Editorial: Model-informed decision making in the preclinical stages of pharmaceutical research and development
Bibliographic record
Abstract
Editorial on the Research Topic Model-informed decision making in the preclinical stages of pharmaceutical research and developmentAlthough late-stage clinical attrition has been long considered as the most significant issue facing the pharmaceutical industry, the probability of technical success in the clinic is largely related to decisions made years earlier in the preclinical stages of Research and Development (R&D); it is at these early stages that decisions are made regarding the molecular target, modality of intervention, drug design and clinical candidate selection.Accordingly, model-informed drug development approaches that have proven useful in the clinic (e.g., quantitative systems pharmacology (QSP) modeling, physiologically based pharmacokinetic (PBPK) modeling, pharmacokinetic-pharmacodynamic (PKPD) modeling) are increasingly leveraged to support decisions in the earlier preclinical stages of R&D.These advances, however, have not been well-represented in the literature.This topic illustrates efforts to apply modeling in target verification, lead compound optimization, clinical candidate selection, and human efficacious dose prediction, with an emphasis on how modeling and simulation is being used to advance hypothesis driven research and support decision making in preclinical research.As a collection, the papers included in this topic will allow researchers to better understand the impact and limitations that such modeling has in real-world drug research, and, in turn, facilitate insight and guidance for future research in quantitative pharmacological modeling and simulation.Presented as a high-level overview, authors from several pharmaceutical companies shared their collective experiences about how modeling and simulation approaches have been used to inform various decision points from discovery to first-in-human clinical trials (Kondic et al., 2022).Target validation is considered as one of the main areas where QSP can impact drug discovery, however adoption of this approach is slow due to the multiscale nature and complexity of typical QSP models (Chelliah and van der Graaf, 2022).Diving in
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.037 | 0.024 |
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".