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Guo Q, Fu B, Tian Y, Xu S, Meng X. Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development. Curr Med Res Opin 2024; 40:1483-1493. [PMID: 39083361 DOI: 10.1080/03007995.2024.2387187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024]
Abstract
Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming, and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. It does that by synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.
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Affiliation(s)
- Qi Guo
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Bo Fu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Yuan Tian
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Shujun Xu
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Xin Meng
- School of Pharmacy, Heilongjiang University of Chinese Medicine, Harbin, P. R. China
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2
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Luo Y, Shan W, Peng L, Luo L, Ding P, Liang W. A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning. IEEE J Biomed Health Inform 2024; 28:4503-4511. [PMID: 38607707 DOI: 10.1109/jbhi.2024.3387937] [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: 04/14/2024]
Abstract
Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.
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Tükel EY, Ateş O, Kiraz Y. In Silico Drug Repurposing Against PSMB8 as a Potential Target for Acute Myeloid Leukemia Treatment. Mol Biotechnol 2024:10.1007/s12033-024-01224-4. [PMID: 38954355 DOI: 10.1007/s12033-024-01224-4] [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: 10/03/2023] [Accepted: 06/21/2024] [Indexed: 07/04/2024]
Abstract
PSMB8 emerges as a prominent gene associated with cancer survival, yet its potential therapeutic role in acute myeloid leukemia (AML) remains unexplored within the existing literature. The principal aim of this study is to systematically screen an expansive library of molecular entities, curated from various databases to identify the prospective inhibitory agents with an affinity for PSMB8. A comprehensive assortment of molecular compounds obtained from the ZINC15 database was subjected to molecular docking simulations with PSMB8 by using the AutoDock tool in PyRx (version 0.9.9) to elucidate binding affinities. Following the docking simulations, a select subset of molecules underwent further investigation through comprehensive ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis employing AdmetSar and SwissADME tools. Finally, RMSD, RMSF, Rg, and H bond analyses were conducted via GROMACS to determine the best conformationally dynamic molecule that represents the candidate agent for the study. Following rigorous evaluation, Adozelesin, Fiduxosin, and Rimegepant have been singled out based on considerations encompassing bioavailability scores, compliance with filter criteria, and acute oral toxicity levels. Additionally, ligand interaction analysis indicates that Adozelesin and Fiduxosin exhibit an augmented propensity for hydrogen bond formation, a factor recognized for its facilitative role in protein-ligand interactions. After final analyses, we report that Fiduxosin may offer a treatment possibility by reversing the low survival rates caused by PSMB8 high activation in AML. This study represents a strategic attempt to repurpose readily available pharmaceutical agents, potentially obviating the need for de novo drug development, and thereby offering promising avenues for therapeutic intervention in specific diseases.
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Affiliation(s)
- Ezgi Yağmur Tükel
- Department of Genetics and Bioengineering, Faculty of Engineering, İzmir University of Economics, Sakarya st. No:156, 35330, Balçova, İzmir, Turkey
| | - Onur Ateş
- Department of Genetics and Bioengineering, Faculty of Engineering, İzmir University of Economics, Sakarya st. No:156, 35330, Balçova, İzmir, Turkey
| | - Yağmur Kiraz
- Department of Genetics and Bioengineering, Faculty of Engineering, İzmir University of Economics, Sakarya st. No:156, 35330, Balçova, İzmir, Turkey.
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Shukla H, John D, Banerjee S, Tiwari AK. Drug repurposing for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:249-319. [PMID: 38942541 DOI: 10.1016/bs.pmbts.2024.03.035] [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: 06/30/2024]
Abstract
Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.
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Affiliation(s)
- Halak Shukla
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Diana John
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Shuvomoy Banerjee
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Anand Krishna Tiwari
- Genetics and Developmental Biology Laboratory, Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India.
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Waseem T, Rajput TA, Mushtaq MS, Babar MM, Rajadas J. Computational biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:91-109. [PMID: 38789189 DOI: 10.1016/bs.pmbts.2024.03.018] [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: 05/26/2024]
Abstract
The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.
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Affiliation(s)
- Tanya Waseem
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Tausif Ahmed Rajput
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | | | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan; Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States.
| | - Jayakumar Rajadas
- Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [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: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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Shahab M, Ziyu P, Waqas M, Zheng G, Bin Jardan YA, Fentahun Wondmie G, Bouhrhia M. Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer. Sci Rep 2024; 14:6768. [PMID: 38514638 PMCID: PMC10958019 DOI: 10.1038/s41598-024-55321-0] [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: 09/13/2023] [Accepted: 02/22/2024] [Indexed: 03/23/2024] Open
Abstract
Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.
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Affiliation(s)
- Muhammad Shahab
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China
| | - Peng Ziyu
- School of chemistry and chemical engineering, Wuhan University of Science and Technology, Wuhan, 430081, People's Republic of China
| | - Muhammad Waqas
- Natural and Medical Sciences Research Center, University of Nizwa, Birkat Al-Mouz, 616, Nizwa, Oman
| | - Guojun Zheng
- State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
| | - Yousef A Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, P. O. BOX 2455, 11451, Riyadh, Saudi Arabia
| | | | - Mohammed Bouhrhia
- Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, 80060, Agadir, Morocco
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Cirinciani M, Da Pozzo E, Trincavelli ML, Milazzo P, Martini C. Drug Mechanism: A bioinformatic update. Biochem Pharmacol 2024:116078. [PMID: 38402909 DOI: 10.1016/j.bcp.2024.116078] [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: 12/13/2023] [Revised: 02/01/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
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Affiliation(s)
- Martina Cirinciani
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy
| | - Eleonora Da Pozzo
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Maria Letizia Trincavelli
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Paolo Milazzo
- Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy; Department of Computer Science, University of Pisa, Largo Pontecorvo, 3, 56127 Pisa, Italy
| | - Claudia Martini
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
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Korlepara DB, C S V, Srivastava R, Pal PK, Raza SH, Kumar V, Pandit S, Nair AG, Pandey S, Sharma S, Jeurkar S, Thakran K, Jaglan R, Verma S, Ramachandran I, Chatterjee P, Nayar D, Priyakumar UD. PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications. Sci Data 2024; 11:180. [PMID: 38336857 PMCID: PMC10858175 DOI: 10.1038/s41597-023-02872-y] [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: 07/20/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.
