1
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Frederick MI, Abdesselam D, Clouvel A, Croteau L, Hassan S. Leveraging PARP-1/2 to Target Distant Metastasis. Int J Mol Sci 2024; 25:9032. [PMID: 39201718 PMCID: PMC11354653 DOI: 10.3390/ijms25169032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/14/2024] [Accepted: 08/14/2024] [Indexed: 09/03/2024] Open
Abstract
Poly (ADP-Ribose) Polymerase (PARP) inhibitors have changed the outcomes and therapeutic strategy for several cancer types. As a targeted therapeutic mainly for patients with BRCA1/2 mutations, PARP inhibitors have commonly been exploited for their capacity to prevent DNA repair. In this review, we discuss the multifaceted roles of PARP-1 and PARP-2 beyond DNA repair, including the impact of PARP-1 on chemokine signalling, immune modulation, and transcriptional regulation of gene expression, particularly in the contexts of angiogenesis and epithelial-to-mesenchymal transition (EMT). We evaluate the pre-clinical role of PARP inhibitors, either as single-agent or combination therapies, to block the metastatic process. Efficacy of PARP inhibitors was demonstrated via DNA repair-dependent and independent mechanisms, including DNA damage, cell migration, invasion, initial colonization at the metastatic site, osteoclastogenesis, and micrometastasis formation. Finally, we summarize the recent clinical advancements of PARP inhibitors in the prevention and progression of distant metastases, with a particular focus on specific metastatic sites and PARP-1 selective inhibitors. Overall, PARP inhibitors have demonstrated great potential in inhibiting the metastatic process, pointing the way for greater use in early cancer settings.
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Affiliation(s)
- Mallory I. Frederick
- Faculty of Medicine, Université de Montréal, Montreal, QC H3C 3T5, Canada; (M.I.F.); (D.A.); (L.C.)
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), l’Institut de Cancer de Montreal, Montreal, QC H2X 0A9, Canada;
| | - Djihane Abdesselam
- Faculty of Medicine, Université de Montréal, Montreal, QC H3C 3T5, Canada; (M.I.F.); (D.A.); (L.C.)
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), l’Institut de Cancer de Montreal, Montreal, QC H2X 0A9, Canada;
| | - Anna Clouvel
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), l’Institut de Cancer de Montreal, Montreal, QC H2X 0A9, Canada;
| | - Laurent Croteau
- Faculty of Medicine, Université de Montréal, Montreal, QC H3C 3T5, Canada; (M.I.F.); (D.A.); (L.C.)
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), l’Institut de Cancer de Montreal, Montreal, QC H2X 0A9, Canada;
| | - Saima Hassan
- Faculty of Medicine, Université de Montréal, Montreal, QC H3C 3T5, Canada; (M.I.F.); (D.A.); (L.C.)
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), l’Institut de Cancer de Montreal, Montreal, QC H2X 0A9, Canada;
- Division of Surgical Oncology, Department of Surgery, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC H2X 0C1, Canada
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2
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Cheng L, Rui Y, Wang Y, Chen S, Su J, Yu XF. A glimpse into viral warfare: decoding the intriguing role of highly pathogenic coronavirus proteins in apoptosis regulation. J Biomed Sci 2024; 31:70. [PMID: 39003473 PMCID: PMC11245872 DOI: 10.1186/s12929-024-01062-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 06/18/2024] [Indexed: 07/15/2024] Open
Abstract
Coronaviruses employ various strategies for survival, among which the activation of endogenous or exogenous apoptosis stands out, with viral proteins playing a pivotal role. Notably, highly pathogenic coronaviruses such as SARS-CoV-2, SARS-CoV, and MERS-CoV exhibit a greater array of non-structural proteins compared to low-pathogenic strains, facilitating their ability to induce apoptosis via multiple pathways. Moreover, these viral proteins are adept at dampening host immune responses, thereby bolstering viral replication and persistence. This review delves into the intricate interplay between highly pathogenic coronaviruses and apoptosis, systematically elucidating the molecular mechanisms underpinning apoptosis induction by viral proteins. Furthermore, it explores the potential therapeutic avenues stemming from apoptosis inhibition as antiviral agents and the utilization of apoptosis-inducing viral proteins as therapeutic modalities. These insights not only shed light on viral pathogenesis but also offer novel perspectives for cancer therapy.
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Affiliation(s)
- Leyi Cheng
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Yajuan Rui
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Yanpu Wang
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Shiqi Chen
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Jiaming Su
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China.
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - Xiao-Fang Yu
- Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310009, China.
- Cancer Center, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
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3
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Chen Y, Sun S, Liu X, Li H, Huan S, Xiong B, Zhang XB. Plasmonic Imaging of Multivalent NTD-Nucleic Acid Interactions for Broad-Spectrum Antiviral Drug Analysis. Anal Chem 2024; 96:9551-9560. [PMID: 38787915 DOI: 10.1021/acs.analchem.4c01037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The discovery and identification of broad-spectrum antiviral drugs are of great significance for blocking the spread of pathogenic viruses and corresponding variants of concern. Herein, we proposed a plasmonic imaging-based strategy for assessing the efficacy of potential broad-spectrum antiviral drugs targeting the N-terminal domain of a nucleocapsid protein (NTD) and nucleic acid (NA) interactions. With NTD and NA conjugated gold nanoparticles as core and satellite nanoprobes, respectively, we found that the multivalent binding interactions could drive the formation of core-satellite nanostructures with enhanced scattering brightness due to the plasmonic coupling effect. The core-satellite assembly can be suppressed in the presence of antiviral drugs targeting the NTD-NA interactions, allowing the drug efficacy analysis by detecting the dose-dependent changes in the scattering brightness by plasmonic imaging. By quantifying the changes in the scattering brightness of plasmonic nanoprobes, we uncovered that the constructed multivalent weak interactions displayed a 500-fold enhancement in affinity as compared with the monovalent NTD-NA interactions. We demonstrated the plasmonic imaging-based strategy for evaluating the efficacy of a potential broad-spectrum drug, PJ34, that can target the NTD-NA interactions, with the IC50 as 24.35 and 14.64 μM for SARS-CoV-2 and SARS-CoV, respectively. Moreover, we discovered that ceftazidime holds the potential as a candidate drug to inhibit the NTD-NA interactions with an IC50 of 22.08 μM from molecular docking and plasmonic imaging-based drug analysis. Finally, we validated that the potential antiviral drug, 5-benzyloxygramine, which can induce the abnormal dimerization of nucleocapsid proteins, is effective for SARS-CoV-2, but not effective against SARS-CoV. All these demonstrations indicated that the plasmonic imaging-based strategy is robust and can be used as a powerful strategy for the discovery and identification of broad-spectrum drugs targeting the evolutionarily conserved viral proteins.
