An online monitoring‐based adaptive quality control framework for a continuous pharmaceutical cyber‐physical system
Bibliographic record
Abstract
Abstract In Pharma 4.0, continuous pharmaceutical manufacturing is pivotal for quality control‐driven pharmaceutical development. Pharmaceutical quality control (PQC) in a continuous pharmaceutical cyber–physical system (PCPS) plays a crucial role in ensuring the quality of drug products. However, variations or disturbances in raw materials, equipment conditions, or environmental factors may lead to deviations in critical quality attributes of drugs from their acceptable ranges. This article introduces a generalized online monitoring‐based adaptive PQC framework for a continuous PCPS, structured around two phases—variational autoencoder‐based online process monitoring and data and knowledge fusion driven adaptive PQC based on a fuzzy‐rules emulated network. Next, a case study is presented to preliminarily explore the application of the proposed framework in a simulated pharmaceutical feeding–blending‐based twin screw granulation process. Finally, a series of simulation experiments are designed to verify the feasibility and effectiveness of the simulation modelling and the proposed PQC framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".