Chemical and Toxicological Analysis of Antiretroviral Drugs
Bibliographic record
Abstract
Introduction. Human Immunodeficiency Virus (HIV) is one of the main socially significant infection all over the world. HIV-positive patients take medical care, including antiretroviral drugs (ARVs) pharmacotherapy. Like all drugs, ARVs have lots of side effects that should be taken when prescribing drugs as part of highly active antiretroviral therapy. There are many cases when side effects of ARVs caused patients to enter the toxicology department. Therefore, the development of new methods for the analysis of ARV in biological fluids for the timely diagnosis of treatment of poisoning of this group of drugs is relevant today. Aim. The aim of this study is development of screening analysis of atazanavir, abacavir, nevirapine, ritonavir, lopinavir, zidovudine, darunavir and efavirenz in the urine to identify these drugs as possible toxicants for poisoning by high-performance liquid chromatography with tandem massselective detection (HPLC-MS/MS). Materials and methods. Identification of ARV was performed by HPLC-MS/MS. Methanol precipitation method was used as a sample preparation. Results and discussion. The optimal conditions for sample preparation, chromatographic separation, and mass-spectrometric detection were selected to determine the studied ARVs. This method was tested on urine samples from patients in the Department of Acute Poisoning and Somatopsychiatric Disorders (OOSPD) with acute ARV poisoning. Conclusion. This screening method for analyse atazanavir, abacavir, nevirapine, ritonavir, lopinavir, zidovudine, darunavir and efavirenz in human urine has been developed by HPLC-MS/MS. The developed method can be used to identify these drugs as possible toxicants in case of poisoning. The prospect for the development of the topic is the inclusion of new molecules in the method and quantitative determination of the studied ARVs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".