OC 8469 REVIEWING INVESTIGATIONAL PRODUCT’S QUALITY ASSURANCE DOCUMENTATION IN MAJOR CLINICAL TRIAL REGISTRIES FOR POST-MARKETING CLINICAL TRIAL STUDIES
Bibliographic record
Abstract
Background The proven worrisome quality of medicines marketed in developing countries also affects clinical trials (CTs) as they may be used as Investigational Medicinal Products (IMPs). By regulation, CT sponsors should assure IMP’s quality and describe their quality measures in CT protocols that should be registered in a CT Registry (CTR). To check compliance with this regulation, we reviewed major CTRs to assess the availabilities of data fields on IMP quality for post-marketing CTs. Methods Two reviewers independently assessed English versions of CTRs of International Committee of Medical Journal Editors (ICJME) and WHO platforms in July 2017. Each CTR was checked for availability of data fields on: brand name; manufacturer’s name; regulatory approval status; approving regulator; manufacturer’s compliance with Good Manufacturing Practices (cGMP); and quality testing (IMP appearance, impurities, microbial contamination, dosing). In case of discrepancy, consensus was sought. Results Of 19 CTRs identified, 8 and 6 belonged to WHO and ICMJE, respectively, and 5 were equally part of both platforms. All CTRs had an ‘intervention’ data field to capture data on IMPs and IMP comparators. Unlike all others, the Canadian CTR used ‘drug name’ rather than ‘intervention’. Only the EU CTR had data fields for ‘manufacturer’s name’, ‘product approval status’, and ‘approving authority’. None of the CTRs had data fields on ‘cGMP’ or ‘quality testing’. Conclusion None of the CTRs of ICMJE and ICTRP has adequate data fields to establish that the source of post-marketing IMPs is of assured quality. This is astonishing given the extensive requirements in WHO and ICMJE guidelines. The gap of quality assurance fields should be bridged by adding them to CTRs. Specifically, IMP quality testing should be conducted before, during, and after clinical trial completion. Until adoption of these measures, EU-CTR should be favoured for registration of CTs conducted in developing countries.
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.
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 | 0.282 | 0.532 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".