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Hakami MA. Harnessing machine learning potential for personalised drug design and overcoming drug resistance. J Drug Target 2024; 32:918-930. [PMID: 38842417 DOI: 10.1080/1061186x.2024.2365934] [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: 05/09/2024] [Revised: 06/01/2024] [Accepted: 06/04/2024] [Indexed: 06/07/2024]
Abstract
Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.
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Affiliation(s)
- Mohammed Ageeli Hakami
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Shaqra University, Al-Quwayiyah, Riyadh, Saudi Arabia
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2
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Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, Boon A, Qin X, Liu L, Gu H. Artificial Intelligence in Metabolomics: A Current Review. Trends Analyt Chem 2024; 178:117852. [PMID: 39071116 PMCID: PMC11271759 DOI: 10.1016/j.trac.2024.117852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.
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Affiliation(s)
- Jinhua Chi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Jingmin Shu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Ming Li
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA
- University of Arizona College of Medicine, Phoenix, AZ 85004, USA
| | - Rekha Mudappathi
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Yan Jin
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Freeman Lewis
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Alexandria Boon
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
| | - Xiaoyan Qin
- College of Liberal Arts and Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
- Center for Translational Science, Florida International University, Port St. Lucie, FL 34987, USA
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Muegge I, Bentzien J, Ge Y. Perspectives on current approaches to virtual screening in drug discovery. Expert Opin Drug Discov 2024:1-11. [PMID: 39132881 DOI: 10.1080/17460441.2024.2390511] [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: 06/29/2024] [Accepted: 08/06/2024] [Indexed: 08/13/2024]
Abstract
INTRODUCTION For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods. AREAS COVERED The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS. EXPERT OPINION The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.
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Affiliation(s)
- Ingo Muegge
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Jörg Bentzien
- Research department, Alkermes, Inc, Waltham, MA, USA
| | - Yunhui Ge
- Research department, Alkermes, Inc, Waltham, MA, USA
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Aittokallio T, Fang EF. Editorial overview-Artificial intelligence methodologies in structural biology: Bridging the gap to medical applications. Curr Opin Struct Biol 2024; 87:102862. [PMID: 38870638 DOI: 10.1016/j.sbi.2024.102862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Affiliation(s)
- Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway; Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway; iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
| | - Evandro Fei Fang
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway.
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Sinha K, Parwez S, Mv S, Yadav A, Siddiqi MI, Banerjee D. Machine learning and biological evaluation-based identification of a potential MMP-9 inhibitor, effective against ovarian cancer cells SKOV3. J Biomol Struct Dyn 2024; 42:6823-6841. [PMID: 37504963 DOI: 10.1080/07391102.2023.2240416] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023]
Abstract
MMP-9, also known as gelatinase B, is a zinc-metalloproteinase family protein that plays a key role in the degradation of the extracellular matrix (ECM). The normal function of MMP-9 includes the breakdown of ECM, a process that aids in normal physiological processes such as embryonic development, angiogenesis, etc. Interruptions in these processes due to the over-expression or downregulation of MMP-9 are reported to cause some pathological conditions like neurodegenerative diseases and cancer. In the present study, an integrated approach for ML-based virtual screening of the Maybridge library was carried out and their biological activity was tested in an attempt to identify novel small molecule scaffolds that can inhibit the activity of MMP-9. The top hits were identified and selected for target-based activity against MMP-9 protein using the kit (Biovision K844). Further, MTT assay was performed in various cancer cell lines such as breast (MCF-7, MDA-MB-231), colorectal (HCT119, DL-D-1), cervical (HeLa), lung (A549) and ovarian cancer (SKOV3). Interestingly, one compound viz., RJF02215 exhibited anti-cancer activity selectively in SKOV3. Wound healing assay and colony formation assay performed on SKOV3 cell line in the presence of RJF02215 confirmed that the compound had a significant inhibitory effect on this cell line. Thus, we have identified a novel molecule that can inhibit MMP-9 activity in vitro and inhibits the proliferation of SKOV3 cells. Novel molecules based on the structure of RJF02215 may become a good value addition for the treatment of ovarian cancer by exhibiting selective MMP-9 activity.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Khushboo Sinha
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shahid Parwez
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Shahana Mv
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Ananya Yadav
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
| | - Mohammad Imran Siddiqi
- Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Dibyendu Banerjee
- Cancer Biology Division, CSIR-Central Drug Research Institute, Lucknow, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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Mohammadzadeh-Vardin T, Ghareyazi A, Gharizadeh A, Abbasi K, Rabiee HR. DeepDRA: Drug repurposing using multi-omics data integration with autoencoders. PLoS One 2024; 19:e0307649. [PMID: 39058696 PMCID: PMC11280260 DOI: 10.1371/journal.pone.0307649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.
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Affiliation(s)
- Taha Mohammadzadeh-Vardin
- Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran
| | - Amin Ghareyazi
- Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran
| | - Ali Gharizadeh
- Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran
| | - Karim Abbasi
- Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran
- Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran
| | - Hamid R. Rabiee
- Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. [Translated article] Introducing artificial intelligence to hospital pharmacy departments. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:TS35-TS44. [PMID: 39097375 DOI: 10.1016/j.farma.2024.04.001] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, Spain.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
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González-Pérez Y, Montero Delgado A, Martinez Sesmero JM. Approaching artificial intelligence to Hospital Pharmacy. FARMACIA HOSPITALARIA 2024; 48 Suppl 1:S35-S44. [PMID: 39097366 DOI: 10.1016/j.farma.2024.02.007] [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: 09/14/2023] [Revised: 01/03/2024] [Accepted: 02/14/2024] [Indexed: 08/05/2024] Open
Abstract
Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.
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Affiliation(s)
- Yared González-Pérez
- Servicio de Farmacia, Hospital Universitario de Canarias, San Cristóbal de La Laguna, España.
| | - Alfredo Montero Delgado
- Servicio de Farmacia, Hospital Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, España
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Gauss C, Stone LD, Ghafouri M, Quan D, Johnson J, Fribley AM, Amm HM. Overcoming Resistance to Standard-of-Care Therapies for Head and Neck Squamous Cell Carcinomas. Cells 2024; 13:1018. [PMID: 38920648 PMCID: PMC11201455 DOI: 10.3390/cells13121018] [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: 05/29/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Although there have been some advances during in recent decades, the treatment of head and neck squamous cell carcinoma (HNSCC) remains challenging. Resistance is a major issue for various treatments that are used, including both the conventional standards of care (radiotherapy and platinum-based chemotherapy) and the newer EGFR and checkpoint inhibitors. In fact, all the non-surgical treatments currently used for HNSCC are associated with intrinsic and/or acquired resistance. Herein, we explore the cellular mechanisms of resistance reported in HNSCC, including those related to epigenetic factors, DNA repair defects, and several signaling pathways. This article discusses these mechanisms and possible approaches that can be used to target different pathways to sensitize HNSCC to the existing treatments, obtain better responses to new agents, and ultimately improve the patient outcomes.
