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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [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: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Schulman A, Rousu J, Aittokallio T, Tanoli Z. Attention-based approach to predict drug-target interactions across seven target superfamilies. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae496. [PMID: 39115379 DOI: 10.1093/bioinformatics/btae496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/12/2024] [Accepted: 08/06/2024] [Indexed: 08/29/2024]
Abstract
MOTIVATION Drug-target interactions (DTIs) hold a pivotal role in drug repurposing and elucidation of drug mechanisms of action. While single-targeted drugs have demonstrated clinical success, they often exhibit limited efficacy against complex diseases, such as cancers, whose development and treatment is dependent on several biological processes. Therefore, a comprehensive understanding of primary, secondary and even inactive targets becomes essential in the quest for effective and safe treatments for cancer and other indications. The human proteome offers over a thousand druggable targets, yet most FDA-approved drugs bind to only a small fraction of these targets. RESULTS This study introduces an attention-based method (called as MMAtt-DTA) to predict drug-target bioactivities across human proteins within seven superfamilies. We meticulously examined nine different descriptor sets to identify optimal signature descriptors for predicting novel DTIs. Our testing results demonstrated Spearman correlations exceeding 0.72 (P < 0.001) for six out of seven superfamilies. The proposed method outperformed fourteen state-of-the-art machine learning, deep learning and graph-based methods and maintained relatively high performance for most target superfamilies when tested with independent bioactivity data sources. We computationally validated 185 676 drug-target pairs from ChEMBL-V33 that were not available during model training, achieving a reasonable performance with Spearman correlation >0.57 (P < 0.001) for most superfamilies. This underscores the robustness of the proposed method for predicting novel DTIs. Finally, we applied our method to predict missing bioactivities among 3492 approved molecules in ChEMBL-V33, offering a valuable tool for advancing drug mechanism discovery and repurposing existing drugs for new indications. AVAILABILITY AND IMPLEMENTATION https://github.com/AronSchulman/MMAtt-DTA.
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Affiliation(s)
- Aron Schulman
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
| | - Juho Rousu
- Department of Computer Science, Aalto University, Espoo, 02150, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, 0379, Norway
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, 0372, Norway
| | - Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, 00014, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, 00014, Finland
- Drug Discovery and Chemical Biology (DDCB) Consortium, Biocenter, Helsinki, 00014, Finland
- BioICAWtech, Helsinki, Helsinki, 00410, Finland
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3
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Singh S, Parthasarathi KTS, Bhat MY, Gopal C, Sharma J, Pandey A. Profiling Kinase Activities for Precision Oncology in Diffuse Gastric Cancer. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:76-89. [PMID: 38271566 DOI: 10.1089/omi.2023.0173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B (BAZ1B), WNK lysine-deficient protein kinase 1 (WNK1), and myosin light-chain kinase (MLCK) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 (AAK1), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor (HDGF), heat shock protein 90 (HSP90), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.
