A machine learning-based screen of marine natural products to identify new leads for the treatment of Acanthamoeba eye infection

基于机器学习的海洋天然产品筛选，以确定治疗棘阿米巴眼部感染的新线索

• 批准号: 10511577
• 负责人: Anjan Debnath
• 金额: $ 23.7万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10511577
• 关键词: Acanthamoeba; Acanthamoeba Keratitis; Actinobacteria class; Adverse effects; Agar; Algorithms; Amoeba genus; Automation; Bacteria; Biological Assay; Biology; Bioluminescence; Blindness; Chlorhexidine; Clinical; Collaborations; Computers; Contact Lenses; Cornea; Cyst; Data; Detection; Development; Drug Kinetics; Drug Screening; Emerging Communicable Diseases; Epithelial Cells; Eye Infections; Failure; Formulation; Funding; Future; Genotype; Gluconates; Goals; Human; Image; In Vitro; Individual; Infection; Keratitis; Kidney; Laboratories; Lead; Left; Libraries; Literature; Liver; Machine Learning; Mammalian Cell; Manuals; Methods; Microbiology; Miniaturization; Modernization; Motivation; National Institute of Allergy and Infectious Disease; Natural Product Drug; Natural Products; Natural Products Chemistry; Outcome; Pain; Parasites; Parasitology; Patients; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Predisposition; Proliferating; Recurrence; Research; Research Personnel; Sampling; Specificity; Speed; Structure; Techniques; Testing; Tissues; Training; Trauma; Visual impairment; Work; aggressive therapy; antimicrobial; base; convolutional neural network; corneal epithelium; cytotoxicity; deep ocean; drug development; drug discovery; effective therapy; efficacy study; excystation; experience; improved; in vivo; inhibitor; innovation; interest; marine natural product; microbial; microscopic imaging; neural network; novel; opportunistic pathogen; polyhexamethylene biguanide; recurrent infection; resilience; screening; skills; therapeutically effective; therapy development; tool

PROJECT SUMMARY Painful blinding keratitis is caused by the free-living amoeba Acanthamoeba and can occur in healthy individuals wearing contact lenses. Acanthamoeba can exist as a trophozoite or cyst and both stages are able to cause Acanthamoeba keratitis. While effective therapies, such as chlorhexidine gluconate and polyhexamethylene biguanide, exist to treat Acanthamoeba keratitis, the trophozoites can encyst in the ocular tissue to resist current therapies. Infection recurrence occurs in approximately 10% of cases due to the lack of efficient drugs that can kill both trophozoites and cysts. Therefore, discovery of therapeutics that are effective against both stages is a critical unmet need to avert blindness. The urgency of the issue is underscored by the NIAID's listing of acanthamoebiasis as an Emerging Infectious Disease. Current efforts to identify new anti-Acanthamoeba compounds rely primarily upon trophocidal assays that target the trophozoite stage of the parasite. Standard cysticidal assays are laborious and depend on manual observation of compound-treated cysts. Considering the manual and low-throughput approaches used in the cysticidal assays, we hypothesized that any development in automation and miniaturization could significantly increase the throughput of cysticidal drug screens to yield new cysticidal compounds. We adapted and trained a YOLOv3 machine learning object-detection neural network to recognize A. castellanii trophozoites and cysts in microscopy images. We utilized this trained neural network as a tool to count excysted trophozoites in compound-treated wells to determine if a compound was cysticidal. We validated this novel screen with literature-relevant cysticidal and non-cysticidal reference compounds by determining their minimum cysticidal concentrations. Our machine learning-based cysticidal assay improved throughput, demonstrated high specificity and an exquisite ability to identify non-cysticidal compounds. We combined this cysticidal assay with our bioluminescence-based trophocidal assay to screen about 9,000 structurally unique marine microbial metabolites against A. castellanii. Our preliminary screen identified a marine microbial metabolite that was both trophocidal and cysticidal. Based on these data, we propose to utilize our machine learning-based high-throughput cysticidal assay to 1) screen >20,000 marine microbial natural products against trophozoites and cysts of a reference strain, and isolate, dereplicate and assign the structures of the active compounds, 2) evaluate susceptibility of trophozoites and cysts of a reference strain, and relevant mammalian cells to purified compounds, and 3) confirm trophocidal and cysticidal activities of less toxic purified compounds against multiple genotypes of Acanthamoeba. The goal of this work will be to identify 1-3 molecules that are potent inhibitors of A. castellanii trophozoites and cysts. To successfully achieve the aims, we rely on our collaboration that combines the unique expertise of Dr. Debnath (PI) in Acanthamoeba parasite biology and Dr. Fenical in marine natural products drug discovery (Co-Investigator). Drs. Debnath and Fenical's expertise and experience has potential to elevate our drug discovery platform to a translational level.

