(M-065) Pharmacometrics and Machine Learning-Based Modeling to Assess the Impact of Vaginal Microbiome on Tenofovir Exposures in the Female Genital Tract of African Women

Shen Cheng, PhD – Department of Experimental and Clinical Pharmacology – College of Pharmacy, University of Minnesota, Minneapolis, MN; Diqin Yan, MS – Department of Experimental and Clinical Pharmacology – College of Pharmacy, University of Minnesota, Minneapolis, MN; Moataz Mohamed, MS – Department of Experimental and Clinical Pharmacology – College of Pharmacy, University of Minnesota, Minneapolis, MN; Thomas Kaiser-Powers, MD – Division of Basic and Translational Research, Department of Surgery – Medical School, University of Minnesota, Minneapolis, MN; Flavia Kiweewa, PhD – Department of College of Health Sciences – Makerere University, Kampala, Uganda; Pamala Jacobson, PharmD – Department of Experimental and Clinical Pharmacology – College of Pharmacy, University of Minnesota, Minneapolis, MN; Christopher Staley, PhD – Division of Basic and Translational Research, Department of Surgery – Medical School, University of Minnesota, Minneapolis, MN; Melanie Nicol, PharmD, PhD – Department of Experimental and Clinical Pharmacology – College of Pharmacy, University of Minnesota, Minneapolis, MN

Objectives: Antiretrovirals are effective for HIV pre-exposure prophylaxis (PrEP), but treatment outcomes vary by genders [1]. Truvada, a U.S. FDA-approved oral combination of tenofovir (TFV) and emtricitabine (FTC), reduces HIV infection risk by over 70% with only two doses per week in men who have sex with men [2]. However, despite similar adherence, similar benefits have not been observed in heterosexual women [3]. This discrepancy may partly be attributed to differential mucosal tissue penetration and other host factors. Being important in mucosal health, the vaginal microbiome is emerging as a potential modulator of drug responses. However, its high dimensionality poses a challenge to identify specific taxa influencing drug exposure, especially in the context of other clinical variables. This study aimed to develop a combined pharmacometrics (PMx) and machine learning (ML) framework to investigate the impact of the vaginal microbiome on TFV cervical tissue exposure.

Methods: Data were collected from a single-visit clinical study (NCT03377608) involving 50 premenopausal Uganda women living with HIV and stable on a TFV disoproxil fumarate (TDF)/ lamivudine regimen [4]. Steady-state TFV concentrations in plasma and TFV diphosphate (TFVdp, its active metabolite) concentrations in cervical tissues were collected at a single time point. Vaginal microbiome compositions were acquired using 16S ribosomal RNA sequencing.
A published TFV pharmacokinetic (PK) model incorporating plasma and cervical tissue compartments was extracted [5]. After validating model performance via visual predictive checks, empirical Bayes estimates (EBEs) were derived using Maximum A Posterior (MAP) Bayesian method in mapbayr package [6]. Simulations using EBEs were conducted to derive individual TFV exposures (TFV AUC0-240 hours in plasma and TFVdp AUC 0-240 hours in cervical tissues) under a 2-1-1 dosing on-demand PrEP regimen (600 mg before sex followed by two 300 mg once daily doses). Microbiome data were transformed using centered log-ratio methods. Correlation heatmaps provided an initial assessment of genus-level associations with drug exposures. Ridge and LASSO regressions (with λ optimized via leave-one-out cross-validation) were applied to identify impactful predictors of TFVdp cervical AUC.

Results: The PK model adequately characterized the observed plasma and cervical concentrations. EBEs were derived to drive the simulation of Individualized exposures. Correlation heatmaps demonstrated both positive (e.g., Gemela, β=0.41) and negative (e.g. Megasphaera, β=-0.30) associations between genera and TFVdp cervical AUC. Besides TFV plasma AUC (β=0.083) and weight (β=-0.048), LASSO analyses identified Gemella, Falsiporphyromonas, Streptobacillus, and Megasphaera based on penalized regression coefficients (βs: 0.38, -0.0023, -0.097 and -0.23) as influential genera on TFVdp cervical AUC.

Conclusions: This study presents a feasible two-step PMx-ML approach to investigate the impact of high-dimensional microbiome on drug exposure. Our analyses identified several vaginal microbiome genera that may significantly affect TFV tissue exposures, providing insights into the observed variability in PrEP efficacy among women.

Citations: [1] M. R. Nicol, J. L. Adams, et al., Clin Investig (Lond) 2013, 3.
[2] P. L. Anderson, D. V. Glidden, et al., Sci Transl Med 2012, 4, 151ra125.
[3] L. Van Damme, A. Corneli, et al., N Engl J Med 2012, 367, 411-422.
[4] M. R. Nicol, P. Eneh, et al., Clin Infect Dis 2020, 70, 1717-1724.
[5] E. Leung, M. L. Cottrell, et al., CPT Pharmacometrics Syst Pharmacol 2023, 12, 1922-1930.
[6] F. Le Louedec, F. Puisset, et al., CPT Pharmacometrics Syst Pharmacol 2021, 10, 1208-1220.

Keywords: drug exposures, vaginal microbiome, HIV