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Updated: May 12, 2026

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Published on: March 30, 2014

Forecasting drug resistant HIV protease evolution.

Manu Aggarwal¹, Vipul Periwal¹

¹National Institutes of Health, Bethesda, MD.

Biorxiv : the Preprint Server for Biology

|June 26, 2025

Summary

This summary is machine-generated.

Forecasting drug resistance in human immunodeficiency virus (HIV) is crucial. A new model predicts viral evolution and identifies key mutations, suggesting Atazanavir (ATV) and Ritonavir (RTV) combination therapy is least resistant.

Keywords:

HIV treatment drug resistance protein evolution statistical physics

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Last Updated: May 12, 2026

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

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Published on: March 30, 2014

Rapid Screening of HIV Reverse Transcriptase and Integrase Inhibitors

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Published on: April 10, 2014

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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Published on: May 9, 2025

Area of Science:

Computational biology
Virology
Drug resistance studies

Background:

Protease inhibitors (PIs) are vital for human immunodeficiency virus (HIV) treatment.
Drug-resistant strains emerge, compromising PI efficacy.
Forecasting drug resistance is essential for effective treatment strategies.

Purpose of the Study:

To develop a probabilistic model for inferring epistatic interactions and predicting viral evolution.
To identify critical mutations driving drug resistance.
To evaluate the drug resistance of different PI combination therapies.

Main Methods:

Developed a probabilistic large-deviation model to infer epistatic interactions.
Simulated stochastic evolutionary paths weighted by mutation transition probabilities.
Trained classification models on in vitro susceptibility data to infer drug resistance.
Predicted drug resistance along simulated evolutionary paths.

Main Results:

Low-probability mutations are necessary for viral populations to evolve diverse, fit genotypes.
The combination therapy of Atazanavir (ATV) and Ritonavir (RTV) showed the least drug resistance.
The model predicted known primary and secondary PI-resistant mutations without prior knowledge.

Conclusions:

The developed model effectively infers mechanistic relationships from sparse sequence data.
The model can predict drug resistance and identify critical mutations in viral evolution.
The ATV/RTV combination therapy is predicted to be the most effective against drug resistance.