Challenges for the Clinical Development of New Nucleoside Reverse Transcriptase Inhibitors for HIV Infection
Bibliographic record
Abstract
There is a need for new antiretroviral drugs with activity against HIV isolates resistant to currently available agents and improved short and long-term tolerability profiles. Clinical trial designs for nucleotide and nucleoside reverse transcriptase inhibitors (NRTIs) are restricted by the characteristics of these agents (for example, their cross-resistance, resistance threshold and interaction profiles), the ethical need to ensure that patients are not maintained on suboptimal regimens, and regulatory requirements (for example, with regards to trial designs and patient populations). For example, consideration of cross-resistance profiles must influence the way in which an NRTI in development is sequenced to minimize any impact on future treatment options. The resistance threshold is determined by the number of mutations required to diminish sensitivity to a given drug. Pharmacokinetic or pharmacodynamic interactions restrict how NRTIs may be combined during clinical development. Doses may be selected on the basis of results from short-term monotherapy studies in treatment-naive patients, but such studies cannot establish the long-term efficacy or tolerability of new agents used in combination regimens. Confirmatory studies in treatment-naive populations do not meet the medical and regulatory needs for clinical data in treatment-experienced populations, while studies in treatment-experienced populations are subject to numerous clinical and logistical difficulties. Intensification, switch and hybrid study designs all offer suitable approaches to the evaluation of NRTIs with novel resistance profiles. Switch studies are particularly useful for agents with resistance profiles that suggest a specific sequencing approach in treatment and for those with the potential, based on pharmacokinetic data, for interactions with other agents. The successful development of new NRTIs will depend upon a thorough appreciation of these many and complex issues, not only among those involved in the design of clinical studies, but also those contributing to their review and conduct.
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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 | 0.119 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".