Development and Validation of HPLC-UV Method for the Determination of Favipiravir in Human Plasma
Bibliographic record
Abstract
Introduction. Coronavirus disease (COVID-19) is an acute infectious disease caused by SARS-CoV-2 (severe acute respiratory syndrome-related coronavirus 2). Favipiravir is a synthetic prodrug with antiviral activity used for the treatment of COVID-19. There are oral and parenteral dosage forms of favipiravir. Compared with oral administration, parenteral administration has some advantages. Developing a method for the determination of favipiravir in human blood plasma is necessary for performing the analytical part of clinical studies of favipiravir for parenteral administration as an infusion, studying pharmacokinetics, and choosing the optimal dosage of the drug. Aim. The aim of this study is to develop and validate a method for quantitative determination of favipiravir in human plasma by high-performance liquid chromatography with ultraviolet detection (HPLC-UV) for pharmacokinetic studies. Materials and methods. Determination of favipiravir in human plasma by HPLC-UV. The UV detection was set at 323 ± 2 nm. The samples were processed by methanol protein precipitation. Internal standard: raltegravir. Mobile phase: 0.1 % formic acid in water with 0.08 % aqueous ammonia (eluent A), 0.1 % formic acid in acetonitrile with 0.08 % aqueous ammonia (eluent B). Column: Phenomenex Kinetex®, C18, 150 × 4.6 mm, 5 μm. Analytical range: 0.25–200.00 μg/mL. Results and discussion. This method was validated by selectivity, calibration curve, accuracy, precision, spike recovery, the lower limit of quantification, carry-over effect and stability. Conclusion. We developed and validated the method of quantitative determination of favipiravir in human plasma by HPLC-UV. The analytical range was 0.25–200.00 μg/mL in human plasma. The method could be applied in pharmacokinetics studies of favipiravir.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".