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Affiliation(s)
- Chester Gauss
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
| | - Logan D. Stone
- Oral & Maxillofacial Surgery, School of Dentistry, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Mehrnoosh Ghafouri
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
| | - Daniel Quan
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
| | - Jared Johnson
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
| | - Andrew M. Fribley
- Carman and Ann Adams Department of Pediatrics, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (C.G.); (M.G.)
- Department of Otolaryngology Head and Neck Surgery, School of Medicine, Wayne State University, Detroit, MI 48202, USA; (D.Q.)
- Molecular Therapeutics Program, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48202, USA
| | - Hope M. Amm
- Oral & Maxillofacial Surgery, School of Dentistry, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
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Malla R, Viswanathan S, Makena S, Kapoor S, Verma D, Raju AA, Dunna M, Muniraj N. Revitalizing Cancer Treatment: Exploring the Role of Drug Repurposing. Cancers (Basel) 2024; 16:1463. [PMID: 38672545 PMCID: PMC11048531 DOI: 10.3390/cancers16081463] [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: 03/02/2024] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer persists as a global challenge necessitating continual innovation in treatment strategies. Despite significant advancements in comprehending the disease, cancer remains a leading cause of mortality worldwide, exerting substantial economic burdens on healthcare systems and societies. The emergence of drug resistance further complicates therapeutic efficacy, underscoring the urgent need for alternative approaches. Drug repurposing, characterized by the utilization of existing drugs for novel clinical applications, emerges as a promising avenue for addressing these challenges. Repurposed drugs, comprising FDA-approved (in other disease indications), generic, off-patent, and failed medications, offer distinct advantages including established safety profiles, cost-effectiveness, and expedited development timelines compared to novel drug discovery processes. Various methodologies, such as knowledge-based analyses, drug-centric strategies, and computational approaches, play pivotal roles in identifying potential candidates for repurposing. However, despite the promise of repurposed drugs, drug repositioning confronts formidable obstacles. Patenting issues, financial constraints associated with conducting extensive clinical trials, and the necessity for combination therapies to overcome the limitations of monotherapy pose significant challenges. This review provides an in-depth exploration of drug repurposing, covering a diverse array of approaches including experimental, re-engineering protein, nanotechnology, and computational methods. Each of these avenues presents distinct opportunities and obstacles in the pursuit of identifying novel clinical uses for established drugs. By examining the multifaceted landscape of drug repurposing, this review aims to offer comprehensive insights into its potential to transform cancer therapeutics.
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Affiliation(s)
- RamaRao Malla
- Cancer Biology Laboratory, Department of Biochemistry and Bioinformatics, GITAM School of Science, GITAM (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
| | - Sathiyapriya Viswanathan
- Department of Biochemistry, ACS Medical College and Hospital, Chennai 600007, Tamil Nadu, India;
| | - Sree Makena
- Maharajah’s Institute of Medical Sciences and Hospital, Vizianagaram 535217, Andhra Pradesh, India
| | - Shruti Kapoor
- Department of Genetics, University of Alabama, Birmingham, AL 35233, USA
| | - Deepak Verma
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | | | - Manikantha Dunna
- Center for Biotechnology, Jawaharlal Nehru Technological University, Hyderabad 500085, Telangana, India
| | - Nethaji Muniraj
- Center for Cancer and Immunology Research, Children’s National Hospital, 111, Michigan Ave NW, Washington, DC 20010, USA
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Jabarin A, Shtar G, Feinshtein V, Mazuz E, Shapira B, Ben-Shabat S, Rokach L. Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model. Brief Bioinform 2024; 25:bbae108. [PMID: 38647152 PMCID: PMC11033730 DOI: 10.1093/bib/bbae108] [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: 12/06/2023] [Revised: 02/04/2024] [Accepted: 02/25/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) remains a serious threat to health, with limited effective therapeutic options, especially due to advanced stage at diagnosis and its inherent resistance to chemotherapy, making it one of the leading causes of cancer-related deaths worldwide. The lack of clear treatment directions underscores the urgent need for innovative approaches to address and manage this deadly condition. In this research, we repurpose drugs with potential anti-cancer activity using machine learning (ML). METHODS We tackle the problem by using a neural network trained on drug-target interaction information enriched with drug-drug interaction information, which has not been used for anti-cancer drug repurposing before. We focus on eravacycline, an antibacterial drug, which was selected and evaluated to assess its anti-cancer effects. RESULTS Eravacycline significantly inhibited the proliferation and migration of BxPC-3 cells and induced apoptosis. CONCLUSION Our study highlights the potential of drug repurposing for cancer treatment using ML. Eravacycline showed promising results in inhibiting cancer cell proliferation, migration and inducing apoptosis in PDAC. These findings demonstrate that our developed ML drug repurposing models can be applied to a wide range of new oncology therapeutics, to identify potential anti-cancer agents. This highlights the potential and presents a promising approach for identifying new therapeutic options.
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Affiliation(s)
- Adi Jabarin
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Guy Shtar
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Valeria Feinshtein
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Eyal Mazuz
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Bracha Shapira
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Shimon Ben-Shabat
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev (BGU), P.O.B. 653, Beer-Sheva 8410501, Israel
| | - Lior Rokach
- Department of Information Systems and Software Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 8410501, Israel
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Dakilah I, Harb A, Abu-Gharbieh E, El-Huneidi W, Taneera J, Hamoudi R, Semreen MH, Bustanji Y. Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine. Front Pharmacol 2024; 15:1324001. [PMID: 38313315 PMCID: PMC10834672 DOI: 10.3389/fphar.2024.1324001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/04/2024] [Indexed: 02/06/2024] Open
Abstract
The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.