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Affiliation(s)
- Smrita Singh
- Manipal Academy of Higher Education (MAHE), Manipal, India
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Center for Molecular Medicine, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, India
| | - K T Shreya Parthasarathi
- Manipal Academy of Higher Education (MAHE), Manipal, India
- Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Mohd Younis Bhat
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Amrita School of Biotechnology, Amrita Vishwapeetham University, Kollam, India
| | - Champaka Gopal
- Department of Pathology, Kidwai Memorial Institute of Oncology, Bangalore, India
| | - Jyoti Sharma
- Manipal Academy of Higher Education (MAHE), Manipal, India
- Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Akhilesh Pandey
- Manipal Academy of Higher Education (MAHE), Manipal, India
- Center for Molecular Medicine, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, India
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
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4
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Xiaolin X, Xiaozhi L, Guoping H, Hongwei L, Jinkuo G, Xiyun B, Zhen T, Xiaofang M, Yanxia L, Na X, Chunyan Z, Rui G, Kuan W, Cheng Z, Cuancuan W, Mingyong L, Xinping D. Overfit deep neural network for predicting drug-target interactions. iScience 2023; 26:107646. [PMID: 37680476 PMCID: PMC10480310 DOI: 10.1016/j.isci.2023.107646] [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: 02/12/2022] [Revised: 06/28/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Drug-target interactions (DTIs) prediction is an important step in drug discovery. As traditional biological experiments or high-throughput screening are high cost and time-consuming, many deep learning models have been developed. Overfitting must be avoided when training deep learning models. We propose a simple framework, called OverfitDTI, for DTI prediction. In OverfitDTI, a deep neural network (DNN) model is overfit to sufficiently learn the features of the chemical space of drugs and the biological space of targets. The weights of trained DNN model form an implicit representation of the nonlinear relationship between drugs and targets. Performance of OverfitDTI on three public datasets showed that the overfit DNN models fit the nonlinear relationship with high accuracy. We identified fifteen compounds that interacted with TEK, a receptor tyrosine kinase contributing to vascular homeostasis, and the predicted AT9283 and dorsomorphin were experimentally demonstrated as inhibitors of TEK in human umbilical vein endothelial cells (HUVECs).
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Affiliation(s)
- Xiao Xiaolin
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Xiaozhi
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - He Guoping
- Geriatrics Department, Traditional Chinese Medicine Hospital of Binhai New Area, Tianjin, China
| | - Liu Hongwei
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei, China
- Department of Anesthesiology, Tangshan Maternal and Child Health Hospital, Tangshan, Hebei, China
| | - Guo Jinkuo
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
| | - Bian Xiyun
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Tian Zhen
- Deepwater Technology Research Institute, China National Offshore Oil Corporation, Tianjin, China
| | - Ma Xiaofang
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Li Yanxia
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Xue Na
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Chunyan
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Gao Rui
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Kuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Cheng
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Cuancuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Mingyong
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Department of Urology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Du Xinping
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
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5
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Williams AH, Zhan CG. Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management. BioDrugs 2023; 37:649-674. [PMID: 37464099 DOI: 10.1007/s40259-023-00611-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/28/2023] [Indexed: 07/20/2023]
Abstract
In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.
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Affiliation(s)
- Alexander H Williams
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA
- GSK Upper Providence, 1250 S. Collegeville Road, Collegeville, PA, 19426, USA
| | - Chang-Guo Zhan
- Molecular Modeling and Biopharmaceutical Center, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky, 789 South Limestone Street, Lexington, KY, 40536, USA.
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6
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Park H, Hong S, Lee M, Kang S, Brahma R, Cho KH, Shin JM. AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors. Sci Rep 2023; 13:10268. [PMID: 37355672 PMCID: PMC10290719 DOI: 10.1038/s41598-023-37456-8] [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: 04/10/2023] [Accepted: 06/22/2023] [Indexed: 06/26/2023] Open
Abstract
The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.
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Affiliation(s)
- Hyejin Park
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sujeong Hong
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Myeonghun Lee
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sungil Kang
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Min Shin
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.
