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Zamora Z, Wang S, Chen YW, Diamante G, Yang X. Systematic transcriptome-wide meta-analysis across endocrine disrupting chemicals reveals shared and unique liver pathways, gene networks, and disease associations. ENVIRONMENT INTERNATIONAL 2024; 183:108339. [PMID: 38043319 DOI: 10.1016/j.envint.2023.108339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/03/2023] [Accepted: 11/19/2023] [Indexed: 12/05/2023]
Abstract
Cardiometabolic disorders (CMD) are a growing public health problem across the world. Among the known cardiometabolic risk factors are compounds that induce endocrine and metabolic dysfunctions, such as endocrine disrupting chemicals (EDCs). To date, how EDCs influence molecular programs and cardiometabolic risks has yet to be fully elucidated, especially considering the complexity contributed by species-, chemical-, and dose-specific effects. Moreover, different experimental and analytical methodologies employed by different studies pose challenges when comparing findings across studies. To explore the molecular mechanisms of EDCs in a systematic manner, we established a data-driven computational approach to meta-analyze 30 human, mouse, and rat liver transcriptomic datasets for 4 EDCs, namely bisphenol A (BPA), bis(2-ethylhexyl) phthalate (DEHP), tributyltin (TBT), and perfluorooctanoic acid (PFOA). Our computational pipeline uniformly re-analyzed pre-processed quality-controlled microarray data and raw RNAseq data, derived differentially expressed genes (DEGs) and biological pathways, modeled gene regulatory networks and regulators, and determined CMD associations based on gene overlap analysis. Our approach revealed that DEHP and PFOA shared stable transcriptomic signatures that are enriched for genes associated with CMDs, suggesting similar mechanisms of action such as perturbations of peroxisome proliferator-activated receptor gamma (PPARγ) signaling and liver gene network regulators VNN1 and ACOT2. In contrast, TBT exhibited highly divergent gene signatures, pathways, network regulators, and disease associations from the other EDCs. In addition, we found that the rat, mouse, and human BPA studies showed highly variable transcriptomic patterns, providing molecular support for the variability in BPA responses. Our work offers insights into the commonality and differences in the molecular mechanisms of various EDCs and establishes a streamlined data-driven workflow to compare molecular mechanisms of environmental substances to elucidate the underlying connections between chemical exposure and disease risks.
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Affiliation(s)
- Zacary Zamora
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Susanna Wang
- Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Yen-Wei Chen
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Graciel Diamante
- Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA.
| | - Xia Yang
- Molecular Toxicology Interdepartmental Program, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Integrative Biology and Physiology, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA; Institute for Quantitative and Computational Biosciences, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA.
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Egners A, Cramer T, Wallach I, Berndt N. Kinetic Modeling of Hepatic Metabolism and Simulation of Treatment Effects. Methods Mol Biol 2024; 2769:211-225. [PMID: 38315400 DOI: 10.1007/978-1-0716-3694-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
Mathematical modeling is a promising strategy to fill the experimentally unapproachable knowledge gaps about the relative contribution of various molecular processes to cellular metabolic function. To this end, we developed detailed kinetic models of the central metabolism of different cell types, comprising multiple metabolic functionalities. We used the model to simulate metabolic changes in several cell types under different experimental settings in health and disease. In this way, we show that it is possible to decipher and characterize the relative influence of various metabolic pathways and enzymes to overall metabolic performance and phenotype.Quantitative Systems Metabolism (QSM™) allows quantitative assessment of metabolic functionality and metabolic profiling based on proteomic data. Here, we describe the technique, namely, molecular resolved kinetic modeling, underlying QSM™. We explain the necessary steps for the generation of cell-specific models to functionally interpret proteomic data and point out some unresolved challenges and open questions.
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Affiliation(s)
- Antje Egners
- Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, Aachen, Germany
| | - Thorsten Cramer
- Molecular Tumor Biology, Department of General, Visceral and Transplantation Surgery, RWTH University Hospital, Aachen, Germany
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Iwona Wallach
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nikolaus Berndt
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany.
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Pandiri AR, Auerbach SS, Stevens JL, Blomme EAG. Toxicogenomics Approaches to Address Toxicity and Carcinogenicity in the Liver. Toxicol Pathol 2023; 51:470-481. [PMID: 38288963 PMCID: PMC11014763 DOI: 10.1177/01926233241227942] [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] [Indexed: 04/13/2024]
Abstract
Toxicogenomic technologies query the genome, transcriptome, proteome, and the epigenome in a variety of toxicological conditions. Due to practical considerations related to the dynamic range of the assays, sensitivity, cost, and technological limitations, transcriptomic approaches are predominantly used in toxicogenomics. Toxicogenomics is being used to understand the mechanisms of toxicity and carcinogenicity, evaluate the translational relevance of toxicological responses from in vivo and in vitro models, and identify predictive biomarkers of disease and exposure. In this session, a brief overview of various transcriptomic technologies and practical considerations related to experimental design was provided. The advantages of gene network analyses to define mechanisms were also discussed. An assessment of the utility of toxicogenomic technologies in the environmental and pharmaceutical space showed that these technologies are being increasingly used to gain mechanistic insights and determining the translational relevance of adverse findings. Within the environmental toxicology area, there is a broader regulatory consideration of benchmark doses derived from toxicogenomics data. In contrast, these approaches are mainly used for internal decision-making in pharmaceutical development. Finally, the development and application of toxicogenomic signatures for prediction of apical endpoints of regulatory concern continues to be area of intense research.
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Affiliation(s)
- Arun R Pandiri
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Scott S Auerbach
- National Institute of Environmental Health Sciences, Durham, North Carolina, USA
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Xie J, Ma A, Fennell A, Ma Q, Zhao J. It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data. Brief Bioinform 2020; 20:1449-1464. [PMID: 29490019 DOI: 10.1093/bib/bby014] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/16/2018] [Indexed: 12/12/2022] Open
Abstract
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.
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Shimada K, Mitchison TJ. Unsupervised identification of disease states from high-dimensional physiological and histopathological profiles. Mol Syst Biol 2019; 15:e8636. [PMID: 30782979 PMCID: PMC6380462 DOI: 10.15252/msb.20188636] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The liver and kidney in mammals play central roles in protecting the organism from xenobiotics and are at high risk of xenobiotic-induced injury. Xenobiotic-induced tissue injury has been extensively studied from both classical histopathological and biochemical perspectives. Here, we introduce a machine-learning approach to analyze toxicological response. Unsupervised characterization of physiological and histological changes in a large toxicogenomic dataset revealed nine discrete toxin-induced disease states, some of which correspond to known pathology, but others were novel. Analysis of dynamics revealed transitions between disease states at constant toxin exposure, mostly toward decreased pathology, implying induction of tolerance. Tolerance correlated with induction of known xenobiotic defense genes and decrease of novel ferroptosis sensitivity biomarkers, suggesting ferroptosis as a druggable driver of tissue pathophysiology. Lastly, mechanism of body weight decrease, a known primary marker for xenobiotic toxicity, was investigated. Combined analysis of food consumption, body weight, and molecular biomarkers indicated that organ injury promotes cachexia by whole-body signaling through Gdf15 and Igf1, suggesting strategies for therapeutic intervention that may be broadly relevant to human disease.
