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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 261] [Impact Index Per Article: 87.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Katsila T, Balasopoulou A, Tsagaraki I, Patrinos GP. Pharmacomicrobiomics informs clinical pharmacogenomics. Pharmacogenomics 2019; 20:731-739. [PMID: 31368841 DOI: 10.2217/pgs-2019-0027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Aim: Microbiota-host-xenobiotics interactions in humans become of prime interest when clinical pharmacogenomics is to be implemented. Despite the advent of technology, information still needs to be translated into knowledge for optimum patient stratification and disease management. Material & methods: Herein, we mined metagenomic, pharmacometagenomic and pharmacomicrobiomic datasets to map microbiota-host-drugs networks. Results: Datasets were multifaceted and voluminous. Interoperability, data sparsity and scarcity remain a challenge. Mapping microbiota-host-drugs networks allowed the prediction of drug response/toxicity and modulation of the microbiota-host-drugs interplay. Conclusion: Our approach triangulated microbiota, host and drug networks revealing the need for contextual data and open science via microattribution to accelerate knowledge growth. Our findings may serve as a data storehouse for a user-friendly query system, coupled with databanks and databases.
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Affiliation(s)
- Theodora Katsila
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece.,Institute of Chemical Biology, National Hellenic Research Centre, Athens, Greece
| | - Angeliki Balasopoulou
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
| | - Ioanna Tsagaraki
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
| | - George P Patrinos
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece.,Department of Pathology, College of Medicine & Health Sciences, Al Ain, United Arab Emirates.,Zayed Center of Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
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Smirnov P, Kofia V, Maru A, Freeman M, Ho C, El-Hachem N, Adam GA, Ba-Alawi W, Safikhani Z, Haibe-Kains B. PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies. Nucleic Acids Res 2019; 46:D994-D1002. [PMID: 30053271 PMCID: PMC5753377 DOI: 10.1093/nar/gkx911] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 09/27/2017] [Indexed: 12/18/2022] Open
Abstract
Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.
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Affiliation(s)
- Petr Smirnov
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Victor Kofia
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Alexander Maru
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Mark Freeman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Chantal Ho
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Nehme El-Hachem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - George-Alexandru Adam
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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Sivadas A, Scaria V. Population-scale genomics-Enabling precision public health. ADVANCES IN GENETICS 2018; 103:119-161. [PMID: 30904093 DOI: 10.1016/bs.adgen.2018.09.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.
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Affiliation(s)
- Ambily Sivadas
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India; Academy of Scientific and Innovative Research (AcSIR), New Delhi, India.
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Patrinos GP, Katsila T. Pharmacogenomics education and research at the Department of Pharmacy, University of Patras, Greece. Pharmacogenomics 2016; 17:1865-1872. [DOI: 10.2217/pgs-2016-0142] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
The Pharmacogenomics and Personalized Medicine group belongs to the Laboratory of Molecular Biology and Immunology, Department of Pharmacy and is active since 2009 mainly in the field of pharmacogenomics and personalized medicine. Herein, we describe the research interests, collaborations and accomplishments of the Pharmacogenomics and Personalized Medicine group together with the teaching activities of the group that greatly enhance the pharmacogenomics knowledge of graduate/postgraduate students and healthcare professionals.
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Affiliation(s)
- George P Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
- Department of Pathology, College of Medicine & Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
- Department of Bioinformatics, Faculty of Health Sciences, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Theodora Katsila
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
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Viennas E, Komianou A, Mizzi C, Stojiljkovic M, Mitropoulou C, Muilu J, Vihinen M, Grypioti P, Papadaki S, Pavlidis C, Zukic B, Katsila T, van der Spek PJ, Pavlovic S, Tzimas G, Patrinos GP. Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies. Nucleic Acids Res 2016; 45:D846-D853. [PMID: 27924022 PMCID: PMC5210643 DOI: 10.1093/nar/gkw949] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Accepted: 10/12/2016] [Indexed: 01/20/2023] Open
Abstract
FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leading mostly to monogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnic mutation databases, all built around Microsoft's PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The abovementioned updates further enhance the impact of FINDbase, as a key resource for Genomic Medicine applications.
