Role of genetic diversity amongst HIV-1 non-B subtypes in drug resistance: a systematic review of virologic and biochemical evidence.
Bibliographic record
Abstract
The genetic diversity of HIV-1 has required its classification into types and subtypes. There is controversy as to how and to what extent genetic diversity may affect the emergence of antiretroviral drug resistance in HIV-1 subtypes other than B. To better understand the impact of genetic diversity (represented by different HIV-1 subtypes) on resistance to reverse transcriptase and protease inhibitor drugs, a systematic review was conducted on virologic and biochemical evidence obtained from work with non-B HIV-1 subtypes. We searched 11 databases and retrieved 3,486 citations on all aspects of non-B subtype-related resistance research. Twenty-seven studies with virologic and/or biochemical data met the eligibility criteria for our systematic review. Nineteen studies were found that reported phenotypes in non-B subtypes (304 from naive isolates and 242 from drug-exposed isolates) and 11 studies that used molecular biology techniques to study non-B resistance to antiretroviral drugs. Compared to the NL4-3 laboratory strain, lower baseline susceptibilities of recombinant A/G subtype virus to protease inhibitors were observed and a substantial proportion of subtype C isolates displayed higher IC50 at baseline for atazanavir. Some A/G isolates were found to have reduced susceptibility to abacavir. Mutations not typical of B subtypes include the reverse transcriptase mutation V106M and the protease mutations M89I/V and N83T. Virologic and biochemical data suggest that K65R is more likely to emerge in subtype C HIV-1. There is evidence to suggest differential effects of other mutations according to subtype, e.g. the protease inhibitor mutations I93L and M89I/V. Importantly, the most widely used commercial phenotyping systems do not take into account gag variations among natural isolates, which could limit the accuracy of measured susceptibility. Enzymatic and virologic data support the concept that naturally occurring polymorphisms in different non-B subtypes can affect the susceptibility of HIV-1 to different antiretroviral drugs, the magnitude of resistance conferred by major mutations, and the propensity to acquire some resistance mutations. Tools may need to be optimized to accurately measure drug susceptibility of non-B subtypes, especially for protease inhibitors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".