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Affiliation(s)
- Kenichi Shimada
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Timothy J Mitchison
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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AlWahsh M, Othman A, Hamadneh L, Telfah A, Lambert J, Hikmat S, Alassi A, Mohamed FEZ, Hergenröder R, Al-Qirim T, Dooley S, Hammad S. Second exposure to acetaminophen overdose is associated with liver fibrosis in mice. EXCLI JOURNAL 2019; 18:51-62. [PMID: 30956639 PMCID: PMC6449668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 01/28/2019] [Indexed: 11/17/2022]
Abstract
Repeated administration of hepatotoxicants is usually accompanied by liver fibrosis. However, the difference in response as a result of repeated exposures of acetaminophen (APAP) compared to a single dose is not well-studied. Therefore, in the current study, the liver response after a second dose of APAP was investigated. Adult fasted Balb/C mice were exposed to two toxic doses of 300 mg/kg APAP, which were administered 72 h apart from each other. Subsequently, blood and liver from the treated mice were collected 24 h and 72 h after both APAP administrations. Liver transaminase, i.e. alanine amino transferase (ALT) and aspartate amino transferase (AST) levels revealed that the fulminant liver damage was reduced after the second APAP administration compared to that observed at the same time point after the first treatment. These results correlated with the necrotic areas as indicated by histological analyses. Surprisingly, Picro Sirius Red (PSR) staining showed that the accumulation of extracellular matrix after the second dose coincides with the upregulation of some fibrogenic signatures, e.g., alpha smooth muscle actin. Non-targeted liver tissue metabolic profiling indicates that most alterations occur 24 h after the first dose of APAP. However, the levels of most metabolites recover to basal values over time. This organ adaptation process is also confirmed by the upregulation of antioxidative systems like e.g. superoxide dismutase and catalase. From the results, it can be concluded that there is a different response of the liver to APAP toxic doses, if the liver has already been exposed to APAP. A necroinflammatory process followed by a liver regeneration was observed after the first APAP exposure. However, fibrogenesis through the accumulation of extracellular matrix is observed after a second challenge. Therefore, further studies are required to mechanistically understand the so called "liver memory".
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Affiliation(s)
- Mohammad AlWahsh
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan,Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany,*To whom correspondence should be addressed: Mohammad AlWahsh, Leibniz Institut für Analytische Wissenschaften - ISAS e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany; Tel: +49 231 1392 192, E-mail:
| | - Amnah Othman
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Lama Hamadneh
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Ahmad Telfah
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Jörg Lambert
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Suhair Hikmat
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Amin Alassi
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Fatma El Zahraa Mohamed
- Molecular Hepatology Section, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167-Mannheim, Germany,Department of Pathology, Faculty of Medicine, Minia University, 11432-Minia, Egypt
| | - Roland Hergenröder
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
| | - Tariq Al-Qirim
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Steven Dooley
- Molecular Hepatology Section, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167-Mannheim, Germany
| | - Seddik Hammad
- Molecular Hepatology Section, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, 68167-Mannheim, Germany,Department of Forensic Medicine and Veterinary Toxicology, Faculty of Veterinary Medicine, South Valley University, 83523-Qena, Egypt
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7
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Jin X, Zimmers TA, Zhang Z, Koniaris LG. Resveratrol Improves Recovery and Survival of Diet-Induced Obese Mice Undergoing Extended Major (80%) Hepatectomy. Dig Dis Sci 2019; 64:93-101. [PMID: 30284135 DOI: 10.1007/s10620-018-5312-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 09/28/2018] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Loss of hepatic epidermal growth factor receptor (EGFR) expression is a cause for the increased perioperative risk for complications and death in patients with obesity and fatty liver undergoing liver resection. Herein, we set out to identify agents that might increase EGFR expression and improve recovery for patients with fatty liver undergoing resection. Using the diet-induced obese (DIO) mouse model of fatty liver, we examined resveratrol as a therapy to induce EGFR expression and improve outcomes following 80% partial hepatectomy (PH) in a murine model. METHODS DIO mice were fed resveratrol or carrier control by gavage. EGFR expression and the response to major (80%) PH were examined. RESULTS Based on an Illumina analysis, resveratrol was identified as increasing EGFR gene expression in A549 cells. Resveratrol was observed to also increase EGFR protein expression in A549 cells. DIO mice fed resveratrol by gavage (75 mg/kg) demonstrated an increased EGFR expression without the identified hepatic toxicity. Resveratrol and control mice subjected to 80% PH, a model of high mortality hepatectomy in DIO mice, demonstrated macroscopically decreased fatty liver and fewer liver hemorrhagic petechiae. Resveratrol pretreatment ameliorated liver injury and accelerated regeneration of the hepatic remnant after 80% PH including decreasing serum ALT and bilirubin, while increasing hepatic PCNA expression. Resveratrol increased induction of p-STAT3 and p-AKT after 80% hepatectomy. Resveratrol pretreatment significantly improved survival rates in DIO mice undergoing extended 80% PH. CONCLUSIONS Oral resveratrol restores EGFR expression in fatty liver. Resveratrol may be a promising protective agent in instances where extensive hepatic resection of fatty liver is required.
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Affiliation(s)
- Xiaoling Jin
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Teresa A Zimmers
- Department of Surgery, Indiana University School of Medicine, EH 511 SGEN, Indianapolis, IN, 46202, USA
| | - Zongxiu Zhang
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Leonidas G Koniaris
- Department of Surgery, Indiana University School of Medicine, EH 511 SGEN, Indianapolis, IN, 46202, USA.
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Auerbach SS, Paules RS. Genomic dose response: Successes, challenges, and next steps. CURRENT OPINION IN TOXICOLOGY 2018. [DOI: 10.1016/j.cotox.2019.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Jin X, Zimmers TA, Jiang Y, Milgrom DP, Zhang Z, Koniaris LG. Meloxicam increases epidermal growth factor receptor expression improving survival after hepatic resection in diet-induced obese mice. Surgery 2018; 163:1264-1271. [PMID: 29361369 DOI: 10.1016/j.surg.2017.11.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Revised: 11/08/2017] [Accepted: 11/28/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND Patients with fatty liver have delayed regenerative responses, increased hepatocellular injury, and increased risk for perioperative mortality. Currently, no clinical therapy exists to prevent liver failure or improve regeneration in patients with fatty liver. Previously we demonstrated that obese mice have markedly reduced levels of epidermal growth factor receptor in liver. We sought to identify pharmacologic agents to increase epidermal growth factor receptor expression to improve hepatic regeneration in the setting of fatty liver resection. METHODS Lean (20% calories from fat) and diet-induced obese mice (60% calories from fat) were subjected to 70% or 80% hepatectomy. RESULTS Using the BaseSpace Correlation Engine of deposited gene arrays we identified agents that increased hepatic epidermal growth factor receptor. Meloxicam was identified as inducing epidermal growth factor receptor expression across species. Meloxicam improved hepatic steatosis in diet-induced obese mice both grossly and histologically. Immunohistochemistry and Western blot analysis demonstrated that meloxicam pretreatment of diet-induced obese mice dramatically increased epidermal growth factor receptor protein expression in hepatocytes. After 70% hepatectomy, meloxicam pretreatment ameliorated liver injury and significantly accelerated mitotic rates of hepatocytes in obese mice. Recovery of liver mass was accelerated in obese mice pretreated with meloxicam (by 26% at 24 hours and 38% at 48 hours, respectively). After 80% hepatectomy, survival was dramatically increased with meloxicam treatment. CONCLUSION Low epidermal growth factor receptor expression is a common feature of fatty liver disease. Meloxicam restores epidermal growth factor receptor expression in steatotic hepatocytes. Meloxicam pretreatment may be applied to improve outcome after fatty liver resection or transplantation with steatotic graft.