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7
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Murumägi A, Ungureanu D, Khan S, Arjama M, Välimäki K, Ianevski A, Ianevski P, Bergström R, Dini A, Kanerva A, Koivisto-Korander R, Tapper J, Lassus H, Loukovaara M, Mägi A, Hirasawa A, Aoki D, Pietiäinen V, Pellinen T, Bützow R, Aittokallio T, Kallioniemi O. Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma. Br J Cancer 2023; 128:678-690. [PMID: 36476658 PMCID: PMC9938120 DOI: 10.1038/s41416-022-02067-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Many efforts are underway to develop novel therapies against the aggressive high-grade serous ovarian cancers (HGSOCs), while our understanding of treatment options for low-grade (LGSOC) or mucinous (MUCOC) of ovarian malignancies is not developing as well. We describe here a functional precision oncology (fPO) strategy in epithelial ovarian cancers (EOC), which involves high-throughput drug testing of patient-derived ovarian cancer cells (PDCs) with a library of 526 oncology drugs, combined with genomic and transcriptomic profiling. HGSOC, LGSOC and MUCOC PDCs had statistically different overall drug response profiles, with LGSOCs responding better to targeted inhibitors than HGSOCs. We identified several subtype-specific drug responses, such as LGSOC PDCs showing high sensitivity to MDM2, ERBB2/EGFR inhibitors, MUCOC PDCs to MEK inhibitors, whereas HGSOCs showed strongest effects with CHK1 inhibitors and SMAC mimetics. We also explored several drug combinations and found that the dual inhibition of MEK and SHP2 was synergistic in MAPK-driven EOCs. We describe a clinical case study, where real-time fPO analysis of samples from a patient with metastatic, chemorefractory LGSOC with a CLU-NRG1 fusion guided clinical therapy selection. fPO-tailored therapy with afatinib, followed by trastuzumab and pertuzumab, successfully reduced tumour burden and blocked disease progression over a five-year period. In summary, fPO is a powerful approach for the identification of systematic drug response differences across EOC subtypes, as well as to highlight patient-specific drug regimens that could help to optimise therapies to individual patients in the future.
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Affiliation(s)
- Astrid Murumägi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
| | - Daniela Ungureanu
- Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Suleiman Khan
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Mariliina Arjama
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Katja Välimäki
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Philipp Ianevski
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Rebecka Bergström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Alice Dini
- Applied Tumor Genomics Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Anna Kanerva
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Riitta Koivisto-Korander
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Tapper
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Heini Lassus
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Mikko Loukovaara
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | - Akira Hirasawa
- Department of Clinical Genomic Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Daisuke Aoki
- Department of Obstetrics and Gynecology, Keio University School of Medicine, Tokyo, Japan
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Teijo Pellinen
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
| | - Ralf Bützow
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, 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
| | - Olli Kallioniemi
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland.
- Science for Life Laboratory (SciLifeLab), Department of Oncology and Pathology, Karolinska Institutet, Solna, Sweden.
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Moret M, Pachon Angona I, Cotos L, Yan S, Atz K, Brunner C, Baumgartner M, Grisoni F, Schneider G. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat Commun 2023; 14:114. [PMID: 36611029 PMCID: PMC9825622 DOI: 10.1038/s41467-022-35692-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method's scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model's ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.
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Affiliation(s)
- Michael Moret
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Irene Pachon Angona
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Shen Yan
- University of Zurich, University Children's Hospital, Children's Research Center, Pediatric Molecular Neuro-Oncology Research, Lengghalde 5, 8008, Zurich, Switzerland
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Cyrill Brunner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Martin Baumgartner
- University of Zurich, University Children's Hospital, Children's Research Center, Pediatric Molecular Neuro-Oncology Research, Lengghalde 5, 8008, Zurich, Switzerland
| | - Francesca Grisoni
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
- Eindhoven University of Technology, Institute for Complex Molecular Systems and Eindhoven Artificial Intelligence Systems Institute, Department of Biomedical Engineering, Groene Loper 7, 5612AZ, Eindhoven, The Netherlands.
- Center for 393 Living Technologies, Alliance TU/e, WUR, UU, UMC 394 Utrecht, Utrecht, 3584 CB, The Netherlands.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
- ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 138602, Singapore.
