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

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Comparison of drug resistance scores for tipranavir in protease inhibitor-naive patients infected with HIV-1 B and non-B subtypes.

Genotypes of samples from protease inhibitor-naïve patients in Frankfurt's HIV Cohort were analyzed with five tipranavir resistance prediction algorithms. Mean scores were higher in non-B than in B subtypes. The proportion of non-B subtypes increased with increasing scores, except in weighted algorithms. Virtual and in vitro phenotype analyses of samples with increased scores showed no reduced tipranavir susceptibility. Current algorithms appear suboptimal for interpretation of resistance to tipranavir in non-B subtypes; increased scores might reflect algorithm bias rather than "natural resistance."

A comparison of HIV-1 drug susceptibility as provided by conventional phenotyping and by a phenotype prediction tool based on viral genotype.

Concordance between the conventional HIV-1 phenotypic drug resistance assay, PhenoSense (PS), and vircoTYPE HIV-1 (vT), a drug resistance assay based on prediction of the phenotype, was investigated in a data set from the Stanford HIV Resistance database (hivdb). Depending on the drug, between 287 and 902 genotype-phenotype data pairs were available for comparisons. Test results (fold-change values) in the two assays were highly correlated, with an overall mean correlation coefficient of 0.90 using single PS measurements. This coefficient rose to 0.94 when the vT results were compared to the mean of repeat PS measurements. These results are comparable with the corresponding correlation coefficients of 0.87 and 0.95, calculated using single measurements, and the mean of repeat measurements, respectively, as obtained in the Antivirogram assay, the conventional HIV-1 phenotypic drug resistance test on which vT is based. The proportion of resistance calls resulting in a "major" discordance (fully susceptible or maximal response by one assay but fully resistant or minimal response by the other) ranged from 0% to 8.1% for drugs for which two clinical test cut-offs were available in both assays (didanosine, abacavir, tenofovir, saquinavir/r, fosamprenavir/r, and lopinavir/r), from 2.4% to 8.1% for the drugs for which two clinical test cut-offs were available in the vT assay and one clinical test cut-off in the PS assay (lamivudine, stavudine, indinavir/r, and atazanavir/r) and from 3.1% to 10.3% for drugs for which biological test cut-offs were used (zidovudine, nevirapine, delavirdine, efavirenz, indinavir, ritonavir, nelfinavir, saquinavir, and fosamprenavir). Our analyses suggest that these assays provide comparable resistance information, which will be of value to physicians who may be presented with either or both types of test report in their practice.

Clinical cut-offs for HIV-1 phenotypic resistance estimates: update based on recent pivotal clinical trial data and a revised approach to viral mixtures.

The clinical utility of HIV-1 resistance testing is dependent upon accurate interpretation and application of results. The development of clinical cut-offs (CCOs) for most HIV antiretroviral drugs assessed by the vircoTYPE HIV-1 resistance test has been described previously. Updated CCOs based on new methodology and new data from clinical cohorts and pivotal clinical studies are presented in this communication. Data for analysis included the original records for CCO derivation from eight clinical trials and two cohort studies plus new records from the clinical cohorts and from the TITAN, POWER, and DUET clinical studies. Drug-specific linear regression models were developed to describe the relationship between baseline characteristics (phenotypic resistance as estimated by virtualPhenotype-LM using methods revised recently for handling mixed viral sequences; viral load; and treatment history), new treatment regimen, and 8-week virologic outcome. The clinical cut-offs were defined as the estimated phenotypic resistance levels (fold change, FC) associated with a 20% and 80% loss of drug activity. The development dataset included 6550 records with an additional 2299 reserved for validation. The updated, v.4.2 CCOs were generally close to the v4.1 values, with a trend observed toward marginally higher cut-offs for the NRTIs. These results suggest that the updated CCOs provide a relevant tool for estimating the contribution to virological response of individual antiviral drugs in antiretroviral drug combinations as used currently in clinical practice.

Compilation and prevalence of mutations associated with resistance to non-nucleoside reverse transcriptase inhibitors.

BACKGROUND: Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are an important component of antiretroviral therapy for HIV type-1 (HIV-1)-infected patients. Development of NNRTI resistance can lead to treatment failure and is conferred by the presence of specific resistance-associated mutations (RAMs) in the reverse transcriptase. In addition to the widely used list of NNRTI RAMs provided by the International AIDS Society-USA HIV-1 Drug Resistance Mutation Group, which were identified on the basis of clinical experience with the approved NNRTIs, a more comprehensive list of NNRTI RAMs is needed to guide the study of baseline and emerging resistance to new NNRTIs. METHODS: We conducted an extensive review of the existing literature on NNRTI resistance, together with several in vitro and in vivo studies on the mechanism of HIV-1 resistance to approved NNRTIs and to NNRTIs formerly or currently in clinical development. RESULTS: In total, 44 NNRTI RAMs were identified. These included V90I, A98G, L100I, K1O1E/P/Q, K103H/N/S/T, V106A/I/M, V108I, E138G/K/Q, V179D/E/F/G/I, Y181C/I/V, Y188C/H/L, V189I, G190A/C/E/Q/S, H221Y, P225H, F227C/L, M230I/L, P236L, K238N/T and Y318F. These NNRTI RAMs were observed, either alone or in combination with others, ranging in frequency from 0.02% to 56.96% in a panel of 101,679 NNRTI-resistant isolates submitted to Virco BVBA (Mechelen, Belgium) for routine clinical resistance testing. Phenotypical data from site-directed mutants helped to establish the contribution of each mutation to NNRTI resistance. CONCLUSIONS: The list of 44 NNRTI RAMs compiled in this study provides a comprehensive overview of mutations that play a role in HIV-1 NNRTI resistance and can be used to guide further in vitro and in vivo research on the mechanisms of HIV-1 NNRTI resistance.

Silent mutations are selected in HIV-1 reverse transcriptase and affect enzymatic efficiency.

OBJECTIVE: Missense mutations in HIV-1 reverse transcriptase are frequently selected in response to therapy; we examined whether silent mutations were also selected for by HIV therapy. DESIGN: Retrospective, observational analysis. Biochemical assays. METHODS: A comparison of the reverse transcriptase gene, from antiretroviral- naive (N = 812) and experienced individuals (N = 2212), reveals two silent mutations (K65K and K66K) that are strongly associated with treatment experience. To assess reverse transcription efficiency, steady-state kinetic assays were carried out using recombinant purified HIV-1 reverse transcriptase and a series of synthetic RNA/DNA template/primer substrates. The RNA templates spanned codons 60-77 in the reverse transcriptase and included different combinations of mutations at codons 65, 66, 67, and 70. RESULTS: Silent AAG mutations (or mixtures) at reverse transcriptase codons 65 and/or 66 were observed in 812 samples from 351 patients and 2129 samples from 829 patients, respectively. In clade B samples, there was a very strong relationship between the silent mutations and the thymidine analogue mutations, in particular D67N. Steady-state kinetic experiments demonstrated that HIV-1 reverse transcriptase exhibited a strong tendency to pause and/or dissociate at codons 65 and 66 on RNA templates that contained the D67N and K70R mutations. However, when the K66 or K66 AAA to AAG mutations were added to the background of the 67 and 70 mutational changes, these pausing and/or dissociation events were largely alleviated. CONCLUSION: Silent mutations at codons 65 and/or 66 are strongly coselected with thymidine analogue mutations. These data provide the first evidence for an RNA-level mechanism of direct relevance to drug resistance.