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Affiliation(s)
- Xiaoling Jin
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Teresa A Zimmers
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yanlin Jiang
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Daniel P Milgrom
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Zongxiu Zhang
- Department of Surgery, Thomas Jefferson University School of Medicine, Philadelphia, PA, USA
| | - Leonidas G Koniaris
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, USA.
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Delavan B, Roberts R, Huang R, Bao W, Tong W, Liu Z. Computational drug repositioning for rare diseases in the era of precision medicine. Drug Discov Today 2017; 23:382-394. [PMID: 29055182 DOI: 10.1016/j.drudis.2017.10.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 09/19/2017] [Accepted: 10/11/2017] [Indexed: 12/12/2022]
Abstract
There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.
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Affiliation(s)
- Brian Delavan
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA; University of Arkansas at Little Rock, Little Rock, AR 72204, USA
| | - Ruth Roberts
- ApconiX, BioHub at Alderley Park, Alderley Edge SK10 4TG, UK; University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health Rockville, MD 20850, USA
| | | | - Weida Tong
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Zhichao Liu
- National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
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Kohonen P, Parkkinen JA, Willighagen EL, Ceder R, Wennerberg K, Kaski S, Grafström RC. A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury. Nat Commun 2017; 8:15932. [PMID: 28671182 PMCID: PMC5500850 DOI: 10.1038/ncomms15932] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 05/15/2017] [Indexed: 01/17/2023] Open
Abstract
Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion'-concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.
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Affiliation(s)
- Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
| | - Juuso A Parkkinen
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, P.O. Box 15400, 00076 Aalto, Finland
| | - Egon L Willighagen
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden.,Department of Bioinformatics-BiGCaT, Maastricht University, Universiteitssingel 50, P.O. Box 616, UNS 50 Box19, NL-6200 MD Maastricht, The Netherlands
| | - Rebecca Ceder
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
| | - Krister Wennerberg
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Tukholmankatu 8, P.O. Box 20, FI-00014 Helsinki, Finland
| | - Samuel Kaski
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Konemiehentie 2, P.O. Box 15400, 00076 Aalto, Finland.,Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, P.O. Box 68, FI-00014 Helsinki, Finland
| | - Roland C Grafström
- Institute of Environmental Medicine, Karolinska Institutet, Nobels väg 13, Box 210, SE-17177 Stockholm, Sweden
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12
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Demonstration Study: A Protocol to Combine Online Tools and Databases for Identifying Potentially Repurposable Drugs. DATA 2017. [DOI: 10.3390/data2020015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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13
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AbdulHameed MDM, Ippolito DL, Stallings JD, Wallqvist A. Mining kidney toxicogenomic data by using gene co-expression modules. BMC Genomics 2016; 17:790. [PMID: 27724849 PMCID: PMC5057266 DOI: 10.1186/s12864-016-3143-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 09/29/2016] [Indexed: 12/15/2022] Open
Abstract
Background Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease. Results We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury. Conclusions Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA
| | - Danielle L Ippolito
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Jonathan D Stallings
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA.
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El-Hachem N, Grossmann P, Blanchet-Cohen A, Bateman AR, Bouchard N, Archambault J, Aerts HJ, Haibe-Kains B. Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:313-20. [PMID: 26173225 PMCID: PMC4786983 DOI: 10.1289/ehp.1409157] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2014] [Accepted: 07/09/2015] [Indexed: 05/03/2023]
Abstract
BACKGROUND Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system (TG-GATEs) project generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals. OBJECTIVES To assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs data set to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals with those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro. METHODS We developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using pathways from the Reactome database with biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species. RESULTS We found that some chemicals induced conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat genomes. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support the transforming growth factor beta receptor (TGF-βR) signaling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes. CONCLUSIONS Our integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants.
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Affiliation(s)
- Nehme El-Hachem
- Integrative systems biology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
- Department of Medicine, University of Montreal, Montréal, Quebec, Canada
| | - Patrick Grossmann
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alain R. Bateman
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Nicolas Bouchard
- Department of Medicine, University of Montreal, Montréal, Quebec, Canada
- Molecular Biology of Neural Development, Institut de Recherches Cliniques de Montréal, Montreal, Canada
| | - Jacques Archambault
- Laboratory of Molecular Virology, Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada
| | - Hugo J.W.L. Aerts
- Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Address correspondence to B. Haibe-Kains, Princess Margaret Cancer Centre, University Health Network, 101 College St., Toronto, ON, M5G 1L7, Canada. Telephone: 1 (416) 581-7628. E-mail: , or to H.J.W.L. Aerts, Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA. E-mail:
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Medical Biophysics Department, University of Toronto, Toronto, Ontario, Canada
- Address correspondence to B. Haibe-Kains, Princess Margaret Cancer Centre, University Health Network, 101 College St., Toronto, ON, M5G 1L7, Canada. Telephone: 1 (416) 581-7628. E-mail: , or to H.J.W.L. Aerts, Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA. E-mail:
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15
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Grafström RC, Nymark P, Hongisto V, Spjuth O, Ceder R, Willighagen E, Hardy B, Kaski S, Kohonen P. Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of 'Omics' Data from Human Cell Cultures. Altern Lab Anim 2016; 43:325-32. [PMID: 26551289 DOI: 10.1177/026119291504300506] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This paper outlines the work for which Roland Grafström and Pekka Kohonen were awarded the 2014 Lush Science Prize. The research activities of the Grafström laboratory have, for many years, covered cancer biology studies, as well as the development and application of toxicity-predictive in vitro models to determine chemical safety. Through the integration of in silico analyses of diverse types of genomics data (transcriptomic and proteomic), their efforts have proved to fit well into the recently-developed Adverse Outcome Pathway paradigm. Genomics analysis within state-of-the-art cancer biology research and Toxicology in the 21st Century concepts share many technological tools. A key category within the Three Rs paradigm is the Replacement of animals in toxicity testing with alternative methods, such as bioinformatics-driven analyses of data obtained from human cell cultures exposed to diverse toxicants. This work was recently expanded within the pan-European SEURAT-1 project (Safety Evaluation Ultimately Replacing Animal Testing), to replace repeat-dose toxicity testing with data-rich analyses of sophisticated cell culture models. The aims and objectives of the SEURAT project have been to guide the application, analysis, interpretation and storage of 'omics' technology-derived data within the service-oriented sub-project, ToxBank. Particularly addressing the Lush Science Prize focus on the relevance of toxicity pathways, a 'data warehouse' that is under continuous expansion, coupled with the development of novel data storage and management methods for toxicology, serve to address data integration across multiple 'omics' technologies. The prize winners' guiding principles and concepts for modern knowledge management of toxicological data are summarised. The translation of basic discovery results ranged from chemical-testing and material-testing data, to information relevant to human health and environmental safety.
