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Ravegnini G, Valori G, Zhang Q, Ricci R, Hrelia P, Angelini S. Pharmacogenetics in the treatment of gastrointestinal stromal tumors - an updated review. Expert Opin Drug Metab Toxicol 2020; 16:797-808. [PMID: 32597248 DOI: 10.1080/17425255.2020.1789589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
INTRODUCTION Gastrointestinal stromal tumors (GIST) are the best example of a targeted therapy in solid tumors. The introduction of tyrosine kinase inhibitors (TKIs) deeply improved the prognosis of this tumor. However, a degree of inter-patient variability is still reported in response rates and pharmacogenetics may play an important role in the final clinical outcome. AREAS COVERED In this review, the authors provide an updated overview of the pharmacogenetic literature analyzing the role of polymorphisms in both GIST treatment efficacy and toxicity. EXPERT OPINION Besides the primary role of somatic DNA in dictating the clinical response to TKIs, several polymorphisms influencing their pharmacokinetics and pharmacodynamics have been identified as being potentially involved. In the last 10 years, many potential biomarkers have been proposed to predict clinical response and toxicity after TKI administration. However, the evidence is still too limited to promote a clinical translation. To date, the somatic mutational status represents the main player in clinical response to TKIs in GIST treatment; however, pharmacogenetics could still explain the degree of inter-patient variability observed in GIST patients. A combination of different theoretical approaches, experimental model systems, and statistical methods is clearly needed, in order to translate pharmacogenetics to clinical practice in the near future.
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Affiliation(s)
- Gloria Ravegnini
- Department of Pharmacy and Biotechnology, University of Bologna , Bologna, Italy
| | - Giorgia Valori
- Department of Pharmacy and Biotechnology, University of Bologna , Bologna, Italy
| | - Qianqian Zhang
- UOC di Anatomia Patologica, Fondazione Policlinico Universitario 'A. Gemelli' IRCCS , Rome, Italy
| | - Riccardo Ricci
- UOC di Anatomia Patologica, Fondazione Policlinico Universitario 'A. Gemelli' IRCCS , Rome, Italy.,Department of Pathology, Universita Cattolica del Sacro Cuore , Rome, Italy
| | - Patrizia Hrelia
- Department of Pharmacy and Biotechnology, University of Bologna , Bologna, Italy
| | - Sabrina Angelini
- Department of Pharmacy and Biotechnology, University of Bologna , Bologna, Italy
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Multiple genetic factors affecting the pharmacokinetic and pharmacodynamic processes of tacrolimus in Chinese myasthenia gravis patients. Eur J Clin Pharmacol 2020; 76:659-671. [PMID: 31955224 DOI: 10.1007/s00228-019-02803-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 11/20/2019] [Indexed: 12/14/2022]
Abstract
PURPOSE Tacrolimus is a novel effective immunosuppressant for myasthenia gravis (MG) patients. However, the narrow therapeutic window, and high inter- and intrapatient variation in bioavailability largely limited its clinical application. This article intended to find the SNPs influencing clinical outcome and discover the possible mechanisms. METHODS Based on the tagSNPs genotyped by Improved Multiple Ligase Detection Reaction, Plink 1.07 was used to find the SNPs having close interaction to tacrolimus serum concentration, QMG score changes or even reasonable drug dose. Then we searched several databases to predict the possible miRNA binding rs15524 sequence. Based on the prediction, dual-luciferase reporter assay and miRNA transfection were used to discover the mechanism of how SNP rs15524 controls tacrolimus serum concentration through influencing CYP3A5 expression. RESULTS In this article, we found multiple SNPs on CYP3A4, CYP3A5, FKBP1A, NFATC2 genes were predicted closely related to tacrolimus serum concentration, therapeutic effect which reflected by QMG score changes or even reasonable drug dose. After in silico miRNA selection, possible relationship between hsa-miR-500a and rs15524 was found. With the help of dual-luciferase reporter assay, wild-type rs15524 (T allele) was found having a stronger binding affinity for hsa-miR-500a. Higher expression of CYP3A5 may also led by lower hsa-miR-500a level. CONCLUSIONS SNP rs15524 may control CYP3A5 expression by affecting the binding affinity between CYP3A5 3'UTR and hsa-miR-500a. Wild type (T allele) 3'UTR of CYP3A5 has stronger binding affinity to hsa-miR-500a and cause lower CYP3A5 expression and higher tacrolimus serum concentration.
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Zhang Z, Zhao Y, Liao X, Shi W, Li K, Zou Q, Peng S. Deep learning in omics: a survey and guideline. Brief Funct Genomics 2020; 18:41-57. [PMID: 30265280 DOI: 10.1093/bfgp/ely030] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 07/31/2018] [Accepted: 08/30/2018] [Indexed: 01/17/2023] Open
Abstract
Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning.
