The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.

中文翻译：

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SARS-CoV-2 感染的形态细胞分析确定了 COVID-19 的药物再利用候选者。

严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 和相关疾病 COVID-19 的全球传播需要能够迅速转化为临床护理的治疗干预措施。不幸的是，传统的药物发现方法的失败率 > 90%，从目标识别到临床使用可能需要 10-15 年。相比之下，药物再利用可以显着加速翻译。我们开发了一种定量高通量筛选，以识别针对 SARS-CoV-2 的有效单一药物和联合疗法。定量高内涵形态分析与基于人工智能的机器学习策略相结合，对细胞特征进行感染和压力分类。该测定检测到多种抗病毒作用机制 (MOA)，包括抑制病毒进入、传播、和调节宿主细胞反应。从 1,425 种 FDA 批准的化合物和临床候选药物库中，我们确定了 16 种具有抗病毒作用的剂量反应性化合物。特别是，我们发现乳铁蛋白是一种有效的 SARS-CoV-2 感染抑制剂，IC50 为 308 nM，并且它增强了瑞德西韦和羟氯喹的功效。乳铁蛋白还刺激抗病毒宿主细胞反应，并在 iPSC 衍生的肺泡上皮细胞（一种主要感染部位的模型）中保留抑制活性。鉴于其在人体中的安全性，这些数据表明乳铁蛋白是一种易于转化的 COVID-19 治疗辅助剂。此外，发现几种常用处方药会加剧病毒感染，需要进行临床研究。