Resistance Issues With New Nucleoside/Nucleotide Backbone Options
Bibliographic record
Abstract
The nucleoside and nucleotide reverse transcriptase inhibitors (NRTIs/NtRTIs) remain an enduring feature of combination therapy. As NRTI/NtRTI options continue to expand, questions arise about how best to combine these agents to create effective dual NRTI/NtRTI backbones in antiretroviral regimens while avoiding treatment-emergent drug resistance. Clinicians must consider how NRTIs/NtRTIs such as tenofovir DF (TDF), abacavir (ABC), and emtricitabine (FTC), as well as new once-daily and coformulated NRTIs/NtRTIs, interact with older agents when combined in novel regimens and how sequencing the new NRTIs can preserve future treatment options. Resistance data from clinical trials have revealed important information on the patterns, prevalence, and effects of resistance seen among patients experiencing virologic failure. In recent years, the prevalence of some mutations such as M184V and Q151M has remained relatively constant, while the L74V mutation, the 69 insertions, and thymidine analogue mutations have decreased in prevalence. Other mutations such as K65R and Y115F, while still relatively uncommon, are increasing in prevalence. This increase may be due to the use of new treatment combinations that select for these mutations at a higher rate. Clinical trials suggest that new regimens containing TDF or ABC select for K65R and that this mutation is observed more frequently with TDF; in contrast, L74V is observed more frequently in ABC-containing regimens but is not commonly selected by TDF-containing regimens. Several lines of evidence are converging to suggest that the presence of zidovudine may decrease the risk of L74V and K65R in ABC- or TDF-containing regimens. This review summarizes the clinical implications of resistance profiles associated with new NRTI/NtRTI regimens in current use and in advanced clinical studies.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".