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9
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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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10
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Bhatnagar R, Sardar S, Beheshti M, Podichetty JT. How can natural language processing help model informed drug development?: a review. JAMIA Open 2022; 5:ooac043. [PMID: 35702625 PMCID: PMC9188322 DOI: 10.1093/jamiaopen/ooac043] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/28/2022] [Accepted: 05/26/2022] [Indexed: 01/20/2023] Open
Abstract
Objective To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement. Materials and Methods Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed. The applications were stratified into 3 stages: drug discovery, clinical trials, and pharmacovigilance. Key NLP functionalities used for these applications were assessed. Programming libraries and open-source resources for the implementation of NLP functionalities in MIDD were identified. Results NLP has been utilized to aid various processes in drug development lifecycle such as gene-disease mapping, biomarker discovery, patient-trial matching, adverse drug events detection, etc. These applications commonly use NLP functionalities of named entity recognition, word embeddings, entity resolution, assertion status detection, relation extraction, and topic modeling. The current state-of-the-art for implementing these functionalities in MIDD applications are transformer models that utilize transfer learning for enhanced performance. Various libraries in python, R, and Java like huggingface, sparkNLP, and KoRpus as well as open-source platforms such as DisGeNet, DeepEnroll, and Transmol have enabled convenient implementation of NLP models to MIDD applications. Discussion Challenges such as reproducibility, explainability, fairness, limited data, limited language-support, and security need to be overcome to ensure wider adoption of NLP in MIDD landscape. There are opportunities to improve the performance of existing models and expand the use of NLP in newer areas of MIDD. Conclusions This review provides an overview of the potential and pitfalls of current NLP approaches in MIDD.
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Affiliation(s)
- Roopal Bhatnagar
- Data Science, Data Collaboration Center, Critical Path Institute , Tucson, Arizona, USA
| | - Sakshi Sardar
- Quantitative Medicine, Critical Path Institute , Tucson, Arizona, USA
| | - Maedeh Beheshti
- Quantitative Medicine, Critical Path Institute , Tucson, Arizona, USA
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11
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Polypharmacology: The science of multi-targeting molecules. Pharmacol Res 2022; 176:106055. [PMID: 34990865 DOI: 10.1016/j.phrs.2021.106055] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/23/2021] [Accepted: 12/31/2021] [Indexed: 12/28/2022]
Abstract
Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.
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12
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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13
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Galletti C, Bota PM, Oliva B, Fernandez-Fuentes N. Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6408542. [PMID: 34679164 PMCID: PMC8533369 DOI: 10.1093/database/baab068] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 01/05/2023]
Abstract
The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.
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Affiliation(s)
- Cristiano Galletti
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Carrer Laura 13, Vic, Catalonia 08500, Spain
| | - Patricia Mirela Bota
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Carrer Laura 13, Vic, Catalonia 08500, Spain.,Department of Experimental and Health Sciences, Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain
| | - Baldo Oliva
- Department of Experimental and Health Sciences, Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Catalonia 08003, Spain
| | - Narcis Fernandez-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Carrer Laura 13, Vic, Catalonia 08500, Spain
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14
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Tanoli Z, Aldahdooh J, Alam F, Wang Y, Seemab U, Fratelli M, Pavlis P, Hajduch M, Bietrix F, Gribbon P, Zaliani A, Hall MD, Shen M, Brimacombe K, Kulesskiy E, Saarela J, Wennerberg K, Vähä-Koskela M, Tang J. Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments. Brief Bioinform 2021; 23:6361039. [PMID: 34472587 PMCID: PMC8769689 DOI: 10.1093/bib/bbab350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 08/03/2021] [Accepted: 08/02/2021] [Indexed: 12/29/2022] Open
Abstract
Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.
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Affiliation(s)
- Ziaurrehman Tanoli
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
| | - Jehad Aldahdooh
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
| | - Farhan Alam
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
| | - Yinyin Wang
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
| | - Umair Seemab
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
| | | | - Petr Pavlis
- Institute of Molecular and Translational Medicine, Czech
| | - Marian Hajduch
- Institute of Molecular and Translational Medicine, Czech
| | | | - Philip Gribbon
- Fraunhofer Institute for Molecular Biology and Applied Ecology, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Molecular Biology and Applied Ecology, Germany
| | - Matthew D Hall
- National Center for Advancing Translational Sciences, USA
| | - Min Shen
- National Center for Advancing Translational Sciences, USA
| | | | - Evgeny Kulesskiy
- Institute for Molecular Medicine Finland, University of Helsinki, Finland
| | - Jani Saarela
- Institute for Molecular Medicine Finland, University of Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Denmark
| | | | - Jing Tang
- Research Program in Systems Oncology, Faculty of medicine, University of Helsinki, Finland
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15
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Dotolo S, Marabotti A, Facchiano A, Tagliaferri R. A review on drug repurposing applicable to COVID-19. Brief Bioinform 2021; 22:726-741. [PMID: 33147623 PMCID: PMC7665348 DOI: 10.1093/bib/bbaa288] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 12/11/2022] Open
Abstract
Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug–disease or drug–target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.
