Simplification of Therapeutic Drug Monitoring for Twice-Daily Regimens of Lopinavir/Ritonavir for HIV infection
Bibliographic record
Abstract
Cost and inconvenience limit the application of full 12-hour pharmacokinetic (PK) analysis for routine therapeutic drug monitoring of antiretroviral medications. We explore whether lopinavir (LPV) and ritonavir (RTV) exposures can be estimated with limited sampling for patients taking twice-daily LPV/RTV. One hundred and one PK profiles from 81 patients, most receiving salvage therapies including twice-daily LPV/RTV, were obtained for the analysis. After a minimum of 2 weeks on a stable regimen, blood was drawn immediately before and at 1, 2, 4, 6, 8, 10, and 12 hours after a timed medication dose. Plasma drug concentrations were determined by a validated HPLC-MS-MS assay. Peak concentrations, evening troughs, and AUC0-12 h were entered into linear and log10-log10 linear regression models to determine the best correlation with LPV and RTV plasma concentrations using a maximum of 2 time points. The accuracy and precision of PK parameter estimates of the resultant models were tested on data collected for an additional 25 patients. Twelve models using various combinations of 2 timed LPV concentrations afforded accurate (maximum % bias = -6.45) and precise (relative standard deviation < 15%) estimates for the LPV peak concentration or AUC0-12h. Four sets of 2 concentrations provided simultaneous estimates of both PK parameters, with the best estimates derived from data collected at 2 and 6 hours postdose. Evening trough concentrations were the best estimators of the daily nadir; however, no adequate substitute for collecting blood 12 hours postdose emerged from this analysis.
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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) | 0.001 | 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".