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Affiliation(s)
- Yancao Chen
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Shijie Sun
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Xixuan Liu
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Huiwen Li
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Shuangyan Huan
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Bin Xiong
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Xiao-Bing Zhang
- State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
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4
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Rezaei A, Moqadami A, Khalaj-Kondori M. Minocycline as a prospective therapeutic agent for cancer and non-cancer diseases: a scoping review. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2024; 397:2835-2848. [PMID: 37991540 DOI: 10.1007/s00210-023-02839-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/05/2023] [Indexed: 11/23/2023]
Abstract
Minocycline is an FDA-approved secondary-generation tetracycline antibiotic. It is a synthetic antibiotic having many biological effects, such as antioxidant, anti-inflammatory, anti-cancer, and neuroprotective functions. This study discusses the pharmacological mechanisms of preventive and therapeutic effects of minocycline. Specifically, it provides a comprehensive overview of the molecular pathways by which minocycline acts on the different cancers, including ovarian, breast, glioma, colorectal, liver, pancreatic, lung, prostate, melanoma, head and neck, leukemia, and non-cancer diseases such as Alzheimer's disease, Parkinson, schizophrenia, multiple sclerosis, Huntington, polycystic ovary syndrome, and coronavirus disease 19. Minocycline may be a potential medication for these disorders due to its strong blood-brain barrier penetrance. It is also widely accepted as a specific medication, has a well-known side-effect characteristic, is reasonably priced, making it appropriate for continuous use in managing diseases, and has been demonstrated as an oral approach because it is effectively absorbed and accomplished almost all of the body's parts.
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Affiliation(s)
- Abedeh Rezaei
- Department of Animal Biology¸ Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
| | - Amin Moqadami
- Department of Animal Biology¸ Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
| | - Mohammad Khalaj-Kondori
- Department of Animal Biology¸ Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.
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5
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Saingam P, Jain T, Woicik A, Li B, Candry P, Redcorn R, Wang S, Himmelfarb J, Bryan A, Winkler MKH, Gattuso M. Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications. WATER RESEARCH 2024; 254:121415. [PMID: 38479175 DOI: 10.1016/j.watres.2024.121415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 02/28/2024] [Accepted: 03/03/2024] [Indexed: 04/06/2024]
Abstract
Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 - February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.
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Affiliation(s)
- Prakit Saingam
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States.
| | - Tanisha Jain
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Addie Woicik
- Department of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Bo Li
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Pieter Candry
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Raymond Redcorn
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Sheng Wang
- Department of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| | - Jonathan Himmelfarb
- Kidney Research Institute, University of Washington, Seattle, WA, United States; Center for Dialysis Innovation, University of Washington, Seattle, WA, United States
| | - Andrew Bryan
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
| | - Mari K H Winkler
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Meghan Gattuso
- Seattle Public Utilities, Project Delivery and Engineering, 700 5th Ave, Seattle, WA 98104, United States
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6
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Yang R, Fu Y, Zhang Q, Zhang L. GCNGAT: Drug-disease association prediction based on graph convolution neural network and graph attention network. Artif Intell Med 2024; 150:102805. [PMID: 38553169 DOI: 10.1016/j.artmed.2024.102805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 01/22/2024] [Accepted: 02/08/2024] [Indexed: 04/02/2024]
Abstract
Predicting drug-disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug-disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug-disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug-disease information, a drug-disease heterogeneous graph structure is constructed based on all known drug-disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug-disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug-disease associations by learning drug-disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug-disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.
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Affiliation(s)
- Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Yao Fu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Qian Zhang
- Heze Institute of Science and Technology Information, Heze, 274000, China.
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
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7
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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8
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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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9
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Hakmi M, Bouricha EM, Soussi A, Bzioui IA, Belyamani L, Ibrahimi A. Computational Drug Design Strategies for Fighting the COVID-19 Pandemic. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 1457:199-214. [PMID: 39283428 DOI: 10.1007/978-3-031-61939-7_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/08/2024]
Abstract
The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.
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Affiliation(s)
- Mohammed Hakmi
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco.
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco.
| | - El Mehdi Bouricha
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
| | - Abdellatif Soussi
- Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145, Genova, Italy
| | - Ilias Abdeslam Bzioui
- Department of Gynecology and Obstetrics, Faculty of Medicine, Abdelmalek Essaâdi University Hospital, Tangier, Morocco
| | - Lahcen Belyamani
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
- Emergency Department, Medical and Pharmacy School, Military Hospital Mohammed V, Mohammed V University, Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), Faculty of Medicine and Pharmacy, Bioinova Research Center, Mohammed Vth University, Rabat, Morocco
- Mohammed VI Center for Research and Innovation (CM6), Rabat, Morocco
- Mohammed VI University of Health Sciences (UM6SS), Casablanca, Morocco
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10
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Tang R, Sun C, Huang J, Li M, Wei J, Liu J. Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network. IEEE J Biomed Health Inform 2023; 27:5675-5684. [PMID: 37672364 DOI: 10.1109/jbhi.2023.3312374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.
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11
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Huda NU, Wasim M, Nawaz H, Majeed MI, Javed MR, Rashid N, Iqbal MA, Tariq A, Hassan A, Akram MW, Jamil F, Imran M. Synthesis, Characterization, and Evaluation of Antifungal Activity of 1-Butyl-3-hexyl-1 H-imidazol-2(3 H)-selenone by Surface-Enhanced Raman Spectroscopy. ACS OMEGA 2023; 8:36460-36470. [PMID: 37810682 PMCID: PMC10552477 DOI: 10.1021/acsomega.3c05436] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/08/2023] [Indexed: 10/10/2023]
Abstract
In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.
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Affiliation(s)
- Noor ul Huda
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Wasim
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Haq Nawaz
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Irfan Majeed
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Rizwan Javed
- Department
of Bioinformatics and Biotechnology, Government
College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Nosheen Rashid
- Department
of Chemistry, University of Education, Faisalabad Campus, Faisalabad 38000, Pakistan
| | - Muhammad Adnan Iqbal
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Anam Tariq
- Department
of Biochemistry, Government College University
Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Ahmad Hassan
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Waseem Akram
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Faisal Jamil
- Department
of Chemistry, University of Agriculture
Faisalabad, Faisalabad 38000, Pakistan
| | - Muhammad Imran
- Department
of Chemistry, Faculty of Science, King Khalid
University, P.O. Box 9004, Abha 61413, Saudi Arabia
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12
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Narovlyansky AN, Filimonova MV, Tsyshkova NG, Pronin AV, Grebennikova TV, Karamov EV, Larichev VF, Kornilayeva GV, Fedyakina IT, Dolzhikova IV, Mezentseva MV, Isaeva EI, Poloskov VV, Koval LS, Marinchenko VP, Surinova VI, Filimonov AS, Shitova AA, Soldatova OV, Sanin AV, Zubashev IK, Ponomarev AV, Veselovsky VV, Kozlov VV, Stepanov AV, Khomich AV, Kozlov VS, Ivanov SA, Shegai PV, Kaprin AD, Ershov FI, Gintsburg AL. In Vitro Antiviral Activity of a New Indol-3-carboxylic Acid Derivative Against SARS-CoV-2. Acta Naturae 2023; 15:83-91. [PMID: 38234608 PMCID: PMC10790354 DOI: 10.32607/actanaturae.26623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 10/26/2023] [Indexed: 01/19/2024] Open
Abstract
The coronavirus disease (COVID-19) pandemic has brought into sharp relief the threat posed by coronaviruses and laid the foundation for a fundamental analysis of this viral family, as well as a search for effective anti-COVID drugs. Work is underway to update existent vaccines against COVID-19, and screening for low-molecular-weight anti-COVID drug candidates for outpatient medicine continues. The opportunities and ways to accelerate the development of antiviral drugs against other pathogens are being discussed in the context of preparing for the next pandemic. In 2012-2015, Tsyshkova et al. synthesized a group of water-soluble low-molecular-weight compounds exhibiting an antiviral activity, whose chemical structure was similar to that of arbidol. Among those, there were a number of water-soluble compounds based on 5-methoxyindole-3-carboxylic acid aminoalkyl esters. Only one member of this rather extensive group of compounds, dihydrochloride of 6-bromo-5-methoxy-1-methyl-2-(1-piperidinomethyl)-3-(2-diethylaminoethoxy) carbonylindole, exhibited a reliable antiviral effect against SARS-CoV-2 in vitro. At a concentration of 52.0 μM, this compound completely inhibited the replication of the SARS-CoV-2 virus with an infectious activity of 106 TCID50/mL. The concentration curves of the analyzed compound indicate the specificity of its action. Interferon-inducing activity, as well as suppression of syncytium formation induced by the spike protein (S-glycoprotein) of SARS-CoV-2 by 89%, were also revealed. In view of its synthetic accessibility - high activity (IC50 = 1.06 μg/mL) and high selectivity index (SI = 78.6) - this compound appears to meets the requirements for the development of antiviral drugs for COVID-19 prevention and treatment.
