Adherence–resistance relationships for protease and non-nucleoside reverse transcriptase inhibitors explained by virological fitness
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of resistance by adherence level in patients treated with non-nucleoside reverse transcriptase inhibitors (NNRTI) or protease inhibitors (PI). Also to examine the mechanism of differential class-specific adherence-resistance relationships, focusing on the patient-derived capacity of wild-type and drug-resistant recombinant variants to replicate in vitro in the presence of variable drug levels. METHODS: Participants received unannounced pill count measures to assess adherence, viral load monitoring, and genotypic resistance testing. The replicative capacity of drug-susceptible and drug-resistant recombinants was determined using a single-cycle recombinant phenotypic susceptibility assay. Drug exposure was estimated using population-averaged pharmacological measurements adjusted by participant-specific levels of adherence. RESULTS: In the NNRTI-treated group, 69% had resistance at 0-48% adherence compared to 13% at 95-100% (P = 0.01). PI resistance was less common than NNRTI resistance at 0-48% adherence (69% versus 23%; P = 0.01). In multivariate analysis, the odds for PI resistance increased (P = 0.03) while the odds for NNRTI resistance decreased (P = 0.04) with improving adherence. Individuals with drug-resistant variants were more likely to have levels of drug exposure where the resistant variant was more fit than the drug-susceptible variant in vitro, while those with drug-susceptible virus were more likely to have levels of drug exposure where the drug-susceptible virus was more fit than the drug-resistant variant (P = 0.005). CONCLUSIONS: NNRTI resistance was more common than PI resistance at low levels of adherence. Class-specific adherence-resistance relationships are associated with the relative replicative capacity of drug-resistant versus wild-type variants to replicate in the presence of clinically relevant drug levels.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".