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Affiliation(s)
- Divya B Korlepara
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Divison of Physics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, 600127, India
| | - Vasavi C S
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Department of Artificial Intelligence, School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, India
| | - Rakesh Srivastava
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Pradeep Kumar Pal
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Saalim H Raza
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Vishal Kumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivam Pandit
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Aathira G Nair
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Sanjana Pandey
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shubham Sharma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shruti Jeurkar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Kavita Thakran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Reena Jaglan
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivangi Verma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Indhu Ramachandran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Prathit Chatterjee
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Divya Nayar
- Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - U Deva Priyakumar
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India.
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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Esteban-Medina M, Loucera C, Rian K, Velasco S, Olivares-González L, Rodrigo R, Dopazo J, Peña-Chilet M. The mechanistic functional landscape of retinitis pigmentosa: a machine learning-driven approach to therapeutic target discovery. J Transl Med 2024; 22:139. [PMID: 38321543 PMCID: PMC10848380 DOI: 10.1186/s12967-024-04911-7] [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: 07/13/2023] [Accepted: 01/20/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Retinitis pigmentosa is the prevailing genetic cause of blindness in developed nations with no effective treatments. In the pursuit of unraveling the intricate dynamics underlying this complex disease, mechanistic models emerge as a tool of proven efficiency rooted in systems biology, to elucidate the interplay between RP genes and their mechanisms. The integration of mechanistic models and drug-target interactions under the umbrella of machine learning methodologies provides a multifaceted approach that can boost the discovery of novel therapeutic targets, facilitating further drug repurposing in RP. METHODS By mapping Retinitis Pigmentosa-related genes (obtained from Orphanet, OMIM and HPO databases) onto KEGG signaling pathways, a collection of signaling functional circuits encompassing Retinitis Pigmentosa molecular mechanisms was defined. Next, a mechanistic model of the so-defined disease map, where the effects of interventions can be simulated, was built. Then, an explainable multi-output random forest regressor was trained using normal tissue transcriptomic data to learn causal connections between targets of approved drugs from DrugBank and the functional circuits of the mechanistic disease map. Selected target genes involvement were validated on rd10 mice, a murine model of Retinitis Pigmentosa. RESULTS A mechanistic functional map of Retinitis Pigmentosa was constructed resulting in 226 functional circuits belonging to 40 KEGG signaling pathways. The method predicted 109 targets of approved drugs in use with a potential effect over circuits corresponding to nine hallmarks identified. Five of those targets were selected and experimentally validated in rd10 mice: Gabre, Gabra1 (GABARα1 protein), Slc12a5 (KCC2 protein), Grin1 (NR1 protein) and Glr2a. As a result, we provide a resource to evaluate the potential impact of drug target genes in Retinitis Pigmentosa. CONCLUSIONS The possibility of building actionable disease models in combination with machine learning algorithms to learn causal drug-disease interactions opens new avenues for boosting drug discovery. Such mechanistically-based hypotheses can guide and accelerate the experimental validations prioritizing drug target candidates. In this work, a mechanistic model describing the functional disease map of Retinitis Pigmentosa was developed, identifying five promising therapeutic candidates targeted by approved drug. Further experimental validation will demonstrate the efficiency of this approach for a systematic application to other rare diseases.
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Affiliation(s)
- Marina Esteban-Medina
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, Spain
| | - Carlos Loucera
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, Spain
| | - Kinza Rian
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain
- Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, Spain
| | - Sheyla Velasco
- Group of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF), 46012, Valencia, Spain
| | - Lorena Olivares-González
- Group of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF), 46012, Valencia, Spain
| | - Regina Rodrigo
- Group of Pathophysiology and Therapies for Vision Disorders, Príncipe Felipe Research Center (CIPF), 46012, Valencia, Spain
- Biomedical Research Networking Center in Rare Diseases (CIBERER), Health Institute Carlos III, 28029, Madrid, Spain
- Department of Physiology, University of Valencia (UV), 46100, Burjassot, Spain
- Department of Anatomy and Physiology, Catholic University of Valencia San Vicente Mártir, 46001, Valencia, Spain
- Joint Research Unit on Endocrinology, Nutrition and Clinical Dietetics UV-IIS La Fe, 46026, Valencia, Spain
| | - Joaquin Dopazo
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
- Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, Spain.
- Biomedical Research Networking Center in Rare Diseases (CIBERER), Health Institute Carlos III, 28029, Madrid, Spain.
| | - Maria Peña-Chilet
- Andalusian Platform for Computational Medicine, Andalusian Public Foundation Progress and Health-FPS, Seville, Spain.
- Systems and Computational Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Seville, 41013, Seville, Spain.
- Biomedical Research Networking Center in Rare Diseases (CIBERER), Health Institute Carlos III, 28029, Madrid, Spain.
- BigData, AI, Biostatistics & Bioinformatics Platform, Health Research Institute La Fe (IISLaFe), 46026, Valencia, Spain.
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Singh N, Singh AK. A comprehensive review on structural and therapeutical insight of Cerebroside sulfotransferase (CST) - An important target for development of substrate reduction therapy against metachromatic leukodystrophy. Int J Biol Macromol 2024; 258:128780. [PMID: 38104688 DOI: 10.1016/j.ijbiomac.2023.128780] [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: 06/26/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023]
Abstract
This review is an effort towards the development of substrate reduction therapy using cerebroside sulfotransferase (CST) as a target protein for the development of inhibitors intended to treat pathophysiological condition resulting from the accumulation of sulfatide, a product from the catalytic action of CST. Accumulation of sulfatides leads to progressive impairment and destruction of the myelin structure, disruption of normal physiological transmission of electrical impulse between nerve cells, axonal loss in the central and peripheral nervous system and cumulatively gives a clinical manifestation of metachromatic leukodystrophy. Thus, there is a need to develop specific and potent CST inhibitors to positively control sulfatide accumulation. Structural similarity and computational studies revealed that LYS85, SER172 and HIS141 are key catalytic residues that determine the catalytic action of CST through the transfer of sulfuryl group from the donor PAPS to the acceptor galactosylceramide. Computational studies revealed catalytic site of CST consists two binding site pocket including PAPS binding pocket and substrate binding pocket. Specific substrate site residues in CST can be targeted to develop specific CST inhibitors. This review also explores the challenges of CST-directed substrate reduction therapy as well as the opportunities available in natural products for inhibitor development.