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Affiliation(s)
- A. N. Narovlyansky
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - M. V. Filimonova
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - N. G. Tsyshkova
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - A. V. Pronin
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - T. V. Grebennikova
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - E. V. Karamov
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - V. F. Larichev
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - G. V. Kornilayeva
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - I. T. Fedyakina
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - I. V. Dolzhikova
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - M. V. Mezentseva
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - E. I. Isaeva
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - V. V. Poloskov
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - L. S. Koval
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - V. P. Marinchenko
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - V. I. Surinova
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - A. S. Filimonov
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - A. A. Shitova
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - O. V. Soldatova
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - A. V. Sanin
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - I. K. Zubashev
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - A. V. Ponomarev
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - V. V. Veselovsky
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, 119991 Russian Federation
| | - V. V. Kozlov
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - A. V. Stepanov
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, 119991 Russian Federation
| | | | - V. S. Kozlov
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - S. A. Ivanov
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - P. V. Shegai
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - A. D. Kaprin
- National Medical Research Center for Radiology, Ministry of Health of the Russian Federation, Obninsk, 249036 Russian Federation
| | - F. I. Ershov
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
| | - A. L. Gintsburg
- National Research Centre for Epidemiology and Microbiology named after the honorary academician N.F. Gamaleya, Ministry of Health of the Russian Federation, Moscow, 123098 Russian Federation
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13
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Jamrat S, Sukasem C, Sratthaphut L, Hongkaew Y, Samanchuen T. A precision medicine approach to personalized prescribing using genetic and nongenetic factors for clinical decision-making. Comput Biol Med 2023; 165:107329. [PMID: 37611418 DOI: 10.1016/j.compbiomed.2023.107329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/14/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Screening potential drug-drug interactions, drug-gene interactions, contraindications, and other factors is crucial in clinical practice. However, implementing these screening concepts in real-world settings poses challenges. This work proposes an approach towards precision medicine that combines genetic and nongenetic factors to facilitate clinical decision-making. The approach focuses on raising the performance of four potential interaction screenings in the prescribing process, including drug-drug interactions, drug-gene interactions, drug-herb interactions, drug-social lifestyle interactions, and two potential considerations for patients with liver or renal impairment. The work describes the design of a curated knowledge-based model called the knowledge model for potential interaction and consideration screening, the screening logic for both the detection module and inference module, and the personalized prescribing report. Three case studies have demonstrated the proof-of-concept and effectiveness of this approach. The proposed approach aims to reduce decision-making processes for healthcare professionals, reduce medication-related harm, and enhance treatment effectiveness. Additionally, the recommendation with a semantic network is suggested to assist in risk-benefit analysis when health professionals plan therapeutic interventions with new medicines that have insufficient evidence to establish explicit recommendations. This approach offers a promising solution to implementing precision medicine in clinical practice.
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Affiliation(s)
- Samart Jamrat
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand; Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand; Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center, Ramathibodi Hospital, Bangkok, 10400, Thailand; Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Lawan Sratthaphut
- Artificial Intelligence and Metabolomics Research Group, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand; Department of Biomedicine and Health Informatics, Faculty of Pharmacy, Silpakorn University, Nakhon Pathom, 73000, Thailand
| | - Yaowaluck Hongkaew
- Bumrungrad Genomic Medicine Institute, Bumrungrad International Hospital, Bangkok, 10110, Thailand; Research and Development Laboratory, Bumrungrad International Hospital, Bangkok, 10110, Thailand
| | - Taweesak Samanchuen
- Technology of Information System Management Division, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand.
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14
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Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
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Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
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15
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Lima JPO, da Fonseca AM, Marinho GS, da Rocha MN, Marinho EM, dos Santos HS, Freire RM, Marinho ES, de Lima-Neto P, Fechine PBA. De novo design of bioactive phenol and chromone derivatives for inhibitors of Spike glycoprotein of SARS-CoV-2 in silico. 3 Biotech 2023; 13:301. [PMID: 37588795 PMCID: PMC10425314 DOI: 10.1007/s13205-023-03695-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/29/2023] [Indexed: 08/18/2023] Open
Abstract
This work presents the synthesis of 12 phenol and chromone derivatives, prepared by the analogs, and the possibility of conducting an in silico study of its derivatives as a therapeutic alternative to combat the SARS-CoV-2, pathogen responsible for COVID-19 pandemic, using its S-glycoprotein as a macromolecular target. After the initial screening for the ranking of the products, it was chosen which structure presented the best energy bond with the target. As a result, derivative 4 was submitted to a molecular growth study using artificial intelligence, where 8436 initial structures were obtained that passed through the interaction filters and similarity to the active glycoprotein pocket through the MolAICal computational package. Thus, 557 Hits with active configuration were generated, which is very promising compared to the BLA reference link for inhibiting the biological target. Molecular dynamics also simulated these compounds to verify their stability within the active protein site to seek new therapeutic propositions to fight against the pandemic. The Hit 48 and 250 are the most active compounds against SARS-CoV-2. In summary, the results show that the Hit 250 would be more active than the natural compound, which could be further developed for further testing against SARS-CoV-2. The study employs the de novo approach to design new drugs, combining artificial intelligence and molecular dynamics simulations to create efficient molecular structures. This research aims to contribute to the development of effective therapeutic strategies against the pandemic.
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Affiliation(s)
- Joan Petrus Oliveira Lima
- Advanced Materials Chemistry Group (GQMat)-Department of Analytical Chemistry and Physical Chemistry, Federal University of Ceará, Campus Pici, Fortaleza, Ceará 60455-970 Brazil
| | - Aluísio Marques da Fonseca
- Mestrado Acadêmico em Sociobiodiversidades e Tecnologias Sustentáveis-MASTS, Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE 62785-000 Brazil
| | - Gabrielle Silva Marinho
- Faculdade de Filosofia Dom Aureliano Matos-FAFIDAM, Universidade Estadual do Ceará, Centro, Limoeiro do Norte, CE 62930-000 Brazil
| | - Matheus Nunes da Rocha
- Faculdade de Filosofia Dom Aureliano Matos-FAFIDAM, Universidade Estadual do Ceará, Centro, Limoeiro do Norte, CE 62930-000 Brazil
| | - Emanuelle Machado Marinho
- Advanced Materials Chemistry Group (GQMat)-Department of Analytical Chemistry and Physical Chemistry, Federal University of Ceará, Campus Pici, Fortaleza, Ceará 60455-970 Brazil
| | | | | | - Emmanuel Silva Marinho
- Faculdade de Filosofia Dom Aureliano Matos-FAFIDAM, Universidade Estadual do Ceará, Centro, Limoeiro do Norte, CE 62930-000 Brazil
| | - Pedro de Lima-Neto
- Advanced Materials Chemistry Group (GQMat)-Department of Analytical Chemistry and Physical Chemistry, Federal University of Ceará, Campus Pici, Fortaleza, Ceará 60455-970 Brazil
| | - Pierre Basílio Almeida Fechine
- Advanced Materials Chemistry Group (GQMat)-Department of Analytical Chemistry and Physical Chemistry, Federal University of Ceará, Campus Pici, Fortaleza, Ceará 60455-970 Brazil
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16
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Elkashlan M, Ahmad RM, Hajar M, Al Jasmi F, Corchado JM, Nasarudin NA, Mohamad MS. A review of SARS-CoV-2 drug repurposing: databases and machine learning models. Front Pharmacol 2023; 14:1182465. [PMID: 37601065 PMCID: PMC10436567 DOI: 10.3389/fphar.2023.1182465] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/06/2023] [Indexed: 08/22/2023] Open
Abstract
The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.
