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Abstract
Abstract
The prediction of toxicological endpoints has gained broad acceptance; it is widely applied in early stages of drug discovery as well as for impurities obtained in the production of generic or equivalent products. In this work, we describe methodologies for the prediction of toxicological endpoints compounds, with a particular focus on secondary metabolites. Case studies include toxicity prediction of natural compound databases with anti-diabetic, anti-malaria and anti-HIV properties.
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Qian T, Zhu S, Hoshida Y. Use of big data in drug development for precision medicine: an update. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2019; 4:189-200. [PMID: 31286058 PMCID: PMC6613936 DOI: 10.1080/23808993.2019.1617632] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 05/08/2019] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios. AREAS COVERED Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery. EXPERT OPINION In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.
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Affiliation(s)
- Tongqi Qian
- Department of Genetics and Genomic Sciences and Icahn
Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount
Sinai, New York, NY, USA
| | - Shijia Zhu
- Liver Tumor Translational Research Program, Simmons
Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of
Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
75390, USA
| | - Yujin Hoshida
- Liver Tumor Translational Research Program, Simmons
Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of
Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
75390, USA
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Yang H, Sun L, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. Front Chem 2018. [PMID: 29515993 PMCID: PMC5826228 DOI: 10.3389/fchem.2018.00030] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
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Affiliation(s)
- Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Lixia Sun
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Abstract
In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.
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Khan SR, Baghdasarian A, Fahlman RP, Michail K, Siraki AG. Current status and future prospects of toxicogenomics in drug discovery. Drug Discov Today 2014; 19:562-78. [DOI: 10.1016/j.drudis.2013.11.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Revised: 09/27/2013] [Accepted: 11/01/2013] [Indexed: 01/03/2023]
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Pan Y, Cheng T, Wang Y, Bryant SH. Pathway analysis for drug repositioning based on public database mining. J Chem Inf Model 2014; 54:407-18. [PMID: 24460210 PMCID: PMC3956470 DOI: 10.1021/ci4005354] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
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Sixteen FDA-approved
drugs were investigated to elucidate their
mechanisms of action (MOAs) and clinical functions by pathway analysis
based on retrieved drug targets interacting with or affected by the
investigated drugs. Protein and gene targets and associated pathways
were obtained by data-mining of public databases including the MMDB,
PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez
E-Utilities were applied, and in-house Ruby scripts were developed
for data retrieval and pathway analysis to identify and evaluate relevant
pathways common to the retrieved drug targets. Pathways pertinent
to clinical uses or MOAs were obtained for most drugs. Interestingly,
some drugs identified pathways responsible for other diseases than
their current therapeutic uses, and these pathways were verified retrospectively
by in vitro tests, in vivo tests, or clinical trials. The pathway
enrichment analysis based on drug target information from public databases
could provide a novel approach for elucidating drug MOAs and repositioning,
therefore benefiting the discovery of new therapeutic treatments for
diseases.
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Affiliation(s)
- Yongmei Pan
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , 8600 Rockville Pike, Bethesda, Maryland 20894, United States
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Willighagen EL, Jeliazkova N, Hardy B, Grafström RC, Spjuth O. Computational toxicology using the OpenTox application programming interface and Bioclipse. BMC Res Notes 2011; 4:487. [PMID: 22075173 PMCID: PMC3264531 DOI: 10.1186/1756-0500-4-487] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2011] [Accepted: 11/10/2011] [Indexed: 11/10/2022] Open
Abstract
Background Toxicity is a complex phenomenon involving the potential adverse effect on a range of biological functions. Predicting toxicity involves using a combination of experimental data (endpoints) and computational methods to generate a set of predictive models. Such models rely strongly on being able to integrate information from many sources. The required integration of biological and chemical information sources requires, however, a common language to express our knowledge ontologically, and interoperating services to build reliable predictive toxicology applications. Findings This article describes progress in extending the integrative bio- and cheminformatics platform Bioclipse to interoperate with OpenTox, a semantic web framework which supports open data exchange and toxicology model building. The Bioclipse workbench environment enables functionality from OpenTox web services and easy access to OpenTox resources for evaluating toxicity properties of query molecules. Relevant cases and interfaces based on ten neurotoxins are described to demonstrate the capabilities provided to the user. The integration takes advantage of semantic web technologies, thereby providing an open and simplifying communication standard. Additionally, the use of ontologies ensures proper interoperation and reliable integration of toxicity information from both experimental and computational sources. Conclusions A novel computational toxicity assessment platform was generated from integration of two open science platforms related to toxicology: Bioclipse, that combines a rich scriptable and graphical workbench environment for integration of diverse sets of information sources, and OpenTox, a platform for interoperable toxicology data and computational services. The combination provides improved reliability and operability for handling large data sets by the use of the Open Standards from the OpenTox Application Programming Interface. This enables simultaneous access to a variety of distributed predictive toxicology databases, and algorithm and model resources, taking advantage of the Bioclipse workbench handling the technical layers.
