The evaluation of candidate microbicide antiretrovirals against wild type and drug resistant HIV-1 «in vitro»
Bibliographic record
Abstract
Over the last 30 years, the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) mêlée has endured despite valiant efforts made by the research community. Although great strides in treatment efficacy have been made, an effective vaccine remains beyond our reach. Anti- HIV microbicides, (topically applied agents to prevent the transmission of HIV), offer an infection prevention strategy that may buy us time until an effective vaccine is discovered. Indeed, antiretroviral (ARV)-based microbicides have proven more effective than any vaccine clinical trial to date. Improved formulations, including combination ARV- based microbicides, may prove to be even more clinically efficacious in the future. However, the pre-clinical evaluation of candidate microbicide ARVs is prudent, lest a formulation proves inimitably toxic or useless during human trials. Of course, pre-clinical drug assessments have limitations; what we learn from in vitro tissue culture experiments does not necessarily translate seamlessly to in vivo efficacy. Pre-clinical assessment may, nevertheless, aid in advancing the best possible candidate ARVs, permitting each candidate microbicide ARV to be ranked in the context of better toxicity, efficacy, and drug resistance development profiles. The following thesis describes the pre-clinical assessment of the lead candidate microbicide ARVs, tenofovir (TFV) (a nucleotide reverse transcriptase inhibitor (NtRTI)), dapivirine (DAP) (a nonnucleoside reverse transcriptase inhibitor(NNRTI)), and BMS- 599793, an HIV-1 entry inhibitor. How these drugs perform as HIV-1 infection preventatives in the context of wild type (WT) and drug resistant (DR)HIV-1 transmission in vitro is focused upon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".