Ireland Danielle, Rabeler Christina, Rao Sagar, Richardson Rudy J, Collins Eva-Maria S
Department of Biology, Swarthmore College, Swarthmore, Pennsylvania, United States of America.
Department of Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS One. 2025 Jan 30;20(1):e0315394. doi: 10.1371/journal.pone.0315394. eCollection 2025.
Mental illnesses put a tremendous burden on afflicted individuals and society. Identification of novel drugs to treat such conditions is intrinsically challenging due to the complexity of neuropsychiatric diseases and the need for a systems-level understanding that goes beyond single molecule-target interactions. Thus far, drug discovery approaches focused on target-based in silico or in vitro high-throughput screening (HTS) have had limited success because they cannot capture pathway interactions or predict how a compound will affect the whole organism. Organismal behavioral testing is needed to fill the gap, but mammalian studies are too time-consuming and cost-prohibitive for the early stages of drug discovery. Behavioral medium-throughput screening (MTS) in small organisms promises to address this need and complement in silico and in vitro HTS to improve the discovery of novel neuroactive compounds. Here, we used cheminformatics and MTS in the freshwater planarian Dugesia japonica-an invertebrate system used for neurotoxicant testing-to evaluate the extent to which complementary insight could be gained from the two data streams. In this pilot study, our goal was to classify 19 neuroactive compounds into their functional categories: antipsychotics, anxiolytics, and antidepressants. Drug classification was performed with the same computational methods, using either physicochemical descriptors or planarian behavioral profiling. As it was not obvious a priori which classification method was most suited to this task, we compared the performance of four classification approaches. We used principal coordinate analysis or uniform manifold approximation and projection, each coupled with linear discriminant analysis, and two types of machine learning models-artificial neural net ensembles and support vector machines. Classification based on physicochemical properties had comparable accuracy to classification based on planarian profiling, especially with the machine learning models that all had accuracies of 90-100%. Planarian behavioral MTS correctly identified drugs with multiple therapeutic uses, thus yielding additional information compared to cheminformatics. Given that planarian behavioral MTS is an inexpensive true 3R (refine, reduce, replace) alternative to vertebrate testing and requires zero a priori knowledge about a chemical, it is a promising experimental system to complement in silico cheminformatics to identify new drug candidates.
精神疾病给患者个人和社会带来了巨大负担。由于神经精神疾病的复杂性以及需要超越单分子 - 靶点相互作用的系统层面理解，识别用于治疗此类疾病的新型药物本质上具有挑战性。到目前为止，专注于基于靶点的计算机模拟或体外高通量筛选（HTS）的药物发现方法取得的成功有限，因为它们无法捕捉通路相互作用，也无法预测化合物将如何影响整个生物体。需要进行生物体行为测试来填补这一空白，但哺乳动物研究对于药物发现的早期阶段来说耗时过长且成本过高。小型生物中的行为中通量筛选（MTS）有望满足这一需求，并补充计算机模拟和体外HTS，以改进新型神经活性化合物的发现。在这里，我们在淡水涡虫日本三角涡虫（一种用于神经毒物测试的无脊椎动物系统）中使用了化学信息学和MTS，以评估从这两个数据流中可以获得互补见解的程度。在这项初步研究中，我们的目标是将19种神经活性化合物分类为它们的功能类别：抗精神病药、抗焦虑药和抗抑郁药。使用相同的计算方法进行药物分类，使用物理化学描述符或涡虫行为谱分析。由于事先不清楚哪种分类方法最适合这项任务，我们比较了四种分类方法的性能。我们使用主坐标分析或均匀流形近似与投影，每种方法都与线性判别分析相结合，以及两种类型的机器学习模型——人工神经网络集成和支持向量机。基于物理化学性质的分类与基于涡虫谱分析的分类具有相当的准确性，特别是对于所有准确率都在90% - 100%的机器学习模型。涡虫行为MTS正确识别了具有多种治疗用途的药物，因此与化学信息学相比产生了额外的信息。鉴于涡虫行为MTS是一种廉价的真正替代脊椎动物测试的3R（优化、减少、替代）方法，并且不需要关于化学物质的先验知识，它是一个有前途的实验系统，可以补充计算机模拟化学信息学以识别新的候选药物。