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Affiliation(s)
- Nivedita Singh
- Department of Dravyaguna, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
| | - Anil Kumar Singh
- Department of Dravyaguna, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
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12
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Pattnaik S, Murmu S, Prasad Rath B, Singh MK, Kumar S, Mohanty C. In silico screening of phytoconstituents as potential anti-inflammatory agents targeting NF-κB p65: an approach to promote burn wound healing. J Biomol Struct Dyn 2024:1-29. [PMID: 38287503 DOI: 10.1080/07391102.2024.2306199] [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: 05/29/2023] [Accepted: 01/10/2024] [Indexed: 01/31/2024]
Abstract
Chronic burn wounds are frequently characterised by a prolonged and dysregulated inflammatory phase that is mediated by over-activation of NF-κB p65. Synthetic wound healing drugs used for treatment of inflammation are primarily associated with several shortcomings which reduce their therapeutic index. In this scenario, phytoconstituents that exhibit multifaceted biological activities including anti-inflammatory effects have emerged as a promising therapeutic alternative. However, identification and isolation of phytoconstituents from medicinal herbs is a cumbersome method that is linked to profound uncertainty. Hence, present study aimed to identify prospective phytoconstituents as inhibitors of RHD of NF-κB p65 by utilizing in silico approach. Virtual screening of 2821 phytoconstituents was performed against protein model. Out of 2821 phytoconstituents, 162 phytoconstituents displayed a higher binding affinity (≤ -8.0 kcal/mol). These 162 phytoconstituents were subjected to ADMET predictions, and 15 of them were found to satisfy Lipinski's rule of five and showed favorable pharmacokinetic properties. Among these 15 phytoconstituents, 5 phytoconstituents with high docking scores i.e. silibinin, bismurrayaquinone A, withafastuosin B, yuccagenin, (+)-catechin 3-gallate were selected for molecular dynamics (MD) simulation analysis. Results of MD simulation indicated that withafastuosin B, (+)-catechin 3-gallate and yuccagenin produced a compact and stable complex with protein without significant variations in conformation. Relative binding energy analysis of best hit molecules indicate that withafastuosin B, and (+)-catechin 3-gallate exhibit high binding affinity with target protein among other lead molecules. Findings of study suggest that these phytoconstituents could serve as promising anti-inflammatory agents for treatment of burn wounds by inhibiting the RHD of NF-κB p65.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Saswati Pattnaik
- School of Applied Sciences, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India
| | - Sneha Murmu
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, India
| | - Bibhu Prasad Rath
- School of Applied Sciences, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India
| | - Mahender Kumar Singh
- Data Science Laboratory, National Brain Research Centre, Gurgaon, Haryana, India
| | - Sunil Kumar
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, India
| | - Chandana Mohanty
- School of Applied Sciences, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India
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13
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Haghir Ebrahim Abadi MH, Ghasemlou A, Bayani F, Sefidbakht Y, Vosough M, Mozaffari-Jovin S, Uversky VN. AI-driven covalent drug design strategies targeting main protease (m pro) against SARS-CoV-2: structural insights and molecular mechanisms. J Biomol Struct Dyn 2024:1-29. [PMID: 38287509 DOI: 10.1080/07391102.2024.2308769] [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: 11/09/2023] [Accepted: 01/17/2024] [Indexed: 01/31/2024]
Abstract
The emergence of new SARS-CoV-2 variants has raised concerns about the effectiveness of COVID-19 vaccines. To address this challenge, small-molecule antivirals have been proposed as a crucial therapeutic option. Among potential targets for anti-COVID-19 therapy, the main protease (Mpro) of SARS-CoV-2 is important due to its essential role in the virus's life cycle and high conservation. The substrate-binding region of the core proteases of various coronaviruses, including SARS-CoV-2, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), could be used for the generation of new protease inhibitors. Various drug discovery methods have employed a diverse range of strategies, targeting both monomeric and dimeric forms, including drug repurposing, integrating virtual screening with high-throughput screening (HTS), and structure-based drug design, each demonstrating varying levels of efficiency. Covalent inhibitors, such as Nirmatrelvir and MG-101, showcase robust and high-affinity binding to Mpro, exhibiting stable interactions confirmed by molecular docking studies. Development of effective antiviral drugs is imperative to address potential pandemic situations. This review explores recent advances in the search for Mpro inhibitors and the application of artificial intelligence (AI) in drug design. AI leverages vast datasets and advanced algorithms to streamline the design and identification of promising Mpro inhibitors. AI-driven drug discovery methods, including molecular docking, predictive modeling, and structure-based drug repurposing, are at the forefront of identifying potential candidates for effective antiviral therapy. In a time when COVID-19 potentially threat global health, the quest for potent antiviral solutions targeting Mpro could be critical for inhibiting the virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | - Fatemeh Bayani
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Yahya Sefidbakht
- Protein Research Center, Shahid Beheshti University, Tehran, Iran
| | - Massoud Vosough
- Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Sina Mozaffari-Jovin
- Department of Medical Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Vladimir N Uversky
- Department of Molecular Medicine, University of South Florida, Tampa, FL, USA
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14
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Cordell GA. The contemporary nexus of medicines security and bioprospecting: a future perspective for prioritizing the patient. NATURAL PRODUCTS AND BIOPROSPECTING 2024; 14:11. [PMID: 38270809 PMCID: PMC10811317 DOI: 10.1007/s13659-024-00431-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 01/14/2024] [Indexed: 01/26/2024]
Abstract
Reacting to the challenges presented by the evolving nexus of environmental change, defossilization, and diversified natural product bioprospecting is vitally important for advancing global healthcare and placing patient benefit as the most important consideration. This overview emphasizes the importance of natural and synthetic medicines security and proposes areas for global research action to enhance the quality, safety, and effectiveness of sustainable natural medicines. Following a discussion of some contemporary factors influencing natural products, a rethinking of the paradigms in natural products research is presented in the interwoven contexts of the Fourth and Fifth Industrial Revolutions and based on the optimization of the valuable assets of Earth. Following COP28, bioprospecting is necessary to seek new classes of bioactive metabolites and enzymes for chemoenzymatic synthesis. Focus is placed on those performance and practice modifications which, in a sustainable manner, establish the patient, and the maintenance of their prophylactic and treatment needs, as the priority. Forty initiatives for natural products in healthcare are offered for the patient and the practitioner promoting global action to address issues of sustainability, environmental change, defossilization, quality control, product consistency, and neglected diseases to assure that quality natural medicinal agents will be accessible for future generations.
