Multilocus genetic interactions and response to efavirenz-containing regimens: an Adult AIDS Clinical Trials Group study
Bibliographic record
Abstract
OBJECTIVE: For the HIV-1 reverse transcriptase inhibitor efavirenz, variant drug transporter gene ABCB1 may predict virologic response but not plasma efavirenz exposure. Conversely, variant drug metabolizing enzyme gene CYP2B6 predicts greater plasma efavirenz exposure but not virologic response. We examined whether long-term responses to efavirenz, and/or plasma efavirenz exposure, are better predicted by multilocus genetic interactions than by individual polymorphisms. MATERIALS AND METHODS: We studied antiretroviral-naïve study participants randomized to receive efavirenz (with or without nelfinavir) plus two nucleoside analogues in study ACTG 384, and who had DNA available for analysis. Participants were followed up for up to 3 years. Nine single nucleotide polymorphisms in ABCB1, CYP2B6, CYP3A4, CYP3A5 and CYP2C19 were identified. Gene-gene interactions were identified using multifactor dimensionality reduction. RESULTS: Among 340 efavirenz recipients, higher efavirenz AUC24 h values were associated with a single locus model involving CYP2B6 516G>T (73% accuracy; P<0.001). This was also the best model among blacks (69% accuracy; P<0.001), whereas among whites the best model involved a gene-gene interaction between CYP2B6 516G>T and ABCB1 2677G>T (82% accuracy, P<0.001). Among 155 participants who received efavirenz without nelfinavir, virologic failure was associated with a two-locus interaction between ABCB1 2677G>T and CYP2B6 516G>T (65% accuracy, P<0.001). Toxicity failure was best predicted by an interaction between ABCB1 2677G>T and ABCB1 3435C>T (71% accuracy, P<0.001). CONCLUSIONS: Multilocus genetic interactions between variant drug metabolism and transporter genes may predict efavirenz pharmacokinetics and treatment responses. This finding may have implications for better individualizing antiretroviral therapy.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".