PubMed · 12737644
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2003. Resistance data also presented.. https://doi.org/10.1089/108729103321619818
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OBJECTIVE: To assess responses to indinavir (IDV)-ritonavir (RTV)-based regimens among HIV-1 infected patients with prior failure of protease inhibitors, and to assess the effects of adherence to therapy and pre-existing genotypic and phenotypic resistance on this response. METHODS: Twenty-eight patients initiating salvage regimens with IDV-RTV (800 mg and 200 mg twice daily, respectively) plus one or more reverse transcriptase inhibitor (RTI) were identified retrospectively. Genotypic and phenotypic susceptibilities to multiple antiretroviral agents were determined on viral samples collected at initiation of the salvage regimens, and adherence to therapy was determined through patient self-reporting. Response to therapy (viral RNA </= 400 copies/ml) was assessed at the end of and beyond 6 months of follow-up. RESULTS: Based on responses measured in the first 6 months of follow-up, 16 responders and 12 non-responders were identified without differences in baseline demographic factors, laboratory parameters, extent of prior antiretroviral therapy, or characteristics of the RTI components of the new IDV-RTV-based regimens. Adequate adherence was associated with virologic responses (P = 0.005). There were trends for genotypic and phenotypic resistance to be associated with adequate adherence, and, surprisingly, phenotypic resistance to IDV was associated with virologic response rather than with therapeutic failure (P = 0.02). Beyond 6 months of follow-up (mean follow-up 69 weeks), adequate adherence was still associated with virologic response (P = 0.001), but genotypic or phenotypic resistance to IDV were not associated with therapeutic failure. CONCLUSIONS: These results suggest that IDV-RTV-based regimens may be able to overcome IDV resistance. This underscores the importance of drug adherence, potency, and exposure in determining virologic responses to antiretroviral therapy.
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.
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