ArticleCPT: pharmacometrics & systems pharmacology2017
A Mathematical Model to Predict HIV Virological Failure and Elucidate the Role of Lymph Node Drug Penetration.
Article in CPT: pharmacometrics & systems pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Impact of Dolutegravir Plus Lamivudine as First-line Antiretroviral Treatment on the Human Immunodeficiency Virus Type 1 Reservoir and Inflammatory Markers in Peripheral Blood.The Journal of infectious diseases · 2025Trial
- Using chronobiology-based second-generation artificial intelligence digital system for overcoming antimicrobial drug resistance in chronic infections.Annals of medicine · 2023Review
- Article
- Long-acting drugs and formulations for the treatment and prevention of HIV infection.International journal of antimicrobial agents · 2021Review
- Insight into treatment of HIV infection from viral dynamics models.Immunological reviews · 2018Review
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preventing virological failure following HIV treatment remains a difficult task that is further complicated by the emergence of drug resistance. We have developed a mathematical model able to explain and predict HIV virological outcomes for various compounds and patients' drug intake patterns. Compared to current approaches, this model considers, altogether, drug penetration into lymph nodes, a refined adherence representation accounting for the propensity for long drug holidays, population pharmacokinetic and pharmacodynamic variability, drug interaction, and crossresistance. In silico results are consistent with clinical observations for treatment with efavirenz, efavirenz in association with tenofovir DF and emtricitabine, or boosted darunavir. Our findings indicate that limited lymph node drug penetration can account for a large proportion of cases of virological failure and drug resistance. Since a limited amount of information is required by the model, it can be of use in the process of drug discovery and to guide clinical treatment strategies.
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