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Affiliation(s)
- Ibraheem Dakilah
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Amani Harb
- Department of Basic Sciences, Faculty of Arts and Sciences, Al-Ahliyya Amman University, Amman, Jordan
| | - Eman Abu-Gharbieh
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Waseem El-Huneidi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Jalal Taneera
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rifat Hamoudi
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Division of Surgery and Interventional Science, University College London, London, United Kingdom
| | - Mohammed H Semreen
- College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
| | - Yasser Bustanji
- Research Institute of Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- School of Pharmacy, The University of Jordan, Amman, Jordan
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13
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Modh DH, Kulkarni VM. Anticancer Drug Discovery By Structure-Based Repositioning Approach. Mini Rev Med Chem 2024; 24:60-91. [PMID: 37165589 DOI: 10.2174/1389557523666230509123036] [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: 12/04/2022] [Revised: 03/07/2023] [Accepted: 03/28/2023] [Indexed: 05/12/2023]
Abstract
Despite the tremendous progress that has occurred in recent years in cell biology and oncology, in chemical, physical and computer sciences, the disease cancer has continued as the major cause of death globally. Research organizations, academic institutions and pharmaceutical companies invest huge amounts of money in the discovery and development of new anticancer drugs. Though much effort is continuing and whatever available approaches are being attempted, the success of bringing one effective drug into the market has been uncertain. To overcome problems associated with drug discovery, several approaches are being attempted. One such approach has been the use of known, approved and marketed drugs to screen these for new indications, which have gained considerable interest. This approach is known in different terms as "drug repositioning or drug repurposing." Drug repositioning refers to the structure modification of the active molecule by synthesis, in vitro/ in vivo screening and in silico computational applications where macromolecular structure-based drug design (SBDD) is employed. In this perspective, we aimed to focus on the application of repositioning or repurposing of essential drug moieties present in drugs that are already used for the treatment of some diseases such as diabetes, human immunodeficiency virus (HIV) infection and inflammation as anticancer agents. This review thus covers the available literature where molecular modeling of drugs/enzyme inhibitors through SBDD is reported for antidiabetics, anti-HIV and inflammatory diseases, which are structurally modified and screened for anticancer activity using respective cell lines.
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Affiliation(s)
- Dharti H Modh
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Erandwane, Pune, 411038, Maharashtra, India
| | - Vithal M Kulkarni
- Department of Pharmaceutical Chemistry, Poona College of Pharmacy, Bharati Vidyapeeth (Deemed to be) University, Erandwane, Pune, 411038, Maharashtra, India
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14
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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15
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Rassy E, Andre F. A forgotten dimension of big data in drug repositioning. Eur J Cancer 2023; 192:113277. [PMID: 37647850 DOI: 10.1016/j.ejca.2023.113277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Affiliation(s)
- Elie Rassy
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; CESP, INSERM U1018, Université Paris-Saclay, Villejuif, France.
| | - Fabrice Andre
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; Gustave Roussy, INSERM U981, Université Paris-Saclay, Villejuif, France
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16
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Dobbs Spendlove M, M. Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets. PeerJ 2023; 11:e16088. [PMID: 37790614 PMCID: PMC10544355 DOI: 10.7717/peerj.16088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
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Affiliation(s)
- Mauri Dobbs Spendlove
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Trenton M. Gibson
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Shaney McCain
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Benjamin C. Stone
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | | | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
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17
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Kumbhar N, Nimal S, Patil D, Kaiser VF, Haupt J, Gacche RN. Repurposing of neprilysin inhibitor 'sacubitrilat' as an anti-cancer drug by modulating epigenetic and apoptotic regulators. Sci Rep 2023; 13:9952. [PMID: 37336927 DOI: 10.1038/s41598-023-36872-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/12/2023] [Indexed: 06/21/2023] Open
Abstract
Modifications in the epigenetic landscape have been considered a hallmark of cancer. Histone deacetylation is one of the crucial epigenetic modulations associated with the aggressive progression of various cancer subtypes. Herein, we have repurposed the neprilysin inhibitor sacubitrilat as a potent anticancer agent using in-silico protein-ligand interaction profiler (PLIP) analysis, molecular docking, and in vitro studies. The screening of PLIP profiles between vorinostat/panobinostat and HDACs/LTA4H followed by molecular docking resulted in five (Sacubitrilat, B65, BDS, BIR, and NPV) FDA-approved, experimental and investigational drugs. Sacubitrilat has demonstrated promising anticancer activity against colorectal cancer (SW-480) and triple-negative breast cancer (MDA-MB-231) cells, with IC50 values of 14.07 μg/mL and 23.02 μg/mL, respectively. FACS analysis revealed that sacubitrilat arrests the cell cycle at the G0/G1 phase and induces apoptotic-mediated cell death in SW-480 cells. In addition, sacubitrilat inhibited HDAC isoforms at the transcriptomic level by 0.7-0.9 fold and at the proteomic level by 0.5-0.6 fold as compared to the control. Sacubitrilat increased the protein expression of tumor-suppressor (p53) and pro-apoptotic makers (Bax and Bid) by 0.2-2.5 fold while decreasing the expression of anti-apoptotic Bcl2 and Nrf2 proteins by 0.2-0.5 fold with respect to control. The observed cleaved PARP product indicates that sacubitrilat induces apoptotic-mediated cell death. This study may pave the way to identify the anticancer potential of sacubitrilat and can be explored in human clinical trials.
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Affiliation(s)
- Navanath Kumbhar
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | - Snehal Nimal
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | - Deeksha Patil
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India
| | | | | | - Rajesh N Gacche
- Department of Biotechnology, Savitribai Phule Pune University, Pune, Maharashtra (MS), 411007, India.
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18
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Cavalla D, Crichton G. Drug repurposing: Known knowns to unknown unknowns - Network analysis of the repurposome. Drug Discov Today 2023:103639. [PMID: 37236525 DOI: 10.1016/j.drudis.2023.103639] [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: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/19/2023] [Indexed: 05/28/2023]
Abstract
DrugRepurposing Online is a database of well-curated literature examples of drug repurposing, structured by reference to compounds and indications, via a generalisation layer (within specific datasets) of mechanism. References are categorised by level of relevance to human application to assist users in prioritising repurposing hypotheses. Users can search freely between any two of the three categories in either direction; results can then be extended to the third category. The concatenation of two (or more) direct relationships to create an indirect, hypothetical new repurposing relationship is intended to offer novel and non-obvious opportunities that can be both patented and efficiently developed. A natural language processing (NLP) powered search capability extends the opportunities from the hand-curated foundation to identify further opportunities.