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Affiliation(s)
- Zhiqiang Zhang
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Yi Zhao
- Institute of Computing Technology,Chinese Academy of Sciences, Beijing, China
| | - Xiangke Liao
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Wenqiang Shi
- School of Computer Science, National University of Defense Technology, Changsha, China
| | - Kenli Li
- College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Shaoliang Peng
- School of Computer Science, National University of Defense Technology, Changsha, China.,College of Computer Science and Electronic Engineering & National Supercomputer Centre in Changsha, Hunan University, Changsha, China
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Liu Y, Wang D, He F, Wang J, Joshi T, Xu D. Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean. Front Genet 2019; 10:1091. [PMID: 31824557 PMCID: PMC6883005 DOI: 10.3389/fgene.2019.01091] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/09/2019] [Indexed: 12/21/2022] Open
Abstract
Genomic selection uses single-nucleotide polymorphisms (SNPs) to predict quantitative phenotypes for enhancing traits in breeding populations and has been widely used to increase breeding efficiency for plants and animals. Existing statistical methods rely on a prior distribution assumption of imputed genotype effects, which may not fit experimental datasets. Emerging deep learning technology could serve as a powerful machine learning tool to predict quantitative phenotypes without imputation and also to discover potential associated genotype markers efficiently. We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency maps. The missing values of SNPs are treated as a new genotype for the input of the deep learning model. We tested our framework on both simulation data and experimental datasets of soybean. The results show that the deep learning model can bypass the imputation of missing values and achieve more accurate results for predicting quantitative phenotypes than currently available other well-known statistical methods. It can also effectively and efficiently identify significant markers of SNPs and SNP combinations associated in genome-wide association study.
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Affiliation(s)
- Yang Liu
- Institute of Data Science and Informatics, University of Missouri, Columbia, MO, United States.,Department of Electrical Engineer and Computer Science, University of Missouri, Columbia, MO, United States
| | - Duolin Wang
- Department of Electrical Engineer and Computer Science, University of Missouri, Columbia, MO, United States.,Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO, United States
| | - Fei He
- Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO, United States.,Department of Computer Science and Information Technology, Northeast Normal University, Changchun, China
| | - Juexin Wang
- Department of Electrical Engineer and Computer Science, University of Missouri, Columbia, MO, United States.,Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO, United States
| | - Trupti Joshi
- Institute of Data Science and Informatics, University of Missouri, Columbia, MO, United States.,Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO, United States.,Department of Health Management and Informatics, School of Medicine, University of Missouri, Columbia, MO, United States
| | - Dong Xu
- Institute of Data Science and Informatics, University of Missouri, Columbia, MO, United States.,Department of Electrical Engineer and Computer Science, University of Missouri, Columbia, MO, United States.,Christopher S. Bond Life Science Center, University of Missouri, Columbia, MO, United States
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Meng HY, Jin WL, Yan CK, Yang H. The Application of Machine Learning Techniques in Clinical Drug Therapy. Curr Comput Aided Drug Des 2019; 15:111-119. [PMID: 29804538 DOI: 10.2174/1573409914666180525124608] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 05/15/2018] [Accepted: 05/22/2018] [Indexed: 12/19/2022]
Abstract
INTRODUCTION The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. METHODS According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. RESULTS AND CONCLUSION In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development.
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Affiliation(s)
- Huan-Yu Meng
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Wan-Lin Jin
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Cheng-Kai Yan
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Huan Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
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Kalinin AA, Higgins GA, Reamaroon N, Soroushmehr S, Allyn-Feuer A, Dinov ID, Najarian K, Athey BD. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 2018; 19:629-650. [PMID: 29697304 PMCID: PMC6022084 DOI: 10.2217/pgs-2018-0008] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 03/09/2018] [Indexed: 01/02/2023] Open
Abstract
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.
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Affiliation(s)
- Alexandr A Kalinin
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource (SOCR), University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
| | - Gerald A Higgins
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Narathip Reamaroon
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Sayedmohammadreza Soroushmehr
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Ari Allyn-Feuer
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Ivo D Dinov
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Statistics Online Computational Resource (SOCR), University of Michigan School of Nursing, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Brian D Athey
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
- Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI 48109, USA
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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P Tafti A, Badger J, LaRose E, Shirzadi E, Mahnke A, Mayer J, Ye Z, Page D, Peissig P. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure. JMIR Med Inform 2017; 5:e51. [PMID: 29222076 PMCID: PMC5741828 DOI: 10.2196/medinform.9170] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 11/07/2017] [Accepted: 11/08/2017] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. METHODS We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. RESULTS The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. CONCLUSIONS To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.
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Affiliation(s)
- Ahmad P Tafti
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Jonathan Badger
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Eric LaRose
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Ehsan Shirzadi
- Institute of Electrical and Electronics Engineers, Dublin, Ireland
| | - Andrea Mahnke
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - John Mayer
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Zhan Ye
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - David Page
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
| | - Peggy Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
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Johnson KW, Shameer K, Glicksberg BS, Readhead B, Sengupta PP, Björkegren JLM, Kovacic JC, Dudley JT. Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine. ACTA ACUST UNITED AC 2017; 2:311-327. [PMID: 30062151 PMCID: PMC6034501 DOI: 10.1016/j.jacbts.2016.11.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 11/29/2016] [Accepted: 11/30/2016] [Indexed: 12/20/2022]
Abstract
The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.
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Affiliation(s)
- Kipp W Johnson
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York.,Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Khader Shameer
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York.,Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin S Glicksberg
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York.,Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ben Readhead
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York.,Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Partho P Sengupta
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Medical Biochemistry and Biophysics Vascular Biology Unit, Karolinska Institutet, Stockholm, Sweden
| | - Jason C Kovacic
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joel T Dudley
- Institute for Next Generation Healthcare, Mount Sinai Health System, New York, New York.,Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
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Kim IW, Oh JM. Deep learning: from chemoinformatics to precision medicine. JOURNAL OF PHARMACEUTICAL INVESTIGATION 2017. [DOI: 10.1007/s40005-017-0332-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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