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Affiliation(s)
- Marim Elkashlan
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Rahaf M Ahmad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Malak Hajar
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Fatma Al Jasmi
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
- Division of Metabolic Genetics, Department of Pediatrics, Tawam Hospital, Al Ain, United Arab Emirates
| | - Juan Manuel Corchado
- Departamento de Informática y Automática, Facultad de Ciencias, Grupo de Investigación BISITE, Instituto de Investigación Biomédica de Salamanca, University of Salamanca, Salamanca, Spain
| | - Nurul Athirah Nasarudin
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Mohd Saberi Mohamad
- Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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17
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Zaeri N. Artificial intelligence and machine learning responses to COVID-19 related inquiries. J Med Eng Technol 2023; 47:301-320. [PMID: 38625639 DOI: 10.1080/03091902.2024.2321846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 02/18/2024] [Indexed: 04/17/2024]
Abstract
Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
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Affiliation(s)
- Naser Zaeri
- Faculty of Computer Studies, Arab Open University, Kuwait
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18
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Cüvitoğlu A, Isik Z. Network neighborhood operates as a drug repositioning method for cancer treatment. PeerJ 2023; 11:e15624. [PMID: 37456868 PMCID: PMC10340098 DOI: 10.7717/peerj.15624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 06/01/2023] [Indexed: 07/18/2023] Open
Abstract
Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.
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Affiliation(s)
- Ali Cüvitoğlu
- The Graduate School of Natural and Applied Sciences, Dokuz Eylül University, Izmir, Turkiye
| | - Zerrin Isik
- Computer Engineering Department, Engineering Faculty, Dokuz Eylül University, Izmir, Turkiye
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19
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Rampogu S, Jung TS, Ha MW, Lee KW. Repurposing and computational design of PARP inhibitors as SARS-CoV-2 inhibitors. Sci Rep 2023; 13:10583. [PMID: 37386052 PMCID: PMC10310815 DOI: 10.1038/s41598-023-36342-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/01/2023] [Indexed: 07/01/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.
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Affiliation(s)
- Shailima Rampogu
- Department of Bio and Medical Big Data (BK4 Program), Division of Life Sciences, Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Republic of Korea.
| | - Tae Sung Jung
- Laboratory of Aquatic Animal Diseases, College of Veterinary Medicine, Research Institute of Natural Science, Gyeongsang National University, Jinju, 52828, Republic of Korea.
| | - Min Woo Ha
- Interdisciplinary Graduate Program in Advanced Convergence Technology and Science, Jeju National University, 102 Jejudaehak-ro, Jeju, 63243, Republic of Korea.
| | - Keun Woo Lee
- Department of Bio and Medical Big Data (BK4 Program), Division of Life Sciences, Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), Jinju, Republic of Korea.
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20
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Ghosh N, Saha I, Gambin A. Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19. ACS OMEGA 2023; 8:13840-13854. [PMID: 37163139 PMCID: PMC10084923 DOI: 10.1021/acsomega.3c00030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/22/2023] [Indexed: 05/11/2023]
Abstract
COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.
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Affiliation(s)
- Nimisha Ghosh
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland
- Department of Computer Science and Information Technology, Institute of Technical Education and Research, Siksha 'O' Anusandhan, Bhubaneswar, 751030 Odisha, India
| | - Indrajit Saha
- Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata, 700106 West Bengal, India
| | - Anna Gambin
- Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland
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21
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Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. Sci Rep 2023; 13:3643. [PMID: 36871056 PMCID: PMC9985643 DOI: 10.1038/s41598-023-30095-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/15/2023] [Indexed: 03/06/2023] Open
Abstract
The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.
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22
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Wang X, Wang H, Yin G, Zhang YD. Network-based drug repurposing for the treatment of COVID-19 patients in different clinical stages. Heliyon 2023; 9:e14059. [PMID: 36855680 PMCID: PMC9951095 DOI: 10.1016/j.heliyon.2023.e14059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 03/02/2023] Open
Abstract
In the severe acute respiratory coronavirus disease 2019 (COVID-19) pandemic, there is an urgent need to develop effective treatments. Through a network-based drug repurposing approach, several effective drug candidates are identified for treating COVID-19 patients in different clinical stages. The proposed approach takes advantage of computational prediction methods by integrating publicly available clinical transcriptome and experimental data. We identify 51 drugs that regulate proteins interacted with SARS-CoV-2 protein through biological pathways against COVID-19, some of which have been experimented in clinical trials. Among the repurposed drug candidates, lovastatin leads to differential gene expression in clinical transcriptome for mild COVID-19 patients, and estradiol cypionate mainly regulates hormone-related biological functions to treat severe COVID-19 patients. Multi-target mechanisms of drug candidates are also explored. Erlotinib targets the viral protein interacted with cytokine and cytokine receptors to affect SARS-CoV-2 attachment and invasion. Lovastatin and testosterone block the angiotensin system to suppress the SARS-CoV-2 infection. In summary, our study has identified effective drug candidates against COVID-19 for patients in different clinical stages and provides comprehensive understanding of potential drug mechanisms.
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Affiliation(s)
- Xin Wang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Han Wang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.,Department of Mathematics, Imperial College London, London, The United Kingdom
| | - Yan Dora Zhang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China.,Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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23
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Joshi PB. Navigating with chemometrics and machine learning in chemistry. Artif Intell Rev 2023; 56:1-26. [PMID: 36714038 PMCID: PMC9870782 DOI: 10.1007/s10462-023-10391-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/09/2023] [Indexed: 01/25/2023]
Abstract
Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.
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Affiliation(s)
- Payal B. Joshi
- Operations and Method Development, Shefali Research Laboratories, Ambernath (East), Thane, Maharashtra 421501 India
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24
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Sharun K, Tiwari R, Yatoo MI, Natesan S, Megawati D, Singh KP, Michalak I, Dhama K. A comprehensive review on pharmacologic agents, immunotherapies and supportive therapeutics for COVID-19. NARRA J 2022; 2:e92. [PMID: 38449903 PMCID: PMC10914132 DOI: 10.52225/narra.v2i3.92] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/06/2022] [Indexed: 03/08/2024]
Abstract
The emergence of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected many countries throughout the world. As urgency is a necessity, most efforts have focused on identifying small molecule drugs that can be repurposed for use as anti-SARS-CoV-2 agents. Although several drug candidates have been identified using in silico method and in vitro studies, most of these drugs require the support of in vivo data before they can be considered for clinical trials. Several drugs are considered promising therapeutic agents for COVID-19. In addition to the direct-acting antiviral drugs, supportive therapies including traditional Chinese medicine, immunotherapies, immunomodulators, and nutritional therapy could contribute a major role in treating COVID-19 patients. Some of these drugs have already been included in the treatment guidelines, recommendations, and standard operating procedures. In this article, we comprehensively review the approved and potential therapeutic drugs, immune cells-based therapies, immunomodulatory agents/drugs, herbs and plant metabolites, nutritional and dietary for COVID-19.