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19
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De Vita S, Chini MG, Bifulco G, Lauro G. Target identification by structure-based computational approaches: Recent advances and perspectives. Bioorg Med Chem Lett 2023; 83:129171. [PMID: 36739998 DOI: 10.1016/j.bmcl.2023.129171] [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: 08/05/2022] [Revised: 12/15/2022] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.
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Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy
| | - Maria Giovanna Chini
- Department of Biosciences and Territory, University of Molise, Contrada Fonte Lappone, 86090 Pesche (IS), Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy.
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano (SA), Italy.
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20
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Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 2023; 10:1086097. [PMID: 36873878 PMCID: PMC9975164 DOI: 10.3389/fmed.2023.1086097] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas S. Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Jamie Overbeek
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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21
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Senirkentli GB, İnce Bingöl S, Ünal M, Bostancı E, Güzel MS, Açıcı K. Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study. Technol Health Care 2023; 31:1723-1735. [PMID: 36970921 DOI: 10.3233/thc-220563] [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] [Indexed: 06/18/2023]
Abstract
BACKGROUND Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases. OBJECTIVE This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding. METHODS The dataset of 116 patients who were previously treated by senior orthodontists and divided into two groups according to their treatment modalities were examined. Machine Learning algorithms including Multilayer Perceptron, Linear Logistic Regression, k-nearest Neighbors, Naïve Bayes, and Random Forest were trained on this dataset. Several metrics were used for the evaluation of accuracy, precision, recall, and kappa statistic. RESULTS The most important 12 features were determined with the feature selection algorithm. While all algorithms achieved over 90% accuracy, Random Forest yielded 95% accuracy, with high reliability values (kappa = 0.90). CONCLUSION The employment of machine learning methods for the treatment decision with or without extraction in the early treatment of patients in the mixed dentition can be particularly useful for pedodontists and general practitioners.
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Affiliation(s)
| | | | - Metehan Ünal
- Computer Engineering Department, Faculty of Engineering, Ankara University, Ankara, Turkey
| | - Erkan Bostancı
- Computer Engineering Department, Faculty of Engineering, Ankara University, Ankara, Turkey
| | - Mehmet Serdar Güzel
- Computer Engineering Department, Faculty of Engineering, Ankara University, Ankara, Turkey
| | - Koray Açıcı
- Artificial Intelligence and Data Engineering Department, Faculty of Engineering, Ankara University, Ankara, Turkey
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22
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Yang J, Zhang D, Cai Y, Yu K, Li M, Liu L, Chen X. Computational Prediction of Drug Phenotypic Effects Based on Substructure-Phenotype Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:256-265. [PMID: 35239490 DOI: 10.1109/tcbb.2022.3155453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Identifying drug phenotypic effects, including therapeutic effects and adverse drug reactions (ADRs), is an inseparable part for evaluating the potentiality of new drug candidates (NDCs). However, current computational methods for predicting phenotypic effects of NDCs are mainly based on the overall structure of an NDC or a related target. These approaches often lead to inconsistencies between the structures and functions and limit the prediction space of NDCs. In this study, first, we constructed quantitative associations of substructure-domain, domain-ADR, and domain-ATC (Anatomical Therapeutic Chemical Classification System code) through L1LOG and L1SVM machine learning models. These associations represent relationships between phenotypes (ADRs and ATCs) and local structures of drugs and proteins. Then, based on these established associations, substructure-phenotype relationships were constructed which were utilized to quantify drug-phenotype relationships. Thus, this approach could achieve high-throughput and effective evaluations of the druggability of NDCs by referring to the established substructure-phenotype relationships and structural information of NDCs without additional prior knowledge. Using this computational pipeline, 83,205 drug-ATC relationships (including 1,479 drugs and 178 ATCs) and 306,421 drug-ADR relationships (including 1,752 drugs and 454 ADRs) were predicted in total. The prediction results were validated at four levels: five-fold cross validation, public databases, literature, and molecular docking. Furthermore, three case studies demonstrated the feasibility of our method. 79 ATCs and 269 ADRs were predicted to be related to Maraviroc, an approved drug, including the existing antiviral effect in clinical use. Additionally, we also found risk substructures of severe ADRs, for example, SUB215 (>= 1, saturated or only aromatic carbon ring size 7) can result in shock. And we analyzed the mechanism of action (MOA) of interested drugs based on the established drug-substructure-domain-protein associations. In a word, this approach through establishing drug-substructure-phenotype relationships can achieve quantitative prediction of phenotypes for a given NDC or drug without any prior knowledge except its structure information. Using that way, we can directly obtain the relationships between substructure and phenotype of a compound, which is more convenient to analyze the phenotypic mechanism of drugs and accelerate the process of rational drug design.
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23
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Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease. Pharmaceutics 2022; 15:pharmaceutics15010083. [PMID: 36678712 PMCID: PMC9865219 DOI: 10.3390/pharmaceutics15010083] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.
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24
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Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer. Int J Mol Sci 2022; 24:ijms24010046. [PMID: 36613487 PMCID: PMC9820223 DOI: 10.3390/ijms24010046] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/09/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.
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25
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Sahayasheela VJ, Lankadasari MB, Dan VM, Dastager SG, Pandian GN, Sugiyama H. Artificial intelligence in microbial natural product drug discovery: current and emerging role. Nat Prod Rep 2022; 39:2215-2230. [PMID: 36017693 PMCID: PMC9931531 DOI: 10.1039/d2np00035k] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.
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Affiliation(s)
- Vinodh J Sahayasheela
- Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-Ku, Kyoto 606-8502, Japan.
| | - Manendra B Lankadasari
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Vipin Mohan Dan
- Microbiology Division, Jawaharlal Nehru Tropical Botanic Garden and Research Institute, Thiruvananthapuram, Kerala, India
| | - Syed G Dastager
- NCIM Resource Centre, Division of Biochemical Sciences, CSIR - National Chemical Laboratory, Pune, Maharashtra, India
| | - Ganesh N Pandian
- Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida-Ushinomaecho, Sakyo-Ku, Kyoto 606-8501, Japan
| | - Hiroshi Sugiyama
- Department of Chemistry, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwakecho, Sakyo-Ku, Kyoto 606-8502, Japan.
