A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods Pub Date : 2024-08-24 DOI:10.1016/j.jneumeth.2024.110256

Danil A. Lukovikov , Tatiana O. Kolesnikova , Aleksey N. Ikrin , Nikita O. Prokhorenko , Anton D. Shevlyakov , Andrei A. Korotaev , Longen Yang , Vea Bley , Murilo S. de Abreu , Allan V. Kalueff

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引用次数: 0

Abstract

Background

Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications have empowered automated image recognition and video-tracking to ensure more efficient behavioral testing.

New method

Capitalizing on several AI tools that most recently became available, here we present a novel open-access AI-driven platform to analyze tracks of adult zebrafish collected from in vivo neuropharmacological experiments. For this, we trained the AI system to distinguish zebrafish behavioral patterns following systemic treatment with several well-studied psychoactive drugs - nicotine, caffeine and ethanol.

Results

Experiment 1 showed the ability of the AI system to distinguish nicotine and caffeine with 75 % and ethanol with 88 % probability and high (81 %) accuracy following a post-training exposure to these drugs. Experiment 2 further validated our system with additional, previously unexposed compounds (cholinergic arecoline and varenicline, and serotonergic fluoxetine), used as positive and negative controls, respectively.

Comparison with existing methods

The present study introduces a novel open-access AI-driven approach to analyze locomotor activity of adult zebrafish.

Conclusions

Taken together, these findings support the value of custom-made AI tools for unlocking full potential of zebrafish CNS drug research by monitoring, processing and interpreting the results of in vivo experiments.

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利用成年斑马鱼开发中枢神经系统药物的新型开放式人工智能驱动平台。

背景：尽管斑马鱼在生物医学中越来越多地被用于中枢神经系统疾病建模和药物发现，但由此产生的大数据需要客观、精确和可重复的分析。人工智能（AI）应用赋予了自动图像识别和视频跟踪功能，以确保更高效的行为测试：新方法：利用最近出现的几种人工智能工具，我们在此介绍一种新型的开放式人工智能驱动平台，用于分析从体内神经药理学实验中收集的成年斑马鱼的轨迹。为此，我们首先对人工智能系统进行了训练，以区分斑马鱼在接受几种经过充分研究的精神活性药物（尼古丁、咖啡因和乙醇）系统治疗后的行为模式：实验 1 显示，人工智能系统能以 75% 的概率区分尼古丁和咖啡因，并能以 88% 的概率和较高的准确率（81%）区分训练后暴露于这些药物的乙醇。实验 2 进一步验证了我们的系统，使用了更多以前未接触过的化合物（胆碱能类药物异丁苯丙酸和伐尼克兰，以及血清素能类药氟西汀），分别作为阳性对照和阴性对照：与现有方法的比较：本研究引入了一种新的开放式人工智能驱动方法来分析成年斑马鱼的运动活动：综上所述，这些研究结果支持定制人工智能工具的价值，通过监测、处理和解释体内实验结果，充分挖掘斑马鱼中枢神经系统药物研究的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Neuroscience Methods 医学-神经科学

CiteScore

7.10

自引率

3.30%

发文量

226

审稿时长

52 days

期刊介绍： The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.