On-site management of investigational products and drug delivery systems in conformity with Good Clinical Practices (GCPs)
Bibliographic record
Abstract
BACKGROUND: Investigators and research teams participating in clinical trials have to deal with complex investigational products, study designs, and research environments. The emergence of new drug delivery systems and investigational products combining more than one drug and the development of biodrugs such as monoclonal antibodies, peptides, siRNA, and gene therapy to treat orphan or common diseases constitute a new challenge for investigators and clinical sites. PURPOSE: We describe the requirements and challenges of drug management in conformity with Good Clinical Practices (GCPs) for investigators and sites participating in clinical trials. Review At all sites participating in clinical trials, standard operating procedures (SOPs) covering the critical path of drug and drug delivery systems management are required. All steps should be auditable, including reception, validation, storage, access, preparation, distribution, techniques of administration, use, return, and destruction of research products. Biodrugs require traceability and specific SOPs on the management of potential immune reactions. Investigational products must be stored under standard auditable conditions. The traceability of storage conditions (including temperature) requires these conditions to be monitored on a continuous basis. A dedicated space with restricted access limited to authorized qualified personnel facilitates the monitoring. CONCLUSIONS: The development of standardized, auditable settings and the application of dedicated, site-specific SOPs for the management of investigational products and drug delivery systems contribute to guarantee the compliance to GCP requirements.
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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 | 0.159 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".