Evaluating Vaginal Microbiome Effects on Antiretroviral Exposure in the Female Genital Tract Using Pharmacometric and Machine Learning Approaches.

Authors

Diqin Yan, Lindsey B Collins, Christopher Staley, Flavia Kiweewa Matovu, Pamala Jacobson, Melanie Nicol, Shen Cheng

Published in

Clinical pharmacology and therapeutics. Oct 03, 2026. Epub Oct 03, 2026.

Abstract

Antiretroviral pre-exposure prophylaxis (PrEP) is less effective in women than expected, partly because drug exposure within the female genital tract is variable and may be influenced by the vaginal microbiome. We developed an integrated pharmacometrics and machine learning framework to evaluate associations between vaginal microbiome composition and cervical antiretroviral exposure in women with human immunodeficiency virus (HIV) receiving tenofovir (TFV)-, lamivudine (3TC)-, or emtricitabine (FTC)-containing regimens. Pharmacokinetic and microbiome data were obtained from two single-center studies in Uganda and the United States. Published population pharmacokinetic models were used to estimate individual parameters by maximum a posteriori Bayesian estimation and to simulate plasma and cervical tissue exposure metrics under sexual-activity-driven dosing that were not studied in these trials. Microbiome diversity was assessed using alpha and beta diversity analyses. Associations between microbial genera and cervical drug exposure were evaluated using correlation analysis, differential abundance testing, and three machine learning approaches: LASSO, random forest, and XGBoost. Overall vaginal microbiome diversity was not associated with cervical exposure to TFV, 3TC, or FTC. In contrast, specific taxa-level associations with cervical drug exposure were identified. For TFV, Gemella was positively associated with cervical TFV-diphosphate exposure, whereas Megasphaera and Falsiporphyromonas were negatively associated across multiple models. For 3TC, Dialister, Prevotella, Atopobium, Streptobacillus, and Gardnerella were repeatedly identified. For FTC, Bifidobacteriaceae was consistently selected, with Lachnospiraceae and Prevotellaceae also implicated. This pharmacometrics-machine learning framework identified specific vaginal taxa associated with cervical antiretroviral exposure, supporting the future development of microbiome-based biomarkers and precision PrEP strategies for women.

PMID:
42827404
Bibliographic data and abstract were imported from PubMed on 03 Oct 2026.

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