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Affiliation(s)
| | | | | | - Roberto Tagliaferri
- Artificial Intelligence, Statistical Pattern Recognition, Clustering, Biomedical imaging and Bioinformatics
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16
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Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform 2021; 22:1656-1678. [PMID: 32055842 PMCID: PMC7986597 DOI: 10.1093/bib/bbaa003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/09/2019] [Indexed: 02/07/2023] Open
Abstract
Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Umair Seemab
- Haartman Institute, University of Helsinki, Finland
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Denmark
| | - Jing Tang
- Faculty of medicine, University of Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
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17
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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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18
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Ianevski A, Lahtela J, Javarappa KK, Sergeev P, Ghimire BR, Gautam P, Vähä-Koskela M, Turunen L, Linnavirta N, Kuusanmäki H, Kontro M, Porkka K, Heckman CA, Mattila P, Wennerberg K, Giri AK, Aittokallio T. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. SCIENCE ADVANCES 2021; 7:eabe4038. [PMID: 33608276 PMCID: PMC7895436 DOI: 10.1126/sciadv.abe4038] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/08/2021] [Indexed: 06/12/2023]
Abstract
The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.
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Affiliation(s)
- Aleksandr Ianevski
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, Finland
| | - Jenni Lahtela
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Komal K Javarappa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Philipp Sergeev
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Bishwa R Ghimire
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Prson Gautam
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Laura Turunen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Nora Linnavirta
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Heikki Kuusanmäki
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Biotech Research and Innovation Centre (BRIC) and Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, Copenhagen, Denmark
- Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Mika Kontro
- Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland
| | - Kimmo Porkka
- Helsinki University Hospital Comprehensive Cancer Center, Hematology Research Unit Helsinki, iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Biotech Research and Innovation Centre (BRIC) and Novo Nordisk Foundation Center for Stem Cell Biology (DanStem), University of Copenhagen, Copenhagen, Denmark
| | - Anil K Giri
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, Aalto University, Espoo, 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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19
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Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith O, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res 2021; 49:D1144-D1151. [PMID: 33237278 PMCID: PMC7778926 DOI: 10.1093/nar/gkaa1084] [Citation(s) in RCA: 439] [Impact Index Per Article: 146.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 12/11/2022] Open
Abstract
The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.
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Affiliation(s)
- Sharon L Freshour
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Susanna Kiwala
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Kelsy C Cotto
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Adam C Coffman
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Joshua F McMichael
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Jonathan J Song
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
| | - Malachi Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Obi L Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Alex H Wagner
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO 63108, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH 43215, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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20
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Antunes ASLM, de Almeida V, Crunfli F, Carregari VC, Martins-de-Souza D. Proteomics for Target Identification in Psychiatric and Neurodegenerative Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1286:251-264. [PMID: 33725358 DOI: 10.1007/978-3-030-55035-6_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Psychiatric and neurodegenerative disorders such as schizophrenia (SCZ), Parkinson's disease (PD), and Alzheimer's disease (AD) continue to grow around the world with a high impact on health, social, and economic outcomes for the patient and society. Despite efforts, the etiology and pathophysiology of these disorders remain unclear. Omics technologies have contributed to the understanding of the molecular mechanisms that underlie these complex disorders and have suggested novel potential targets for treatment and diagnostics. Here, we have highlighted the unique and common pathways shared between SCZ, PD, and AD and highlight the main proteomic findings over the last 5 years using in vitro models, postmortem brain samples, and cerebrospinal fluid (CSF) or blood of patients. These studies have identified possible therapeutic targets and disease biomarkers. Further studies including target validation, the use of large sample sizes, and the integration of omics findings with bioinformatics tools are required to provide a better comprehension of pharmacological targets.
