Alberto Vegas Rodriguez 1,3, Iñaki F. Trocóniz 1,2,3
1 Department of Pharmaceutical Sciences, School of Pharmacy and Nutrition, University of Navarra (Pamplona, España), 2 Navarra Institute for Health Research (IdiSNA) (Pamplona, España), 3 Institute of Data Science and Artificial Intelligence (DATAI), University of Navarra (Pamplona, España)
Introduction.
Human immunodeficiency virus (HIV) remains a major global public health challenge, affecting millions worldwide, with the highest prevalence in Africa and other resource-limited regions. Despite effective antiretroviral therapies, no cure exists, and prevention strategies such as pre-exposure prophylaxis (PrEP) are essential. Daily oral regimens often face adherence challenges, reducing effectiveness. This has motivated the development of different new agents, where models can guide dose optimization and support the design of more effective PrEP strategies.
Objectives.
The aim of this work is to develop an in-silico platform capable of simulating HIV incidence across diverse populations, allowing the assessment of factors such as risky sexual encounters, condom use and partner HIV status, enabling also the evaluation of PrEP interventions. The platform aims to support clinical trials by providing insights into treatment optimization and potential outcomes.
Methods.
We developed a stochastic model to simulate HIV transmission and the incidence in different populations. The model considers viral dynamics at the cellular level, where infection is established if the virus completes a full replication cycle. Viral elimination and progression through each stage are treated as discrete reactions, allowing calculation of the probability of extinction at each step. The prophylactic efficacy of any chosen drug is estimated by comparing Pinf with and without the drug.
The model operates at the population level, assigning individual characteristics such as HIV prevalence, condom use, and sexual activity. Time intervals between sexual encounters are generated from an exponential distribution, and acts are classified by type (vaginal or anal), each with specific transmission risks. Condom use and partner viremia are incorporated probabilistically, affecting transmission probability.
A drug X with a specific dosing regimen is simulated, calculating drug concentrations at the times of sexual encounters and the resulting Pinf for each event. Outcomes, including cumulative person-years at risk and infection incidence, were compared with published clinical trial data.
Results.
The model first estimated individual HIV infection probability, showing consistent values with some variability due to T cell counts and virion numbers. Transmission probabilities by route aligned with literature, with vaginal transmission around 0.3% and higher risk for receptive anal intercourse. In 100 simulations of the study population, background HIV incidence was 3.28 per 100 person-years, close to the reported 3.38, validating the model. Incidence increased linearly with weekly sexual encounters, highlighting parameter effects in high-risk groups. Simulated PrEP dosing with the drug X maintained prophylactic efficacy above 90–95%, reducing incidence by 97% compared to no drug, while some infections still occurred. Variant-specific EC50 values identified resistant strains, and overall, the model successfully replicated clinical trial outcomes.
Conclusions.
The model accurately simulates HIV incidence in virtual populations by incorporating behavioural and biological factors such as condom use, sexual act type, partner serostatus and encounter frequency. It integrates different drugs and dosing regimens, predicting prophylactic efficacy consistent with clinical trials and enabling exploration of resistant viral strains. Results confirm that long-acting PrEP is highly effective and allow scenario testing, including missed doses or alternative regimens. Some factors, such as inflammation or co-infections, are not modelled and could increase infection risk.
References:
1. Global HIV Programme. Accessed December 3, 2023. https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hiv/strategic-information/hiv-data-and-statistics
2. Okwundu CI, Uthman OA, Okoromah CA. Antiretroviral pre-exposure prophylaxis (PrEP) for preventing HIV in high-risk individuals. Cochrane Database of Systematic Reviews. 2012;2012(12). doi:10.1002/14651858.CD007189.pub3
4. Duwal S, Sunkara V, Von Kleist M. Multiscale Systems-Pharmacology Pipeline to Assess the Prophylactic Efficacy of NRTIs Against HIV-1. CPT Pharmacometrics Syst Pharmacol. 2016;5(7):377-387. doi:10.1002/psp4.12095
5. Levy JA. HIV and the Pathogenesis of AIDS. ASM Press; 2007. doi:10.1128/9781555815653
6. Duwal S, Dickinson L, Khoo S, von Kleist M. Mechanistic framework predicts drug-class specific utility of antiretrovirals for HIV prophylaxis. PLoS Comput Biol. 2019;15(1). doi:10.1371/journal.pcbi.1006740
Reference: PAGE 34 (2026) Abstr 12213 [www.page-meeting.org/?abstract=12213]
Poster: Drug/Disease Modelling - Infection