Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks.
Our objective was to accurately predict, from complex mutation patterns, human immunodeficiency virus type 1 resistance to the protease inhibitor lopinavir, by use of artificial intelligence. Two neural network models were constructed: 1 based on changes at 11 positions in the protease that were previously recognized as being significant for lopinavir resistance and another based on a newly derived set of 28 mutations that were identified by performing category prevalence analysis. Both models were trained, validated, and tested with 1322 clinical samples. A procedure of determining the optimal neural network parameters was proposed to speed up the training processes. The results suggested that the 28-mutation set was a more accurate predictor of lopinavir susceptibility (correlation coefficient, R2=0.88). We identified potentially significant new mutations associated with lopinavir resistance and demonstrated the utility of neural network models in predicting phenotypic susceptibility from complex genotypes.
Wang
Dechao
D
Virco, Cambridge, United Kingdom. dechaowang@hivrdi.org
Larder
Brendan
B
D024882:Drug Resistance, Viral / Q000235:genetics*; D005838:Genotype; D015658:HIV Infections / Q000821:virology; D016333:HIV Protease / Q000235:genetics; D017320:HIV Protease Inhibitors / Q000494:pharmacology*; D015497:HIV-1 / Q000187:drug effects* / Q000201:enzymology / Q000235:genetics; D006801:Humans; D061466:Lopinavir; D008826:Microbial Sensitivity Tests / Q000379:methods; D009154:Mutation; D016571:Neural Networks, Computer; D010641:Phenotype; D011237:Predictive Value of Tests; D011744:Pyrimidinones / Q000494:pharmacology*
D017320:HIV Protease Inhibitors; D011744:Pyrimidinones; D061466:Lopinavir; D016333:HIV Protease