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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要痛苦的盲目角膜炎是由自由活动的变形虫acanthamoeba引起的，可以在健康的个体中发生穿着隐形眼镜。 acanthamoeba可以作为滋养体或囊肿存在，并且两个阶段都能引起阿斯塔莫巴角膜炎。虽然有效的疗法，例如氯己定和聚己二甲基 Biguanide，存在治疗阿甘塔米巴角膜炎的存在，滋养体可以在眼组织中封装以抵抗电流疗法。感染复发发生在大约10％的病例中，因为缺乏有效的药物杀死滋养体和囊肿。因此，发现在两个阶段有效的治疗剂是一个避免失明的严重未满足。该问题的紧迫性被NIAID的清单强调了阿斯塔木病作为一种新兴的传染病。目前为识别新的反acanthamoeba的努力化合物主要依赖于靶向寄生虫的滋养体阶段的人颗粒化测定。标准囊性测定很费力，并取决于对化合物处理的囊肿的手动观察。考虑到手动和低通量方法用于杀性测定法，我们假设自动化和微型化可以显着增加杀性药筛查的吞吐量，以产生新的囊性化合物。我们对Yolov3机器学习对象检测神经网络进行了调整和训练在显微镜图像中识别A. castellanii滋养体和囊肿。我们利用了这个训练有素的神经网络一种在化合物处理的井中计算脱粒的滋养体的工具，以确定化合物是否为囊性。我们通过与文学相关的囊性和非囊化参考化合物验证了这个新颖的屏幕确定其最小囊性浓度。我们的基于机器学习的囊性测定得到了改善吞吐量，表现出很高的特异性和精致的识别非囊性化合物的能力。我们将这种囊性测定与我们的生物发光的人颗粒化测定结合在一起，以筛选约9,000 在结构上独特的海洋微生物代谢产物针对曲曲霉。我们的初步屏幕确定了海军陆战队微生物代谢产物既是颗粒质又是囊性的。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性测定1）屏幕> 20,000海洋微生物天然产品针对参考应变的滋养体和囊肿，并分离，脱皮并分配了活性化合物，2）评估参考菌株的滋养体和囊肿的敏感性以及相关的哺乳动物细胞以纯化化合物，3）证实毒性较小的纯净纯化的营养和囊性活性针对阿甘达莫巴多种基因型的化合物。这项工作的目标是识别1-3个分子是Castellanii滋养体和囊肿的有效抑制剂。为了成功实现目标，我们依靠我们的合作结合了Acanthamoeba寄生虫生物学和海洋天然产品药物发现（共同评估器）的Fenical博士。博士。 Debnath和Fenical的专业知识经验有可能将我们的药物发现平台提升到转化水平。