A Comparative Study of Strategies for Incorporating Uncertainty in Design Space Determination for Pharmaceutical Manufacturing
Bibliographic record
Abstract
A case study concerned with batch synthesis of 2,6-difluoropurine-9-tetrahydropyran (THP) is used to compare the effectiveness of different methods for determining design spaces (DSs) where process operation results in satisfactory product quality. A mechanistic model is used to map a deterministic design space (DDS) and various probabilistic design spaces (PDSs). Uncertainties in model parameters, process parameters, and final measurement errors for quality variables lead to the shrinkage of the DDS, helping to avoid undesirable process outcomes. This case study reveals that ignoring correlated effects of model parameters and ignoring model nonlinearity leads to unreliable results. By contrast, parametric bootstrapping, which accounts for nonlinearity and parameter correlation, provides reliable information about the influence of uncertain parameters on the DS. For this case study, model parameter uncertainty reduces the size of the DS by ∼5%. Incorporating uncertainty in key process parameters further reduces the size of the DS by ∼20%. Additional shrinkage of the DS by ∼12% occurs when uncertainties in final quality variables are considered. The largest rectangular region within the resulting DS is obtained using optimization. Contour plots reveal that operation at the center of this rectangular region would lead to a ∼2% reduction in yield compared with operation at other satisfactory points in the DS. This comparative analysis offers important guidance for selecting approaches for handling uncertainties when constructing DSs for pharmaceutical development. The value of integrating mechanistic modeling, robust uncertainty quantification, and optimization for reliable DS determination is illustrated.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".