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Affiliation(s)
- André S L M Antunes
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil.
| | - Valéria de Almeida
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Fernanda Crunfli
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Victor C Carregari
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, Brazil
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas, SP, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria, Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo, Brazil
- D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
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21
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He M, Zhou Y. How to identify “Material basis–Quality markers” more accurately in Chinese herbal medicines from modern chromatography-mass spectrometry data-sets: Opportunities and challenges of chemometric tools. CHINESE HERBAL MEDICINES 2021; 13:2-16. [PMID: 36117762 PMCID: PMC9476807 DOI: 10.1016/j.chmed.2020.05.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 03/26/2020] [Accepted: 05/25/2020] [Indexed: 12/20/2022] Open
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22
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Montalvo-Casimiro M, González-Barrios R, Meraz-Rodriguez MA, Juárez-González VT, Arriaga-Canon C, Herrera LA. Epidrug Repurposing: Discovering New Faces of Old Acquaintances in Cancer Therapy. Front Oncol 2020; 10:605386. [PMID: 33312959 PMCID: PMC7708379 DOI: 10.3389/fonc.2020.605386] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022] Open
Abstract
Gene mutations are strongly associated with tumor progression and are well known in cancer development. However, recently discovered epigenetic alterations have shown the potential to greatly influence tumoral response to therapy regimens. Such epigenetic alterations have proven to be dynamic, and thus could be restored. Due to their reversible nature, the promising opportunity to improve chemotherapy response using epigenetic therapy has arisen. Beyond helping to understand the biology of the disease, the use of modern clinical epigenetics is being incorporated into the management of the cancer patient. Potential epidrug candidates can be found through a process known as drug repositioning or repurposing, a promising strategy for the discovery of novel potential targets in already approved drugs. At present, novel epidrug candidates have been identified in preclinical studies and some others are currently being tested in clinical trials, ready to be repositioned. This epidrug repurposing could circumvent the classic paradigm where the main focus is the development of agents with one indication only, while giving patients lower cost therapies and a novel precision medical approach to optimize treatment efficacy and reduce toxicity. This review focuses on the main approved epidrugs, and their druggable targets, that are currently being used in cancer therapy. Also, we highlight the importance of epidrug repurposing by the rediscovery of known chemical entities that may enhance epigenetic therapy in cancer, contributing to the development of precision medicine in oncology.
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Affiliation(s)
- Michel Montalvo-Casimiro
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Rodrigo González-Barrios
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Marco Antonio Meraz-Rodriguez
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | | | - Cristian Arriaga-Canon
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Luis A. Herrera
- Unidad de Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
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23
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Choi Y, Shin B, Kang K, Park S, Beck BR. Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model. Viruses 2020; 12:E1325. [PMID: 33218024 PMCID: PMC7698791 DOI: 10.3390/v12111325] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 11/05/2020] [Accepted: 11/16/2020] [Indexed: 12/15/2022] Open
Abstract
Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.
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Affiliation(s)
- Yoonjung Choi
- Deargen, Inc., Daejeon 34051, Korea; (Y.C.); (B.S.); (S.P.)
| | - Bonggun Shin
- Deargen, Inc., Daejeon 34051, Korea; (Y.C.); (B.S.); (S.P.)
| | - Keunsoo Kang
- Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan 31116, Korea;
| | - Sungsoo Park
- Deargen, Inc., Daejeon 34051, Korea; (Y.C.); (B.S.); (S.P.)
| | - Bo Ram Beck
- Deargen, Inc., Daejeon 34051, Korea; (Y.C.); (B.S.); (S.P.)
