MétaCan
Menu
Back to cohort

Development and Validation of Atazanavir and Ritonavir Determination in Human Plasma by HPLC-MS Method

2020· article· en· W3008140779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDrug development & registration · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsCanadian Public Health Association
Fundersnot available
KeywordsAtazanavirRitonavirPharmacokineticsChromatographyHuman plasmaProtein precipitationTherapeutic drug monitoringChemistryPharmacologyHuman immunodeficiency virus (HIV)MedicineAntiretroviral therapyViral loadVirology

Abstract

fetched live from OpenAlex

Introduction . HIV infection is one of the most relevant diseases from a medical, epidemiological and social point of view. Timely diagnosis, detection and control of the disease, adequate prescription of antiretroviral therapy can sufficiently reduce the viral load on the patient's body, reduce the risk of transmission of infection. Currently, combinations of various antiretroviral drugs are increasingly being prescribed as therapy. One of the most important is combination of atazanavir and ritonavir. The most important stage for the study of pharmacokinetics, studies of comparative pharmacokinetics and bioequivalence is the development of an analytical method that allows you to determine the investigated substances in human plasma. There are currently no published methods for the determination of atazanavir and ritonavir in human plasma using high performance liquid chromatography with mass selective detection using a single quadrupole mass detector. In this article presents the development and validation of a method for the determination of atazanavir and ritonavir in blood plasma after sample preparation by the method of protein precipitation. Aim . The aim of the study is to develop a method for the quantitative determination of atazanavir and ritonavir in human plasma by HPLC with mass spectrometric detection for performing the analytical part of pharmacokinetic studies. Materials and methods . Determination of atazanavir and ritonavir in human plasma by HPLC with mass spectrometric detection. A sample was prepared using protein deposition. Results and discussion . The method was validated of selectivity, matrix effect, calibration curve, accuracy, precision, limit of quantification, carry-over effect and sample stability. Conclusion . The method of the determination of atazanavir and ritonavir in human plasma was developed and validated by HPLC-MS. The analytical range of the was 50.0–10000.0 ng/mL in plasma for atazanavir and 10.0–2500.0 ng/mL in plasma for ritonavir. Method could be applied to determination of atazanavir and ritonavir in plasma for PK and BE studies.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.295
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it