- Institute for Integrated Cell-Material Sciences (WPI-iCeMS), Kyoto University, Yoshida-Ushinomaecho, Sakyo-Ku, Kyoto 606-8501, Japan
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26
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Wang Y, Aldahdooh J, Hu Y, Yang H, Vähä-Koskela M, Tang J, Tanoli Z. DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features. Sci Rep 2022; 12:21116. [PMID: 36477604 PMCID: PMC9729186 DOI: 10.1038/s41598-022-24980-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
The drug development process consumes 9-12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data to repurpose drugs for 669 diseases from 22 groups, including various cancers, musculoskeletal, infections, cardiovascular, and skin diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurposed scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. At DrugRepo score ≥ 0.4, we repurposed 516 approved drugs across 545 diseases. Moreover, hundreds of novel predicted compounds can be matched with ongoing studies at clinical trials. Our analysis is supported by a web tool available at: http://drugrepo.org/ .
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Affiliation(s)
- Yinyin Wang
- grid.7737.40000 0004 0410 2071Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jehad Aldahdooh
- grid.7737.40000 0004 0410 2071Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yingying Hu
- grid.7737.40000 0004 0410 2071Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hongbin Yang
- grid.5335.00000000121885934Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Markus Vähä-Koskela
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Jing Tang
- grid.7737.40000 0004 0410 2071Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ziaurrehman Tanoli
- grid.7737.40000 0004 0410 2071Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland ,BioICAWtech, Helsinki, Finland
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27
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Fonseca-Montaño MA, Blancas S, Herrera-Montalvo LA, Hidalgo-Miranda A. Cancer Genomics. Arch Med Res 2022; 53:723-731. [PMID: 36460546 DOI: 10.1016/j.arcmed.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022]
Abstract
In the past decade, genomics has fundamentally changed our view of cancer biology, allowing comprehensive analyses of mutations, copy number alterations, structural variants, gene expression and DNA methylation profiles in large-scale studies across different cancer types. Efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have fostered international collaborations for cancer genomic analyses and have generated public databases that give scientists around the world access to thoroughly curated data, which have been extensively used as a tool for further hypothesis driven research on several aspects of cancer biology. In parallel, some of these findings are being translated into specific clinical benefits for cancer patients. In this review, we provide a brief historical description of the evolution of international public cancer genome projects and related databases, as well as we discuss about their impact on general cancer research.
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Affiliation(s)
- Marco A Fonseca-Montaño
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | - Susana Blancas
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Cátedras Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | | | - Alfredo Hidalgo-Miranda
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México.
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28
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Zhang L, Fan S, Vera J, Lai X. A network medicine approach for identifying diagnostic and prognostic biomarkers and exploring drug repurposing in human cancer. Comput Struct Biotechnol J 2022; 21:34-45. [PMID: 36514340 PMCID: PMC9732137 DOI: 10.1016/j.csbj.2022.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Shiwei Fan
- College of Computer Science, Sichuan University, Chengdu, China
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany,Deutsches Zentrum Immuntherapie, Erlangen, Germany,Comprehensive Cancer Center Erlangen, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany,Deutsches Zentrum Immuntherapie, Erlangen, Germany,Comprehensive Cancer Center Erlangen, Erlangen, Germany,BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland,Corresponding author at: Universitätsklinikum Erlangen, Erlangen, Germany; Tampere University, Tampere, Finland.
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29
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Kakoti BB, Bezbaruah R, Ahmed N. Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues. Front Pharmacol 2022; 13:1007315. [PMID: 36263141 PMCID: PMC9574100 DOI: 10.3389/fphar.2022.1007315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/12/2022] [Indexed: 11/21/2022] Open
Abstract
Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.
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Affiliation(s)
- Bibhuti Bhusan Kakoti
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, India
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30
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Ozdemir ES, Ranganathan SV, Nussinov R. How has artificial intelligence impacted COVID-19 drug repurposing and what lessons have we learned? Expert Opin Drug Discov 2022; 17:1061-1065. [PMID: 36154343 DOI: 10.1080/17460441.2022.2128333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- E Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Srivathsan V Ranganathan
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Leidos Biomedical Research, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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31
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Abstract
Glioblastoma is the most aggressive primary brain tumor with a poor prognosis. The 2021 WHO CNS5 classification has further stressed the importance of molecular signatures in diagnosis although therapeutic breakthroughs are still lacking. In this review article, updates on the current and novel therapies in IDH-wildtype GBM will be discussed.
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Affiliation(s)
- Jawad M Melhem
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - James R Perry
- Division of Neurology, Department of Medicine, Faculty of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
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32
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Pan X, Lin X, Cao D, Zeng X, Yu PS, He L, Nussinov R, Cheng F. Deep learning for drug repurposing: Methods, databases, and applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1597] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xiaoqin Pan
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Xuan Lin
- School of Computer Science Xiangtan University Xiangtan China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Xiangxiang Zeng
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Philip S. Yu
- Department of Computer Science University of Illinois at Chicago Chicago Illinois USA
| | - Lifang He
- Department of Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick Frederick Maryland USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Cleveland Ohio USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio USA
- Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland Ohio USA
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33
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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34
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Padhi D, Govindaraju T. Mechanistic Insights for Drug Repurposing and the Design of Hybrid Drugs for Alzheimer's Disease. J Med Chem 2022; 65:7088-7105. [PMID: 35559617 DOI: 10.1021/acs.jmedchem.2c00335] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The heterogeneity and complex nature of Alzheimer's disease (AD) is attributed to several genetic risk factors and molecular culprits. The slow pace and increasing failure rate of conventional drug discovery has led to the exploration of complementary strategies based on repurposing approved drugs to treat AD. Drug repurposing (DR) is a cost-effective, low-risk, and efficient approach for identifying novel therapeutic candidates for AD treatment. Similarly, hybrid drug design through the integration of distinct pharmacophores from known or failed drugs and natural products is an interesting strategy to target the multifactorial nature of AD. In this Perspective, we discuss the potential of DR and highlight promising drug candidates that can be advanced for clinical trials, backed by a detailed discussion on their plausible mechanisms of action. Our article fosters research on the hidden potential of DR and hybrid drug design with the goal of unravelling new drugs and targets to tackle AD.
