A Simple Screening Approach to Reduce B*5701-Associated Abacavir Hypersensitivity on the Basis of Sequence Variation in HIV Reverse Transcriptase
Bibliographic record
Abstract
BACKGROUND: Abacavir hypersensitivity is strongly associated with the human leukocyte antigen (HLA)-B*5701 allele; however, the cost of routine high-resolution HLA typing before initiation of therapy remains prohibitive. We propose a simple approach to reduce B*5701-associated abacavir hypersensitivity based on the screening of human immunodeficiency virus (HIV) reverse transcriptase (RT) for a signature B*5701-associated cytotoxic T lymphocyte escape mutation at RT codon 245. METHODS: The correlation between HLA-B*5701 and RT codon 245 variation was investigated in 392 HIV-infected, antiretroviral-naive adults who were initiating highly active antiretroviral therapy. The relationship between codon 245 variation and premature abacavir discontinuation was investigated in a larger cohort of treated individuals (n=982). Associations between HLA-B*5701 and codon 245 variants were determined using Fisher's exact test or the chi (2) test. RESULTS: A very strong association between HLA-B*5701 and RT codon 245 variation was observed. Only 1 (4.2%) of 24 subjects with B*5701 harbored virus with the clade B "wild-type" amino acid 245V, compared with 278 (75.5%) of 368 who did not have B*5701 (P<.001). The sensitivity and specificity of codon 245 substitutions for predicting HLA-B*5701 were 96% and 75%, respectively, and the positive and negative predictive values were 20% and 99.6%, respectively. This association remained robust even after antiretroviral treatment was administered (negative predictive value, 100%; n=269). In abacavir-treated individuals (n=982), codon 245 substitutions were predictive of premature abacavir discontinuation (P=.02). CONCLUSIONS: As HIV RT sequence is incidentally obtained as a part of routine drug-resistance testing, the examination of sequence variation at RT codon 245 could be adopted as a simple, low-cost screening method to identify individuals who could be safely treated with abacavir and/or who could benefit from HLA characterization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".