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Affiliation(s)
- Geoffrey A Cordell
- Natural Products Inc., 1320 Ashland Avenue, Evanston, IL, 60201, USA.
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL, 32610, USA.
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15
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Liu Y, Sang G, Liu Z, Pan Y, Cheng J, Zhang Y. MPTN: A message-passing transformer network for drug repurposing from knowledge graph. Comput Biol Med 2024; 168:107800. [PMID: 38043469 DOI: 10.1016/j.compbiomed.2023.107800] [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: 09/15/2023] [Revised: 11/09/2023] [Accepted: 11/29/2023] [Indexed: 12/05/2023]
Abstract
Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.
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Affiliation(s)
- Yuanxin Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Guoming Sang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Zhi Liu
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yilin Pan
- School of Artificial Intelligence, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Junkai Cheng
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, Liaoning, China.
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16
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Zhang C, Liu J, Sui Y, Liu S, Yang M. In silico drug repurposing carvedilol and its metabolites against SARS-CoV-2 infection using molecular docking and molecular dynamic simulation approaches. Sci Rep 2023; 13:21404. [PMID: 38049492 PMCID: PMC10696093 DOI: 10.1038/s41598-023-48398-6] [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: 08/18/2023] [Accepted: 11/26/2023] [Indexed: 12/06/2023] Open
Abstract
The pandemic of coronavirus disease 2019 (COVID-19) caused by the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a significant impact on the economy and public health worldwide. Therapeutic options such as drugs and vaccines for this newly emerged disease are eagerly desired due to the high mortality. Using the U.S. Food and Drug Administration (FDA) approved drugs to treat a new disease or entirely different diseases, in terms of drug repurposing, minimizes the time and cost of drug development compared to the de novo design of a new drug. Drug repurposing also has some other advantages such as reducing safety evaluation to accelerate drug application on time. Carvedilol, a non-selective beta-adrenergic blocker originally designed to treat high blood pressure and manage heart disease, has been shown to impact SARS-CoV-2 infection in clinical observation and basic studies. Here, we applied computer-aided approaches to investigate the possibility of repurposing carvedilol to combat SARS-CoV-2 infection. The molecular mechanisms and potential molecular targets of carvedilol were identified by evaluating the interactions of carvedilol with viral proteins. Additionally, the binding affinities of in vivo metabolites of carvedilol with selected targets were evaluated. The docking scores for carvedilol and its metabolites with RdRp were - 10.0 kcal/mol, - 9.8 kcal/mol (1-hydroxyl carvedilol), - 9.7 kcal/mol (3-hydroxyl carvedilol), - 9.8 kcal/mol (4-hydroxyl carvedilol), - 9.7 kcal/mol (5-hydroxyl carvedilol), - 10.0 kcal/mol (8-hydroxyl carvedilol), and - 10.1 kcal/mol (O-desmethyl carvedilol), respectively. Using the molecular dynamics simulation (100 ns) method, we further confirmed the stability of formed complexes of RNA-dependent RNA polymerase (RdRp) and carvedilol or its metabolites. Finally, the drug-target interaction mechanisms that contribute to the complex were investigated. Overall, this study provides the molecular targets and mechanisms of carvedilol and its metabolites as repurposed drugs to fight against SARS-CoV-2 infection.
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Affiliation(s)
- Chunye Zhang
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65212, USA
| | - Jiazheng Liu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau, 999078, China
| | - Yuxiang Sui
- School of Life Science, Shanxi Normal University, Linfen, 041004, Shanxi, China
| | - Shuai Liu
- The First Affiliated Hospital, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Ming Yang
- Department of Surgery, University of Missouri, Columbia, MO, 65212, USA.
- NextGen Precision Health Institution, University of Missouri, Columbia, MO, 65212, USA.
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17
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Amoroso N, Gambacorta N, Mastrolorito F, Togo MV, Trisciuzzi D, Monaco A, Pantaleo E, Altomare CD, Ciriaco F, Nicolotti O. Making sense of chemical space network shows signs of criticality. Sci Rep 2023; 13:21335. [PMID: 38049451 PMCID: PMC10696027 DOI: 10.1038/s41598-023-48107-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy.
| | - Nicola Gambacorta
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
- Division of Medical Genetics, Fondazione IRCCS-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
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18
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Ogbodo UC, Enejoh OA, Okonkwo CH, Gnanasekar P, Gachanja PW, Osata S, Atanda HC, Iwuchukwu EA, Achilonu I, Awe OI. Computational identification of potential inhibitors targeting cdk1 in colorectal cancer. Front Chem 2023; 11:1264808. [PMID: 38099190 PMCID: PMC10720044 DOI: 10.3389/fchem.2023.1264808] [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: 07/21/2023] [Accepted: 11/13/2023] [Indexed: 12/17/2023] Open
Abstract
Introduction: Despite improved treatment options, colorectal cancer (CRC) remains a huge public health concern with a significant impact on affected individuals. Cell cycle dysregulation and overexpression of certain regulators and checkpoint activators are important recurring events in the progression of cancer. Cyclin-dependent kinase 1 (CDK1), a key regulator of the cell cycle component central to the uncontrolled proliferation of malignant cells, has been reportedly implicated in CRC. This study aimed to identify CDK1 inhibitors with potential for clinical drug research in CRC. Methods: Ten thousand (10,000) naturally occurring compounds were evaluated for their inhibitory efficacies against CDK1 through molecular docking studies. The stability of the lead compounds in complex with CDK1 was evaluated using molecular dynamics simulation for one thousand (1,000) nanoseconds. The top-scoring candidates' ADME characteristics and drug-likeness were profiled using SwissADME. Results: Four hit compounds, namely, spiraeoside, robinetin, 6-hydroxyluteolin, and quercetagetin were identified from molecular docking analysis to possess the least binding scores. Molecular dynamics simulation revealed that robinetin and 6-hydroxyluteolin complexes were stable within the binding pocket of the CDK1 protein. Discussion: The findings from this study provide insight into novel candidates with specific inhibitory CDK1 activities that can be further investigated through animal testing, clinical trials, and drug development research for CRC treatment.