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24
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Crawford KA, Berlow NE, Tsay J, Lazich M, Mancini M, Noakes C, Huang T, Keller C. Case report for an adolescent with germline RET mutation and alveolar rhabdomyosarcoma. Cold Spring Harb Mol Case Stud 2020; 6:mcs.a004853. [PMID: 32532875 PMCID: PMC7304354 DOI: 10.1101/mcs.a004853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 04/13/2020] [Indexed: 11/25/2022] Open
Abstract
In this case report we evaluate the genetics of and scientific basis of therapeutic options for a 14-yr-old male patient diagnosed with metastatic PAX3–FOXO1 fusion positive alveolar rhabdomyosarcoma. A distinguishing genetic feature of this patient was a germline RET C634F mutation, which is a known driver of multiple endocrine neoplasia type 2A (MEN2A) cancer. Through sequential DNA and RNA sequencing analyses over the patient's clinical course, a set of gene mutations, amplifications, and overexpressed genes were identified and biological hypotheses generated to explore the biology of RET and coexisting signaling pathways in rhabdomyosarcoma. Somatic genetic abnormalities identified include CDK4 amplification and FGFR4 G388R polymorphism. Because of the initial lack of patient-derived primary cell cultures, these hypotheses were evaluated using several approaches including western blot analysis and pharmacological evaluation with molecularly similar alveolar rhabdomyosarcoma cell lines. Once a primary cell culture became available, the RET inhibitor cabozantinib was tested but showed no appreciable efficacy in vitro, affirming with the western blot negative for RET protein expression that RET germline mutation could be only incidental. In parallel, the patient was treated with cabozantinib without definitive clinical benefit. Parallel chemical screens identified PI3K and HSP90 as potential tumor-specific biological features. Inhibitors of PI3K and HSP90 were further validated in drug combination synergy experiments and shown to be synergistic in the patient-derived culture. We also evaluated the use of JAK/STAT pathway inhibitors in the context of rhabdomyosarcomas bearing the FGFR4 G388R coding variant. Although the patient succumbed to his disease, study of the patient's tumor has generated insights into the biology of RET and other targets in rhabdomyosarcoma.
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Affiliation(s)
- Kenneth A Crawford
- Children's Cancer Therapy Development Institute, Beaverton, Oregon 97005, USA
| | - Noah E Berlow
- Children's Cancer Therapy Development Institute, Beaverton, Oregon 97005, USA
| | - Jennifer Tsay
- 2016 Pediatric Cancer Nanocourse, Children's Cancer Therapy Development Institute, Beaverton, Oregon 97005, USA
| | - Michael Lazich
- 2016 Pediatric Cancer Nanocourse, Children's Cancer Therapy Development Institute, Beaverton, Oregon 97005, USA
| | - Maria Mancini
- Champions Oncology, Hackensack, New Jersey 07601, USA
| | | | - Tannie Huang
- Kaiser Permanente Santa Clara Medical Center, Santa Clara, California 95051, USA
| | - Charles Keller
- Children's Cancer Therapy Development Institute, Beaverton, Oregon 97005, USA
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25
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Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 2020; 18:784-790. [PMID: 32280433 PMCID: PMC7118541 DOI: 10.1016/j.csbj.2020.03.025] [Citation(s) in RCA: 400] [Impact Index Per Article: 100.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 03/23/2020] [Accepted: 03/25/2020] [Indexed: 12/15/2022] Open
Abstract
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.
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Affiliation(s)
| | - Bonggun Shin
- Deargen, Inc., Daejeon, Republic of Korea
- Department of Computer Science, Emory University, Atlanta, GA, United States
| | | | | | - Keunsoo Kang
- Department of Microbiology, College of Science & Technology, Dankook University, Cheonan, Republic of Korea
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26
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Beck BR, Shin B, Choi Y, Park S, Kang K. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput Struct Biotechnol J 2020; 18:784-790. [PMID: 32280433 DOI: 10.1101/2020.01.31.929547] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 03/23/2020] [Accepted: 03/25/2020] [Indexed: 05/18/2023] Open
Abstract
The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd < 1000 nM. In addition, we also found that several antiviral agents, such as Kaletra (lopinavir/ritonavir), could be used for the treatment of SARS-CoV-2. Overall, we suggest that the list of antiviral drugs identified by the MT-DTI model should be considered, when establishing effective treatment strategies for SARS-CoV-2.
