Quality-by-Design-Driven RP-HPLC Method Development and Validation for Impurity Analysis of Elexacaftor, a Cystic Fibrosis Drug, with LC-MS/MS-Based Degradant Identification
Bibliographic record
Abstract
The development and validation of an analytical technique based on RP-HPLC for the determination of impurities in Elexacaftor (ELX). Using a straightforward and stability-indicating HPLC technique, six known contaminants were precisely assessed with the right resolutions and peak forms. In a number of separation studies, the chromatographic separation was accomplished using an XBridge Shield RP 18, 150 mm × 4.6 mm, 3.5 μm column. The mobile phases consisted of methanol, acetonitrile, 2-propanol, and 10 mM potassium dihydrogen orthophosphate buffer used for the analysis. The gradient elution process took 90 min to complete, and the column temperature was 27 °C. Photodiode array optimization was accomplished at 220 nm with a 0.7 mL/min flow rate. The developed procedure was validated following ICH requirements and discovered to be robust, linear, accurate, specific, selective, and exact. The method range extended from LOQ to ∼0.45% for impurities and LOQ to ∼120% for the Elexacaftor drug substance. The method robustness was established by utilizing the DoEs in the part of the QbD concept. The method's stability was evaluated under diverse conditions including acid and base hydrolysis, oxidative and water hydrolysis, and thermal and photolytic degradation and was helpful for process development as well as quality checks in bulk drug manufacturing.
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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 | 0.009 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".