Campaign manufacturing of highly active or sensitizing drugs: a comparison between the GMPs of various Regulatory Agencies.
Bibliographic record
Abstract
BACKGROUND: Cross-contamination and mix-ups are among the problems which could have a negative impact on the quality of the finished product during the production of highly active or sensitizing drugs with campaign manufacturing. Standardised, validated procedures ensure quality standards are maintained during production. In spite of this, the operating conditions and applicability of methods adopted by the various regulatory agencies manifest significant differences which could consequently compromise the safety of the finished product. This work has analysed and compared the GMP of various Regulatory Agencies to examine issues connected to campaign manufacturing highly active or sensitizing drugs. METHODS: The GMP of the following Regulatory Agencies have been studied: EMA, CFDA, COFEPRIS, FDA, Health Canada, ANVISA, CDSCO, PIC/S and WHO. The study was carried out for the purpose of understanding which agencies consent to the use of campaign manufacturing for the following categories of medicinal products: hormones, immunosuppressants, cytotoxic agents, highly active pharmaceutical ingredients (APIs), biological preparations, steroids, sensitizing pharmaceutical materials, antibiotics, cephalosporins, penicillins, carbapenems and beta-lactam derivatives. RESULTS: The GMP of Health Canada, EMA, PIC/S and FDA show a number of similarities, starting with the fact that they allow campaign manufacturing for similar categories of pharmaceutical products after an appropriate risk evaluation has been performed. CFDA, WHO, ANVISA authorise campaign manufacturing in "exceptional circumstances", though they do not always define what they mean by this. COFEPRIS authorises campaign manufacturing for certain classes of drugs, while there is no mention of campaign manufacturing in the CDSCO regulations. CONCLUSIONS: Quite a few significant differences have been found in the various regulations concerning the use of campaign manufacturing and the classes of drugs that can be produced with this method. In the light of this, it is obvious that efforts to harmonise legislation internationally have not yet been successful: currently, states can adopt different quality standards. The pharmaceutical industry could use this situation to its advantage by delocalising production on the basis of existing standards. The need to harmonise GMPs is a priority which must be achieved as soon as possible.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".