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Affiliation(s)
- Roland C Grafström
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Vesa Hongisto
- Toxicology Department, Misvik Biology Corporation, Turku, Finland
| | - Ola Spjuth
- Department of Medical Epidemiology and Biostatistics, Swedish e-Science Research Centre, Karolinska Institutet, Stockholm, Sweden and Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Rebecca Ceder
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Egon Willighagen
- Department of Bioinformatics-BiGCat, Maastricht University, Maastricht, The Netherlands
| | - Barry Hardy
- Douglas Connect GmbH, Zeiningen, Switzerland
| | - Samuel Kaski
- Helsinki Institute for Information Technology, Aalto University, Department of Computer Science, Helsinki, Finland
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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Martin TM, Plautz SA, Pannier AK. Temporal endogenous gene expression profiles in response to lipid-mediated transfection. J Gene Med 2015; 17:14-32. [PMID: 25663588 DOI: 10.1002/jgm.2821] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 02/01/2015] [Accepted: 02/03/2015] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Design of efficient nonviral gene delivery systems is limited as a result of the rudimentary understanding of the specific molecules and processes that facilitate DNA transfer. METHODS Lipoplexes formed with Lipofectamine 2000 (LF2000) and plasmid-encoding green fluorescent protein (GFP) were delivered to the HEK 293T cell line. After treating cells with lipoplexes, HG-U133 Affymetrix microarrays were used to identify endogenous genes differentially expressed between treated and untreated cells (2 h exposure) or between flow-separated transfected cells (GFP+) and treated, untransfected cells (GFP-) at 8, 16 and 24 h after lipoplex treatment. Cell priming studies were conducted using pharmacologic agents to alter endogenous levels of the identified differentially expressed genes to determine effect on transfection levels. RESULTS Relative to untreated cells 2 h after lipoplex treatment, only downregulated genes were identified ≥ 30-fold: ALMS1, ITGB1, FCGR3A, DOCK10 and ZDDHC13. Subsequently, relative to GFP- cells, the GFP+ cell population showed at least a five-fold upregulation of RAP1A and PACSIN3 (8 h) or HSPA6 and RAP1A (16 and 24 h). Pharmacologic studies altering endogenous levels for ALMS1, FCGR3A, and DOCK10 (involved in filopodia protrusions), ITGB1 (integrin signaling), ZDDHC13 (membrane trafficking) and PACSIN3 (proteolytic shedding of membrane receptors) were able to increase or decrease transgene production. CONCLUSIONS RAP1A, PACSIN3 and HSPA6 may help lipoplex-treated cells overcome a transcriptional shutdown due to treatment with lipoplexes and provide new targets for investigating molecular mechanisms of transfection or for enhancing transfection through cell priming or engineering of the nonviral gene delivery system.
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Affiliation(s)
- Timothy M Martin
- Department of Pharmaceutical Sciences, Durham Research Center II, University of Nebraska-Medical Center, Omaha, NE, USA
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Martin TM, Plautz SA, Pannier AK. Temporal endogenous gene expression profiles in response to polymer-mediated transfection and profile comparison to lipid-mediated transfection. J Gene Med 2015; 17:33-53. [PMID: 25663627 DOI: 10.1002/jgm.2822] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Revised: 02/01/2015] [Accepted: 02/03/2015] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Design of efficient nonviral gene delivery systems is limited by the rudimentary understanding of specific molecules that facilitate transfection. METHODS Polyplexes using 25-kDa polyethylenimine (PEI) and plasmid-encoding green fluorescent protein (GFP) were delivered to HEK 293T cells. After treating cells with polyplexes, microarrays were used to identify endogenous genes differentially expressed between treated and untreated cells (2 h of exposure) or between flow-separated transfected cells (GFP+) and treated, untransfected cells (GFP-) at 8, 16 and 24 h after lipoplex treatment. Cell priming studies were conducted using pharmacologic agents to alter endogenous levels of the identified differentially expressed genes to determine effect on transfection levels. Differentially expressed genes in polyplex-mediated transfection were compared with those differentially expressed in lipoplex transfection to identify DNA carrier-dependent molecular factors. RESULTS Differentially expressed genes were RGS1, ARHGAP24, PDZD2, SNX24, GSN and IGF2BP1 after 2 h; RAP1A and ACTA1 after 8 h; RAP1A, WDR78 and ACTA1 after 16 h; and RAP1A, SCG5, ATF3, IREB2 and ACTA1 after 24 h. Pharmacologic studies altering endogenous levels for ARHGAP24, GSN, IGF2BP1, PDZD2 and RGS1 were able to increase or decrease transgene production. Comparing differentially expressed genes for polyplexes and lipoplexes, no common genes were identified at the 2-h time point, whereas, after the 8-h time point, RAP1A, ATF3 and HSPA6 were similarly expressed. SCG5 and PGAP1 were only upregulated in polyplex-transfected cells. CONCLUSIONS The identified genes and pharmacologic agents provide targets for improving transfection systems, although polyplex or lipoplex dependencies must be considered.
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Affiliation(s)
- Timothy M Martin
- Department of Pharmaceutical Sciences, Durham Research Center II, University of Nebraska-Medical Center, Omaha, NE, USA
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Kim H, Kim JH, Kim SY, Jo D, Park HJ, Kim J, Jung S, Kim HS, Lee K. Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity. PLoS One 2015; 10:e0136698. [PMID: 26335687 PMCID: PMC4559398 DOI: 10.1371/journal.pone.0136698] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 08/05/2015] [Indexed: 11/19/2022] Open
Abstract
Undesirable toxicity is one of the main reasons for withdrawing drugs from the market or eliminating them as candidates in clinical trials. Although numerous studies have attempted to identify biomarkers capable of predicting pharmacotoxicity, few have attempted to discover robust biomarkers that are coherent across various species and experimental settings. To identify such biomarkers, we conducted meta-analyses of massive gene expression profiles for 6,567 in vivo rat samples and 453 compounds. After applying rigorous feature reduction procedures, our analyses identified 18 genes to be related with toxicity upon comparisons of untreated versus treated and innocuous versus toxic specimens of kidney, liver and heart tissue. We then independently validated these genes in human cell lines. In doing so, we found several of these genes to be coherently regulated in both in vivo rat specimens and in human cell lines. Specifically, mRNA expression of neuronal regeneration-related protein was robustly down-regulated in both liver and kidney cells, while mRNA expression of cathepsin D was commonly up-regulated in liver cells after exposure to toxic concentrations of chemical compounds. Use of these novel toxicity biomarkers may enhance the efficiency of screening for safe lead compounds in early-phase drug development prior to animal testing.
