Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

Heiser, Katie; McLean, Peter F.; Davis, Chadwick T.; Fogelson, Ben; Gordon, Hannah; Jacobson, Pamela; Hurst, Brett L.; Miller, Ben; Alfa, Ronald W.; Earnshaw, Berton; Victors, Mason; Chong, Yolanda T.; Haque, Imran S.; Low, Adeline S.; Gibson, Christopher C.

doi:10.1101/2020.04.21.054387

Cited by 64 publications

(83 citation statements)

References 20 publications

(19 reference statements)

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“…For example, our finding that SARS-CoV-2 infects CMs via an endolysosomal route may indicate that clinical trials which target TMPRSS2 to prevent COVID-19 42,43 may not afford effective cardioprotection without orthogonal targeting of endosomal proteases 44 . Additionally, although cell-based drug screens exist for many pathogens, including SARS-CoV-2 45 , the unique cytoarchitecture of cardiomyocytes and the specific impacts of SARS-CoV-2 on these cells offer novel screening possibilities. For example, myocyte-specific myofibrillar fragmentation enables cell-based drug screens with an exceptionally sensitive reporter of CM infection.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients

Pérez-Bermejo

Kang

Rockwood

et al. 2020

Preprint

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COVID-19 causes cardiac dysfunction in up to 50% of patients, but the pathogenesis remains unclear. Infection of human iPSC-derived cardiomyocytes with SARS-CoV-2 revealed robust transcriptomic and morphological signatures of damage in cardiomyocytes. These morphological signatures include a distinct pattern of sarcomere fragmentation, with specific cleavage of thick filaments, and numerous iPSC-cardiomyocytes that lacked nuclear DNA. Human autopsy specimens from COVID-19 patients also displayed marked sarcomeric disruption and similar fragmentation, as well as prevalently enucleated cardiomyocytes. These striking transcriptomic and cytopathic changes provide a roadmap to understand the mechanisms of COVID-19 cardiac damage, search for potential treatments, and determine the basis for prolonged cardiac morbidity observed in this pandemic.

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Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients

Pérez-Bermejo

Kang

Rockwood

et al. 2020

Preprint

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“…This research conducted its experiments based on the dataset presented in research (Heiser et al 2020). The dataset attribute description is presented in detail in Table 1.…”

Section: Datasets Characteristicsmentioning

confidence: 99%

RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

et al. 2020

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Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

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“…While most of the drug sets in the library are from studies that utilized computational methods, several key studies are from large-scale drug screens that include mostly Food and Drug Administration-approved drugs. 1 , 2 , 3 , 4 , 5 , 6 Using a Venn diagram, we compared the results from these six in vitro SARS-CoV-2 drug screen studies ( Figure 3 ). Overall, there is little overlap across these screens, with only 11 drugs shared across two or more studies ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…Drug-repurposing studies present a promising avenue for quickly offering a treatment because many of these drugs have known safety profiles. So far, drug-repurposing studies can be categorized into two groups, in vitro screens 1 , 2 , 3 , 4 , 5 , 6 and computational predictions. Computational predictions are mostly based on structural biology methods, 7 , 8 , 9 , 10 but some are based on network analysis and transcriptomics.…”

Section: Introductionmentioning

confidence: 99%

The COVID-19 Drug and Gene Set Library

et al. 2020

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In a short period, many research publications that report sets of experimentally validated drugs as potential COVID-19 therapies have emerged. To organize this accumulating knowledge, we developed the COVID-19 Drug and Gene Set Library ( https://amp.pharm.mssm.edu/covid19/ ), a collection of drug and gene sets related to COVID-19 research from multiple sources. The platform enables users to view, download, analyze, visualize, and contribute drug and gene sets related to COVID-19 research. To evaluate the content of the library, we compared the results from six in vitro drug screens for COVID-19 repurposing candidates. Surprisingly, we observe low overlap across screens while highlighting overlapping candidates that should receive more attention as potential therapeutics for COVID-19. Overall, the COVID-19 Drug and Gene Set Library can be used to identify community consensus, make researchers and clinicians aware of new potential therapies, enable machine-learning applications, and facilitate the research community to work together toward a cure.

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Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

Cited by 64 publications

References 20 publications

SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients

SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients

RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

The COVID-19 Drug and Gene Set Library

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