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Affiliation(s)
- Khan Sharun
- Division of Surgery, ICAR-Indian Veterinary Research Institute, Izatnagar, India
| | - Ruchi Tiwari
- Department of Veterinary Microbiology and Immunology, College of Veterinary Sciences, UP Pandit Deen Dayal Upadhayay Pashu Chikitsa Vigyan Vishwavidyalay Evum Go-Anusandhan Sansthan (DUVASU), Mathura, India
| | - Mohd I. Yatoo
- Division of Veterinary Clinical Complex, Faculty of Veterinary Sciences and Animal Husbandry, Shuhama, Alusteng Srinagar, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Jammu and Kashmir, India
| | - Senthilkumar Natesan
- Department of Infectious Diseases, Indian Institute of Public Health Gandhinagar, Opp to Airforce station HQ, Gandhinagar, India
| | - Dewi Megawati
- Department of Microbiology and Parasitology, Faculty of Medicine and Health Sciences, Warmadewa University, Denpasar, Indonesia
- Department of Medical Microbiology and Immunology, University of California, Davis, California, USA
| | - Karam P. Singh
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, India
| | - Izabela Michalak
- Faculty of Chemistry, Department of Advanced Material Technologies, Wrocław University of Science and Technology, Wrocław, Poland
| | - Kuldeep Dhama
- Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, India
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25
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Karri R, Chen YPP, Burrell AJC, Penny-Dimri JC, Broadley T, Trapani T, Deane AM, Udy AA, Plummer MP. Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients. PLoS One 2022; 17:e0276509. [PMID: 36288359 PMCID: PMC9604987 DOI: 10.1371/journal.pone.0276509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 10/07/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE(S) To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. MAIN OUTCOME MEASURES Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. RESULTS 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50-69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. CONCLUSION Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.
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Affiliation(s)
- Roshan Karri
- Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Yi-Ping Phoebe Chen
- Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, Victoria, Australia
| | - Aidan J. C. Burrell
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | | | - Tessa Broadley
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Tony Trapani
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
| | - Adam M. Deane
- Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia
| | - Andrew A. Udy
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventative Medicine, Monash University, Melbourne, Victoria, Australia
- Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Mark P. Plummer
- Intensive Care Unit, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Critical Care, Melbourne Medical School, Melbourne, Victoria, Australia
- * E-mail:
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26
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Lu L, Qin J, Chen J, Yu N, Miyano S, Deng Z, Li C. Recent computational drug repositioning strategies against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5713-5728. [PMID: 36277237 PMCID: PMC9575573 DOI: 10.1016/j.csbj.2022.10.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022] Open
Abstract
We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data. We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19. Integrating different levels of data may improve the success rate for drug repositioning.
Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.
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Affiliation(s)
- Lu Lu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiale Qin
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China
| | - Jiandong Chen
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,School of Public Health, Undergraduate School of Zhejiang University, Hangzhou, China
| | - Na Yu
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Zhenzhong Deng
- Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
| | - Chen Li
- Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China,Zhejiang Provincial Key Laboratory of Genetic & Developmental Disorders, Zhejiang University School of Medicine, Hangzhou, China,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China,Corresponding authors at: Department of Human Genetics, Department of Ultrasound, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China (C. Li).
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27
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Wang J, Liu J, Luo M, Cui H, Zhang W, Zhao K, Dai H, Song F, Chen K, Yu Y, Zhou D, Li MJ, Yang H. Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference. iScience 2022; 25:105348. [PMID: 36267550 PMCID: PMC9556799 DOI: 10.1016/j.isci.2022.105348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/02/2022] [Accepted: 10/11/2022] [Indexed: 02/08/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.
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Affiliation(s)
- Jianhua Wang
- Department of Epidemiology and Biostatistics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China,Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jiaojiao Liu
- Department of Pathogen Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Menghan Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hui Cui
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Wenwen Zhang
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Ke Zhao
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Fangfang Song
- Department of Epidemiology and Biostatistics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ying Yu
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Dongming Zhou
- Department of Pathogen Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Corresponding author
| | - Mulin Jun Li
- Department of Epidemiology and Biostatistics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China,Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Corresponding author
| | - Hongxi Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China,Corresponding author
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28
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Ai D, Wu J, Cai H, Zhao D, Chen Y, Wei J, Xu J, Zhang J, Wang L. A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors. Front Pharmacol 2022; 13:971369. [PMID: 36304149 PMCID: PMC9592829 DOI: 10.3389/fphar.2022.971369] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/14/2022] [Indexed: 08/16/2024] Open
Abstract
PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.
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Affiliation(s)
- Daiqiao Ai
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Jingxing Wu
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Hanxuan Cai
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Duancheng Zhao
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Yihao Chen
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Jiajia Wei
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
| | - Jianrong Xu
- Department of Pharmacology and Chemical Biology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Academy of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiquan Zhang
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, College of Pharmacy, Guizhou Medical University, Guiyang, China
| | - Ling Wang
- School of Biology and Biological Engineering, Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, South China University of Technology, Guangzhou, China
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29
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Ozdemir ES, Ranganathan SV, Nussinov R. How has artificial intelligence impacted COVID-19 drug repurposing and what lessons have we learned? Expert Opin Drug Discov 2022; 17:1061-1065. [PMID: 36154343 DOI: 10.1080/17460441.2022.2128333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- E Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Srivathsan V Ranganathan
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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30
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Xiong Z, Huang F, Wang Z, Liu S, Zhang W. A Multimodal Framework for Improving in Silico Drug Repositioning With the Prior Knowledge From Knowledge Graphs. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2623-2631. [PMID: 34375284 DOI: 10.1109/tcbb.2021.3103595] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Drug repositioning/repurposing is a very important approach towards identifying novel treatments for diseases in drug discovery. Recently, large-scale biological datasets are increasingly available for pharmaceutical research and promote the development of drug repositioning, but efficiently utilizing these datasets remains challenging. In this paper, we develop a novel multimodal framework, termed GraphPK (Graph-based Prior Knowledge) for improving in silico drug repositioning via using the prior knowledge from a drug knowledge graph. First, we construct a knowledge graph by integrating relevant bio-entities (drugs, diseases, etc.) and associations/interactions among them, and apply the knowledge graph embedding technique to extract prior knowledge of drugs and diseases. Moreover, we make use of the known drug-disease association, and obtain known association-based features from an association bipartite graph through graph embedding, and also take into account biological domain features, i.e., drug chemical structures and disease semantic similarity. Finally, we design a multimodal neural network to combine three types of features from the knowledge graph, the known associations and the biological domain, and build the prediction model for predicting drug-disease associations. Massive experiments show that our method outperforms other state-of-the-art methods in terms of most metrics, and the ablation analysis regarding the three types of features reveals that prior knowledge from knowledge graphs can not only lift the predictive power of in silico drug repositioning, but also enhance the model's robustness to different scenarios. The results of case studies offer support that GraphPK has the potential for actual use.
