How to achieve safe, high-quality clinical studies with non-Medicinal Investigational Products? A practical guideline by using intra-bronchial carbon nanoparticles as case study
Bibliographic record
Abstract
BACKGROUND: Clinical studies investigating medicinal products need to comply with laws concerning good clinical practice (GCP) and good manufacturing practice (GMP) to guarantee the quality and safety of the product, to protect the health of the participating individual and to assure proper performance of the study. However, there are no specific regulations or guidelines for non-Medicinal Investigational Products (non-MIPs) such as allergens, enriched food supplements, and air pollution components. As a consequence, investigators will avoid clinical research and prefer preclinical models or in vitro testing for e.g. toxicology studies. THE AIM OF THIS ARTICLE IS TO: 1) briefly review the current guidelines and regulations for Investigational Medicinal Products; 2) present a standardised approach to ensure the quality and safety of non-MIPs in human in vivo research; and 3) discuss some lessons we have learned. METHODS AND RESULTS: We propose a practical line of approach to compose a clarifying product dossier (PD), comprising the description of the production process, the analysis of the raw and final product, toxicological studies, and a thorough risk-benefit-analysis. This is illustrated by an example from a human in vivo research model to study exposure to air pollutants, by challenging volunteers with a suspension of carbon nanoparticles (the component of ink cartridges for laser printers). CONCLUSION: With this novel risk-based approach, the members of competent authorities are provided with standardised information on the quality of the product in relation to the safety of the participants, and the scientific goal of the study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".