Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs

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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Publication Details

Journal
Biomimetics
Published
2025-11-24
DOI
https://doi.org/10.3390/biomimetics10120796
Citations
3
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
3.14
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article

Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs

Tooba Yousefi, Maryam Sadat Mirlohi, Amir Seyfoori, Amir Reza Aref
3 citations
Biomimetics
Cancer Cells and Metastasis
3.14
article

Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs

Tooba Yousefi, Maryam Sadat Mirlohi, Amir Seyfoori, Amir Reza Aref
article en
3 citations

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.

BiomimeticsVol. 10(12)
University of Victoria (CA), Massachusetts General Hospital (US), Shahid Beheshti University of Medical Sciences (IR)
Openalex Percentile: Top 7%
Cancer Cells and Metastasis
3.14
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Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs — Tooba Yousefi, Maryam Sadat Mirlohi, et al. · Biomimetics (2025) | TGRS Research Map | TGRS