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Affiliation(s)
| | - Ojochenemi A. Enejoh
- Genomics and Bioinformatics Department, National Biotechnology Development Agency, Abuja, Nigeria
| | - Chinelo H. Okonkwo
- Department of Pharmacology and Toxicology, University of Nigeria, Nsukka, Nigeria
| | | | - Pauline W. Gachanja
- Department of Biochemistry and Biotechnology, Pwani University, Kilifi, Kenya
| | - Shamim Osata
- Department of Biochemistry, University of Nairobi, Nairobi, Kenya
| | - Halimat C. Atanda
- Biotechnology Department, Federal University of Technology, Akure, Nigeria
| | - Emmanuel A. Iwuchukwu
- Protein Structure-Function Research Unit, School of Molecular and Cell Biology, Faculty of Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Ikechukwu Achilonu
- Protein Structure-Function Research Unit, School of Molecular and Cell Biology, Faculty of Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Olaitan I. Awe
- Department of Computer Science, University of Ibadan, Ibadan, Nigeria
- African Society for Bioinformatics and Computational Biology, Cape Town, South Africa
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19
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Mullowney MW, Duncan KR, Elsayed SS, Garg N, van der Hooft JJJ, Martin NI, Meijer D, Terlouw BR, Biermann F, Blin K, Durairaj J, Gorostiola González M, Helfrich EJN, Huber F, Leopold-Messer S, Rajan K, de Rond T, van Santen JA, Sorokina M, Balunas MJ, Beniddir MA, van Bergeijk DA, Carroll LM, Clark CM, Clevert DA, Dejong CA, Du C, Ferrinho S, Grisoni F, Hofstetter A, Jespers W, Kalinina OV, Kautsar SA, Kim H, Leao TF, Masschelein J, Rees ER, Reher R, Reker D, Schwaller P, Segler M, Skinnider MA, Walker AS, Willighagen EL, Zdrazil B, Ziemert N, Goss RJM, Guyomard P, Volkamer A, Gerwick WH, Kim HU, Müller R, van Wezel GP, van Westen GJP, Hirsch AKH, Linington RG, Robinson SL, Medema MH. Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov 2023; 22:895-916. [PMID: 37697042 DOI: 10.1038/s41573-023-00774-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 09/13/2023]
Abstract
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.
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Affiliation(s)
| | - Katherine R Duncan
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Somayah S Elsayed
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Neha Garg
- School of Chemistry and Biochemistry, Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
| | - Nathaniel I Martin
- Biological Chemistry Group, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Barbara R Terlouw
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Friederike Biermann
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Kai Blin
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Marina Gorostiola González
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
- ONCODE institute, Leiden, The Netherlands
| | - Eric J N Helfrich
- Institute of Molecular Bio Science, Goethe-University Frankfurt, Frankfurt am Main, Germany
- LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt am Main, Germany
| | - Florian Huber
- Center for Digitalization and Digitality, Hochschule Düsseldorf, Düsseldorf, Germany
| | - Stefan Leopold-Messer
- Institut für Mikrobiologie, Eidgenössische Technische Hochschule (ETH) Zürich, Zürich, Switzerland
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Jena, Germany
| | - Tristan de Rond
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
| | - Jeffrey A van Santen
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Maria Sorokina
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller University, Jena, Germany
- Pharmaceuticals R&D, Bayer AG, Berlin, Germany
| | - Marcy J Balunas
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Mehdi A Beniddir
- Équipe "Chimie des Substances Naturelles", Université Paris-Saclay, CNRS, BioCIS, Orsay, France
| | - Doris A van Bergeijk
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | - Laura M Carroll
- Structural and Computational Biology Unit, EMBL, Heidelberg, Germany
| | - Chase M Clark
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Chao Du
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
| | | | - Francesca Grisoni
- Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Centre for Living Technologies, Alliance TU/e, WUR, UU, UMC Utrecht, Utrecht, The Netherlands
| | | | - Willem Jespers
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Olga V Kalinina
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Drug Bioinformatics, Medical Faculty, Saarland University, Homburg, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | | | - Hyunwoo Kim
- College of Pharmacy and Integrated Research Institute for Drug Development, Dongguk University Seoul, Goyang-si, Republic of Korea
| | - Tiago F Leao
- Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Joleen Masschelein
- Center for Microbiology, VIB-KU Leuven, Heverlee, Belgium
- Department of Biology, KU Leuven, Heverlee, Belgium
| | - Evan R Rees
- Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, USA
| | - Raphael Reher
- Institute of Pharmaceutical Biology and Biotechnology, University of Marburg, Marburg, Germany
- Institute of Pharmacy, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Daniel Reker
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Duke Microbiome Center, Duke University, Durham, NC, USA
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence, Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Michael A Skinnider
- Adapsyn Bioscience, Hamilton, Ontario, Canada
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Allison S Walker
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Egon L Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Barbara Zdrazil
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridgeshire, UK
| | - Nadine Ziemert
- Interfaculty Institute for Microbiology and Infection Medicine Tuebingen (IMIT), Institute for Bioinformatics and Medical Informatics (IBMI), University of Tuebingen, Tuebingen, Germany
| | | | - Pierre Guyomard
- Bonsai team, CRIStAL - Centre de Recherche en Informatique Signal et Automatique de Lille, Université de Lille, Villeneuve d'Ascq Cedex, France
| | - Andrea Volkamer
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- In silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - William H Gerwick
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany
- Department of Pharmacy, Saarland University, Saarbrücken, Germany
- German Center for infection research (DZIF), Braunschweig, Germany
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany
| | - Gilles P van Wezel
- Department of Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands
- Netherlands Institute of Ecology, NIOO-KNAW, Wageningen, The Netherlands
| | - Gerard J P van Westen
- Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden, The Netherlands.
| | - Anna K H Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Saarbrücken, Germany.
- Department of Pharmacy, Saarland University, Saarbrücken, Germany.
- German Center for infection research (DZIF), Braunschweig, Germany.
- Helmholtz International Lab for Anti-Infectives, Saarbrücken, Germany.
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, British Columbia, Canada.
| | - Serina L Robinson
- Department of Environmental Microbiology, Eawag: Swiss Federal Institute for Aquatic Science and Technology, Dübendorf, Switzerland.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Institute of Biology, Leiden University, Leiden, The Netherlands.
