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

• 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
• 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

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.

项目总结/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。虽然这种速度 药物开发是必要的，它也伴随着生产具有显著副作用的治疗的风险增加， 方面的影响.一些COVID-19药物的一个可能的副作用是听力损失。药物引起听力的可能性 在药物开发或临床试验期间通常不评估损失。我们的研究将迅速评估 根据NOT-DC-20-008，用于潜在耳毒性的COVID-19药物，以确定“来自 与COVID-19相关的治疗或疫苗接种。”我们的目标是促进安全COVID-19的发展 副作用最小的药物。一些用于COVID-19临床试验的药物与听力损失有关， 这些药物的耳毒性潜力是基于个别病例报告或体外实验， 耳毒性负荷在很大程度上是未知的。我们不应该把病人作为改变生活的负面因素的试验台- 当有快速低成本的替代品可用时，本提案的目的是迅速查明 使用计算机模拟和体内方法研究COVID-19治疗剂的耳毒性潜力。我们将实现 该目标有三个具体目的：1）预测COVID-19治疗的耳毒性潜力 学习（ML），2）确定COVID-19疗法在斑马鱼侧线中的相对毛细胞毒性， 和3）确定预测的耳毒素导致大鼠听力损失的程度。我们的创新ML模型 正确分类耳毒素与非耳毒素，准确率为87%。在这个项目中，我们将使用我们目前的 该模型用于即刻耳毒性检测，并进一步优化模型以获得更好的预测准确性。在 与ML模型平行，我们将在斑马鱼幼虫侧线中筛选COVID-19治疗剂，这是一种 快速筛选耳毒性的优良模型。我们小组和其他人先前的工作证明了 侧线作为有效发现耳毒素的平台。最后，我们将验证预测的耳毒素， 目的1和2在大鼠中使用生理学和形态学测定。我们的研究意义重大，因为 我们将确定新的或重新利用的COVID-19治疗药物的耳毒性潜力，这可以为我们提供信息， 努力1）推进无耳毒性的有效候选物，2）修改成功但有耳毒性的COVID-19 药物和/或开发耳保护性联合治疗，以保持治疗效果，同时最大限度地减少耳毒性副作用， 影响，和3）确定哪些患者由于他们接受的药物而需要听力监测。我们 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 用于大规模的COVID-19治疗人体试验。我们的研究极有可能发现耳毒性COVID-19 药物，促进开发更安全的药物疗法，以对抗这一致命的流行病。此外，许多 用于COVID-19临床试验的药物已经被批准用于其他适应症。我们的研究将 为临床上用于非COVID相关疾病的药物提供重要数据，增加额外价值。