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31
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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Jiang S, Hou H. A Secure Artificial Intelligence-Enabled Critical Sars Crisis Management Using Random Sigmoidal Artificial Neural Networks. Front Public Health 2022; 10:901294. [PMID: 35602132 PMCID: PMC9114671 DOI: 10.3389/fpubh.2022.901294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Since December 2019, the pandemic COVID-19 has been connected to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early identification and diagnosis are essential goals for health practitioners because early symptoms correlate with those of other common illnesses including the common cold and flu. RT-PCR is frequently used to identify SARS-CoV-2 viral infection. Although this procedure can take up to 2 days to complete and sequential monitoring may be essential to figure out the potential of false-negative findings, RT-PCR test kits are apparently in low availability, highlighting the urgent need for more efficient methods of diagnosing COVID-19 patients. Artificial intelligence (AI)-based healthcare models are more effective at diagnosing and controlling large groups of people. Hence, this paper proposes a novel AI-enabled SARS detection framework. Here, the input CT images are collected and preprocessed using a block-matching filter and histogram equalization (HE). Segmentation is performed using Compact Entropy Rate Superpixel (CERS) technique. Features of segmented output are extracted using Histogram of Gradient (HOG). Feature selection is done using Principal Component Analysis (PCA). The suggested Random Sigmoidal Artificial Neural Networks (RS-ANN) based classification approach effectively diagnoses the existence of the disease. The performance of the suggested Artificial intelligence model is analyzed and related to existing approaches. The suggested AI system may help identify COVID-19 patients more quickly than conventional approaches.
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Affiliation(s)
- Shiwei Jiang
- School of Politics and Public Administration, Zhenghzhou University, Zhengzhou, China
| | - Hongwei Hou
- School of Politics and Public Administration, Zhenghzhou University, Zhengzhou, China
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Zhou H, Ni WJ, Huang W, Wang Z, Cai M, Sun YC. Advances in Pathogenesis, Progression, Potential Targets and Targeted Therapeutic Strategies in SARS-CoV-2-Induced COVID-19. Front Immunol 2022; 13:834942. [PMID: 35450063 PMCID: PMC9016159 DOI: 10.3389/fimmu.2022.834942] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 03/07/2022] [Indexed: 01/18/2023] Open
Abstract
As the new year of 2020 approaches, an acute respiratory disease quietly caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease 2019 (COVID-19) was reported in Wuhan, China. Subsequently, COVID-19 broke out on a global scale and formed a global public health emergency. To date, the destruction that has lasted for more than two years has not stopped and has caused the virus to continuously evolve new mutant strains. SARS-CoV-2 infection has been shown to cause multiple complications and lead to severe disability and death, which has dealt a heavy blow to global development, not only in the medical field but also in social security, economic development, global cooperation and communication. To date, studies on the epidemiology, pathogenic mechanism and pathological characteristics of SARS-CoV-2-induced COVID-19, as well as target confirmation, drug screening, and clinical intervention have achieved remarkable effects. With the continuous efforts of the WHO, governments of various countries, and scientific research and medical personnel, the public's awareness of COVID-19 is gradually deepening, a variety of prevention methods and detection methods have been implemented, and multiple vaccines and drugs have been developed and urgently marketed. However, these do not appear to have completely stopped the pandemic and ravages of this virus. Meanwhile, research on SARS-CoV-2-induced COVID-19 has also seen some twists and controversies, such as potential drugs and the role of vaccines. In view of the fact that research on SARS-CoV-2 and COVID-19 has been extensive and in depth, this review will systematically update the current understanding of the epidemiology, transmission mechanism, pathological features, potential targets, promising drugs and ongoing clinical trials, which will provide important references and new directions for SARS-CoV-2 and COVID-19 research.
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Affiliation(s)
- Hong Zhou
- Department of Pharmacy, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wei-Jian Ni
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, The Key Laboratory of Anti-inflammatory of Immune Medicines, Ministry of Education, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China
- Anhui Provincial Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wei Huang
- The Third People’s Hospital of Hefei, The Third Clinical College of Anhui Medical University, Hefei, China
| | - Zhen Wang
- Anhui Provincial Children’s Hospital, Children’s Hospital of Fudan University-Anhui Campus, Hefei, China
| | - Ming Cai
- Department of Pharmacy, The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Yan-Cai Sun
- Department of Pharmacy, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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Zheng M, Schultz MB, Sinclair DA. NAD + in COVID-19 and viral infections. Trends Immunol 2022; 43:283-295. [PMID: 35221228 PMCID: PMC8831132 DOI: 10.1016/j.it.2022.02.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/06/2022] [Accepted: 02/07/2022] [Indexed: 11/24/2022]
Abstract
NAD+, as an emerging regulator of immune responses during viral infections, may be a promising therapeutic target for coronavirus disease 2019 (COVID-19). In this Opinion, we suggest that interventions that boost NAD+ levels might promote antiviral defense and suppress uncontrolled inflammation. We discuss the association between low NAD+ concentrations and risk factors for poor COVID-19 outcomes, including aging and common comorbidities. Mechanistically, we outline how viral infections can further deplete NAD+ and its roles in antiviral defense and inflammation. We also describe how coronaviruses can subvert NAD+-mediated actions via genes that remove NAD+ modifications and activate the NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome. Finally, we explore ongoing approaches to boost NAD+ concentrations in the clinic to putatively increase antiviral responses while curtailing hyperinflammation.
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Affiliation(s)
- Minyan Zheng
- Department of Genetics, Blavatnik Institute, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, Boston, MA, USA
| | - Michael B Schultz
- Department of Genetics, Blavatnik Institute, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, Boston, MA, USA
| | - David A Sinclair
- Department of Genetics, Blavatnik Institute, Paul F. Glenn Center for Biology of Aging Research, Harvard Medical School, Boston, MA, USA.
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Lipták P, Banovcin P, Rosoľanka R, Prokopič M, Kocan I, Žiačiková I, Uhrik P, Grendar M, Hyrdel R. A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization. PeerJ 2022; 10:e13124. [PMID: 35341062 PMCID: PMC8944335 DOI: 10.7717/peerj.13124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background and aim COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant. Results A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST. Conclusion SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.
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Affiliation(s)
- Peter Lipták
- Gastroenterology Clinic, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Peter Banovcin
- Gastroenterology Clinic, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Róbert Rosoľanka
- Clinic of Infectology and Travel Medicine, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Michal Prokopič
- Gastroenterology Clinic, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Ivan Kocan
- Clinic of Pneumology and Phthisiology, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Ivana Žiačiková
- Clinic of Pneumology and Phthisiology, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Peter Uhrik
- Gastroenterology Clinic, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
| | - Marian Grendar
- Laboratory of Bioinformatics and Biostatistics, Biomedical Centre Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovak Republic,Laboratory of Theoretical Methods, Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Rudolf Hyrdel
- Gastroenterology Clinic, University Hospital in Martin, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic
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37
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Drug repurposing in silico screening platforms. Biochem Soc Trans 2022; 50:747-758. [PMID: 35285479 DOI: 10.1042/bst20200967] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.
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Iacopetta D, Ceramella J, Catalano A, Saturnino C, Pellegrino M, Mariconda A, Longo P, Sinicropi MS, Aquaro S. COVID-19 at a Glance: An Up-to-Date Overview on Variants, Drug Design and Therapies. Viruses 2022; 14:573. [PMID: 35336980 PMCID: PMC8950852 DOI: 10.3390/v14030573] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 02/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronavirus family which caused the worldwide pandemic of human respiratory illness coronavirus disease 2019 (COVID-19). Presumably emerging at the end of 2019, it poses a severe threat to public health and safety, with a high incidence of transmission, predominately through aerosols and/or direct contact with infected surfaces. In 2020, the search for vaccines began, leading to the obtaining of, to date, about twenty COVID-19 vaccines approved for use in at least one country. However, COVID-19 continues to spread and new genetic mutations and variants have been discovered, requiring pharmacological treatments. The most common therapies for COVID-19 are represented by antiviral and antimalarial agents, antibiotics, immunomodulators, angiotensin II receptor blockers, bradykinin B2 receptor antagonists and corticosteroids. In addition, nutraceuticals, vitamins D and C, omega-3 fatty acids and probiotics are under study. Finally, drug repositioning, which concerns the investigation of existing drugs for new therapeutic target indications, has been widely proposed in the literature for COVID-19 therapies. Considering the importance of this ongoing global public health emergency, this review aims to offer a synthetic up-to-date overview regarding diagnoses, variants and vaccines for COVID-19, with particular attention paid to the adopted treatments.