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Affiliation(s)
- Dikshaa Padhi
- Bioorganic Chemistry Laboratory, New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur P.O., Bengaluru, Karnataka 560064, India
| | - Thimmaiah Govindaraju
- Bioorganic Chemistry Laboratory, New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur P.O., Bengaluru, Karnataka 560064, India
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35
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Rodrigues R, Duarte D, Vale N. Drug Repurposing in Cancer Therapy: Influence of Patient’s Genetic Background in Breast Cancer Treatment. Int J Mol Sci 2022; 23:ijms23084280. [PMID: 35457144 PMCID: PMC9028365 DOI: 10.3390/ijms23084280] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 12/14/2022] Open
Abstract
Cancer is among the leading causes of death worldwide and it is estimated that in 2040 more than 29 million people will be diagnosed with some type of cancer. The most prevalent type of cancer in women, worldwide, is breast cancer, a type of cancer associated with a huge death rate. This high mortality is mainly a consequence of the development of drug resistance, which is one of the major challenges to overcome in breast cancer treatment. As a result, research has been focused on finding novel therapeutical weapons, specifically ones that allow for a personalized treatment, based on patients’ characteristics. Although the scientific community has been concerned about guaranteeing the quality of life of cancer patients, researchers are also aware of the increasing costs related to cancer treatment, and efforts have been made to find alternatives to the development of new drugs. The development of new drugs presents some disadvantages as it is a multistep process that is time- and money-consuming, involving clinical trials that commonly fail in the initial phases. A strategy to overcome these disadvantages is drug repurposing. In this review, we focused on describing potential repurposed drugs in the therapy of breast cancer, considering their pharmacogenomic profile, to assess the relationship between patients’ genetic variations and their response to a certain therapy. This review supports the need for the development of further fundamental studies in this area, in order to investigate and expand the knowledge of the currently used and novel potential drugs to treat breast cancer. Future clinical trials should focus on developing strategies to group cancer patients according to their clinical and biological similarities and to discover new potential targets, to enable cancer therapy to be more effective and personalized.
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Affiliation(s)
- Rafaela Rodrigues
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal; (R.R.); (D.D.)
| | - Diana Duarte
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal; (R.R.); (D.D.)
- Faculty of Pharmacy of University of Porto, Rua Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal
| | - Nuno Vale
- OncoPharma Research Group, Center for Health Technology and Services Research (CINTESIS), Rua Dr. Plácido da Costa, 4200-450 Porto, Portugal; (R.R.); (D.D.)
- Department of Community Medicine, Health Information and Decision (MEDCIDS), Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
- Associate Laboratory RISE–Health Research Network, Faculty of Medicine, University of Porto, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
- Correspondence: ; Tel.: +351-220426537
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36
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Aittokallio T. What are the current challenges for machine learning in drug discovery and repurposing? Expert Opin Drug Discov 2022; 17:423-425. [PMID: 35255749 DOI: 10.1080/17460441.2022.2050694] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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37
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Aydin B, Yildirim E, Erdogan O, Arga KY, Yilmaz BK, Bozkurt SU, Bayrakli F, Turanli B. Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:115-129. [PMID: 35172108 DOI: 10.1089/omi.2021.0221] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs' treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.
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Affiliation(s)
- Busra Aydin
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Esra Yildirim
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Onur Erdogan
- Department of Neurosurgery, School of Medicine, Marmara University, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
- Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey
| | - Betul Karademir Yilmaz
- Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, Istanbul, Turkey
- Department of Biochemistry and School of Medicine, Marmara University, Istanbul, Turkey
| | - Suheyla Uyar Bozkurt
- Department of Medical Pathology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Fatih Bayrakli
- Department of Neurosurgery, School of Medicine, Marmara University, Istanbul, Turkey
- Institute of Neurological Sciences, Marmara University, Istanbul, Turkey
| | - Beste Turanli
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
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38
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Zhou F, Zhu H, Fu C. Editorial: Clinical Therapeutic Development Against Cancers Resistant to Targeted Therapies. Front Pharmacol 2022; 12:816896. [PMID: 35095531 PMCID: PMC8790167 DOI: 10.3389/fphar.2021.816896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 12/13/2021] [Indexed: 02/01/2023] Open
Affiliation(s)
- Fanfan Zhou
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Hong Zhu
- Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Caiyun Fu
- Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou, China
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39
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Tendulkar R, Chouhan A, Gupta A, Chaudhary A, Dubey C, Shukla S. Structure-Based Drug Design and Development of Novel Synthetic Compounds with Anti-Viral Property against SARS-COV-2. Curr Drug Discov Technol 2022; 19:e280122200663. [PMID: 35088672 DOI: 10.2174/1570163819666220128145724] [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: 08/20/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 11/22/2022]
Abstract
• Background : The world is suffering from health and economic devastation due to the Corona Virus Disease-2019 (COVID-19) pandemic. Given the number of people affected and also the death rate, the virus is definitely a serious threat to humanity. By analogy with previous reports on the Severe Acute Respiratory Syndrome (SARS-CoV-2) virus, the novel replication mechanism of the coronavirus is likely well understood. • Objective : The antiviral activity of various compounds of the flavonoid class was checked against SARS-COVID-19 using diverse tools and software. • Method : From the flavonoid compound class, 100 synthetic compounds with potential antiviral activity were selected and improved for screening and induced fit docking, which was reduced to 25 compounds with good docking score and docking energy. In addition to the apparent match of the molecule with the shape of the binding pocket, a full analysis of the non-covalent interactions in the active site was assessed. • Results : Compounds (nol26), (fla37-fl40), (an32), (an39) showed a maximum docking score, which shows essential interactions for a tight bond. Now, all compounds are synthetic with beneficial drug-like properties. • Conclusion : During the docking study, an increased lipophilic interaction of compounds due to the presence of chlorine in (nol26), (fla37-fl40), (an32), (an39) was discovered. (fla37-fla40) can be investigated as lead molecules against SARS-COV-2 in futuristic drug development.
