Mitigating the Risks of Generic Drug Product Development: An Application of Quality by Design (QbD) and Question based Review (QbR) Approaches.
Bibliographic record
Abstract
This paper discusses the challenges and advantages of implementing Quality by Design (QbD) and Question based Review (QbR) when developing solid dosage formulations and manufacturing processes for generic drugs. Formulation and process development of a drug product is challenging due to the inherent variability of the processes. Regulatory agencies, such as the Food and Drug Administration (FDA) in the USA, demand a QbD approach when developing formulations and processes for new and existing medicinal products. The QbD approach is described in the International Conference on Harmonization (ICH) Guidance Q8 (R2). The regulatory reviewers follow the QbR approach during the review of Chemistry, Manufacturing, and Controls (CMC), which have also adopted some of the elements of the QbD guidance. A systematic application of scientific principles for developing the formulations and processes for generic drug products following the QbD approach is outlined below in three main categories. The categories are product understanding, process understanding, and control strategy. The concept of predefined objectives, quality risk management, and CMC considerations together with the prior knowledge are discussed in detail. The discussions and explanations provided in this paper are based on sound scientific principles, as well as, practical experience applied to resolve product quality and manufacturing issues. Emphasis is given to streamlining formulation and process development that complies with current QbD and QbR principles in order to prevent commonly cited deficiencies. Examples are provided as guiding tools for generic formulation and process development.
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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 | 0.311 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".