Development and validation of reversed-phase HPLC gradient method for the estimation of efavirenz in plasma
Bibliographic record
Abstract
Efavirenz is an anti-viral agent of non-nucleoside reverse transcriptase inhibitor category used as a part of highly active retroviral therapy for the treatment of infections of human immune deficiency virus type-1. A simple, sensitive and rapid reversed-phase high performance liquid chromatographic gradient method was developed and validated for the determination of efavirenz in plasma. The method was developed with high performance liquid chromatography using Waters X-Terra Shield, RP18 50 x 4.6 mm, 3.5 μm column and a mobile phase consisting of phosphate buffer pH 3.5 and Acetonitrile. The elute was monitored with the UV-Visible detector at 260 nm with a flow rate of 1.5 mL/min. Tenofovir disoproxil fumarate was used as internal standard. The method was validated for linearity, precision, accuracy, specificity, robustness and data obtained were statistically analyzed. Calibration curve was found to be linear over the concentration range of 1-300 μg/mL. The retention times of efavirenz and tenofovir disoproxil fumarate (internal standard) were 5.941 min and 4.356 min respectively. The regression coefficient value was found to be 0.999. The limit of detection and the limit of quantification obtained were 0.03 and 0.1 μg/mL respectively. The developed HPLC method can be useful for quantitative pharmacokinetic parameters determination of efavirenz in plasma.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".