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: 10669249
• 负责人: Anjan Debnath
• 金额: $ 19.75万
• 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/10669249
• 关键词: Acanthamoeba; Acanthamoeba Keratitis; Actinobacteria class; Adverse effects; Agar; Algorithms; Amoeba genus; Automation; Bacteria; Biological Assay; Biology; Bioluminescence; Blindness; Chlorhexidine; Classification; 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; 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; 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.

项目摘要 疼痛性致盲性角膜炎是由自由生活的阿米巴原虫引起的，可发生在健康个体中 戴着隐形眼镜阿米巴可以作为滋养体或包囊存在，这两个阶段都能引起 阿米巴角膜炎。虽然有效的治疗，如葡萄糖酸氯己定和聚乙烯醇， 双胍类，存在治疗阿米巴角膜炎，滋养体可在眼组织中成囊以抵抗电流 治疗感染复发发生在约10%的情况下，由于缺乏有效的药物， 杀死滋养体和包囊。因此，发现对这两个阶段都有效的治疗方法是一个挑战。 避免失明的关键未满足需求。该问题的紧迫性是由NIAID的名单强调， 棘阿米巴病是一种新出现的传染病。目前的努力，以确定新的抗阿米巴 化合物主要依赖于靶向寄生虫滋养体阶段的杀滋养体测定。标准 杀囊试验是费力的并且依赖于对化合物处理的囊的人工观察。考虑 人工和低通量方法用于杀囊试验，我们假设， 自动化和小型化可以显著增加杀囊药物筛选的通量， 杀囊化合物。我们调整并训练了YOLOv3机器学习对象检测神经网络， 认识到A. Castellanii滋养体和包囊。我们利用这个经过训练的神经网络， 计数化合物处理的威尔斯孔中脱囊滋养体以确定化合物是否杀囊的工具。我们 用文献相关的杀囊和非杀囊参比化合物验证了这种新的筛选， 确定它们的最小杀囊浓度。我们基于机器学习的杀囊试验得到改进 通量，证明了高特异性和鉴定非杀囊化合物的精确能力。我们 将这种杀囊试验与我们的基于生物发光的杀营养体试验相结合，筛选了约9，000个 针对A.的结构独特的海洋微生物代谢产物。castellanii。我们初步确认了一名海军陆战队员 微生物代谢产物，既杀营养又杀囊。根据这些数据，我们建议利用我们的 基于机器学习的高通量杀囊试验，以1）筛选> 20，000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、去复制和分配 活性化合物，2）评估参考菌株的滋养体和包囊的易感性，以及相关的 哺乳动物细胞对纯化化合物的杀伤活性，和3）证实了毒性较小的纯化化合物的杀滋养体和杀包囊活性。 化合物对抗多种基因型的阿米巴原虫。这项工作的目标将是确定1 - 3个分子 是A.卡氏滋养体和包囊。为了成功实现目标，我们依靠 我们的合作结合了Debnath博士（PI）在阿米巴寄生虫生物学方面的独特专业知识， 博士海洋天然产物药物发现（共同研究员）。Debnath和Fenical博士的专业知识 和经验有可能将我们的药物发现平台提升到转化水平。