Overcoming resistance to existing therapies in HIV‐infected patients: The role of new antiretroviral drugs
Bibliographic record
Abstract
Resistance to available antiretroviral (ARV) agents is of increasing concern, and development of novel agents that address this problem has been identified as a major public health priority. As ARV resistance becomes more prevalent with extended use of existing agents, individuals with HIV infection resistant to all three traditional classes of ARVs, nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs), find themselves increasingly limited with regard to effective treatment options. The need for tolerable new drug regimens that effectively suppress viral replication while being simple to adhere to is increasingly pressing. This article reviews the epidemiology of antiretroviral drug resistance, the factors that contribute to the emergence of resistance, and presents data that support the need for early detection of resistance and maximal virologic suppression in order to delay treatment failure and reduce mortality. Healthcare providers are encouraged to optimize therapy through the use of new agents from existing drug classes, which can minimize cross-resistance, as well as agents with novel mechanisms of action, in order to realize the potential for greater viral containment and to forestall development of resistance mutations. This article evaluates several emerging therapies that are in late-stage clinical development and promise to expand treatment options for highly treatment-experienced patients with the goal of improving outcomes for HIV-infected individuals whose options for sustained antiviral efficacy are increasingly limited.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".