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20
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Mohammadjani N, Karimi S, Moetasam Zorab M, Ashengroph M, Alavi M. Comparative molecular docking and toxicity between carbon-capped metal oxide nanoparticles and standard drugs in cancer and bacterial infections. BIOIMPACTS : BI 2023; 14:27778. [PMID: 38505671 PMCID: PMC10945298 DOI: 10.34172/bi.2023.27778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/20/2023] [Accepted: 08/01/2023] [Indexed: 03/21/2024]
Abstract
Introduction Nanoparticles (NPs) are of great interest in the design of various drugs due to their high surface-to-volume ratio, which result from their unique physicochemical properties. Because of the importance of examining the interactions between newly designed particles with different targets in the case of various diseases, techniques for examining the interactions between these particles with different targets, many of which are proteins, are now very common. Methods In this study, the interactions between metal oxide nanoparticles (MONPs) covered with a carbon layer (Ag2O3, CdO, CuO, Fe2O3, FeO, MgO, MnO, and ZnO NPs) and standard drugs related to the targets of Cancer and bacterial infections were investigated using the molecular docking technique with AutoDock 4.2.6 software tool. Finally, the PRO TOX-II online tool was used to compare the toxicity (LD50) and molecular weight of these MONPs to standard drugs. Results According to the data obtained from the semi flexible molecular docking process, MgO and Fe2O3 NPs performed better than standard drugs in several cases. MONPs typically have a lower 50% lethal dose (LD50) and a higher molecular weight than standard drugs. MONPs have shown a minor difference in binding energy for different targets in three diseases, which probably can be attributed to the specific physicochemical and pharmacophoric properties of MONPs. Conclusion The toxicity of MONPs is one of the major challenges in the development of drugs based on them. According to the results of these molecular docking studies, MgO and Fe2O3 NPs had the highest efficiency among the investigated MONPs.
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Affiliation(s)
- Navid Mohammadjani
- Department of Biological Science, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan, Iran
| | - Sahand Karimi
- Department of Biological Science, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan, Iran
| | | | - Morahem Ashengroph
- Department of Biological Science, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan, Iran
| | - Mehran Alavi
- Department of Biological Science, Faculty of Science, University of Kurdistan, Sanandaj, Kurdistan, Iran
- Nanobiotechnology Department, Faculty of Innovative Science and Technology, Razi University, Kermanshah, Iran
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21
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Navitha Reddy G, Jogvanshi A, Naikwadi S, Sonti R. Nirmatrelvir and ritonavir combination: an antiviral therapy for COVID-19. Expert Rev Anti Infect Ther 2023; 21:943-955. [PMID: 37525997 DOI: 10.1080/14787210.2023.2241638] [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: 03/15/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023]
Abstract
INTRODUCTION The emergence of the Omicron SARS-CoV-2 variant of concern in late November 2021 presaged yet another stage of the COVID-19 pandemic. Paxlovid, a co-packaged dosage form of two antiviral drugs (nirmatrelvir and ritonavir) developed by Pfizer, received its first FDA Emergency Use Authorization (EUA) and conditional marketing by European Medical Agency in patients at high risk of developing severe COVID-19. AREAS COVERED We reviewed the timeline of the drug nirmatrelvir from its discovery to authorization by FDA. After 1 year of its authorization, numerous studies and reports on paxlovid's use and post-use consequences are available. This review summarizes the complete journey of paxlovid from its development, preclinical studies, clinical trials, regulatory approvals, ongoing clinical trials, and safety measures, followed by discussions on recent updates on drug-drug interactions, adverse effects, and relapse of COVID-19. EXPERT OPINION Paxlovid, a new oral antiviral therapy for COVID-19, has shown promising results in clinical trials and has the potential to be effective against the pandemic, particularly for individuals at high risk of severe illness. Comorbidity usage and pharmacovigilance will play a significant stake in the future of paxlovid development. Second-generation Mpro inhibitors play an important role in the upcoming problems associated with COVID-19.
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Affiliation(s)
- Gangireddy Navitha Reddy
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
| | - Akanksha Jogvanshi
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
| | - Sana Naikwadi
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
| | - Rajesh Sonti
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India
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22
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Wu X, Li Z, Chen G, Yin Y, Chen CYC. Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease. Front Mol Biosci 2023; 10:1227371. [PMID: 37441162 PMCID: PMC10334190 DOI: 10.3389/fmolb.2023.1227371] [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: 05/29/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug-target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein-protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA.
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Affiliation(s)
- Xialin Wu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhuojian Li
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Yiyang Yin
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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23
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Kari S, Murugesan A, Thiyagarajan R, Kidambi S, Razzokov J, Selvaraj C, Kandhavelu M, Marimuthu P. Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification. Biomed Pharmacother 2023; 160:114320. [PMID: 36716660 DOI: 10.1016/j.biopha.2023.114320] [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: 12/10/2022] [Revised: 01/17/2023] [Accepted: 01/26/2023] [Indexed: 01/29/2023] Open
Abstract
Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient's is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 -an orphan G protein-coupled receptor- is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs -ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)- as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC50 of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)- silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.
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Affiliation(s)
- Sana Kari
- Molecular Signaling Group, Faculty of Medicine and Health Technology, Tampere University and BioMediTech, P.O.Box 553, 33101 Tampere, Finland
| | - Akshaya Murugesan
- Molecular Signaling Group, Faculty of Medicine and Health Technology, Tampere University and BioMediTech, P.O.Box 553, 33101 Tampere, Finland
| | - Ramesh Thiyagarajan
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Kingdom of Saudi Arabia
| | - Srivatsan Kidambi
- Department of Chemical and Biomolecular Engineering, University of Nebraska-Lincoln, 820 N 16th Street, 207 Othmer Hall, NE 68588, USA
| | - Jamoliddin Razzokov
- Institute of Fundamental and Applied Research, National Research University TIIAME, Kori Niyoziy 39, 100000 Tashkent, Uzbekistan; College of Engineering, Akfa University, Milliy Bog Street 264, 111221 Tashkent, Uzbekistan; Institute of Material Sciences, Academy of Sciences, Chingiz Aytmatov 2b, 100084 Tashkent, Uzbekistan; Department of Physics, National University of Uzbekistan, Universitet 4, 100174 Tashkent, Uzbekistan; Laboratory of Experimental Biophysics, Centre for Advanced Technologies, Universitet 7, 100174 Tashkent, Uzbekistan
| | - Chandrabose Selvaraj
- Department of Biotechnology, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, Tamil Nadu, India
| | - Meenakshisundaram Kandhavelu
- Molecular Signaling Group, Faculty of Medicine and Health Technology, Tampere University and BioMediTech, P.O.Box 553, 33101 Tampere, Finland.
| | - Parthiban Marimuthu
- Pharmaceutical Science Laboratory (PSL - Pharmacy) and Structural Bioinformatics Laboratory (SBL - Biochemistry), Faculty of Science and Engineering, Åbo Akademi University, FI-20520 Turku, Finland.
