Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance.
Drug resistance is a major obstacle to the successful treatment of HIV-1 infection. Genotypic assays are used widely to provide indirect evidence of drug resistance, but the performance of these assays has been mixed. We used standard stepwise linear regression to construct drug resistance models for seven protease inhibitors and 10 reverse transcriptase inhibitors using data obtained from the Stanford HIV drug resistance database. We evaluated these models by hold-one-out experiments and by tests on an independent dataset. Our linear model outperformed other publicly available genotypic interpretation algorithms, including decision tree, support vector machine and four rules-based algorithms (HIVdb, VGI, ANRS and Rega) under both tests. Interestingly, our model did well despite the absence of any terms for interactions between different residues in protease or reverse transcriptase. The resulting linear models are easy to understand and can potentially assist in choosing combination therapy regimens.
Wang
Kai
K
Department of Microbiology, University of Washington, Seattle, Wash., USA.
Jenwitheesuk
Ekachai
E
Samudrala
Ram
R
Mittler
John E
JE
D000465:Algorithms; D030541:Databases, Genetic; D024882:Drug Resistance, Viral / Q000235:genetics*; D005544:Forecasting; D005838:Genotype; D017320:HIV Protease Inhibitors / Q000494:pharmacology*; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D016014:Linear Models; D061466:Lopinavir; D008826:Microbial Sensitivity Tests / Q000706:statistics & numerical data; D017354:Point Mutation / Q000187:drug effects; D011744:Pyrimidinones / Q000494:pharmacology; D018894:Reverse Transcriptase Inhibitors / Q000494:pharmacology*
D017320:HIV Protease Inhibitors; D011744:Pyrimidinones; D018894:Reverse Transcriptase Inhibitors; D061466:Lopinavir