Zhang Moran, Li Qianqian, Li Shunhang, Sun Binxian, Wu Zhuli, Liu Jinxuan, Geng Xingchao, Chen Fangyi
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
National Center for Safety Evaluation of Drugs (NCSED), National Institutes for Food and Drug Control, Beijing 102629, China.
Toxics. 2025 Jul 14;13(7):586. doi: 10.3390/toxics13070586.
Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability-critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose-response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics.
物质使用障碍，尤其是阿片类药物成瘾，仍然是全球主要的健康和毒理学挑战。吗啡依赖在临床实践和临床前研究中都是一个重大问题，特别是在模拟戒断的药效学方面。啮齿动物模型对于研究慢性阿片类药物暴露和戒断的神经毒理学效应仍然不可或缺。然而，传统的行为评估依赖于人工观察，限制了现代药物毒性评估中的客观性、可重复性和可扩展性等关键因素。本研究介绍了MWB_Analyzer，这是一种自动化的高通量系统，旨在定量、客观地评估大鼠的吗啡戒断行为。目标是通过强大、可扩展的行为表型分析来加强对中枢神经系统活性物质的毒理学评估。MWB_Analyzer集成了优化的多角度视频捕获、实时信号处理和机器学习驱动的行为分类。开发了一种改进的基于YOLO的架构，用于在视频帧中准确检测和分类与戒断相关的行为，同时一个并行管道处理音频信号。该系统纳入了特定行为的持续时间阈值，以分离药理学和毒理学相关的行为事件。将实验动物分为高剂量组、低剂量组和对照组。在标准化的毒理学方案下诱导并监测戒断情况。MWB_Analyzer在冗余帧处理方面减少了95%以上，显著提高了计算效率。它表现出高分类准确率：基于视频的行为准确率>94%（在边缘设备上为93%），基于音频的事件准确率>92%。使用行为阈值能够敏感地区分剂量组，揭示明显的剂量反应关系，并支持其在神经药理学和神经毒理学分析中的应用。MWB_Analyzer为啮齿动物模型中阿片类药物戒断综合征的自动化评估提供了一个强大、可重复和客观的平台。它提高了成瘾研究的通量、精度和标准化。重要的是，该工具支持对中枢神经系统药物作用的毒理学研究、临床前药代动力学和药效学评估、药物安全性分析以及新型阿片类药物和中枢神经系统活性治疗药物的监管评估。
