Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems
Bibliographic record
Abstract
Experimental design and robust design (RD) methodologies have received attention from researchers to improve the performance of many different quality characteristics and solve problems at low costs. However, there is room for improvement to simultaneously solve interdisciplinary optimization problems associated with time-oriented, multiple, and hierarchical responses. This paper proposes a new RD modeling and optimization algorithm for drug development based on three research motivations: Firstly, customized experiments and estimation frameworks for representing pharmaceutical quality characteristics (i.e., time-oriented, multiple, and hierarchical responses) and functional relationships between input factors and hierarchical time-oriented output responses are proposed. Secondly, new hierarchical time-oriented robust design (HTRD) optimization models (i.e., priority-based, weight-based, and integrated models) are developed for these interdisciplinary pharmaceutical formulation problems. Finally, the pharmaceutical case study for drug formulation development is conducted for demonstration purposes. Based on the case study results, the proposed approach can provide optimal solutions with significantly small biases and variances.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".