Development and validation of a sensitive HPLC-HESI-MS/MS method for quantitative determination of bitopertin in rat and marmoset plasma
Bibliographic record
Abstract
Bitopertin is a potent glycine transporter 1 (GlyT1) inhibitor that has undergone clinical trials for diverse disorders and has a well-documented pharmacokinetic (PK) profile in humans. Even though pre-clinical studies have demonstrated potential therapeutic effects on cognition and neuropathic pain, the PK profile of bitopertin in the rat has been partly disclosed and no study reporting its PK profile in the common marmoset has been published. The aim of this study was to develop and validate a sensitive and selective high-performance liquid chromatography coupled with heat assisted electrospray ionisation tandem mass spectrometry (HPLC-HESI-MS/MS) assay to quantify bitopertin in the rat (Sprague-Dawley) and the common marmoset (Callithrix jacchus) plasma after administration of 1.0 mg/kg subcutaneously. The analytical method consisted of protein precipitation followed by HPLC-HESI–MS/MS. Chromatographic separation was carried out on a Thermo Scientific Aquasil C18 analytical column (100 x 2.1 mm I.D., 5.0 μm) kept at 50°C using acetonitrile and water both fortified at 0.1% (v/v) with formic acid at a ratio 55:45 as mobile phase with a constant flow rate of 250 μL/min. The calibration function was linear in the range of 0.3-200.0 ng/mL in rat plasma. The intra-day and inter-day precision and accuracy were within ± 15% at all concentrations. The limit of detection (LOD) and quantitation (LOQ) in rat plasma were 0.08 and 0.3 ng/mL, respectively. This method has demonstrated high sensitivity and specificity and was successfully applied to measure bitopertin in rat and marmoset plasma, allowing the investigation of its PK properties in both species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".