Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning
| Autor: | Maanaskumar R. Gantla, Igor F. Tsigelny, Valentina L. Kouznetsova |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
A disintegrin and metalloprotease 17
one- two- three-dimensional Angiotensin II receptor type 1 CXC-chemokine ligand 10 IC50 Nuclear Factor-Kappa B AT1R intensive care unit NF-κB Docking granulocyte colony stimulating factor STAT3 PaDEL Drug Discovery TNFα WEKA Pharmacology (medical) ROC Lung Area under receiver operator characteristic curve ADAM17 Waikato Environment for Knowledge Analysis AUROC interleukin CXCL10 MIP1α Food and Drug Administration receiver operator characteristic curve JAK1 machine learning Infectious Diseases macrophage inflammatory protein 1 5.1 Pharmaceuticals 1D 2D 3D signal transducer and activator of transcription 3 Multi-targeted drug discovery half maximal inhibitory concentration Development of treatments and therapeutic interventions FDA CRS Biotechnology PDB monocyte chemoattractant protein-1 G-CSF tumor necrosis factor α Simplified Molecular-Input Line-Entry System coronavirus disease 2019 Rare Diseases Protein Data Bank Pharmacology Janus kinase 1 SARS-CoV-2 Prevention COVID-19 cytokine release syndrome IL Pneumonia acute respiratory distress syndrome SMILES ML Emerging Infectious Diseases Good Health and Well Being Screening of FDA-approved drugs ICU MCP1 ARDS Pharmaeutical data exploration laboratory |
| Popis: | Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 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 Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 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 COVID‑19. 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. |
| Databáze: | OpenAIRE |
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