项目总结 痛性失明角膜炎是由自由生活的阿米巴棘阿米巴引起的，可发生在健康人身上。 戴着隐形眼镜。棘阿米巴可以滋养体或包囊的形式存在，这两个阶段都能引起 棘阿米巴角膜炎。而有效的治疗方法，如葡萄糖酸氯己定和聚六亚甲基 双胍，用于治疗棘阿米巴角膜炎，滋养体可以包裹在眼组织中抵抗电流 治疗。由于缺乏有效的药物，大约10%的病例会出现感染复发 杀死滋养体和包囊。因此，发现对这两个阶段都有效的疗法是一种 避免失明的关键未得到满足的需要。NIAID列出的这一问题突显了问题的紧迫性 棘阿米巴病作为一种新发传染病。目前寻找新的抗棘阿米巴的努力 化合物主要依赖于针对寄生虫滋养体阶段的杀滋养体试验。标准 杀囊试验费时费力，且依赖人工观察经复合处理的包囊。考虑到 人工和低通量方法用于杀膀胱法，我们假设在 自动化和小型化可以显著增加杀囊剂筛选的吞吐量，以产生新的 杀囊藻类化合物。我们调整和训练了一个YOLOv3机器学习目标检测神经网络 在显微镜图像中识别出卡氏锥虫滋养体和包囊。我们利用这个经过训练的神经网络作为 一种工具，用来计算复合处理井中过量的滋养体，以确定一种化合物是否具有杀囊作用。我们 用文献相关的杀囊剂和非杀囊剂参考化合物验证了这一新的筛选 确定它们的最低杀囊浓度。我们改进了基于机器学习的杀囊剂检测方法 吞吐量，表现出高度的特异性和精湛的能力来鉴定非杀囊类化合物。我们 将这种杀膀胱法与我们基于生物发光的杀虫试验相结合，筛选出约9000 结构独特的海洋微生物代谢物对赤霉菌的抑制作用。我们的初步筛查确认了一名海军陆战队队员 一种微生物代谢物，既能杀死滋养体，又能杀死孢子虫。基于这些数据，我们建议利用我们的 基于机器学习的高通量杀囊试验筛选2万种海洋微生物天然产物 针对参考菌株的滋养体和包囊，并分离、复制和指定 活性化合物，2)评估滋养体和参考菌株包囊的敏感性，并与 对哺乳动物细胞的纯化化合物，以及3)证实了毒性较低的纯化化合物的杀毛滴虫和杀膀胱活性 抗多基因棘阿米巴的化合物。这项工作的目标将是识别1-3个分子 对卡氏疟原虫滋养体和包囊有很强的抑制作用。为了成功地实现这些目标，我们依靠 我们的合作结合了Debnath博士(Pi)在棘阿米巴寄生虫生物学和 Fenical博士从事海洋天然产物药物发现(联合调查员)。Debnath博士和Fenical博士的专业知识 经验有可能将我们的药物发现平台提升到翻译的水平。