Sep 2026· Clinical pharmacology and therapy· 0 citations· 25 references
Medicine
TL;DR
In silico and in vitro NAMs including quantitative systems pharmacology (QSP) modeling for opioid overdose scenarios, (quantitative) structure–activity relationship ((Q)SAR) modeling for toxicity prediction and drug–drug interaction assessment, and multi‐omics approaches for pharmacodynamic biomarker discovery are described.
Abstract
New Approach Methodologies (NAMs) represent a paradigm shift in drug development and regulatory science, offering human‐relevant alternatives to traditional preclinical models. This mini‐review highlights recent advances in NAM development, validation, and regulatory application from FDA's Division of Applied Regulatory Science (DARS). We describe in silico NAMs including quantitative systems pharmacology (QSP) modeling for opioid overdose scenarios, (quantitative) structure–activity relationship ((Q)SAR) modeling for toxicity prediction and drug–drug interaction assessment, and multi‐omics approaches for pharmacodynamic biomarker discovery. Additionally, we present in vitro NAMs including human‐induced pluripotent stem cell (hiPSC)‐derived neural networks for opioid pharmacology assessment, microphysiological systems (MPS) for pulmonary drug permeability, gastrointestinal models for intestinal transport, and standardized cardiac ion channel and hiPSC‐CM MEA assays for proarrhythmia risk prediction. These case studies demonstrate how NAMs can reduce reliance on animal studies while improving translation to clinical outcomes. The NAMs described here range from exploratory research tools to approaches actively informing regulatory submissions review. The integration of in silico and in vitro NAMs into regulatory decision‐making frameworks represents a critical step toward more efficient, human‐relevant drug development and safety assessments.
In recent years, declining success rates and rising development costs have posed major challenges in drug development, highlighting the need for more efficient and rational approaches to evaluating drug efficacy and safety. Although non-clinical studies have traditionally relied primarily on animal experiments, adverse...
Animal-based preclinical testing is increasingly misaligned with the biological and regulatory demands of modern drug development. Persistent failure of drug candidates underscores the limitations of using animal models for human drug development. In oncology and neurodegeneration, where disease mechanisms and therapeu...
C. Meinert, Silvia Cometta, Peter A. Levett et al.· International Journal of Bio...· 0 citations
New approach methodologies (NAMs) encompass a diverse and rapidly evolving set of experimental and computational tools designed to generate human‐relevant mechanistic data for use in drug development and regulatory decision making. Experimental NAMs provide insights into drug disposition, pharmacological activity, and...
Karen Rowland Yeo, P. H. van der Graaf· Clinical pharmacology and th...· 0 citations
This review distills insights from a 2024 American Conference of Pharmacometrics session, highlighting real-world regulatory use cases including pediatric label extension, dose optimization in cell therapy, treatment duration for COVID-19 antivirals, and dosing strategies for T cell redirecting bispecific antibodies.
Wei-Rong Wang, Steven C. Chang, Mengdi Tao et al.· Quantitative Medicine· 0 citations
This review outlines the historical development and conceptual foundations of QSP and summarizes representative clinical applications spanning early, mid, and late development, and considers emerging challenges and opportunities for QSP adoption.
Wei-Rong Wang, Alexander V. Ratushny, Steven C. Chang et al.· Journal of clinical pharmaco...· 0 citations
In silico medicine, which depends on computational modeling and simulation,
is becoming more popular in drug discovery worldwide. Biomedical research is being
rapidly transformed by Artificial Intelligence (AI), but there are risks associated with regulatory
ambiguity. The integration of AI offers significant oppor...
S. Mutta, Naifa Faiz, T. N. et al.· Applied Drug Research, Clini...· 0 citations
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