<i>In Vitro</i> Resistance Profile of the Candidate HIV-1 Microbicide Drug Dapivirine
Bibliographic record
Abstract
Antiretroviral-based microbicides may offer a means to reduce the sexual transmission of HIV-1. Suboptimal use of a microbicide may, however, lead to the development of drug resistance in users that are already, or become, infected with HIV-1. In such cases, the efficacy of treatments may be compromised since the same (or similar) antiretrovirals used in treatments are being developed as microbicides. To help predict which drug resistance mutations may develop in the context of suboptimal use, HIV-1 primary isolates of different subtypes and different baseline resistance profiles were used to infect primary cells in vitro in the presence of increasing suboptimal concentrations of the two candidate microbicide antiretrovirals dapivirine (DAP) and tenofovir (TFV) alone or in combination. Infections were ongoing for 25 weeks, after which reverse transcriptase genotypes were determined and scrutinized for the presence of any clinically recognized reverse transcriptase drug resistance mutations. Results indicated that suboptimal concentrations of DAP alone facilitated the emergence of common nonnucleoside reverse transcriptase inhibitor resistance mutations, while suboptimal concentrations of DAP plus TFV gave rise to fewer mutations. Suboptimal concentrations of TFV alone did not frequently result in the development of resistance mutations. Sensitivity evaluations for stavudine (d4T), nevirapine (NVP), and lamivudine (3TC) revealed that the selection of resistance as a consequence of suboptimal concentrations of DAP may compromise the potential for NVP to be used in treatment, a finding of potential relevance in developing countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".