacometrics Syst Pharmacol, 8(8), 525-537. [4] Bauer RJ (2019). NONMEM tutorial part II: estimation methods and advanced examples. CPT Pharmacometrics Syst Pharmacol, 8(8), 538-556. [5] Crauwels HM et al (2016). Pharmacokinetics of once-daily darunavir/ritonavir in HIV-1–infected pregnant women. HIV Med, 17(9), 643-652. [6] Dodeja P et al (2025). Optimizing drug therapy during pregnancy: a spotlight on population pharmacokinetic modeling. Expert Opin Drug Metab Toxicol, 21(2), 143-160. [7] Eke AC et al. (2020) Darunavir pharmacokinetics with an increased dose during pregnancy. JAIDS J Acquir Immune Defic Syndr 83:373–380. [8] Moltó J et al (2013). Simultaneous pharmacogenetics-based population pharmacokinetic analysis of darunavir and ritonavir in HIV-infected patients. Clin Pharmacokinet, 52, 543-553. [9] Rittweger M et al (2007). Clinical pharmacokinetics of darunavir. Clin Pharmacokinet, 46, 739-756. [10] Stek A et al. (2015). Pharmacokinetics of once versus twice daily darunavir in pregnant HIV-infected women. JAIDS J Acquir Immune Defic Syndr, 70(1), 33-41.
Reference: PAGE 33 (2025) Abstr 11417 [www.page-meeting.org/?abstract=11417]
Poster: Drug/Disease Modelling - Infection