A Comparative Study of Strategies for Incorporating Uncertainty in Design Space Determination for Pharmaceutical Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it