Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

<p>Abstract</p> <p>Background</p> <p>Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.</p> <p>Results</p> <p>Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.</p> <p>Conclusions</p> <p>We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.</p

The impact of the nelfinavir resistance-conferring mutation D30N on the susceptibility of HIV-1 subtype B to other protease inhibitors

The human immunodeficiency virus type 1 (HIV-1) protease mutation D30N is exclusively selected by the protease inhibitor (PI) nelfinavir and confers resistance to this drug. We demonstrate that D30N increases the susceptibility to saquinavir (SQV) and amprenavir in HIV-1 subtype B isolates and that the N88D mutation in a D30N background neutralizes this effect. D30N also suppresses indinavir (IDV) resistance caused by the M46I mutation. Interestingly, in patients with viruses originally containing the D30N mutation who were treated with IDV or SQV, the virus either reversed this mutation or acquired N88D, suggesting an antagonistic effect of D30N upon exposure to these PIs. These findings can improve direct salvage drug treatment in resource limited countries where subtype B is epidemiologically important and extend the value of first and second line PIs in these populations