Antiviral Drugs that Target Cellular Proteins May Play Major Roles in Combating HIV Resistance
Bibliographic record
Abstract
Despite the significant progresses made in antiretroviral therapy, current drugs still cannot cure or prevent HIV infection. And all drugs continue to select for drug-resistant HIV strains. Consequently, new antiretroviral drugs are constantly being developed. To ensure safety, these drugs are usually designed to inhibit viral proteins. But cellular proteins are also emerging as potential targets for new antiretroviral drugs. Two drugs that target cellular proteins inhibit HIV replication in vitro, hydroxyurea (HU) and pharmacological cyclin-dependent kinase inhibitors (PCIs). HU has been tested in clinical trials, commonly in combination therapies. PCIs, which are newer drugs, have just started to be tested in animal models of HIV-induced disease. Herein, we will review the HIV replication cycle and discuss the biological causes why strains resistant to antiviral drugs are so easily selected for. We will then discuss current antiretroviral drugs and HU before focusing on PCIs. PCIs have demonstrated to be effective against wild-type and drug-resistant strains of HIV in vitro, while selecting for no drug resistance. PCIs are additive with conventional antiviral drugs against herpes simplex virus, which suggests that they could also be additive with antiretroviral drugs. Since PCIs are proving surprisingly safe in human clinical trials (against cancer), they may be developed as clinical antiretroviral drugs in the near future. Recent and exciting studies indicate that PCIs ameliorate the pathogenesis of an animal model of HIV-induced nephropathy. We can expect that the full potential of PCIs as antiretroviral drugs will be explored in the coming years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.004 | 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".