Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs
Bibliographic record
Abstract
Traditional animal-based preclinical models, including xenografts and genetically engineered mice, have been used for assessing pharmacodynamics, toxicity, efficacy, and safety for decades. Despite their limited ability to mimic human tumor heterogeneity, immune interactions, and microenvironmental complexity, over 90% of oncology candidates that succeed in animal studies fail in clinical trials. The New Approach Methodologies (NAMs), which include patient-derived organoids, organ-on-chip platforms, and AI-driven computational models, provide human-relevant solutions that can improve predictive validity, mechanistic insight, and ethics. Through these technologies, it will be possible to replicate tumor biology specific to patients, to support co-clinical trial designs, and to facilitate biomarker discovery while reducing animal testing. Several recent regulatory reforms, including the Food and Drug Administration (FDA) Modernization Act 2.0 and the European Medicines Agency's NAM qualification framework, have established clear pathways for the integration of validated NAMs into preclinical drug evaluation. Critically evaluating the scientific rationale, comparative performance, and regulatory context of key NAM platforms in oncology, this review highlights opportunities for synergistic integration, technical refinement, and global harmonization in order to accelerate the development of clinically effective cancer therapeutics based on preclinical findings.
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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 | 0.053 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".