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Affiliation(s)
- Hyosil Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea
| | - Ju-Hwa Kim
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea
| | - So Youn Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Deokyeon Jo
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Ho Jun Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Jihyun Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Sungwon Jung
- Department of Genome Medicine and Science, School of Medicine, Gachon University, Incheon, Korea
- * E-mail: (HSK); (SJ)
| | - Hyun Seok Kim
- Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Korea
- Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Korea
- * E-mail: (HSK); (SJ)
| | - KiYoung Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
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Wu D, Qiu T, Zhang Q, Kang H, Yuan S, Zhu L, Zhu R. Systematic toxicity mechanism analysis of proton pump inhibitors: an in silico study. Chem Res Toxicol 2015; 28:419-30. [PMID: 25626140 DOI: 10.1021/tx5003782] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proton pump inhibitors (PPIs) are extensively used for the treatment of gastric acid-related disorders. PPIs appear to be well tolerated and almost have no short-term side effects. However, the clinical adverse reactions of long-term PPI usage are increasingly reported in recent years. So far, there is no study that elucidates the side effect mechanisms of PPIs comprehensively and systematically. In this study, a well-defined small molecule perturbed microarray data set of 344 compounds and 1695 samples was analyzed. With this high-throughput data set, a new index (Identity, I) was designed to identify PPI-specific differentially expressed genes. Results indicated that (1) up-regulated genes, such as RETSAT, CYP1A1, CYP1A2, and UGT, enhanced vitamin A's metabolism processes in the cellular retinol metabolism pathway; and that (2) down-regulated genes, such as C1QA, C1QC, C4BPA, C4BPB, CFI, and SERPING1, enriched in the complement and coagulation cascades pathway. In addition, strong association was observed between these PPI-specific differentially expressed genes and the reported side effects of PPIs by the gene-disease association network analysis. One potential toxicity mechanism of PPIs as suggested from this systematic PPI-specific gene expression analysis is that PPIs are enriched in acidic organelles where they are activated and inhibit V-ATPases and acid hydrolases, and consequently block the pathways of antigen presentation, the synthesis and secretion of cytokines, and complement component proteins and coagulation factors. The strategies developed in this work could be extended to studies on other drugs.
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Affiliation(s)
- Dingfeng Wu
- Department of Bioinformatics, Tongji University , Shanghai, P.R. China
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21
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Jennen D, Polman J, Bessem M, Coonen M, van Delft J, Kleinjans J. Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data. ACTA ACUST UNITED AC 2014. [DOI: 10.4161/sysb.29400] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Stamper BD. Transcriptional profiling of reactive metabolites for elucidating toxicological mechanisms: a case study of quinoneimine-forming agents. Drug Metab Rev 2014; 47:45-55. [DOI: 10.3109/03602532.2014.978081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Vo NS, Phan V. Exploiting dependencies of pairwise comparison outcomes to predict patterns of gene response. BMC Bioinformatics 2014; 15 Suppl 11:S2. [PMID: 25350806 PMCID: PMC4251046 DOI: 10.1186/1471-2105-15-s11-s2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The analysis of gene expression has played an important role in medical and bioinformatics research. Although it is known that a large number of samples is needed to determine the patterns of gene expression accurately, practical designs of gene expression studies occasionally have insufficient numbers of samples, making it difficult to ascertain true response patterns of variantly expressed genes. RESULTS We describe an approach to cope with the challenge of predicting true orders of gene response to treatments. We show that true patterns of gene response must be orderable sets. In experiments with few samples, we modify the conventional pairwise comparison tests and increase the significance level α intelligently to deduce orderable patterns, which are most likely true orders of gene response. Additionally, motivated by the fact that a gene can be involved in multiple biological functions, our method further resamples experimental replicates and predicts multiple response patterns for each gene. CONCLUSIONS This method can be useful in designing cost-effective experiments with small sample sizes. Patterns of highly-variantly expressed genes can be predicted by varying α intelligently. Furthermore, clusters are labeled meaningfully with patterns that describe precisely how genes in such clusters respond to treatments.
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Bowles M, Shigeta R. Statistical models for predicting liver toxicity from genomic data. ACTA ACUST UNITED AC 2014. [DOI: 10.4161/sysb.24254] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Tawa GJ, AbdulHameed MDM, Yu X, Kumar K, Ippolito DL, Lewis JA, Stallings JD, Wallqvist A. Characterization of chemically induced liver injuries using gene co-expression modules. PLoS One 2014; 9:e107230. [PMID: 25226513 PMCID: PMC4165895 DOI: 10.1371/journal.pone.0107230] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 08/06/2014] [Indexed: 12/19/2022] Open
Abstract
Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.
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Affiliation(s)
- Gregory J. Tawa
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail: (AW); (GJT)
| | - Mohamed Diwan M. AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Xueping Yu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Kamal Kumar
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
| | - Danielle L. Ippolito
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - John A. Lewis
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - Jonathan D. Stallings
- U.S. Army Center for Environmental Health Research, Fort Detrick, Maryland, United States of America
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, United States of America
- * E-mail: (AW); (GJT)
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Garrett SH, Somji S, Sens DA, Zhang KK. Prediction of the number of activated genes in multiple independent Cd(+2)- and As(+3)-induced malignant transformations of human urothelial cells (UROtsa). PLoS One 2014; 9:e85614. [PMID: 24465620 PMCID: PMC3899011 DOI: 10.1371/journal.pone.0085614] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2013] [Accepted: 12/05/2013] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Many toxic environmental agents such as cadmium and arsenic are found to be epidemiologically linked to increasing rates of cancers. In vitro studies show that these toxic agents induced malignant transformation in human cells. It is not clear whether such malignant transformation induced by a single toxic agent is driven by a common set of genes. Although the advancement of high-throughput technology has facilitated the profiling of global gene expression, it remains a question whether the sample size is sufficient to identify this common driver gene set. RESULTS We have developed a statistical method, SOFLR, to predict the number of common activated genes using a limited number of microarray samples. We conducted two case studies, cadmium and arsenic transformed human urothelial cells. Our method is able to precisely predict the number of stably induced and repressed genes and the number of samples to identify the common activated genes. The number of independent transformed isolates required for identifying the common activated genes is also estimated. The simulation study further validated our method and identified the important parameters in this analysis. CONCLUSIONS Our method predicts the degree of similarity and diversity during cell malignant transformation by a single toxic agent. The results of our case studies imply the existence of common driver and passenger gene sets in toxin-induced transformation. Using a pilot study with small sample size, this method also helps microarray experimental design by determining the number of samples required to identify the common activated gene set.
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Affiliation(s)
- Scott H. Garrett
- Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, North Dakota, United States of America
| | - Seema Somji
- Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, North Dakota, United States of America
| | - Donald A. Sens
- Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, North Dakota, United States of America
| | - Ke K. Zhang
- Department of Pathology, School of Medicine and Health Sciences, University of North Dakota, Grand Forks, North Dakota, United States of America
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Gaiteri C, Ding Y, French B, Tseng GC, Sibille E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. GENES, BRAIN, AND BEHAVIOR 2014; 13:13-24. [PMID: 24320616 PMCID: PMC3896950 DOI: 10.1111/gbb.12106] [Citation(s) in RCA: 185] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2013] [Revised: 09/25/2013] [Accepted: 11/10/2013] [Indexed: 12/12/2022]
Abstract
In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene-gene expression correlations are a common source of molecular networks because they can be extracted from high-dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming 'hub genes' direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi-omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease-related prioritization of genes and molecular systems and network meta-analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality-lethality relationship, integration with machine learning approaches and network pharmacology.
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Affiliation(s)
- Chris Gaiteri
- . Modeling, Analysis and Theory Group, Allen Institute for Brain Science, Seattle WA, USA
| | - Ying Ding
- . Carnegie Mellon-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - Beverly French
- . Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - George C. Tseng
- . Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Etienne Sibille
- . Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
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Fernald GH, Altman RB. Using molecular features of xenobiotics to predict hepatic gene expression response. J Chem Inf Model 2013; 53:2765-73. [PMID: 24010729 PMCID: PMC3810861 DOI: 10.1021/ci3005868] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.