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40
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Pantziarka P, Vandeborne L, Bouche G. A Database of Drug Repurposing Clinical Trials in Oncology. Front Pharmacol 2021; 12:790952. [PMID: 34867425 PMCID: PMC8635986 DOI: 10.3389/fphar.2021.790952] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Pan Pantziarka
- The Anticancer Fund, Brussels, Belgium.,The George Pantziarka TP53 Trust, London, United Kingdom
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41
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Sharma PP, Bansal M, Sethi A, Poonam, Pena L, Goel VK, Grishina M, Chaturvedi S, Kumar D, Rathi B. Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Adv 2021; 11:36181-36198. [PMID: 35492747 PMCID: PMC9043418 DOI: 10.1039/d1ra05320e] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/07/2021] [Indexed: 12/19/2022] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.
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Affiliation(s)
- Prem Prakash Sharma
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Meenakshi Bansal
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER) Hyderabad India
| | - Poonam
- Department of Chemistry, Miranda House, University of Delhi Delhi 110007 India
| | - Lindomar Pena
- Department of Virology, Aggeu Magalhaes, Institute (IAM), Oswaldo Cruz Foundation (Fiocruz) Recife 50670-420 Pernambuco Brazil
| | - Vijay Kumar Goel
- School of Physical Sciences, Jawaharlal Nehru University New Delhi 110067 India
| | - Maria Grishina
- South Ural State University, Laboratory of Computational Modelling of Drugs Pr. Lenina 76 454080 Russia
| | - Shubhra Chaturvedi
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences New Delhi 110054 India
| | - Dhruv Kumar
- Amity Institute of Molecular Medicine & Stem Cell Research (AIMMSCR), Amity University Uttar Pradesh Noida 201313 India
| | - Brijesh Rathi
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
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Biswal J, Jayaprakash P, Rayala SK, Venkatraman G, Rangaswamy R, Jeyaraman J. WaterMap and Molecular Dynamic Simulation-Guided Discovery of Potential PAK1 Inhibitors Using Repurposing Approaches. ACS OMEGA 2021; 6:26829-26845. [PMID: 34693105 PMCID: PMC8529594 DOI: 10.1021/acsomega.1c02032] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Indexed: 06/13/2023]
Abstract
p21-Activated kinase 1 (PAK1) is positioned at the nexus of several oncogenic signaling pathways. Currently, there are no approved inhibitors for disabling the transfer of phosphate in the active site directly, as they are limited by lower affinity, and poor kinase selectivity. In this work, a repurposing study utilizing FDA-approved drugs from the DrugBank database was pursued with an initial selection of 27 molecules out of ∼2162 drug molecules, based on their docking energies and molecular interaction patterns. From the molecules that were considered for WaterMap analysis, seven molecules, namely, Mitoxantrone, Labetalol, Acalabrutinib, Sacubitril, Flubendazole, Trazodone, and Niraparib, ascertained the ability to overlap with high-energy hydration sites. Considering many other displaced unfavorable water molecules, only Acalabrutinib, Flubendazole, and Trazodone molecules highlighted their prominence in terms of binding affinity gains through ΔΔG that ranges between 6.44 and 2.59 kcal/mol. Even if Mitoxantrone exhibited the highest docking score and greater interaction strength, it did not comply with the WaterMap and molecular dynamics simulation results. Moreover, detailed MD simulation trajectory analyses suggested that the drug molecules Flubendazole, Niraparib, and Acalabrutinib were highly stable, observed from their RMSD values and consistent interaction pattern with Glu315, Glu345, Leu347, and Asp407 including the hydrophobic interactions maintained in the three replicates. However, the drug molecule Trazodone displayed a loss of crucial interaction with Leu347, which was essential to inhibit the kinase activity of PAK1. The molecular orbital and electrostatic potential analyses elucidated the reactivity and strong complementarity potentials of the drug molecules in the binding pocket of PAK1. Therefore, the CADD-based reposition efforts, reported in this work, helped in the successful identification of new PAK1 inhibitors that requires further investigation by in vitro analysis.
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Affiliation(s)
- Jayashree Biswal
- Structural
Biology and Bio-Computing Laboratory, Department of Bioinformatics,
Science Block, Alagappa University, Karaikudi 630 004, Tamil Nadu, India
| | - Prajisha Jayaprakash
- Structural
Biology and Bio-Computing Laboratory, Department of Bioinformatics,
Science Block, Alagappa University, Karaikudi 630 004, Tamil Nadu, India
| | - Suresh Kumar Rayala
- Department
of Biotechnology, Indian Institute of Technology
Madras, Room No. BT 306, Chennai 600 036, Tamil Nadu, India
| | - Ganesh Venkatraman
- Department
of Human Genetics, College of Biomedical Sciences, Sri Ramachandra University, Porur, Chennai 600 116, Tamil Nadu, India
| | - Raghu Rangaswamy
- Structural
Biology and Bio-Computing Laboratory, Department of Bioinformatics,
Science Block, Alagappa University, Karaikudi 630 004, Tamil Nadu, India
| | - Jeyakanthan Jeyaraman
- Structural
Biology and Bio-Computing Laboratory, Department of Bioinformatics,
Science Block, Alagappa University, Karaikudi 630 004, Tamil Nadu, India
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43
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Li Z, Yao Y, Cheng X, Li W, Fei T. An in silico drug repositioning workflow for host-based antivirals. STAR Protoc 2021; 2:100653. [PMID: 34286288 PMCID: PMC8273420 DOI: 10.1016/j.xpro.2021.100653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).
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Affiliation(s)
- Zexu Li
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of China
| | - Yingjia Yao
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of China
| | - Xiaolong Cheng
- Center for Genetic Medicine Research, Children’s National Hospital, 111 Michigan Ave NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Wei Li
- Center for Genetic Medicine Research, Children’s National Hospital, 111 Michigan Ave NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Ave NW, Washington, DC 20010, USA
| | - Teng Fei
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People’s Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People’s Republic of China
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44
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Kumavath R, Paul S, Pavithran H, Paul MK, Ghosh P, Barh D, Azevedo V. Emergence of Cardiac Glycosides as Potential Drugs: Current and Future Scope for Cancer Therapeutics. Biomolecules 2021; 11:1275. [PMID: 34572488 PMCID: PMC8465509 DOI: 10.3390/biom11091275] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
Cardiac glycosides are natural sterols and constitute a group of secondary metabolites isolated from plants and animals. These cardiotonic agents are well recognized and accepted in the treatment of various cardiac diseases as they can increase the rate of cardiac contractions by acting on the cellular sodium potassium ATPase pump. However, a growing number of recent efforts were focused on exploring the antitumor and antiviral potential of these compounds. Several reports suggest their antitumor properties and hence, today cardiac glycosides (CG) represent the most diversified naturally derived compounds strongly recommended for the treatment of various cancers. Mutated or dysregulated transcription factors have also gained prominence as potential therapeutic targets that can be selectively targeted. Thus, we have explored the recent advances in CGs mediated cancer scope and have considered various signaling pathways, molecular aberration, transcription factors (TFs), and oncogenic genes to highlight potential therapeutic targets in cancer management.