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Affiliation(s)
- Emmanouil Viennas
- University of Patras, Faculty of Engineering, Department of Computer Engineering and Informatics, GR-26504, Patras, Greece
| | - Angeliki Komianou
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece
| | - Clint Mizzi
- Erasmus University Medical Center, Faculty of Medicine and Health Sciences, Department of Bioinformatics, NL-3015 CN, Rotterdam, The Netherlands.,University of Malta, Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, MSD 2090, Malta
| | - Maja Stojiljkovic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Laboratory of Molecular Biomedicine, 11010, Belgrade, Serbia
| | | | - Juha Muilu
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, FI-00014, Helsinki, Finland
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, SE-22100, Lund, Sweden
| | - Panagiota Grypioti
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece
| | - Styliani Papadaki
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece
| | - Cristiana Pavlidis
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece
| | - Branka Zukic
- University of Malta, Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, MSD 2090, Malta
| | - Theodora Katsila
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece
| | - Peter J van der Spek
- Erasmus University Medical Center, Faculty of Medicine and Health Sciences, Department of Bioinformatics, NL-3015 CN, Rotterdam, The Netherlands
| | - Sonja Pavlovic
- University of Malta, Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, MSD 2090, Malta
| | - Giannis Tzimas
- Department of Computer and Informatics Engineering, Technological Educational Institute of Western Greece, GR-30020, Patras, Greece
| | - George P Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, GR-26504, Patras, Greece .,Erasmus University Medical Center, Faculty of Medicine and Health Sciences, Department of Bioinformatics, NL-3015 CN, Rotterdam, The Netherlands
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ePGA: A Web-Based Information System for Translational Pharmacogenomics. PLoS One 2016; 11:e0162801. [PMID: 27631363 PMCID: PMC5025168 DOI: 10.1371/journal.pone.0162801] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Accepted: 08/29/2016] [Indexed: 11/19/2022] Open
Abstract
One of the challenges that arise from the advent of personal genomics services is to efficiently couple individual data with state of the art Pharmacogenomics (PGx) knowledge. Existing services are limited to either providing static views of PGx variants or applying a simplistic match between individual genotypes and existing PGx variants. Moreover, there is a considerable amount of haplotype variation associated with drug metabolism that is currently insufficiently addressed. Here, we present a web-based electronic Pharmacogenomics Assistant (ePGA; http://www.epga.gr/) that provides personalized genotype-to-phenotype translation, linked to state of the art clinical guidelines. ePGA's translation service matches individual genotype-profiles with PGx gene haplotypes and infers the corresponding diplotype and phenotype profiles, accompanied with summary statistics. Additional features include i) the ability to customize translation based on subsets of variants of clinical interest, and ii) to update the knowledge base with novel PGx findings. We demonstrate ePGA's functionality on genetic variation data from the 1000 Genomes Project.
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Abstract
After decades of discovery, inherited variations have been identified in approximately 20 genes that affect about 80 medications and are actionable in the clinic. And some somatically acquired genetic variants direct the choice of 'targeted' anticancer drugs for individual patients. Current efforts that focus on the processes required to appropriately act on pharmacogenomic variability in the clinic are moving away from discovery and towards implementation of an evidenced-based strategy for improving the use of medications, thereby providing a cornerstone for precision medicine.
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Mitropoulos K, Al Jaibeji H, Forero DA, Laissue P, Wonkam A, Lopez-Correa C, Mohamed Z, Chantratita W, Lee MTM, Llerena A, Brand A, Ali BR, Patrinos GP. Success stories in genomic medicine from resource-limited countries. Hum Genomics 2015; 9:11. [PMID: 26081768 PMCID: PMC4485996 DOI: 10.1186/s40246-015-0033-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 06/09/2015] [Indexed: 02/08/2023] Open
Abstract
In recent years, the translation of genomic discoveries into mainstream medical practice and public health has gained momentum, facilitated by the advent of new technologies. However, there are often major discrepancies in the pace of implementation of genomic medicine between developed and developing/resource-limited countries. The main reason does not only lie in the limitation of resources but also in the slow pace of adoption of the new findings and the poor understanding of the potential that this new discipline offers to rationalize medical diagnosis and treatment. Here, we present and critically discuss examples from the successful implementation of genomic medicine in resource-limited countries, focusing on pharmacogenomics, genome informatics, and public health genomics, emphasizing in the latter case genomic education, stakeholder analysis, and economics in pharmacogenomics. These examples can be considered as model cases and be readily replicated for the wide implementation of pharmacogenomics and genomic medicine in other resource-limited environments.
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Affiliation(s)
| | - Hayat Al Jaibeji
- University of Maastricht, Maastricht, The Netherlands. .,Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
| | - Diego A Forero
- Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad Antonio Nariño, Bogotá, Colombia.
| | - Paul Laissue
- Unidad de Genética, Grupo GENIUROS, Escuela de Medicina y Ciencias de la Salud, Universidad del Rosario, Bogotá, Colombia.
| | - Ambroise Wonkam
- Division of Human Genetics, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
| | | | - Zahurin Mohamed
- Department of Pharmacology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
| | - Wasun Chantratita
- Department of Pathology, Medical Genomic Center, Ramathibodi Hospital, Faculty of Medicine, Mahidol University, Bangkok, Thailand.
| | - Ming Ta Michael Lee
- Laboratory for International Alliance on Genomic Research, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan. .,Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Adrian Llerena
- CICAB Clinical Research Center, Extremadura University Hospital and Medical School, Badajoz, Spain.
| | - Angela Brand
- University of Maastricht, Maastricht, The Netherlands.
| | - Bassam R Ali
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
| | - George P Patrinos
- Department of Pharmacy, University of Patras School of Health Sciences, Patras, Greece.
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