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Affiliation(s)
- Guy Haskin Fernald
- Biomedical Informatics Training Program, Stanford University School of Medicine and ‡Departments of Bioengineering and Genetics, Stanford University , Stanford, California 94305, United States
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Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol Syst Biol 2013; 9:662. [PMID: 23632384 PMCID: PMC3658274 DOI: 10.1038/msb.2013.20] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2013] [Accepted: 03/28/2013] [Indexed: 12/14/2022] Open
Abstract
Drug-induced transcriptional modules (biclusters) were identified and annotated in three human cell lines and rat liver. These were used to assess conservation across systems and to infer and experimentally validate novel drug effects and gene functions. ![]()
Biclustering of drug-induced gene expression profiles resulted in modules of drugs and genes, which were enriched in both drug and gene annotations. Identifying drug-induced transcriptional modules separately in three human cell lines and rat liver allows assessment of their conservation across model systems. About 70% of modules are conserved across cell lines, a lower bound of 15% was estimated for their conservation across organisms, and between the in vitro and in vivo systems. Drug-induced transcriptional modules can predict novel gene functions. A conserved module associated with (chole)sterol metabolism revealed novel regulators of cellular cholesterol homeostasis; 10 of them were validated in functional imaging assays. Analysis of drugs clustered into modules can give new insights into their mechanisms of action and provide leads for drug repositioning. We predicted and experimentally validated novel cell cycle inhibitors and modulators of PPARγ, estrogen and adrenergic receptors, with potential for developing new therapies against diabetes and cancer.
In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology.
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Low Y, Sedykh A, Fourches D, Golbraikh A, Whelan M, Rusyn I, Tropsha A. Integrative chemical-biological read-across approach for chemical hazard classification. Chem Res Toxicol 2013; 26:1199-208. [PMID: 23848138 DOI: 10.1021/tx400110f] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here, we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays ("biological" similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from both chemical and biological analogues whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models.
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Affiliation(s)
- Yen Low
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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31
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Kasarskis A, Yang X, Schadt E. Integrative genomics strategies to elucidate the complexity of drug response. Pharmacogenomics 2012; 12:1695-715. [PMID: 22118053 DOI: 10.2217/pgs.11.115] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Pharmacogenomic investigation from both genome-wide association studies and experiments focused on candidate loci involved in drug mechanism and metabolism has yielded a substantial and increasing list of robust genetic effects on drug therapy in humans. At the same time, reasonably comprehensive molecular data such as gene expression, proteomic and metabolomic data are now available for collections of hundreds to thousands of individuals. If these data are structured in a statistically robust and computationally tractable way, such as a network model, they can aid in the analysis of new pharmacogenomics studies by suggesting novel hypotheses for the regulation of genes involved in drug metabolism and response. Similarly, hypotheses taken from these same models can direct genome-wide association studies by focusing the genome-wide association studies analysis on a number of specific hypotheses informed by the relationships customarily seen between a gene's expression or protein activity and genetic variation at a particular locus. Network models based on other sorts of systematic biological data such as cell-based surveys of drug effect on gene expression and mining of literature and electronic medical records for associations between clinical and molecular phenotypes also promise similar utility. Although surely primitive in comparison with what will be developed, these model-based approaches to leveraging the increasing volume of data generated in the course of patient care and medical research nevertheless suggest a huge opportunity to improve our understanding of biological systems involved in pharmacogenomics and apply them to questions of medical relevance.
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Przybylak KR, Cronin MTD. In silico models for drug-induced liver injury--current status. Expert Opin Drug Metab Toxicol 2012; 8:201-17. [PMID: 22248266 DOI: 10.1517/17425255.2012.648613] [Citation(s) in RCA: 67] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Drug-induced liver injury (DILI) is one of the most important reasons for drug attrition at both pre-approval and post-approval stages. Therefore, it is crucial to develop methods that will detect potential hepatotoxicity among drug candidates as early and quickly as possible. However, the complexity of hepatotoxicity endpoint makes it very difficult to predict. In addition, there is still a lack of sensitive and specific biomarkers for DILI that consequently leads to a scarcity of reliable hepatotoxic data, which are the key to any modelling approach. AREAS COVERED This review explores the current status of existing in silico models predicting hepatotoxicity. Over the past decade, attempts have been made to compile hepatotoxicity data and develop in silico models, which can be used as a first-line screening of drug candidates for further testing. EXPERT OPINION Most of the predictive methods discussed in this review are based on the structural properties of chemicals and do not take into account genetic and environmental factors; therefore, their predictions are still uncertain. To improve the predictability of in silico models for DILI, it is essential to better understand its mechanisms as well as to develop sensitive toxicogenomics biomarkers, which show relatively good differentiation between hepatotoxins and non-hepatotoxins.
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Affiliation(s)
- Katarzyna R Przybylak
- Liverpool John Moores University, School of Pharmacy and Chemistry, Byrom Street, Liverpool, L3 3AF, England
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Chang C, Wang J, Zhao C, Fostel J, Tong W, Bushel PR, Deng Y, Pusztai L, Symmans WF, Shi T. Maximizing biomarker discovery by minimizing gene signatures. BMC Genomics 2011; 12 Suppl 5:S6. [PMID: 22369133 PMCID: PMC3287502 DOI: 10.1186/1471-2164-12-s5-s6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background The use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS) based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II), trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications. Results We analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II. Conclusions Our results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the studying endpoints. New classifiers built with MFS exhibit improved performance with both internal and external validation, suggesting that MFS method generally reduces redundancies for features within gene signatures and improves the performance of the model. Consequently, our strategy will be beneficial for the microarray-based clinical applications.
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Affiliation(s)
- Chang Chang
- The Center for Bioinformatics and Institute of Biomedical Sciences, School of Life Science, East China Normal University, 500 Dongchuan Road, Shanghai 200241, China
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Zhang M, Chen M, Tong W. Is Toxicogenomics a More Reliable and Sensitive Biomarker than Conventional Indicators from Rats To Predict Drug-Induced Liver Injury in Humans? Chem Res Toxicol 2011; 25:122-9. [DOI: 10.1021/tx200320e] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Min Zhang
- Center of
Excellence for Bioinformatics, Division of
Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road,
Jefferson, Arkansas 72079, United States
| | - Minjun Chen
- Center of
Excellence for Bioinformatics, Division of
Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road,
Jefferson, Arkansas 72079, United States
| | - Weida Tong
- Center of
Excellence for Bioinformatics, Division of
Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road,
Jefferson, Arkansas 72079, United States
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Iskar M, Zeller G, Zhao XM, van Noort V, Bork P. Drug discovery in the age of systems biology: the rise of computational approaches for data integration. Curr Opin Biotechnol 2011; 23:609-16. [PMID: 22153034 DOI: 10.1016/j.copbio.2011.11.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Accepted: 11/06/2011] [Indexed: 01/08/2023]
Abstract
The increased availability of large-scale open-access resources on bioactivities of small molecules has a significant impact on pharmacology facilitated mainly by computational approaches that digest the vast amounts of data. We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects. We particularly focus on computational methods that leverage various cell-based transcriptional, proteomic and phenotypic profiles of drug response in order to gain a systemic view of drug action at the molecular, cellular and whole-organism scale.
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Affiliation(s)
- Murat Iskar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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Liu J, Jolly RA, Smith AT, Searfoss GH, Goldstein KM, Uversky VN, Dunker K, Li S, Thomas CE, Wei T. Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery. PLoS One 2011; 6:e24233. [PMID: 21935387 PMCID: PMC3174148 DOI: 10.1371/journal.pone.0024233] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2011] [Accepted: 08/03/2011] [Indexed: 01/24/2023] Open
Abstract
Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.