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Affiliation(s)
- Egon L Willighagen
- Department of Pharmaceutical Bioinformatics, Uppsala University, Uppsala, Sweden.
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Kulemina LV, Ostrov DA. Prediction of off-target effects on angiotensin-converting enzyme 2. ACTA ACUST UNITED AC 2011; 16:878-85. [PMID: 21859683 DOI: 10.1177/1087057111413919] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)-approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.
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Affiliation(s)
- Lidia V Kulemina
- Department of Chemistry, University of Florida, Gainesville, FL 32611, USA
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Abstract
IMPORTANCE OF THE FIELD: PubChem is a public molecular information repository, a scientific showcase of the NIH Roadmap Initiative. The PubChem database holds over 27 million records of unique chemical structures of compounds (CID) derived from nearly 70 million substance depositions (SID), and contains more than 449,000 bioassay records with over thousands of in vitro biochemical and cell-based screening bioassays established, with targeting more than 7000 proteins and genes linking to over 1.8 million of substances. AREAS COVERED IN THIS REVIEW: This review builds on recent PubChem-related computational chemistry research reported by other authors while providing readers with an overview of the PubChem database, focusing on its increasing role in cheminformatics, virtual screening and toxicity prediction modeling. WHAT THE READER WILL GAIN: These publicly available datasets in PubChem provide great opportunities for scientists to perform cheminformatics and virtual screening research for computer-aided drug design. However, the high volume and complexity of the datasets, in particular the bioassay-associated false positives/negatives and highly imbalanced datasets in PubChem, also creates major challenges. Several approaches regarding the modeling of PubChem datasets and development of virtual screening models for bioactivity and toxicity predictions are also reviewed. TAKE HOME MESSAGE: Novel data-mining cheminformatics tools and virtual screening algorithms are being developed and used to retrieve, annotate and analyze the large-scale and highly complex PubChem biological screening data for drug design.
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Affiliation(s)
- Xiang-Qun Xie
- Department of Pharmaceutical Sciences, School of Pharmacy; Drug Discovery Institute/Pittsburgh Molecular Library Screening Center (PMLSC); Pittsburgh Chemical Methodologies & Library Development (PCMLD) Center; Departments of Computational Biology and Structural Biology; University of Pittsburgh, Pittsburgh, PA 15260, USA
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Cronin MTD. Finding the Data to Develop and Evaluate (Q)SARs and Populate Categories for Toxicity Prediction. IN SILICO TOXICOLOGY 2010. [DOI: 10.1039/9781849732093-00031] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This chapter describes the sources of data for in silico modelling. It is assumed that the modeller will not normally have the facilities to experimentally determine toxicological data, thus they must rely on existing data. Data can be obtained from in-house sources (e.g. for industry) or from publicly available databases and the scientific literature. For the publicly available data, the sources of toxicologically information and the relevant advantages and disadvantages are defined. The sources include “well-established” datasets and the use of literature searching, through to the use of databases and more global (meta) data portals which call on a number of databases. To use the data collected efficiently, the modeller must define the required endpoint, allow the nature of the data to drive the modelling approach and control the quality of the data and implications for that on in silico models.
