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Nayarisseri A, Abdalla M, Joshi I, Yadav M, Bhrdwaj A, Chopra I, Khan A, Saxena A, Sharma K, Panicker A, Panwar U, Mendonça Junior FJB, Singh SK. Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer. Sci Rep 2024; 14:13251. [PMID: 38858458 PMCID: PMC11164920 DOI: 10.1038/s41598-024-63762-w] [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: 08/15/2023] [Accepted: 05/31/2024] [Indexed: 06/12/2024] Open
Abstract
Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.
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Affiliation(s)
- Anuraj Nayarisseri
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
- Bioinformatics Research Laboratory, LeGene Biosciences Pvt Ltd, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
| | - Mohnad Abdalla
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, 44 Cultural West Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Isha Joshi
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Manasi Yadav
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Anushka Bhrdwaj
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Ishita Chopra
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- School of Medicine and Health Sciences, The George Washington University, Ross Hall, 2300 Eye Street, Washington, D.C., NW, 20037, USA
| | - Arshiya Khan
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Arshiya Saxena
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Khushboo Sharma
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | - Aravind Panicker
- In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India
| | - Umesh Panwar
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India
| | | | - Sanjeev Kumar Singh
- Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India.
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Pavia G, Scarpa F, Ciccozzi A, Romano C, Branda F, Quirino A, Marascio N, Matera G, Sanna D, Ciccozzi M. Changing and Evolution of Influenza Virus: Is It a Trivial Flu? Chemotherapy 2024; 69:185-193. [PMID: 38508151 DOI: 10.1159/000538382] [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: 02/24/2024] [Accepted: 03/09/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Influenza viruses are etiological agents which cause contagious respiratory, seasonal epidemics and, for influenza A subtypes, pandemics. The clinical picture of influenza has undergone continuous change over the years, due to intrinsic viral evolution as well as "reassortment" of its genomic segments. The history of influenza highlights its ability to adapt and to rapidly evolve, without specific circumstances. This reflects the complexity of this pathology and poses the fundamental question about its assumption as a "common illness" and its impact on public health. SUMMARY The global influenza epidemics and pandemics claimed millions of deaths, leaving an indelible mark on public health and showing the need for a better comprehension of the influenza virus. The clear understanding of genetic variations during the influenza seasonal epidemics is a crucial point for developing effective strategies for prevention, treatment, and vaccine design. The recent advance in next-generation sequencing approaches, model systems to virus culture, and bioinformatics pipeline played a key role in the rapid characterization of circulating influenza strains. In particular, the increase in computational power allowed the performance of complex tasks in healthcare settings through machine learning algorithms, which analyze different variables, such as medical and laboratory outputs, to optimize medical research and improve public health systems. The early detection of emerging and reemerging pathogens is a matter of importance to prevent future pandemics. KEY MESSAGES The perception of influenza as a "trivial flu" or a more serious public health concern is a subject of ongoing debate, reflecting the multifaceted nature of this infectious disease. The variability in the severity of influenza sheds light on the unpredictability of the viral characteristics, coupled with the challenges in accurately predicting circulating strains. This adds complexity to the public health burden of influenza and highlights the need for targeted interventions.
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Affiliation(s)
- Grazia Pavia
- Unit of Clinical Microbiology, Department of Health Sciences, "Magna Græcia" University Hospital, Catanzaro, Italy
| | - Fabio Scarpa
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | | | - Chiara Romano
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Francesco Branda
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Angela Quirino
- Unit of Clinical Microbiology, Department of Health Sciences, "Magna Græcia" University Hospital, Catanzaro, Italy
| | - Nadia Marascio
- Unit of Clinical Microbiology, Department of Health Sciences, "Magna Græcia" University Hospital, Catanzaro, Italy
| | - Giovanni Matera
- Unit of Clinical Microbiology, Department of Health Sciences, "Magna Græcia" University Hospital, Catanzaro, Italy
| | - Daria Sanna
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Massimo Ciccozzi
- Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy
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Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Front Pharmacol 2023; 14:1113007. [PMID: 37180722 PMCID: PMC10169664 DOI: 10.3389/fphar.2023.1113007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.
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Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
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Kumari R, Sharma SD, Kumar A, Ende Z, Mishina M, Wang Y, Falls Z, Samudrala R, Pohl J, Knight PR, Sambhara S. Antiviral Approaches against Influenza Virus. Clin Microbiol Rev 2023; 36:e0004022. [PMID: 36645300 PMCID: PMC10035319 DOI: 10.1128/cmr.00040-22] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Preventing and controlling influenza virus infection remains a global public health challenge, as it causes seasonal epidemics to unexpected pandemics. These infections are responsible for high morbidity, mortality, and substantial economic impact. Vaccines are the prophylaxis mainstay in the fight against influenza. However, vaccination fails to confer complete protection due to inadequate vaccination coverages, vaccine shortages, and mismatches with circulating strains. Antivirals represent an important prophylactic and therapeutic measure to reduce influenza-associated morbidity and mortality, particularly in high-risk populations. Here, we review current FDA-approved influenza antivirals with their mechanisms of action, and different viral- and host-directed influenza antiviral approaches, including immunomodulatory interventions in clinical development. Furthermore, we also illustrate the potential utility of machine learning in developing next-generation antivirals against influenza.
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Affiliation(s)
- Rashmi Kumari
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Department of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Suresh D. Sharma
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Amrita Kumar
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Zachary Ende
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Oak Ridge Institute for Science and Education (ORISE), CDC Fellowship Program, Oak Ridge, Tennessee, USA
| | - Margarita Mishina
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Yuanyuan Wang
- Biotechnology Core Facility Branch, Division of Scientific Resources, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
- Association of Public Health Laboratories, Silver Spring, Maryland, USA
| | - Zackary Falls
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA
| | - Jan Pohl
- Biotechnology Core Facility Branch, Division of Scientific Resources, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Paul R. Knight
- Department of Anesthesiology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Suryaprakash Sambhara
- Immunology and Pathogenesis Branch, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
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Mangione W, Falls Z, Samudrala R. Optimal COVID-19 therapeutic candidate discovery using the CANDO platform. Front Pharmacol 2022; 13:970494. [PMID: 36091793 PMCID: PMC9452636 DOI: 10.3389/fphar.2022.970494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 07/07/2022] [Indexed: 01/22/2023] Open
Abstract
The worldwide outbreak of SARS-CoV-2 in early 2020 caused numerous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability of our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.
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Affiliation(s)
| | | | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
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