Recent Regulatory Trends in Pharmaceutical Manufacturing and their Impact on the Industry
Bibliographic record
Abstract
The pharmaceutical industry is one of the most regulated industries in Switzerland. Though the concept of good manufacturing practises (GMP) was implemented for chemical production in the early 1990s, the rules and regulations for our industry are in constant evolution. In this article we will highlight the impact of these changes to the industry using three recent guideline up-dates as examples: the implementation of ICH Q3D 'Guideline for elemental impurities', the EU-GMP Guideline Part III Chapter 'Guideline on setting health based exposure limits for use in risk identification in the manufacture of different medicinal products in shared facilities' from 01. June 2015, and the new guidelines to data integrity such as 'PIC/S 041-1 Good Practices for Data Management and Integrity in regulated GMP/GDP environments'. These examples show how scientific approaches help to modernize the control strategies for our products and increase product quality for a better patient safety. The requirements of data integrity regulations are also of interest to industries and universities not working under GxP requirements as they also support the business to improve data quality (traceability) for patent applications, and reduce risk of data falsification.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".