Integrating New Approach Methodologies and Artificial Intelligence to Advance Central Nervous System Toxicity Prediction: Lessons from Preclinical and Clinical Case Studies

Abstract

Central nervous system (CNS) toxicities remain a major cause of drug attrition and represent a persistent challenge in predicting neurological risk during drug development. Limitations in the predictive resolution and translational relevance of conventional nonclinical paradigms contribute to uncertainty in identifying and interpreting neurotoxicity signals. This manuscript examines key challenges in CNS safety assessment and highlights emerging strategies to improve early detection and prediction of neurological risk. Through a series of case studies, we demonstrate practical approaches for interpreting CNS safety signals and integrating emerging methodologies into nonclinical safety assessment. Examples include sensory and seizure-related endpoints in nonclinical studies and the use of electroencephalography (EEG) to improve detection and characterization of seizure liability. We also highlight the expanding role of advanced sensor technologies and artificial intelligence (AI) in enabling continuous, non-invasive monitoring of animal behavior. In addition, an Integrated Approach to Testing and Assessment (IATA) case study demonstrates how systematic integration of mechanistic data, traditional toxicology findings, and exposure modeling can support regulatory decision-making while aligning with the 3Rs principles (replace, reduce, refine animal testing). Finally, we present regulatory CNS case studies in drug development. Collectively, these approaches enable quantitative assessment of neurological function across circadian cycles, reduce reliance on episodic observer-dependent measurements, and illustrate how integrating refined in vivo methods with New Approach Methodologies (NAMs) and digital technologies can improve prediction of neurological risk and strengthen translation from nonclinical findings to human outcomes in CNS drug development.