Determining the ototoxic potential of COVID-19 therapeutics using machine learning and in vivo approaches

使用机器学习和体内方法确定 COVID-19 疗法的耳毒性潜力

基本信息

批准号：
10732745
负责人：
ALLISON B COFFIN
金额：
$ 40.03万
依托单位：
WASHINGTON STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-07 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10732745
关键词：
Adult Animals Auditory Azithromycin Biological Assay Biometry Blood COVID-19 COVID-19 patient COVID-19 screening COVID-19 therapeutics COVID-19 treatment Case Study Categories Cause of Death Chemical Structure Clinical Clinical Trials Cochlea Computer Models Computers Data Data Set Databases Detection Development Disease Drug Combinations Drug usage FDA approved Goals Hair Cells Hearing Tests Human Human Resources Hydroxychloroquine Immune response Immune system In Vitro Individual Infection Intervention Ivermectin Labyrinth Life Link Machine Learning Methods Modeling Molecular Monitor Morphology Organ Patient Monitoring Patients Pharmaceutical Chemistry Pharmaceutical Preparations Pharmacologic Substance Pharmacotherapy Physiological Physiology Positioning Attribute Preclinical Testing Principal Component Analysis Public Health Publishing Rattus Reporting Research Risk Ritonavir Scientist Sensory Sensory Hair Stavudine Supporting Cell Synapses System Testing Therapeutic Therapeutic Uses Toxic effect Training Treatment Efficacy Vaccination Work Zebrafish anti-viral efficacy combat cost cytotoxic cytotoxicity drug candidate drug development experimental study feature selection hearing impairment in silico in vivo innovation lateral line machine learning model novel novel therapeutics older patient otoprotectant ototoxicity ototoxin pandemic disease pre-clinical preservation rapid detection research clinical testing response screening severe COVID-19 side effect systemic inflammatory response

项目摘要

Project Summary/Abstract There are over 900 drugs and drug combinations currently in clinical trials for COVID-19. While this pace of drug development is necessary, it also comes with increased risk of producing therapies with significant side- effects. One likely side-effect of some COVID-19 drugs is hearing loss. The potential of drugs to cause hearing loss is typically unassessed during drug development or clinical testing. Our research will rapidly assess COVID-19 drugs for ototoxic potential, as per NOT-DC-20-008 to determine the “Potential ototoxicity from therapeutics or vaccination related to COVID-19.” Our goal is to promote the development of safe COVID-19 drugs with minimal side effects. Some drugs in clinical trials for COVID-19 are associated with hearing loss but the ototoxic potential for these drugs is based on individual case reports or in vitro experiments so the true ototoxic burden is largely unknown. We should not use patients as a testbed for a life-altering negative side- effect when there are rapid low-cost alternatives available. The objective of this proposal is to rapidly identify the ototoxic potential of COVID-19 therapeutics using both in silico and in vivo approaches. We will achieve this objective with three Specific Aims: 1) Predict ototoxic potential of COVID-19 therapies with Machine Learning (ML), 2) Determine the relative hair cell toxicity of COVID-19 therapies in the zebrafish lateral line, and 3) Determine the degree to which predicted ototoxins cause hearing loss in rats. Our innovative ML model correctly categorizes ototoxins vs. non-ototoxins with 87% accuracy. In this project we will employ our current model for immediate ototoxicity detection and further optimize the model for better predictive accuracy. In parallel with the ML model, we will screen COVID-19 therapeutics in the larval zebrafish lateral line, which is an excellent model for rapid ototoxicity screening. Prior work by our group and others demonstrates the validity of the lateral line as a platform for effective ototoxin discovery. Finally, we will validate predicted ototoxins from Aims 1 and 2 in rats using both physiological and morphological assays. Our research is significant because we will determine the ototoxic potential of new or repurposed therapeutics for COVID-19, which can inform efforts to 1) advance effective candidates that are not ototoxic, 2) modify successful yet ototoxic COVID-19 drugs and/or develop otoprotective co-therapies to preserve therapeutic efficacy while minimizing ototoxic side- effects, and 3) determine which patients require audiometric monitoring due to the drugs they receive. Our team includes experts in ototoxicity, medicinal chemistry, biostatistics, machine learning, and clinical expertise in large-scale human trials for COVID-19 therapies. Our research is highly likely to identify ototoxic COVID-19 drugs, facilitating development of safer pharmacotherapies to combat this deadly pandemic. Further, many drugs in clinical trials for COVID-19 are already approved for other indications. Our research will therefore provide important data about drugs in clinical use for non-COVID-related disease, adding additional value.

项目概要/摘要目前有超过 900 种药物和药物组合正在进行 COVID-19 的临床试验。药物开发是必要的，但它也伴随着生产具有显着副作用的疗法的风险增加某些 COVID-19 药物可能产生的副作用之一是听力损失。在药物开发或临床测试过程中通常不会评估损失。我们的研究将快速评估。具有潜在耳毒性的 COVID-19 药物，根据 NOT-DC-20-008 确定“潜在耳毒性” 与 COVID-19 相关的治疗或疫苗接种。” 一些正在针对 COVID-19 进行临床试验的药物与听力损失有关，但这些药物的耳毒性潜力是基于个体病例报告或体外实验，因此真实的耳毒性负担在很大程度上是未知的，我们不应该利用患者作为改变生活的负面影响的试验台。当有快速、低成本的替代方案可用时，该提案的目的是快速确定。我们将使用计算机和体内方法来实现 COVID-19 疗法的耳毒性潜力。该目标具有三个具体目标：1) 使用 Machine 预测 COVID-19 疗法的耳毒性潜力学习 (ML)，2) 确定 COVID-19 疗法在斑马鱼侧线的相对毛细胞毒性， 3) 确定预测的耳毒素导致大鼠听力损失的程度。正确分类耳毒素与非耳毒素，准确度为 87% 在此项目中，我们将采用当前的方法。用于立即耳毒性检测的模型，并进一步优化模型以获得更好的预测准确性。与 ML 模型并行，我们将在幼虫斑马鱼侧线中筛选 COVID-19 疗法，这是一种我们小组和其他人之前的工作证明了快速耳毒性筛查的优秀模型。最后，我们将验证预测的耳毒素。我们的研究在大鼠中使用生理学和形态学测定具有重要意义。我们将确定新的或重新调整用途的 COVID-19 疗法的耳毒性潜力，这可以为 1) 开发无耳毒性的有效候选药物，2) 修改成功但有耳毒性的 COVID-19 药物和/或开发耳保护联合疗法，以保持治疗效果，同时最大限度地减少耳毒性副作用效果，以及 3) 确定哪些患者因接受我们的药物而需要进行听力监测。团队包括耳毒性、药物化学、生物统计学、机器学习和临床专业知识方面的专家在针对 COVID-19 疗法的大规模人体试验中，我们的研究极有可能发现具有耳毒性的 COVID-19。此外，许多因此我们的研究将提供有关非新冠肺炎相关疾病临床使用药物的重要数据，增加附加价值。