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Affiliation(s)
- Jiangang Liu
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Robert A. Jolly
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Aaron T. Smith
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - George H. Searfoss
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Keith M. Goldstein
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Vladimir N. Uversky
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
- Department of Molecular Medicine, University of South Florida, Tampa, Florida, United States of America
- Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow Region, Russia
| | - Keith Dunker
- School of Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America
| | - Shuyu Li
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
| | - Craig E. Thomas
- Toxicology, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
| | - Tao Wei
- Translational Science, Lilly Research Laboratories, a Division of Eli Lilly & Co., Indianapolis, Indiana, United States of America
- * E-mail: (TW); (CET)
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Liu Z, Kelly R, Fang H, Ding D, Tong W. Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships. Chem Res Toxicol 2011; 24:1062-70. [PMID: 21627106 DOI: 10.1021/tx2000637] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The primary testing strategy to identify nongenotoxic carcinogens largely relies on the 2-year rodent bioassay, which is time-consuming and labor-intensive. There is an increasing effort to develop alternative approaches to prioritize the chemicals for, supplement, or even replace the cancer bioassay. In silico approaches based on quantitative structure-activity relationships (QSAR) are rapid and inexpensive and thus have been investigated for such purposes. A slightly more expensive approach based on short-term animal studies with toxicogenomics (TGx) represents another attractive option for this application. Thus, the primary questions are how much better predictive performance using short-term TGx models can be achieved compared to that of QSAR models, and what length of exposure is sufficient for high quality prediction based on TGx. In this study, we developed predictive models for rodent liver carcinogenicity using gene expression data generated from short-term animal models at different time points and QSAR. The study was focused on the prediction of nongenotoxic carcinogenicity since the genotoxic chemicals can be inexpensively removed from further development using various in vitro assays individually or in combination. We identified 62 chemicals whose hepatocarcinogenic potential was available from the National Center for Toxicological Research liver cancer database (NCTRlcdb). The gene expression profiles of liver tissue obtained from rats treated with these chemicals at different time points (1 day, 3 days, and 5 days) are available from the Gene Expression Omnibus (GEO) database. Both TGx and QSAR models were developed on the basis of the same set of chemicals using the same modeling approach, a nearest-centroid method with a minimum redundancy and maximum relevancy-based feature selection with performance assessed using compound-based 5-fold cross-validation. We found that the TGx models outperformed QSAR in every aspect of modeling. For example, the TGx models' predictive accuracy (0.77, 0.77, and 0.82 for the 1-day, 3-day, and 5-day models, respectively) was much higher for an independent validation set than that of a QSAR model (0.55). Permutation tests confirmed the statistical significance of the model's prediction performance. The study concluded that a short-term 5-day TGx animal model holds the potential to predict nongenotoxic hepatocarcinogenicity.
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Affiliation(s)
- Zhichao Liu
- Center of Excellence for Bioinformatics, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, USA
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Shi W, Bessarabova M, Dosymbekov D, Dezso Z, Nikolskaya T, Dudoladova M, Serebryiskaya T, Bugrim A, Guryanov A, Brennan RJ, Shah R, Dopazo J, Chen M, Deng Y, Shi T, Jurman G, Furlanello C, Thomas RS, Corton JC, Tong W, Shi L, Nikolsky Y. Functional analysis of multiple genomic signatures demonstrates that classification algorithms choose phenotype-related genes. THE PHARMACOGENOMICS JOURNAL 2010; 10:310-23. [PMID: 20676069 PMCID: PMC2920075 DOI: 10.1038/tpj.2010.35] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Gene expression signatures of toxicity and clinical response benefit both safety assessment and clinical practice; however, difficulties in connecting signature genes with the predicted end points have limited their application. The Microarray Quality Control Consortium II (MAQCII) project generated 262 signatures for ten clinical and three toxicological end points from six gene expression data sets, an unprecedented collection of diverse signatures that has permitted a wide-ranging analysis on the nature of such predictive models. A comprehensive analysis of the genes of these signatures and their nonredundant unions using ontology enrichment, biological network building and interactome connectivity analyses demonstrated the link between gene signatures and the biological basis of their predictive power. Different signatures for a given end point were more similar at the level of biological properties and transcriptional control than at the gene level. Signatures tended to be enriched in function and pathway in an end point and model-specific manner, and showed a topological bias for incoming interactions. Importantly, the level of biological similarity between different signatures for a given end point correlated positively with the accuracy of the signature predictions. These findings will aid the understanding, and application of predictive genomic signatures, and support their broader application in predictive medicine.
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Affiliation(s)
- W Shi
- GeneGo Inc., St Joseph, MI, USA
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Kienhuis AS, Bessems JGM, Pennings JLA, Driessen M, Luijten M, van Delft JHM, Peijnenburg AACM, van der Ven LTM. Application of toxicogenomics in hepatic systems toxicology for risk assessment: acetaminophen as a case study. Toxicol Appl Pharmacol 2010; 250:96-107. [PMID: 20970440 DOI: 10.1016/j.taap.2010.10.013] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2010] [Revised: 10/06/2010] [Accepted: 10/14/2010] [Indexed: 11/18/2022]
Abstract
Hepatic systems toxicology is the integrative analysis of toxicogenomic technologies, e.g., transcriptomics, proteomics, and metabolomics, in combination with traditional toxicology measures to improve the understanding of mechanisms of hepatotoxic action. Hepatic toxicology studies that have employed toxicogenomic technologies to date have already provided a proof of principle for the value of hepatic systems toxicology in hazard identification. In the present review, acetaminophen is used as a model compound to discuss the application of toxicogenomics in hepatic systems toxicology for its potential role in the risk assessment process, to progress from hazard identification towards hazard characterization. The toxicogenomics-based parallelogram is used to identify current achievements and limitations of acetaminophen toxicogenomic in vivo and in vitro studies for in vitro-to-in vivo and interspecies comparisons, with the ultimate aim to extrapolate animal studies to humans in vivo. This article provides a model for comparison of more species and more in vitro models enhancing the robustness of common toxicogenomic responses and their relevance to human risk assessment. To progress to quantitative dose-response analysis needed for hazard characterization, in hepatic systems toxicology studies, generation of toxicogenomic data of multiple doses/concentrations and time points is required. Newly developed bioinformatics tools for quantitative analysis of toxicogenomic data can aid in the elucidation of dose-responsive effects. The challenge herein is to assess which toxicogenomic responses are relevant for induction of the apical effect and whether perturbations are sufficient for the induction of downstream events, eventually causing toxicity.
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Affiliation(s)
- Anne S Kienhuis
- Laboratory for Health Protection Research, National Institute of Public Health and the Environment (RIVM), PO Box 1, 3720 BA Bilthoven, The Netherlands.
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Jennen DGJ, Gaj S, Giesbertz PJ, van Delft JHM, Evelo CT, Kleinjans JCS. Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes. Drug Discov Today 2010; 15:851-8. [PMID: 20708095 DOI: 10.1016/j.drudis.2010.08.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2010] [Revised: 06/25/2010] [Accepted: 08/04/2010] [Indexed: 12/18/2022]
Abstract
In recent decades, our knowledge of the genetics and functional genomics of drug-metabolizing enzymes has increased and a wealth of data on drug-related 'omics' has become available. Despite the availability of large amounts of biological information on xenobiotic biotransformation, the number of available biotransformation pathway maps that can easily be used for visualization of multiple omics data is limited. Here, we created integrated biotransformation pathway maps suitable for multiple omics analysis using PathVisio. The ease of visualizing data on these maps was demonstrated by using published microarray data from human hepatocyte-like cell models, exemplifying - where a sufficient capacity for metabolizing chemicals is a prerequisite for a suited model - how the biotransformation pathway maps can be used for model selection.