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Affiliation(s)
- Domenico Iacopetta
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Jessica Ceramella
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Alessia Catalano
- Department of Pharmacy-Drug Sciences, University of Bari “Aldo Moro”, 70126 Bari, Italy
| | - Carmela Saturnino
- Department of Science, University of Basilicata, 85100 Potenza, Italy; (C.S.); (A.M.)
| | - Michele Pellegrino
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Annaluisa Mariconda
- Department of Science, University of Basilicata, 85100 Potenza, Italy; (C.S.); (A.M.)
| | - Pasquale Longo
- Department of Chemistry and Biology, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy;
| | - Maria Stefania Sinicropi
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
| | - Stefano Aquaro
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Arcavacata di Rende, Italy; (D.I.); (J.C.); (M.P.); (M.S.S.); (S.A.)
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Bent OE, Wachira C, Remy SL, Ogallo W, Walcott-Bryant A. A Framework for Inferring Epidemiological Model Parameters using Bayesian Nonparametrics. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:217-226. [PMID: 35308928 PMCID: PMC8861664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of epidemiological models for decision-making has been prominent during the COVID-19 pandemic. Our work presents the application of nonparametric Bayesian techniques for inferring epidemiological model parameters based on available data sets published during the pandemic, towards enabling predictions under uncertainty during emerging pandemics. We present a methodology and framework that allows epidemiological model drivers to be integrated as input into the model calibration process. We demonstrate our methodology using the stringency index and mobility data for COVID-19 on an SEIRD compartmental model for selected US states. Our results directly compare the use of Bayesian nonparametrics for model predictions based on best parameter estimates with results of inference of parameter values across the US states. The proposed methodology provides a framework for What-If analysis and sequential decision-making methods for disease intervention planning and is demonstrated for COVID-19, while also applicable to other infectious disease models.
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Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery. J Infect Public Health 2022; 15:289-296. [PMID: 35078755 PMCID: PMC8767913 DOI: 10.1016/j.jiph.2022.01.011] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology. METHODS A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized. RESULTS The sequence of SARS- CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability. CONCLUSION The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents for controlling the COVID-19 pandemic.
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Affiliation(s)
- Sali Abubaker Bagabir
- Medical Laboratory Technology Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Nahla Khamis Ibrahim
- Community Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; Epidemiology Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt.
| | - Hala Abubaker Bagabir
- Medical Physiology Department, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia
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Block T, Kuo J. Rationale for Nicotinamide Adenine Dinucleotide (NAD+) Metabolome Disruption as a Pathogenic Mechanism of Post-Acute COVID-19 Syndrome. CLINICAL PATHOLOGY (THOUSAND OAKS, VENTURA COUNTY, CALIF.) 2022; 15:2632010X221106986. [PMID: 35769168 PMCID: PMC9234841 DOI: 10.1177/2632010x221106986] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022]
Abstract
Many acute COVID-19 convalescents experience a persistent sequelae of infection, called post-acute COVID-19 syndrome (PACS). With incidence ranging between 31% and 69%, PACS is becoming increasingly acknowledged as a new disease state in the context of SARS-CoV-2 infection. As SARS-CoV-2 infection can affect several organ systems to varying degrees and durations, the cellular and molecular abnormalities contributing to PACS pathogenesis remain unclear. Despite our limited understanding of how SARS-CoV-2 infection promotes this persistent disease state, mitochondrial dysfunction has been increasingly recognized as a contributing factor to acute SARS-CoV-2 infection and, more recently, to PACS pathogenesis. The biological mechanisms contributing to this phenomena have not been well established in previous literature; however, in this review, we summarize the evidence that NAD+ metabolome disruption and subsequent mitochondrial dysfunction following SARS-CoV-2 genome integration may contribute to PACS biological pathogenesis. We also briefly examine the coordinated and complex relationship between increased oxidative stress, inflammation, and mitochondrial dysfunction and speculate as to how SARS-CoV-2-mediated NAD+ depletion may be causing these abnormalities in PACS. As such, we present evidence supporting the therapeutic potential of intravenous administration of NAD+ as a novel treatment intervention for PACS symptom management.
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AI-powered drug repurposing for developing COVID-19 treatments. REFERENCE MODULE IN BIOMEDICAL SCIENCES 2022. [PMCID: PMC8865759 DOI: 10.1016/b978-0-12-824010-6.00005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Emerging infectious diseases are an ever-present threat to public health, and COVID-19 is the most recent example. There is an urgent need to develop a robust framework to combat the disease with safe and effective therapeutic options. Compared to de novo drug discovery, drug repurposing may offer a lower-cost and faster drug discovery paradigm to explore potential treatment options of existing drugs. This chapter elucidates the advantages of artificial intelligence (AI) in enhancing the drug repurposing process from a data science perspective, using COVID-19 as an example. First, we elaborate on how AI-powered drug repurposing benefits from the accumulated data and knowledge of COVID-19 natural history and pathogenesis. Second, we summarize the pros and cons of AI-powered drug repurposing strategies to facilitate fit-for-purpose selection. Finally, we outline challenges of AI-powered drug repurposing from a regulatory perspective and suggest some potential solutions. AI-powered drug purposing is promising for emerging treatments for COVID-19 infection. Accumulated biological data profiles facilitate AI-based drug repurposing efforts for development of COVID-19 therapies. The ‘fit-for-purpose selection of AI-powered drug repurposing strategies is key to uncovering hidden information among drugs, targets, and diseases. Efforts from different stakeholders boost the adoption of AI-powered drug repurposing in the regulatory setting.
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Chang Z, Zhan Z, Zhao Z, You Z, Liu Y, Yan Z, Fu Y, Liang W, Zhao L. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis 2021; 13:7034-7053. [PMID: 35070385 PMCID: PMC8743418 DOI: 10.21037/jtd-21-747] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 09/02/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. METHODS We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. RESULTS We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. DISCUSSION This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.
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Affiliation(s)
- Zhoulin Chang
- College of Mechanical and Electrical Engineering, Guangdong University of Science and Technology, Dongguan, China
| | - Zhiqing Zhan
- The Third Clinical College, Guangzhou Medical University, Guangzhou, China
| | - Zifan Zhao
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Zhixuan You
- Nanshan College, Guangzhou Medical University, Guangzhou, China
| | - Yang Liu
- School of Information Engineering, Zhengzhou University, Zhengzhou, China
| | - Zhihong Yan
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Yong Fu
- Kuangji Medical Technology (Guangdong Hengqin) Co., Ltd., Zhuhai, China
| | - Wenhua Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Lei Zhao
- Department of Physiology, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
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44
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Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. ROBOTICS AND AUTONOMOUS SYSTEMS 2021; 146:103902. [PMID: 34629751 PMCID: PMC8493645 DOI: 10.1016/j.robot.2021.103902] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 05/05/2023]
Abstract
The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.
