# Bayesian Methods for Optimizing Combination Antiretroviral Therapy for Mentalhealth in People with HIV

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $393,839

## Abstract

Project Summary/Abstract
The use of combination antiretroviral therapy (cART) has significantly reduced HIV-related morbidity and
mortality. However, cART may exacerbate the central nervous system (CNS)-related adverse effects on mental
health for people with HIV (PWH). These adverse effects may result in ART discontinuation with undesirable
downstream consequences such as HIV disease progression, decreased health outcomes, and increased
likelihood of HIV transmission, causing public health concerns. Depression is the most frequently reported mental
health comorbidity caused by CNS injury in PWH, with prevalence ranging from 20% to 60%. Understanding
factors (e.g., drug-drug interactions) contributing to ART-related depression is critical and remains a high priority
NIMH research area. In addition, since PWH must continue cART indefinitely, optimizing sequential cART
treatments over a long-time span tailored to individuals’ evolving clinical characteristics and treatment histories
is important for improving long-term mental health for PWH. However, there are numerous possible drug
combinations with complicated drug-drug interactions and thus creating complex data patterns, such as
heterogeneity, high-dimensionality, and sparseness, making it highly challenging to develop appropriate
statistical models for these problems - which is a critical gap we aim to fill. This proposal will leverage large
public HIV datasets, including Women's Interagency HIV Study (WIHS), to develop data-driven approaches to
facilitate deciphering cART-depression relations and guide more effective cART treatments. This proposal is
organized into three aims: 1) Develop Bayesian methods to learn longitudinal cART effects on depression and
investigate effect modifiers (e.g., polymorphic drug metabolism, aging); 2) Develop Bayesian decision
frameworks to optimize personalized sequential cART assignments with the goal of improving long-term mental
health outcomes for PWH; (3) encapsulate statistical methods and computational algorithms into user-friendly
open-source software for practical use, clinical translation, and dissemination. Findings from this study are
expected to expand our understanding of cART effects on depression, and have potential clinical utility to
facilitate precision medicine in HIV.

## Key facts

- **NIH application ID:** 10833148
- **Project number:** 5R01MH128085-03
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Yanxun Xu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $393,839
- **Award type:** 5
- **Project period:** 2022-06-10 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10833148

## Citation

> US National Institutes of Health, RePORTER application 10833148, Bayesian Methods for Optimizing Combination Antiretroviral Therapy for Mentalhealth in People with HIV (5R01MH128085-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10833148. Licensed CC0.

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