Protein/peptide‐based entry/fusion inhibitors as anti‐HIV therapies: challenges and future direction
Bibliographic record
Abstract
The failures of several first-generation and second-generation small molecule drug-based anti-HIV therapies in various stages of clinical trials are an indication that there is a need for a paradigm shift in the future designs of anti-HIV therapeutics. Over the past several decades, various anti-HIV drugs have been developed, among them, protein/peptide-based therapies. From the first peptide discovered (SJ2176) to the first peptide approved by the Food and Drug Administration (DP178/T20/enfuvirtide/Fuzeon®), anti-HIV proteins/peptides as fusion/entry inhibitors have been shown to provide potent effects and benefits. This review summarizes the past and current endeavors in this area, discusses the potential mechanisms of action for various anti-HIV proteins/peptides, compares the advantages and disadvantages between the different proteins/peptides, and finally, examines the future direction of the field, specifically, strategies that will enhance the therapeutic efficacy of fusion/entry inhibitor-based anti-HIV proteins/peptides. Although there are numerous reviews highlighting the general field of entry/fusion inhibitors, there is a lack of literature focused on protein/peptide-based entry/fusion inhibitors for HIV therapy, and as a result, this review is intended to fill this void by summarizing the past, current, and future development of these macromolecules. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".