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Torabi M, Yasami-Khiabani S, Sardari S, Golkar M, Pérez-Sánchez H, Ghasemi F. Identification of new potential candidates to inhibit EGF via machine learning algorithm. Eur J Pharmacol 2024; 963:176176. [PMID: 38000720 DOI: 10.1016/j.ejphar.2023.176176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023]
Abstract
One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.
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Affiliation(s)
- Mohammadreza Torabi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
| | | | - Soroush Sardari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Majid Golkar
- Department of Parasitology, Pasteur Institute of Iran, Tehran, Iran
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High Performance Computing Reseach Group (BIO-HPC), Computer Engineering Department, UCAM Universidad Católica de Murcia, Murcia, E30107, Spain
| | - Fahimeh Ghasemi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran; Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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2
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Rahban M, Joushi S, Bashiri H, Saso L, Sheibani V. Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment. Front Chem 2024; 11:1325214. [PMID: 38264122 PMCID: PMC10804459 DOI: 10.3389/fchem.2023.1325214] [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: 10/20/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.
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Affiliation(s)
- Mahdie Rahban
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Sara Joushi
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Hamideh Bashiri
- Physiology Research Center, Institute of Neuropharmacology, Department of Physiology and Pharmacology, Medical School, Kerman University of Medical Sciences, Kerman, Iran
| | - Luciano Saso
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University, Rome, Italy
| | - Vahid Sheibani
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
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3
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Gu S, Liu H, Liu L, Hou T, Kang Y. Artificial intelligence methods in kinase target profiling: Advances and challenges. Drug Discov Today 2023; 28:103796. [PMID: 37805065 DOI: 10.1016/j.drudis.2023.103796] [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: 07/12/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023]
Abstract
Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.
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Affiliation(s)
- Shukai Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd, Nanjing 210000, Jiangsu, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
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Athanasiadis P, Ravikumar B, Elliott RJ, Dawson JC, Carragher NO, Clemons PA, Johanssen T, Ebner D, Aittokallio T. Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells. iScience 2023; 26:107209. [PMID: 37485377 PMCID: PMC10359939 DOI: 10.1016/j.isci.2023.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
| | - Richard J.R. Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C. Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O. Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Paul A. Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, United States
| | - Timothy Johanssen
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel Ebner
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tero Aittokallio
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
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5
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Ngo C, Ekanayake A, Zhang C. Identification of Covalent Ligands – from Single Targets to Whole Proteome. Isr J Chem 2023. [DOI: 10.1002/ijch.202200105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Chau Ngo
- Department of Chemistry Loker Hydrocarbon Research Institute University of Southern California 90089 Los Angeles California USA
| | - Arunika Ekanayake
- Department of Chemistry Loker Hydrocarbon Research Institute University of Southern California 90089 Los Angeles California USA
- Current address: Department of Chemistry University of Alberta T6G 2G2 Edmonton AB Canada
| | - Chao Zhang
- Department of Chemistry Loker Hydrocarbon Research Institute University of Southern California 90089 Los Angeles California USA
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6
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Sueca-Comes M, Rusu EC, Grabowska AM, Bates DO. Looking Under the Lamppost: The Search for New Cancer Targets in the Human Kinome. Pharmacol Rev 2022; 74:1136-1145. [PMID: 36180110 DOI: 10.1124/pharmrev.121.000410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 02/02/2022] [Accepted: 02/15/2022] [Indexed: 11/22/2022] Open
Abstract
The number of cancer drugs is increasing as new chemical entities are developed to target molecules, often protein kinases, driving cancer progression. In 2009, Fedorov et al. identified that of the protein kinases in the human kinome, most of the focus has been on a small subset. They highlighted that many poorly investigated protein kinases were cancer drivers, but there was no relationship between publications and involvement in cancer development or progression. Since 2009, there has been a doubling in the number of publications, patents, and drugs targeting the kinome. To determine whether this was an expansion in knowledge of well-studied targets-searching in the light under the lamppost-or an explosion of investigations into previously poorly investigated targets, we searched the literature for publications on each kinase, updating Federov et al.'s assessment of the druggable kinome. The proportion of papers focusing on the 50 most-studied kinases had not changed, and the makeup of those 50 had barely changed. The majority of new drugs (80%) were against the same group of 50 kinases identified as targets 10 years ago, and the proportion of studies investigating previously poorly investigated kinases (<1%) was unchanged. With three exceptions [p38 mitogenactivated protein kinase (p38a), AMP-activated protein kinase catalytic α-subunit 1,2, and B-Raf proto-oncogene (BRAF) serine/threonine kinase], >95% of publications addressing kinases still focused on a relatively small proportion (<50%) of the human kinome independently of their involvement as cancer drivers. There is, therefore, still extensive scope for discovery of therapeutics targeting different protein kinases in cancer and still a bias toward well-characterized targets over the innovative searchlight into the unknown. SIGNIFICANCE STATEMENT: This study presents evidence that drug discovery efforts in cancer are still to some extent focused on a narrow group of well-studied kinases 10 years after the identification of multiple novel cancer targets in the human kinome. This suggests that there is still room for researchers in academia, industry, and the not-for-profit sector to develop new and diverse therapies targeting kinases for cancer.