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Affiliation(s)
- Sujan Sarker
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Lafifa Jamal
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Syeda Faiza Ahmed
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Niloy Irtisam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh
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45
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Lv H, Shi L, Berkenpas JW, Dao FY, Zulfiqar H, Ding H, Zhang Y, Yang L, Cao R. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design. Brief Bioinform 2021; 22:bbab320. [PMID: 34410360 PMCID: PMC8511807 DOI: 10.1093/bib/bbab320] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 12/13/2022] Open
Abstract
The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.
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Affiliation(s)
- Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai 200433, China
| | | | - Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hasan Zulfiqar
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Liming Yang
- Department of Pathophysiology, Harbin Medical University-Daqing, Daqing, 163319, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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46
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Qian K, Schmitt M, Zheng H, Koike T, Han J, Liu J, Ji W, Duan J, Song M, Yang Z, Ren Z, Liu S, Zhang Z, Yamamoto Y, Schuller BW. Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19. IEEE INTERNET OF THINGS JOURNAL 2021; 8:16035-16046. [PMID: 35782182 PMCID: PMC8768988 DOI: 10.1109/jiot.2021.3067605] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/24/2021] [Accepted: 03/17/2021] [Indexed: 05/29/2023]
Abstract
Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.
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Affiliation(s)
- Kun Qian
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Maximilian Schmitt
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Huaiyuan Zheng
- Department of Hand SurgeryWuhan Union Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Tomoya Koike
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Jing Han
- Mobile Systems GroupUniversity of CambridgeCambridgeCB2 1TNU.K.
| | - Juan Liu
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Wei Ji
- Department of Plastic SurgeryWuhan Third Hospital and Tongren Hospital of Wuhan UniversityWuhan430072China
| | - Junjun Duan
- Department of Plastic SurgeryCentral Hospital of Wuhan, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhan430074China
| | - Meishu Song
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zijiang Yang
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zhao Ren
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Shuo Liu
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
| | - Zixing Zhang
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
| | - Yoshiharu Yamamoto
- Educational Physiology Laboratory, Graduate School of EducationThe University of TokyoTokyo113-0033Japan
| | - Björn W. Schuller
- Chair of Embedded Intelligence for Health Care and WellbeingUniversity of Augsburg86159AugsburgGermany
- GLAM—the Group on Language, Audio, and MusicImperial College LondonLondonSW7 2BUU.K.
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47
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Deep learning in target prediction and drug repositioning: Recent advances and challenges. Drug Discov Today 2021; 27:1796-1814. [PMID: 34718208 DOI: 10.1016/j.drudis.2021.10.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
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48
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Liu Q, Wan J, Wang G. A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Brief Bioinform 2021; 23:6384382. [PMID: 34623382 PMCID: PMC8524468 DOI: 10.1093/bib/bbab416] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/25/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022] Open
Abstract
The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [
1–
5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.
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Affiliation(s)
- Qiaoming Liu
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China
| | - Jun Wan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA.,Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Guohua Wang
- Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, Heilongjiang 150001, China.,Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang 150001, China
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49
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Dash S, Dash C, Pandhare J. Therapeutic Significance of microRNA-Mediated Regulation of PARP-1 in SARS-CoV-2 Infection. Noncoding RNA 2021; 7:60. [PMID: 34698261 PMCID: PMC8544662 DOI: 10.3390/ncrna7040060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 09/18/2021] [Accepted: 09/18/2021] [Indexed: 02/07/2023] Open
Abstract
The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 (2019-nCoV) has devastated global healthcare and economies. Despite the stabilization of infectivity rates in some developed nations, several countries are still under the grip of the pathogenic viral mutants that are causing a significant increase in infections and hospitalization. Given this urgency, targeting of key host factors regulating SARS-CoV-2 life cycle is postulated as a novel strategy to counter the virus and its associated pathological outcomes. In this regard, Poly (ADP)-ribose polymerase-1 (PARP-1) is being increasingly recognized as a possible target. PARP-1 is well studied in human diseases such as cancer, central nervous system (CNS) disorders and pathology of RNA viruses. Emerging evidence indicates that regulation of PARP-1 by non-coding RNAs such as microRNAs is integral to cell survival, redox balance, DNA damage response, energy homeostasis, and several other cellular processes. In this short perspective, we summarize the recent findings on the microRNA/PARP-1 axis and its therapeutic potential for COVID-19 pathologies.
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Affiliation(s)
- Sabyasachi Dash
- Weill Cornell Medicine, Department of Pathology and Laboratory Medicine, Cornell University, New York, NY 10065, USA
- Center for AIDS Health Disparities Research, Meharry Medical College, Nashville, TN 37208, USA; (C.D.); (J.P.)
- School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA
| | - Chandravanu Dash
- Center for AIDS Health Disparities Research, Meharry Medical College, Nashville, TN 37208, USA; (C.D.); (J.P.)
- School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA
- Department of Biochemistry, Cancer Biology, Pharmacology and Neuroscience, Meharry Medical College, Nashville, TN 37208, USA
| | - Jui Pandhare
- Center for AIDS Health Disparities Research, Meharry Medical College, Nashville, TN 37208, USA; (C.D.); (J.P.)
- School of Graduate Studies and Research, Meharry Medical College, Nashville, TN 37208, USA
- Department of Microbiology, Immunology and Physiology, Meharry Medical College, Nashville, TN 37208, USA
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50
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Choudhary V, Gupta A, Sharma R, Parmar HS. Therapeutically effective covalent spike protein inhibitors in treatment of SARS-CoV-2. JOURNAL OF PROTEINS AND PROTEOMICS 2021; 12:257-270. [PMID: 34539131 PMCID: PMC8440732 DOI: 10.1007/s42485-021-00074-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 02/08/2023]
Abstract
COVID-19 [coronavirus disease 2019] has resulted in over 204,644,849 confirmed cases and over 4,323,139 deaths throughout the world as of 12 August 2021, a total of 4,428,168,759 vaccine doses have been administered. The lack of potentially effective drugs against the virus is making the situation worse and dangerous. Numerous forces are working on finding an effective treatment against the virus but it is believed that a de novo drug would take several months even if huge financial support is provided. The only solution left with is drug repurposing that would not only provide effective therapy with the already used clinical drugs, but also save time and cost of the de novo drug discovery. The initiation of the COVID-19 infection starts with the attachment of spike glycoprotein of SARS-CoV-2 to the host receptor. Hence, the inhibition of the binding of the virus to the host membrane and the entry of the viral particle into the host cell are one of the main therapeutic targets. This paper not only summarizes the structure and the mechanism of spike protein, but the main focus is on the potential covalent spike protein inhibitors.
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Affiliation(s)
- Vikram Choudhary
- School of Pharmacy, Devi Ahilya Vishwavidyalaya, Takshila Campus, Khandwa Road (Ring Road), Indore, 452001 Madhya Pradesh India
| | - Amisha Gupta
- School of Pharmacy, Devi Ahilya Vishwavidyalaya, Takshila Campus, Khandwa Road (Ring Road), Indore, 452001 Madhya Pradesh India
| | - Rajesh Sharma
- School of Pharmacy, Devi Ahilya Vishwavidyalaya, Takshila Campus, Khandwa Road (Ring Road), Indore, 452001 Madhya Pradesh India
| | - Hamendra Singh Parmar
- School of Biotechnology, Devi Ahilya Vishwavidyalaya, Takshila Campus, Khandwa Road, Indore, 452001 Madhya Pradesh India
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