Negligible Effect of Tenofovir on Atazanavir Trough Concentrations and Genotypic Inhibitory Quotients in the Presence and Absence of Ritonavir
Bibliographic record
Abstract
BACKGROUND: It is recommended to boost atazanavir with ritonavir (ATV/r) when it is combined with tenofovir disoproxil fumarate (TDF) because of drug interactions. For tolerability, unboosted atazanavir (ATV) is sometimes coadministered with TDF. The objective of this study was to evaluate the impact of this interaction on the proportion of patients achieving target ATV C troughs and genotypic inhibitory quotients (GIQ). MATERIALS AND METHODS: A therapeutic drug monitoring database was screened to evaluate ATV concentrations. Differences in C trough and GIQ values among 4 antiretroviral drug combinations were evaluated. RESULTS: Three hundred eight C troughs, 91 GIQs, and 92 viral loads were evaluated for 238, 68, and 69 patients, respectively. Patients receiving ATV/r and TDF compared with ATV and TDF were more likely to have a therapeutic C trough (odds ratio, 2.27; 95% confidence interval: 1.46-3.52; P < 0.001). Among patients on unboosted ATV, the odds of having a therapeutic ATV C trough did not differ between groups with TDF versus without TDF. Although ritonavir increased the GIQ in patients receiving TDF (odds ratio, 3.38; 95% confidence interval: 1.30-8.81; P = 0.013), a similar proportion of patients on TDF and either ATV/r or ATV achieved a therapeutic GIQ. CONCLUSIONS: In patients receiving TDF, ritonavir increased the ATV C trough and GIQ and patients on ATV/r were more likely to have therapeutic C troughs. However, among subjects without ritonavir boosting, TDF compared with other nucleosides did not influence the odds of achieving a therapeutic ATV C trough. These data suggest that ritonavir boosting of ATV is prudent, particularly in patients with resistance mutations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".