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Affiliation(s)
| | - Bonggun Shin
- Deargen, Inc., Daejeon, Republic of Korea
- Department of Computer Science, Emory University, Atlanta, GA, United States
| | | | | | - Keunsoo Kang
- Department of Microbiology, College of Science & Technology, Dankook University, Cheonan, Republic of Korea
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27
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Paananen J, Fortino V. An omics perspective on drug target discovery platforms. Brief Bioinform 2019; 21:1937-1953. [PMID: 31774113 PMCID: PMC7711264 DOI: 10.1093/bib/bbz122] [Citation(s) in RCA: 92] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/23/2019] [Accepted: 07/27/2019] [Indexed: 01/28/2023] Open
Abstract
The drug discovery process starts with identification of a disease-modifying target. This critical step traditionally begins with manual investigation of scientific literature and biomedical databases to gather evidence linking molecular target to disease, and to evaluate the efficacy, safety and commercial potential of the target. The high-throughput and affordability of current omics technologies, allowing quantitative measurements of many putative targets (e.g. DNA, RNA, protein, metabolite), has exponentially increased the volume of scientific data available for this arduous task. Therefore, computational platforms identifying and ranking disease-relevant targets from existing biomedical data sources, including omics databases, are needed. To date, more than 30 drug target discovery (DTD) platforms exist. They provide information-rich databases and graphical user interfaces to help scientists identify putative targets and pre-evaluate their therapeutic efficacy and potential side effects. Here we survey and compare a set of popular DTD platforms that utilize multiple data sources and omics-driven knowledge bases (either directly or indirectly) for identifying drug targets. We also provide a description of omics technologies and related data repositories which are important for DTD tasks.
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Affiliation(s)
- Jussi Paananen
- Institute of Biomedicine, University of Eastern Finland, Finland.,Blueprint Genetics Ltd, Finland
| | - Vittorio Fortino
- Institute of Biomedicine, University of Eastern Finland, Finland
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28
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Tanoli Z, Alam Z, Ianevski A, Wennerberg K, Vähä-Koskela M, Aittokallio T. Interactive visual analysis of drug–target interaction networks using Drug Target Profiler, with applications to precision medicine and drug repurposing. Brief Bioinform 2018; 21:211-220. [PMID: 30566623 DOI: 10.1093/bib/bby119] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 11/01/2018] [Accepted: 11/19/2018] [Indexed: 12/13/2022] Open
Abstract
Knowledge of the full target space of drugs (or drug-like compounds) provides important insights into the potential therapeutic use of the agents to modulate or avoid their various on- and off-targets in drug discovery and precision medicine. However, there is a lack of consolidated databases and associated data exploration tools that allow for systematic profiling of drug target-binding potencies of both approved and investigational agents using a network-centric approach. We recently initiated a community-driven platform, Drug Target Commons (DTC), which is an open-data crowdsourcing platform designed to improve the management, reproducibility and extended use of compound-target bioactivity data for drug discovery and repurposing, as well as target identification applications. In this work, we demonstrate an integrated use of the rich bioactivity data from DTC and related drug databases using Drug Target Profiler (DTP), an open-source software and web tool for interactive exploration of drug-target interaction networks. DTP was designed for network-centric modeling of mode-of-action of multi-targeting anticancer compounds, especially for precision oncology applications. DTP enables users to construct an interaction network based on integrated bioactivity data across selected chemical compounds and their protein targets, further customizable using various visualization and filtering options, as well as cross-links to several drug and protein databases to provide comprehensive information of the network nodes and interactions. We demonstrate here the operation of the DTP tool and its unique features by several use cases related to both drug discovery and drug repurposing applications, using examples of anticancer drugs with shared target profiles. DTP is freely accessible at http://drugtargetprofiler.fimm.fi/.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Zaid Alam
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Aleksandr Ianevski
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology, Aalto University, Espoo, Finland
| | | | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology, Aalto University, Espoo, Finland
- Department of Mathematics and Statistics, University of Turku, Turku, Finland
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