Excellence in research: modeling HIV drug resistance through a biochemical-immunological lens

The National Science Foundation Office of Integrative Activities awarded Howard University $1,065,309 on September 1, 2026, under the Integrative Activities program (CFDA 47.083) to develop computational and mathematical methods for modeling HIV drug resistance to the long-acting injectable cabotegravir (CAB-LA) used in treatment and pre-exposure prophylaxis.

The project integrates molecular, evolutionary, and immunological insights into a unified mathematical framework combining machine learning, biochemical data, evolutionary theory, and virtual clinical trials to advance prediction of drug resistance and inform more effective prevention strategies. The research generates new computational methods addressing real-world complexity in HIV treatment and generates open-source software and educational materials making advanced modeling tools accessible to researchers and students.

The project builds workforce capacity by engaging graduate and undergraduate students at Howard University and Loyola Marymount University in developing skills in mathematical modeling, computation, and data analysis. Cross-institutional training expands participation in computational research, strengthens collaborations between institutions, and prepares students to address emerging challenges in biomedical science and public health.

Performance is located in Washington, DC 20059. The project runs through August 31, 2029.

Abstract

The risk of drug resistance has been a concern since the discovery of penicillin in 1928. Increasing resistance to drugs among pathogens threatens the effectiveness of modern treatments and prevention strategies. This project develops a new approach to modeling using as an example the long-acting injectable drug cabotegravir (CAB-LA), which is used for both HIV treatment and pre-exposure prophylaxis (prep). The innovation of this project lies in the integration of molecular, evolutionary, and immunological insights into a unified mathematical framework. By combining machine learning, biochemical data, evolutionary theory, immunological modeling, and virtual clinical trials, this research advances the prediction of drug resistance and informs the design of more effective prep strategies. The project generates new computational methods for addressing the real-world complexity of HIV treatment and prevention and provides a framework for improving the ability to anticipate and combat drug resistance. The project also helps build the next generation of the scientific workforce. Graduate and undergraduate students at howard university and loyola marymount university work together to develop skills in mathematical modeling, computation, and data analysis. Students contribute to open-source software and educational materials that make advanced modeling tools more accessible to researchers and students. This cross-institutional training expands participation in computational research, strengthens collaborations between institutions, and prepares students to address emerging challenges in biomedical science and public health. This project presents a novel approach for modeling the emergence and dynamics of HIV resistance to pre-exposure prophylaxis (prep) with long-acting injectable cabotegravir (CAB-LA). The framework connects molecular drug target interactions, drug exposure and activity in the body, and virus-immune system dynamics during early HIV infection. In silico predictions of resistance-associated changes in drug binding are generated by integrating molecular docking with physics-informed machine learning (ML) models that predict inhibitor binding energies for wild-type and mutant HIV. An established pharmacokinetic/pharmacodynamic (PK/PD) model then translates these molecular-level predictions into time-varying drug exposure and inhibitory activity. These outputs are coupled with immunological and evolutionary models of early HIV infection to simulate the emergence and selection of resistant viral variants under clinically relevant CAB-LA exposure scenarios. The resulting framework supports data-driven virtual clinical trials to evaluate resistance risk across dosing and patient-specific scenarios. Model predictions are validated against biochemical binding and inhibition assays, data from simian-human-immunodeficiency virus macaque studies, human cohort data, and reported HIV reservoir and rebound kinetics. This integrated framework provides a mechanistic and predictive approach for understanding HIV drug resistance and evaluating strategies to reduce resistance risk during long-acting prep. Students participate at every stage, producing openly released research software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.