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Integrating new approach methodologies and artificial intelligence to advance central nervous system toxicity
Mamta Behl1, Fiona S Daly2,3, Helena T Hogberg2
1Preclinical and Clinical Pharmacology, Neurocrine Biosciences Inc, San Diego, CA 92130, United States.
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, noninvasive 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.
Insights
Predicting central nervous system (CNS) toxicities in drug development is challenging. This study explores advanced methods like AI and EEG to improve early detection of neurological risks, enhancing drug safety assessment.
Area of Science:
- Pharmacology
- Neuroscience
- Toxicology
Background:
- Central nervous system (CNS) toxicities are a significant hurdle in drug development, leading to high attrition rates.
- Current nonclinical methods for assessing neurotoxicity have limitations in predictive accuracy and translational relevance.
- Accurate prediction of neurological risk is crucial for safe and effective drug development.
Purpose of the Study:
- To examine the challenges in CNS safety assessment during drug development.
- To highlight emerging strategies for the early detection and prediction of neurological risks.
- To demonstrate practical approaches for interpreting CNS safety signals and integrating new methodologies.
Main Methods:
- Review of case studies focusing on sensory and seizure-related endpoints.
- Utilizing electroencephalography (EEG) for improved seizure liability detection.
- Application of advanced sensor technologies and artificial intelligence (AI) for continuous animal behavior monitoring.
- Implementation of an Integrated Approach to Testing and Assessment (IATA) combining mechanistic data, toxicology, and exposure modeling.
- Analysis of regulatory CNS case studies in drug development.
Main Results:
- Emerging strategies, including EEG and AI-powered monitoring, enhance the detection and characterization of CNS adverse effects.
- Integrated approaches (IATA) support regulatory decision-making and align with the 3Rs principles (replace, reduce, refine animal testing).
- Advanced methods enable quantitative assessment of neurological function and reduce reliance on subjective measurements.
Conclusions:
- Integrating refined in vivo methods with New Approach Methodologies (NAMs) and digital technologies improves the prediction of neurological risk.
- Enhanced CNS safety assessment strengthens the translation of nonclinical findings to human outcomes.
- These advancements are vital for overcoming challenges in CNS drug development and improving patient safety.
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