Bibliographic record
Abstract
The study examines quality management principles relevant to modern drug discovery and development, with an emphasis on the economic and managerial consequences of inadequate quality in early-stage projects. The research novelty lies in the transfer of pharmaceutical quality system concepts, traditionally focused on manufacturing, into the upstream phases of target identification, hit and lead generation, and preclinical candidate selection. The article describes the main elements of pharmaceutical quality systems, including quality by design, quality risk management, and digital quality management platforms, and analyzes their applicability to discovery workflows. Particular attention is given to AI-enabled decision support, lifecycle-based risk management, and data-driven quality metrics. The objective of this work is to develop an integrated conceptual framework that links quality principles with portfolio decisions and early economic evaluation in drug discovery R&D. To achieve this objective, a narrative review, comparative analysis of regulatory guidance, and conceptual modeling methods are employed. The conclusion outlines managerial implications for pharmaceutical companies and research organizations. The article targets R&D managers, quality professionals, and project leaders working in pharmaceutical and early drug discovery settings.
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.
How this classification was reachedexpand
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".