Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning.
Med Drug Discov
; : 100148, 2022 Nov 29.
Article
in English
| MEDLINE | ID: covidwho-2240856
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARSCoV2) induced cytokine storm is the major cause of COVID19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear FactorKappa B (NFκB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARSCoV2 induced cytokine storm pathway. We developed machine learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.
1D 2D 3D, one- two- three-dimensional; ADAM17, A disintegrin and metalloprotease 17; ARDS, acute respiratory distress syndrome; AT1R, Angiotensin II receptor type 1; AUROC, area under receiver operator characteristic curve, COVID19, coronavirus disease 2019; COVID19; CRS, cytokine release syndrome; CXCL10, CXCchemokine ligand 10; FDA, Food and Drug Administration; GCSF, granulocyte colony stimulating factor; IC50, half maximal inhibitory concentration; ICU, intensive care unit; IL, interleukin; JAK1, Janus kinase 1; MCP1, monocyte chemoattractant protein1; MIP1α, macrophage inflammatory protein 1; ML, machine learning; NFκB, Nuclear FactorKappa B; PDB, Protein Data Bank; PaDEL, Pharmaeutical data exploration laboratory; ROC, receiver operator characteristic curve; SARSCoV2; SMILES, Simplified Molecular-Input Line-Entry System; STAT3, signal transducer and activator of transcription 3; TNFα, tumor necrosis factor α; WEKA, Waikato Environment for Knowledge Analysis; docking; machine learning; multi-targeted drug discovery; screening of FDA-approved drugs
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
Med Drug Discov
Year:
2022
Document Type:
Article
Affiliation country:
J.medidd.2022.100148
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