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Affiliation(s)
- Danyel G J Jennen
- Department of Health Risk Analysis and Toxicology, Maastricht University, The Netherlands.
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Lerapetritou MG, Georgopoulos PG, Roth CM, Androulakis LP. Tissue-level modeling of xenobiotic metabolism in liver: An emerging tool for enabling clinical translational research. Clin Transl Sci 2010; 2:228-37. [PMID: 20443896 DOI: 10.1111/j.1752-8062.2009.00092.x] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
This review summarizes some of the recent developments and identifies critical challenges associated with in vitro and in silico representations of the liver and assesses the translational potential of these models in the quest of rationalizing the process of evaluating drug efficacy and toxicity. It discusses a wide range of research efforts that have produced, during recent years, quantitative descriptions and conceptual as well as computational models of hepatic processes such as biotransport and biotransformation, intra- and intercellular signal transduction, detoxification, etc. The above mentioned research efforts cover multiple scales of biological organization, from molecule-molecule interactions to reaction network and cellular and histological dynamics, and have resulted in a rapidly evolving knowledge base for a "systems biology of the liver." Virtual organ/organism formulations represent integrative implementations of particular elements of this knowledge base, usually oriented toward the study of specific biological endpoints, and provide frameworks for translating the systems biology concepts into computational tools for quantitative prediction of responses to stressors and hypothesis generation for experimental design.
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Affiliation(s)
- Marianthi G Lerapetritou
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey, USA
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Abstract
Of the estimated 10,000 documented human drugs, more than 1000 have been associated with drug-induced liver injury (DILI), although causality has not always been established clearly. Numerous biomarkers for DILI have been explored, but less than ten are adopted or qualified as valid by the US FDA. The biomarkers for DILI are individual or a panel of proteins, nucleic acids or metabolites from various sources, such as the liver, blood and urine. While most DILI biomarkers are drug independent, some possibly 'drug-specific' DILIs have been explored, but specificity and sensitivity of both types need to be improved for the diagnosis of DILI during drug development and in clinical practice. Novel approaches for DILI biomarkers have been actively investigated recently, but produced mainly animal-based biomarkers, which are possibly useful for drug development, but are not suitable or have not been validated for clinical applications. This review summarizes the current practice and future perspectives for DILI biomarkers.
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Affiliation(s)
- Qiang Shi
- Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA
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Steimer JL, Dahl SG, De Alwis DP, Gundert-Remy U, Karlsson MO, Martinkova J, Aarons L, Ahr HJ, Clairambault J, Freyer G, Friberg LE, Kern SE, Kopp-Schneider A, Ludwig WD, De Nicolao G, Rocchetti M, Troconiz IF. Modelling the genesis and treatment of cancer: the potential role of physiologically based pharmacodynamics. Eur J Cancer 2010; 46:21-32. [PMID: 19954965 DOI: 10.1016/j.ejca.2009.10.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2009] [Revised: 09/30/2009] [Accepted: 10/09/2009] [Indexed: 12/01/2022]
Abstract
Physiologically based modelling of pharmacodynamics/toxicodynamics requires an a priori knowledge on the underlying mechanisms causing toxicity or causing the disease. In the context of cancer, the objective of the expert meeting was to discuss the molecular understanding of the disease, modelling approaches used so far to describe the process, preclinical models of cancer treatment and to evaluate modelling approaches developed based on improved knowledge. Molecular events in cancerogenesis can be detected using 'omics' technology, a tool applied in experimental carcinogenesis, but also for diagnostics and prognosis. The molecular understanding forms the basis for new drugs, for example targeting protein kinases specifically expressed in cancer. At present, empirical preclinical models of tumour growth are in great use as the development of physiological models is cost and resource intensive. Although a major challenge in PKPD modelling in oncology patients is the complexity of the system, based in part on preclinical models, successful models have been constructed describing the mechanism of action and providing a tool to establish levels of biomarker associated with efficacy and assisting in defining biologically effective dose range selection for first dose in man. To follow the concentration in the tumour compartment enables to link kinetics and dynamics. In order to obtain a reliable model of tumour growth dynamics and drug effects, specific aspects of the modelling of the concentration-effect relationship in cancer treatment that need to be accounted for include: the physiological/circadian rhythms of the cell cycle; the treatment with combinations and the need to optimally choose appropriate combinations of the multiple agents to study; and the schedule dependence of the response in the clinical situation.
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Thompson K. Toxicogenomics and studies of genomic effects of dietary components. JOURNAL OF NUTRIGENETICS AND NUTRIGENOMICS 2010; 3:251-8. [PMID: 21474956 DOI: 10.1159/000324361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Karol Thompson
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD 20993, USA.
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Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles. PLoS One 2009; 4:e8126. [PMID: 19956587 PMCID: PMC2780314 DOI: 10.1371/journal.pone.0008126] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2009] [Accepted: 11/10/2009] [Indexed: 12/19/2022] Open
Abstract
More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development.
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Jonker MJ, Bruning O, van Iterson M, Schaap MM, van der Hoeven TV, Vrieling H, Beems RB, de Vries A, van Steeg H, Breit TM, Luijten M. Finding transcriptomics biomarkers for in vivo identification of (non-)genotoxic carcinogens using wild-type and Xpa/p53 mutant mouse models. Carcinogenesis 2009; 30:1805-12. [DOI: 10.1093/carcin/bgp190] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
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Kiyosawa N, Ando Y, Manabe S, Yamoto T. Toxicogenomic biomarkers for liver toxicity. J Toxicol Pathol 2009; 22:35-52. [PMID: 22271975 PMCID: PMC3246017 DOI: 10.1293/tox.22.35] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2008] [Accepted: 11/26/2008] [Indexed: 12/15/2022] Open
Abstract
Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.
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Affiliation(s)
- Naoki Kiyosawa
- Medicinal Safety Research Labs., Daiichi Sankyo Co., Ltd., 717 Horikoshi, Fukuroi, Shizuoka 437-0065, Japan
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Chiang AP, Butte AJ. Data-driven methods to discover molecular determinants of serious adverse drug events. Clin Pharmacol Ther 2009; 85:259-68. [PMID: 19177064 PMCID: PMC2726746 DOI: 10.1038/clpt.2008.274] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher-density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs.
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Affiliation(s)
- A P Chiang
- Department of Medicine, Stanford Center for Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
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Abstract
Stem cells have emerged as the starting material of choice for bioprocesses to produce cells and tissues to treat degenerative, genetic, and immunological disease. Translating the biological properties and potential of stem cells into therapies will require overcoming significant cell-manufacturing and regulatory challenges. Bioprocess engineering fundamentals, including bioreactor design and process control, need to be combined with cellular systems biology principles to guide the development of next-generation technologies capable of producing cell-based products in a safe, robust, and cost-effective manner. The step-wise implementation of these bioengineering strategies will enhance cell therapy product quality and safety, expediting clinical development.
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