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Affiliation(s)
- M. T. D. Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University Byrom Street Liverpool L3 3AF UK
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Kavlock R, Dix D. Computational toxicology as implemented by the U.S. EPA: providing high throughput decision support tools for screening and assessing chemical exposure, hazard and risk. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2010; 13:197-217. [PMID: 20574897 DOI: 10.1080/10937404.2010.483935] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Computational toxicology is the application of mathematical and computer models to help assess chemical hazards and risks to human health and the environment. Supported by advances in informatics, high-throughput screening (HTS) technologies, and systems biology, the U.S. Environmental Protection Agency EPA is developing robust and flexible computational tools that can be applied to the thousands of chemicals in commerce, and contaminant mixtures found in air, water, and hazardous-waste sites. The Office of Research and Development (ORD) Computational Toxicology Research Program (CTRP) is composed of three main elements. The largest component is the National Center for Computational Toxicology (NCCT), which was established in 2005 to coordinate research on chemical screening and prioritization, informatics, and systems modeling. The second element consists of related activities in the National Health and Environmental Effects Research Laboratory (NHEERL) and the National Exposure Research Laboratory (NERL). The third and final component consists of academic centers working on various aspects of computational toxicology and funded by the U.S. EPA Science to Achieve Results (STAR) program. Together these elements form the key components in the implementation of both the initial strategy, A Framework for a Computational Toxicology Research Program (U.S. EPA, 2003), and the newly released The U.S. Environmental Protection Agency's Strategic Plan for Evaluating the Toxicity of Chemicals (U.S. EPA, 2009a). Key intramural projects of the CTRP include digitizing legacy toxicity testing information toxicity reference database (ToxRefDB), predicting toxicity (ToxCast) and exposure (ExpoCast), and creating virtual liver (v-Liver) and virtual embryo (v-Embryo) systems models. U.S. EPA-funded STAR centers are also providing bioinformatics, computational toxicology data and models, and developmental toxicity data and models. The models and underlying data are being made publicly available through the Aggregated Computational Toxicology Resource (ACToR), the Distributed Structure-Searchable Toxicity (DSSTox) Database Network, and other U.S. EPA websites. While initially focused on improving the hazard identification process, the CTRP is placing increasing emphasis on using high-throughput bioactivity profiling data in systems modeling to support quantitative risk assessments, and in developing complementary higher throughput exposure models. This integrated approach will enable analysis of life-stage susceptibility, and understanding of the exposures, pathways, and key events by which chemicals exert their toxicity in developing systems (e.g., endocrine-related pathways). The CTRP will be a critical component in next-generation risk assessments utilizing quantitative high-throughput data and providing a much higher capacity for assessing chemical toxicity than is currently available.
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Affiliation(s)
- Robert Kavlock
- National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA.
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Sone H, Okura M, Zaha H, Fujibuchi W, Taniguchi T, Akanuma H, Nagano R, Ohsako S, Yonemoto J. Profiles of Chemical Effects on Cells (pCEC): a toxicogenomics database with a toxicoinformatics system for risk evaluation and toxicity prediction of environmental chemicals. J Toxicol Sci 2010; 35:115-23. [DOI: 10.2131/jts.35.115] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- Hideko Sone
- Research Center for Environmental Risk, National Institute for Environmental Studies
| | - Masahiro Okura
- Research Center for Environmental Risk, National Institute for Environmental Studies
| | - Hiroko Zaha
- Research Center for Environmental Risk, National Institute for Environmental Studies
| | - Wataru Fujibuchi
- Advanced Industrial Science and Technology (AIST), Computational Biology Research Center
| | - Takeaki Taniguchi
- Mitsubishi Research Institute, Inc., Practice Areas and Industry Sectors
| | - Hiromi Akanuma
- Research Center for Environmental Risk, National Institute for Environmental Studies
| | - Reiko Nagano
- Research Center for Environmental Risk, National Institute for Environmental Studies
| | - Seiichiro Ohsako
- Graduate School and Faculty of Medicine, The University of Tokyo
| | - Junzo Yonemoto
- Research Center for Environmental Risk, National Institute for Environmental Studies
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Williams-Devane CR, Wolf MA, Richard AM. Toward a public toxicogenomics capability for supporting predictive toxicology: survey of current resources and chemical indexing of experiments in GEO and ArrayExpress. Toxicol Sci 2009; 109:358-71. [PMID: 19332651 DOI: 10.1093/toxsci/kfp061] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A publicly available toxicogenomics capability for supporting predictive toxicology and meta-analysis depends on availability of gene expression data for chemical treatment scenarios, the ability to locate and aggregate such information by chemical, and broad data coverage within chemical, genomics, and toxicological information domains. This capability also depends on common genomics standards, protocol description, and functional linkages of diverse public Internet data resources. We present a survey of public genomics resources from these vantage points and conclude that, despite progress in many areas, the current state of the majority of public microarray databases is inadequate for supporting these objectives, particularly with regard to chemical indexing. To begin to address these inadequacies, we focus chemical annotation efforts on experimental content contained in the two primary public genomic resources: ArrayExpress and Gene Expression Omnibus. Automated scripts and extensive manual review were employed to transform free-text experiment descriptions into a standardized, chemically indexed inventory of experiments in both resources. These files, which include top-level summary annotations, allow for identification of current chemical-associated experimental content, as well as chemical-exposure-related (or "Treatment") content of greatest potential value to toxicogenomics investigation. With these chemical-index files, it is possible for the first time to assess the breadth and overlap of chemical study space represented in these databases, and to begin to assess the sufficiency of data with shared protocols for chemical similarity inferences. Chemical indexing of public genomics databases is a first important step toward integrating chemical, toxicological and genomics data into predictive toxicology.
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Affiliation(s)
- ClarLynda R Williams-Devane
- U.S. EPA/Office of Research and Development/National Health & Environmental Effects Research Laboratory, Research Triangle Park, NC 27519, USA
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