PubMed Health⌕ Search

PubMed · 17229185

Non-nucleoside reverse transcriptase inhibitors: a review.

Abstract

Non-nucleoside reverse transcriptase inhibitors form the backbone of antiretroviral treatment for many HIV-infected individuals. The tolerability, pill burden and efficacy associated with this class of agents make them a frequent choice for first-line therapy. Here we review nevirapine and efavirenz in terms of efficacy, resistance and toxicity, focusing particularly on the use of nevirapine to prevent mother-to-child transmission in developing countries.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L Waters, L John, M Nelson. 2007. Non-nucleoside reverse transcriptase inhibitors: a review.. https://doi.org/10.1111/j.1742-1241.2006.01146.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Random changepoint modelling of HIV immunologic responses.

We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.

Antiretroviral Therapy, Highly Active↗

Estimation and prediction with HIV-treatment interruption data.

We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.

Antiretroviral Therapy, Highly Active↗