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Affiliation(s)
- Mireia Sueca-Comes
- Division of Cancer and Stem Cells, Centre for Cancer Sciences, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom (M.S.-C., A.M.G., D.O.B.); Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Consejo Superior de Investigaciones Científicas, Valencia, Spain (E.C.R.); and SeqPlexing SL, Valencia, Spain (E.C.R.)
| | - Elena Cristina Rusu
- Division of Cancer and Stem Cells, Centre for Cancer Sciences, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom (M.S.-C., A.M.G., D.O.B.); Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Consejo Superior de Investigaciones Científicas, Valencia, Spain (E.C.R.); and SeqPlexing SL, Valencia, Spain (E.C.R.)
| | - Anna M Grabowska
- Division of Cancer and Stem Cells, Centre for Cancer Sciences, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom (M.S.-C., A.M.G., D.O.B.); Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Consejo Superior de Investigaciones Científicas, Valencia, Spain (E.C.R.); and SeqPlexing SL, Valencia, Spain (E.C.R.)
| | - David O Bates
- Division of Cancer and Stem Cells, Centre for Cancer Sciences, Biodiscovery Institute, University of Nottingham, Nottingham, United Kingdom (M.S.-C., A.M.G., D.O.B.); Institute of Integrative Systems Biology (I2Sysbio), University of Valencia and Consejo Superior de Investigaciones Científicas, Valencia, Spain (E.C.R.); and SeqPlexing SL, Valencia, Spain (E.C.R.)
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7
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Chen Y, Wang ZZ, Hao GF, Song BA. Web support for the more efficient discovery of kinase inhibitors. Drug Discov Today 2022; 27:2216-2225. [DOI: 10.1016/j.drudis.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/16/2022] [Accepted: 04/01/2022] [Indexed: 11/24/2022]
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8
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Kashyap K, Siddiqi MI. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents. Mol Divers 2021; 25:1517-1539. [PMID: 34282519 DOI: 10.1007/s11030-021-10274-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/05/2021] [Indexed: 12/12/2022]
Abstract
Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.
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Affiliation(s)
- Kushagra Kashyap
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India.,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India
| | - Mohammad Imran Siddiqi
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Research Institute (CSIR-CDRI) Campus, Lucknow, India. .,Molecular and Structural Biology Division, CSIR-Central Drug Research Institute (CSIR-CDRI), Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
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9
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Recent advances in drug repurposing using machine learning. Curr Opin Chem Biol 2021; 65:74-84. [PMID: 34274565 DOI: 10.1016/j.cbpa.2021.06.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 12/11/2022]
Abstract
Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.
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10
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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11
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Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16:977-989. [PMID: 33543671 DOI: 10.1080/17460441.2021.1883585] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - 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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12
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Castaldi MP, Hendricks JA, Zhang AX. 'Design, synthesis, and strategic use of small chemical probes toward identification of novel targets for drug development'. Curr Opin Chem Biol 2020; 56:91-97. [PMID: 32375076 DOI: 10.1016/j.cbpa.2020.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Revised: 03/10/2020] [Accepted: 03/10/2020] [Indexed: 02/06/2023]
Abstract
Chemical probes are essential tools used to study and modulate biological systems. Here, we describe some of the recent scientific advancement in the field of chemical biology, as well as how the advent of new technologies is redefining the criteria of 'good' chemical probes and influencing the discovery of valuable drug leads. In this review, we report selected examples of the usage of linkered and linker-free chemical probes for target identification, biological discovery, and general mechanistic understanding. We also discuss the promises of chemogenomics libraries in phenotypic screens, as well as the limitation of their usage to identify the modulation of new targets and biology.
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Affiliation(s)
| | | | - Andrew X Zhang
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Boston, USA
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