New statistical and computational tools for optimization of planarian behavioral chemical screens

目标。迫切需要开发高通量筛选(HTS)非动物模型来取代， 改进和/或减少(“3Rs”)脊椎动物毒理学测试。尤其是高温超导模型的发展 挑战神经毒性(NT)和发育神经毒性(DNT)，其中 不良后果需要在整个生物体水平上进行评估。这项研究的首要目标是 是开发一种非动物生物HTS方法来识别NT和DNT。具体目标是 确定使用最先进的计算方法是否会提高诊断的敏感性和特异性 使用国家毒理学计划87-化合物文库鉴定NT和DNT的脊椎动物HTS (NTP87)。作为NTP神经毒理学筛查策略倡议的合作伙伴，我们之前筛查了 无性淡水中已知和疑似发育神经毒物的NTP87文库 使用9个读数的日本涡虫。利用这个文库，我们证明了脊椎动物的HTS可以识别 已知的(发育)神经毒物，并在斑马鱼发育过程中为筛查增加补充价值。 与线虫和线虫相比，平面动物是中等神经和解剖学复杂性的无脊椎动物 斑马鱼，有易驯服的，进化上保守的神经元电路。行星动物独一无二地允许直接 异种生物对成人神经系统和发育中神经系统影响的比较。对于无性的日本金龟子，它 通过分裂繁殖，神经再生是神经发育的唯一方法，也是保守的关键 脊椎动物神经发育的事件。这些功能和我们以前的工作证明了 用于潜在神经毒物的第一层筛选的脊椎动物高温超导。我们假设我们可以增加 通过使用最新状态重新分析我们的NTP87数据来评估这个非动物模型的敏感性和特异性 艺术机器学习和统计工具。 实验方法。在目标1中，我们将使用18个新的行为和10个新的行为重新分析原始数据 使用计算机视觉和机器学习的形态读数。因此，我们总共分析了37个读数 在完整和再生的生物体中，以5种浓度进行评估。在目标2中，我们将重新分析效力 包括使用基准集中法的所有37个读数，这些读数具有经验确定的终点- 具体的基准响应。这一分析将克服与最低- 先前应用的观察到的效果级别。在目标3中，我们最初将使用贝叶斯统计模型 开发了针对斑马鱼胚胎的筛查，以获得整体毒性总结分数并评估相对 不同读数对浮游生物系统预测能力的重要性。 预期结果。通过将非动物生物行为HTS与最先进的分析技术相结合 方法，该项目将支持开发一种非动物生物HTS方法，该方法可以 与预测生物信息学相结合，以满足填补DNT数据空白的迫切需求。

Objectives. There is an urgent need to develop high-throughput screening (HTS) non-animal models to replace, refine, and/or reduce (“3Rs”) vertebrate toxicology testing. The development of HTS models is especially challenging for neurotoxicity (NT) and developmental neurotoxicity (DNT) where the functional relevancy of adverse outcomes needs to be assessed on the whole organism level. The overarching goal of this research is to develop a non-animal organismal HTS methodology to identify NT and DNT. The specific objective is to determine whether using state-of-the-art computational approaches will increase sensitivity and specificity of planarian HTS to identify NT and DNT using the verified National Toxicology Program 87-compound library (NTP87). As a partner in the NTP Neurotoxicology Screening Strategies Initiative, we previously screened the NTP87 library consisting of known and suspected developmental neurotoxicants in the asexual freshwater planarian Dugesia japonica using 9 readouts. Using this library, we demonstrated that planarian HTS can identify known (developmental-) neurotoxicants and adds complementary value to screens in developing zebrafish. Planarians are invertebrates of intermediate neural and anatomical complexity compared to nematodes and zebrafish and have tractable, evolutionarily conserved neuronal circuits. Planarians uniquely allow for direct comparison of xenobiotic effects on the adult and developing nervous systems. For asexual D. japonica, which reproduce via fission, neuroregeneration is the sole method of neurodevelopment and shares conserved key events with vertebrate neurodevelopment. These features and our previous work demonstrate the value of planarian HTS for first-tier screening of potential neurotoxicants. We hypothesize that we can augment sensitivity and specificity of this non-animal model by re-analyzing our NTP87 data using state-of-the- art machine learning and statistical tools. Experimental approach. In Aim 1, we will re-analyze the raw data using 18 new behavioral and 10 new morphological readouts using computer vision and machine learning. Thus, in total we assay 37 readouts evaluated at 5 concentrations, in intact and regenerating organisms. In Aim 2, we will re-analyze potency including all 37 readouts using a benchmark concentration approach with empirically determined, endpoint- specific benchmark responses. This analysis will overcome the intrinsic limitations associated with lowest- observed-effect levels that was previously applied. In Aim 3, we will use a Bayesian statistical model originally developed for zebrafish embryo screens to obtain a holistic toxicity summary score and evaluate the relative importance of the different readouts for the predictive capabilities of the planarian system. Expected results. By combining non-animal organismal behavioral HTS with state-of-the-art analytical approaches, this project will bolster the development of a non-animal organismal HTS methodology that can be integrated with predictive bioinformatics to meet the urgent need to fill the DNT data gap.