Campaign manufacturing of highly active pr sensitising ingredients: a comparison between the GMPs of various regulatory agencies
Bibliographic record
Abstract
INTRODUCTION \nHighly active or sensitising ingredients (API/SI) need special rules for their production. Cross-contamination and mix-ups represent the greatest dangers related to the manufacturing of API/SI. \nThese risks are obviously greater in campaign manufacturing (CM) and therefore this type of production can only be performed using certified procedures able to guarantee non residue between the two different processes. \n \nNevertheless, as there is no global harmonization of GMPs, some nations require particular working conditions for manufacturing given products, while other countries require different ones. In this paper we have studied the problems related to the CM of API/SI under the different Regulatory Authorities, by analyzing and comparing their Good Manufacturing Practices (GMPs). \n \nMETHODS \nWe studied the GMPs of the different Regulatory Agencies (EMA, CFDA, COFEPRIS, FDA, Health Canada, ANVISA, CDSCO, PIC/S, and WHO) in order to assess which Authorities allow CM for the production of the following categories of drugs: hormones, immunosuppressants, cytotoxic agents, API, biological preparations, steroids, antibiotics, cephalosporins, penicillin, carbapenems, beta-lactam derivatives. \n \nRESULTS \nThe rules of the Regulatory Agencies we studied (regarding the production of API/SI) can be divided into three types: \n1) CM is permitted only where adequate technical/organisational measures are used as well as adequate cleaning measures that can guarantee the safety of the method: Health Canada, EMA, PIC/S, FDA. \n2) CM is permitted for the production of certain classes of API/SI only in exceptional circumstances: CFDA, WHO, ANVISA. CFDA and WHO allow CM only for the production of “certain” API and in exceptional circumstances (without defining in the regulations which circumstances can be defined as exceptional). ANVISA allows the use of CM only in the case of serious emergencies (i.e.: war, fires, floods). \n3) CM is not provided or mentioned: COFEPRIS, CDSCO. \n \nCONCLUSIONS \nSignificant differences emerged between the GMPs Regulatory Authorities both as regards the classes of drugs that can be produced through CM and as regards the Agencies that authorize it. \nThe pharmaceutical industry, in deciding which drugs can be produced according to the principles of CM, will have to use a Quality Risk Management and choose this production method only where the Regulatory Authority allows it and where there are procedures that can guarantee the elimination of the risk of cross-contamination.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".