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24
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Otarigho B, Falade MO. Computational Screening of Approved Drugs for Inhibition of the Antibiotic Resistance Gene mecA in Methicillin-Resistant Staphylococcus aureus (MRSA) Strains. BIOTECH 2023; 12:biotech12020025. [PMID: 37092469 PMCID: PMC10123713 DOI: 10.3390/biotech12020025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Antibiotic resistance is a critical problem that results in a high morbidity and mortality rate. The process of discovering new chemotherapy and antibiotics is challenging, expensive, and time-consuming, with only a few getting approved for clinical use. Therefore, screening already-approved drugs to combat pathogens such as bacteria that cause serious infections in humans and animals is highly encouraged. In this work, we aim to identify approved antibiotics that can inhibit the mecA antibiotic resistance gene found in methicillin-resistant Staphylococcus aureus (MRSA) strains. The MecA protein sequence was utilized to perform a BLAST search against a drug database containing 4302 approved drugs. The results revealed that 50 medications, including known antibiotics for other bacterial strains, targeted the mecA antibiotic resistance gene. In addition, a structural similarity approach was employed to identify existing antibiotics for S. aureus, followed by molecular docking. The results of the docking experiment indicated that six drugs had a high binding affinity to the mecA antibiotic resistance gene. Furthermore, using the structural similarity strategy, it was discovered that afamelanotide, an approved drug with unclear antibiotic activity, had a strong binding affinity to the MRSA-MecA protein. These findings suggest that certain already-approved drugs have potential in chemotherapy against drug-resistant pathogenic bacteria, such as MRSA.
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25
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smProdrugs: A repository of small molecule prodrugs. Eur J Med Chem 2023; 249:115153. [PMID: 36724634 DOI: 10.1016/j.ejmech.2023.115153] [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: 10/20/2022] [Revised: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 01/29/2023]
Abstract
In modern drug discovery and development, the prodrug approach has become a crucial strategy for enhancing the pharmacokinetic profiles of drugs. A prodrug is a chemical compound, which gets metabolized into a pharmacologically active form (drug) inside the body after its administration. In the current work, we report 'smProdrugs' (http://cheminfolab.in/databases/prodrug/), which is one of the first exclusive databases on small molecule prodrugs. It stores the structures, physicochemical properties and experimental ADMET data manually curated from literature. SmProdrugs lists 626 small molecule prodrugs and their active compounds with the above mentioned experimental data from 1808 research articles and 61 patents have been stored. The information page of each record gives the structures and properties of the prodrug and the active drug side by side which makes it easy for the user to instantly compare them. The structural modifications in the prodrug/active drugs are highlighted in a different colour for easy comparison. Experimental data has been curated from the downloaded PubMed and patent articles and were catalogued in a tabular form with more than 25 fields under sub-sections i) name and structures of the prodrugs and their active compounds, ii) mode of activation of the prodrug and enzyme/biocatalyst involved in the conversion, iii) indications/disease, iv) pharmacological target, v) experimental pharmacokinetic properties such as solubility, absorption, volume of distribution, half-life, clearance etc. and vi) information on the purpose/gain from the prodrug strategies. Considering the ever expanding utility of the prodrug approach smProdrugs will be of great use to the scientific community working on rational design of small molecule prodrugs.
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26
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Ali Y, Imtiaz H, Tahir MM, Gul F, Saddozai UAK, ur Rehman A, Ren ZG, Khattak S, Ji XY. Fragment-Based Approaches Identified Tecovirimat-Competitive Novel Drug Candidate for Targeting the F13 Protein of the Monkeypox Virus. Viruses 2023; 15:v15020570. [PMID: 36851785 PMCID: PMC9959752 DOI: 10.3390/v15020570] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/13/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
Monkeypox is a serious public health issue in tropical and subtropical areas. Antivirals that target monkeypox proteins might lead to more effective and efficient therapy. The F13 protein is essential for the growth and maturation of the monkeypox virus. F13 inhibition might be a viable therapeutic target for monkeypox. The in silico fragment-based drug discovery method for developing antivirals may provide novel therapeutic options. In this study, we generated 800 compounds based on tecovirimat, an FDA-approved drug that is efficacious at nanomolar quantities against monkeypox. These compounds were evaluated to identify the most promising fragments based on binding affinity and pharmacological characteristics. The top hits from the chemical screening were docked into the active site of the F13 protein. Molecular dynamics simulations were performed on the top two probable new candidates from molecular docking. The ligand-enzyme interaction analysis revealed that the C2 ligand had lower binding free energy than the standard ligand tecovirimat. Water bridges, among other interactions, were shown to stabilize the C2 molecule. Conformational transitions and secondary structure changes in F13 protein upon C2 binding show more native three-dimensional folding of the protein. Prediction of pharmacological properties revealed that compound C2 may be promising as a drug candidate for monkeypox fever. However, additional in vitro and in vivo testing is required for validation.
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Affiliation(s)
- Yasir Ali
- National Center for Bioinformatics, Quaid-i-Azam University, Islamabad 45320, Pakistan
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Hina Imtiaz
- Tehsil Headquarter Hospital Bhera, Sargodha, Punjab 40540, Pakistan
| | | | - Fouzia Gul
- National Center for Bioinformatics, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Umair Ali Khan Saddozai
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
| | - Ashfaq ur Rehman
- Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, CA 2697-3900, USA
| | - Zhi-Guang Ren
- The First Affiliated Hospital, Henan University, Kaifeng 475004, China
- Correspondence: (Z.-G.R.); (S.K.); (X.-Y.J.)
| | - Saadullah Khattak
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
- Correspondence: (Z.-G.R.); (S.K.); (X.-Y.J.)
| | - Xin-Ying Ji
- Henan International Joint Laboratory for Nuclear Protein Regulation, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China
- Kaifeng Key Laboratory for Infectious Diseases and Biosafety, Kaifeng 475004, China
- Faculty of Basic Medical Subjects, Shu-Qing Medical College of Zhengzhou, Mazhai, Erqi District, Zhengzhou 450064, China
- Correspondence: (Z.-G.R.); (S.K.); (X.-Y.J.)
