PubMed · 11366343
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BACKGROUND: HIV proviral sequencing overcomes the limit of plasma viral load requirement by detecting all the 'archived mutations', but the clinical relevance remains to be evaluated. METHODS: We included 25 participants with available proviral sequences (both intact and defective sequences available) and utilized the genotypic sensitivity score (GSS) to evaluate the level of resistance in their provirus and plasma virus. Defective sequences were further categorized as sequences with and without hypermutations. Personalized GSS score and total GSS score were calculated to evaluate the level of resistance to a whole panel of antiretroviral therapies and to certain antiretroviral therapy that a participant was using. The rate of sequences with drug resistance mutations (DRMs) within each sequence compartment (intact, defective and plasma viral sequences) was calculated for each participant. RESULTS: Defective proviral sequences harbored more DRMs than other sequence compartments, with a median DRM rate of 0.25 compared with intact sequences (0.0, P = 0.014) and plasma sequences (0.095, P = 0.30). Defective sequences with hypermutations were the major source of DRMs, with a median DRM rate of 1.0 compared with defective sequences without hypermutations (0.042, P < 0.001). Certain Apolipoprotein B Editing Complex 3-related DRMs including reverse transcriptase gene mutations M184I, E138K, M230I, G190E and protease gene mutations M46I, D30N were enriched in hypermutated sequences but not in intact sequences or plasma sequences. All the hypermutated sequences had premature stop codons due to Apolipoprotein B Editing Complex 3. CONCLUSION: Proviral sequencing may overestimate DRMs as a result of hypermutations. Removing hypermutated sequences is essential in the interpretation of proviral drug resistance testing.
The plasma level of HIV-RNA has been shown to be a strong prognostic biomarker for clinical progression and death in HIV infected patients and is widely used as the primary outcome in clinical trials to evaluate antiretroviral treatments. Currently approved assays to measure HIV-RNA levels have a lower limit of reliable quantification (LoQ). Current regulatory guidelines recommend using the proportion of patients achieving HIV-RNA levels below the assay limit at a certain time point (e.g. 24 or 48 weeks) as the primary endpoint for regulatory approval. However, a substantial decrease in HIV-RNA that does not go below the LoQ still is considered clinically beneficial for patients with advanced diseases who have failed many other therapies and are unlikely to maximally suppress the virus and achieve HIV-RNA levels below the LoQ. An experimental treatment may not be distinguishable from a control solely in terms of the proportions of patients whose HIV-RNA levels fall below the LoQ. The sensitivity of the comparison between the experimental treatment and the control could be increased by considering as well the difference between the treatments with respect to the HIV-RNA reductions of patients not achieving HIV-RNA levels below the LoQ. In this paper, we introduce a best-rank analysis which assigns the best rank to patients who achieve the HIV-RNA levels below the LoQ and applies the Mann-Whitney-Wilcoxon rank test to compare the two treatment groups. The Mann-Whitney-Wilcoxon statistic is shown to be a weighted sum of two statistics: one to compare the proportions of patients achieving the HIV-RNA levels below the LoQ and one to compare the viral reductions in patients with HIV-RNA levels above the LoQ. The corresponding statistical null and alternative hypotheses and the clinical interpretations of this best-rank test procedure are also discussed. An example is used to illustrate this approach and a simulation study is used to compare this approach with other methods.
The relationship between the superadjacency topochemical index and the anti-HIV activity of HEPT analogs has been investigated in the present study. The values of superadjacency topochemical index of all the analogs involved in the data set were calculated using an in-house computer program. Resulting data were analyzed and a suitable model was developed after identification of the active range. Subsequently, a computed biological activity was assigned to each of the compounds involved in the dataset, which was then compared with the reported anti-HIV activity. Accuracy of prediction was found to be 88% using the said model. The predictive ability of the model indicates that this model can be used for predicting the anti-HIV activity of the compounds prior to synthesis and may prove to be highly beneficial for providing lead structures for development of potent anti-HIV agents.