Advancing regulatory dialogue: In silico models for improved vaccine biomanufacturing - an expert meeting report
Bibliographic record
Abstract
On March 30, 2022, Inno4Vac, a public-private partnership funded by the IMI2/EU/EFPIA Joint Undertaking (IMI2 JU), organised a hybrid workshop, titled "Regulatory Dialogue for Road Maps of Implementation of New Tools in Chemistry, Manufacturing, and Controls Dossiers." This event brought together modellers, regulatory experts, and academic and industry professionals specialising in vaccine process and product development. The sessions discussed key parameters and requirements for model development and verification relevant to vaccine biomanufacturing and shelf life. The stability model was highlighted as having the most significant impact on the common technical document (CTD) due to its potential to streamline data requirements. Regulators are open to considering reliable reduced stability data packages (3-12 months) instead of the standard 36 months, potentially expediting product availability. Appropriate study design reduces uncertainty and therefore the risk of making poor decisions. Upstream models are further from the final product, and their role in the control strategy of the product will define their level of risk and, therefore, requirements for validation and inclusion of information in the file. Regulators may consider downstream models high risk as these can be associated with the monitoring and/or control of critical quality attributes and/or be involved in the release of a product. However, requirements for validation and/or dossier content should always be linked to the intended use of the model and its overall role in the control strategy as per the new EMA Quality Innovation Group Considerations regarding Pharmaceutical Process Models. The success of these models hinges on manufacturers providing enough quality data to prove their accuracy in representing real-world processes. Proactive engagement with regulators, supported by detailed evidence, can foster regulator understanding of new models and potentially lead to new guidelines and pathways for model acceptance.
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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 | 0.054 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".