Practical Considerations of FDA’s CLAP List to Support Testing of Extractables for Pharmaceuticals and Medical Devices
Bibliographic record
Abstract
The Chemical List for Analytical Performance (CLAP) was developed to support the chemical assessment of medical devices. FDA CDRH published the list, along with supporting data and with the intention that the chemicals in the list could be used to perform non-targeted extractable and simulated use extractable studies for devices. The list can be used to establish relative response factors and understand uncertainty in the context of extractable and simulated use extractable studies and can be used to demonstrate system and method suitability. This study utilizes the CLAP list to emphasize the importance of threshold identification, "semi-quantitative" assessment during the non-targeted analysis process of impurities related to Extractable and Leachable (E&L), and biocompatibility testing to ensure meaningful toxicological risk assessments. The current paper also highlights the complexity of the testing process, which involves the use of multiple analytical techniques, addresses challenges associated with screening methods, response factor databases, and the use of internal standards, and highlights a data set based on a modified CLAP list, focusing on volatiles and semivolatiles analyzed by GC-MS. The authors conclude that the CLAP list is a practical tool for establishing a basic database for GC-MS and system suitability evaluation if it has been evaluated at the AET level of the study. It has a reasonable analyte coverage at the highest studied level of 2.5 μg/mL, and the list can be used for evaluating system performance beyond direct injection of liquid extracts. Practical considerations for database generation and response factor applicability at various matrices and concentrations are discussed, with a conclusion that relative responses are generally constant at levels of 2.5 μg/mL or above and are rapidly changing at trace levels.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".