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27
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Rathod S, Shinde K, Porlekar J, Choudhari P, Dhavale R, Mahuli D, Tamboli Y, Bhatia M, Haval KP, Al-Sehemi AG, Pannipara M. Computational Exploration of Anti-cancer Potential of Flavonoids against Cyclin-Dependent Kinase 8: An In Silico Molecular Docking and Dynamic Approach. ACS OMEGA 2023; 8:391-409. [PMID: 36643495 PMCID: PMC9835631 DOI: 10.1021/acsomega.2c04837] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Over the centuries, cancer has been considered one of the significant health threats. It holds the position in the list of deadliest diseases over the globe. In women, breast cancer is the most common among many cancers and is the second most common cancer all over the world, while lung cancer is the first. Cyclin-dependent kinase 8 (CDK8) has been identified as a critical oncogenic driver that is found in breast cancer and associated with tumor progression. Flavonoids were virtually screened against CDK8 using molecular docking, drug-likeness, ADMET prediction, and a molecular dynamics (MD) simulation approach to determine the potential flavonoid structure against CDK8. The results indicated that ZINC000005854718 showed the highest negative binding affinity of -10.7 kcal/mol with the targeted protein and passed all the drug-likeness parameters. Performed molecular dynamics simulation showed that docked complex systems have good conformational stability over 100 ns in different temperatures (298, 300, 305, 310, and 320 K). The comparison between calculated binding free energy via MM/PB(GB)SA methods and binding affinity calculated via molecular docking suggested tight binding of ZINC000005854718 with targeted protein. The results concluded that ZINC000005854718 has drug-like properties with tight and stable binding with the targeted protein.
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Affiliation(s)
- Sanket Rathod
- Department
of Pharmaceutical Chemistry, Bharati Vidyapeeth
College of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Ketaki Shinde
- Department
of Quality Assurance Techniques, Poona College of Pharmacy, Bharati Vidyapeeth Deemed University, Pune 411 038, Maharashtra, India
| | - Jaykedar Porlekar
- Department
of Pharmaceutics, Bharati Vidyapeeth College
of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Prafulla Choudhari
- Department
of Pharmaceutical Chemistry, Bharati Vidyapeeth
College of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Rakesh Dhavale
- Department
of Pharmaceutics, Bharati Vidyapeeth College
of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Deepak Mahuli
- Department
of Pharmacology, Bharati Vidyapeeth College
of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Yasinalli Tamboli
- Wockhardt
Research Centre, D-4, MIDC, Chikalthana, Aurangabad 431 006, Maharashtra, India
| | - Manish Bhatia
- Department
of Pharmaceutical Chemistry, Bharati Vidyapeeth
College of Pharmacy, Kolhapur 416 013, Maharashtra, India
| | - Kishan P. Haval
- Department
of Chemistry, Dr. Babasaheb Ambedkar Marathwada
University Sub Campus, Osmanabad 413501, Maharashtra, India
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Nada H, Elkamhawy A, Lee K. Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations. PeerJ 2022; 10:e14120. [PMID: 36225900 PMCID: PMC9549888 DOI: 10.7717/peerj.14120] [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: 06/10/2022] [Accepted: 09/05/2022] [Indexed: 01/25/2023] Open
Abstract
The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.
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Affiliation(s)
- Hossam Nada
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, South Korea,Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Badr University in Cairo, Cairo, Egypt
| | - Ahmed Elkamhawy
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, South Korea,Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, Egypt
| | - Kyeong Lee
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, South Korea
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29
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Hu C, Zeng Z, Ma D, Yin Z, Zhao S, Chen T, Tang L, Zuo S. Discovery of novel IDH1-R132C inhibitors through structure-based virtual screening. Front Pharmacol 2022; 13:982375. [PMID: 36160383 PMCID: PMC9491111 DOI: 10.3389/fphar.2022.982375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Isocitrate dehydrogenase (IDH) belongs to a family of enzymes involved in glycometabolism. It is found in many living organisms and is one of the most mutated metabolic enzymes. In the current study, we identified novel IDH1-R132C inhibitors using docking-based virtual screening and cellular inhibition assays. A total of 100 molecules with high docking scores were obtained from docking-based virtual screening. The cellular inhibition assay demonstrated five compounds at a concentration of 10 μM could inhibit cancer cells harboring the IDH1-R132C mutation proliferation by > 50%. The compound (T001-0657) showed the most potent effect against cancer cells harboring the IDH1-R132C mutation with a half-maximal inhibitory concentration (IC50) value of 1.311 μM. It also showed a cytotoxic effect against cancer cells with wild-type IDH1 and normal cells with IC50 values of 49.041 μM and >50 μM, respectively. Molecular dynamics simulations were performed to investigate the stability of the kinase structure binding of allosteric inhibitor compound A and the identified compound T001-0657 binds to IDH1-R132C. Root-mean-square deviation, root-mean-square fluctuation, and binding free energy calculations showed that both compounds bind tightly to IDH1-R132C. In conclusion, the compound identified in this study had high selectivity for cancer cells harboring IDH1-R132C mutation and could be considered a promising hit compound for further development of IDH1-R132C inhibitors.
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Affiliation(s)
- Chujiao Hu
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhirui Zeng
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Dan Ma
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- Department of Hematology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Zhixin Yin
- College of Pharmacy, Guizhou Medical University, Guiyang, China
| | - Shanshan Zhao
- College of Pharmacy, Guizhou Medical University, Guiyang, China
| | - Tengxiang Chen
- Transformation Engineering Research Center of Chronic Disease Diagnosis and Treatment, Department of Physiology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
| | - Lei Tang
- State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China
- Guizhou Provincial Engineering Technology Research Center for Chemical Drug R and D, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
| | - Shi Zuo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- Precision Medicine Research Institute of Guizhou, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
- *Correspondence: Tengxiang Chen, ; Lei Tang, ; Shi Zuo,
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