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Affiliation(s)
- Ranjith Kumavath
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (P.O) Kasaragod, Kerala 671320, India;
| | - Sayan Paul
- Department of Biotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu 627012, India;
- Centre for Cardiovascular Biology and Disease, Institute for Stem Cell Science and Regenerative Medicine, Bangalore 560065, India
| | - Honey Pavithran
- Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Tejaswini Hills, Periya (P.O) Kasaragod, Kerala 671320, India;
| | - Manash K. Paul
- Department of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA;
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA;
| | - Debmalya Barh
- Institute of Integrative Omics and Applied Biotechnology (IIOAB), Nonakuri, Purba Medinipur 721172, India;
- Laboratório de Genética Celular e Molecular, Departamento de Genetica, Ecologia e Evolucao, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-001, Brazil;
| | - Vasco Azevedo
- Laboratório de Genética Celular e Molecular, Departamento de Genetica, Ecologia e Evolucao, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais 31270-001, Brazil;
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45
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Rafique R, Islam SR, Kazi JU. Machine learning in the prediction of cancer therapy. Comput Struct Biotechnol J 2021; 19:4003-4017. [PMID: 34377366 PMCID: PMC8321893 DOI: 10.1016/j.csbj.2021.07.003] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/15/2022] Open
Abstract
Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.
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Affiliation(s)
| | - S.M. Riazul Islam
- Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
| | - Julhash U. Kazi
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Lund Stem Cell Center, Department of Laboratory Medicine, Lund University, Lund, Sweden
- Corresponding author at: Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Medicon village Building 404:C3, Scheelevägen 8, 22363 Lund, Sweden.
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46
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Chini MG, Lauro G, Bifulco G. Addressing the Target Identification and Accelerating the Repositioning of Anti‐Inflammatory/Anti‐Cancer Organic Compounds by Computational Approaches. European J Org Chem 2021. [DOI: 10.1002/ejoc.202100245] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Maria Giovanna Chini
- Department of Biosciences and Territory University of Molise C.da Fonte Lappone 86090 Pesche (IS) Italy
| | - Gianluigi Lauro
- Department of Pharmacy University of Salerno Via Giovanni Paolo II 132 84084 Fisciano (SA) Italy
| | - Giuseppe Bifulco
- Department of Pharmacy University of Salerno Via Giovanni Paolo II 132 84084 Fisciano (SA) Italy
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47
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Langer I, Latek D. Drug Repositioning For Allosteric Modulation of VIP and PACAP Receptors. Front Endocrinol (Lausanne) 2021; 12:711906. [PMID: 34867774 PMCID: PMC8637020 DOI: 10.3389/fendo.2021.711906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 10/05/2021] [Indexed: 11/13/2022] Open
Abstract
Vasoactive intestinal peptide (VIP) and pituitary adenylate cyclase-activating polypeptide (PACAP) are two neuropeptides that contribute to the regulation of intestinal motility and secretion, exocrine and endocrine secretions, and homeostasis of the immune system. Their biological effects are mediated by three receptors named VPAC1, VPAC2 and PAC1 that belong to class B GPCRs. VIP and PACAP receptors have been identified as potential therapeutic targets for the treatment of chronic inflammation, neurodegenerative diseases and cancer. However, pharmacological use of endogenous ligands for these receptors is limited by their lack of specificity (PACAP binds with high affinity to VPAC1, VPAC2 and PAC1 receptors while VIP recognizes both VPAC1 and VPAC2 receptors), their poor oral bioavailability (VIP and PACAP are 27- to 38-amino acid peptides) and their short half-life. Therefore, the development of non-peptidic small molecules or specific stabilized peptidic ligands is of high interest. Structural similarities between VIP and PACAP receptors are major causes of difficulties in the design of efficient and selective compounds that could be used as therapeutics. In this study we performed structure-based virtual screening against the subset of the ZINC15 drug library. This drug repositioning screen provided new applications for a known drug: ticagrelor, a P2Y12 purinergic receptor antagonist. Ticagrelor inhibits both VPAC1 and VPAC2 receptors which was confirmed in VIP-binding and calcium mobilization assays. A following analysis of detailed ticagrelor binding modes to all three VIP and PACAP receptors with molecular dynamics revealed its allosteric mechanism of action. Using a validated homology model of inactive VPAC1 and a recently released cryo-EM structure of active VPAC1 we described how ticagrelor could block conformational changes in the region of 'tyrosine toggle switch' required for the receptor activation. We also discuss possible modifications of ticagrelor comparing other P2Y12 antagonist - cangrelor, closely related to ticagrelor but not active for VPAC1/VPAC2. This comparison with inactive cangrelor could lead to further improvement of the ticagrelor activity and selectivity for VIP and PACAP receptor sub-types.
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MESH Headings
- Allosteric Regulation/drug effects
- Binding Sites
- Computer Simulation
- Drug Evaluation, Preclinical/methods
- Drug Repositioning/methods
- Molecular Structure
- Protein Conformation/drug effects
- Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I/chemistry
- Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I/drug effects
- Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I/metabolism
- Receptors, Vasoactive Intestinal Peptide, Type II/chemistry
- Receptors, Vasoactive Intestinal Peptide, Type II/drug effects
- Receptors, Vasoactive Intestinal Peptide, Type II/metabolism
- Receptors, Vasoactive Intestinal Polypeptide, Type I/chemistry
- Receptors, Vasoactive Intestinal Polypeptide, Type I/drug effects
- Receptors, Vasoactive Intestinal Polypeptide, Type I/metabolism
- Ticagrelor/chemistry
- Ticagrelor/pharmacology
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Affiliation(s)
- Ingrid Langer
- Institut de Recherche Interdisciplinaire en Biologie Humaine et Moléculaire (IRIBHM), Faculty of Medicine, Université libre de Bruxelles, Brussels, Belgium
| | - Dorota Latek
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland
- *Correspondence: Dorota Latek,
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