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Drug discovery & development informatics glossary & taxonomy
including In Silico & molecular drug modeling
Evolving terminology for emerging technologies

Suggestions? Comments? Questions?
Mary Chitty MSLS
Last revised January 07, 2020 

Informatics term index:  Related glossaries include:  Drug discovery & development     Molecular Diagnostics     Pharmacogenomics,   
Informatics Algorithms
     Bioinformatics      Cheminformatics    I  information management & interpretation     Protein informatics     Research
Technologies  Sequencing    Biology Pharmaceutical biology     Genetic variations    Protein Structure     

BioIT World  April 16-18, 2019 • Boston, MA Program 

Biosemantics Group:  Addresses concept identification and disambiguation algorithms, meta-analysis and visualization techniques, and biological applications [interconnect genes and proteins, semi-automated annotations of protein functions.] Medical Informatics department of the Erasmus MC University Medical Center of Rotterdam and the Center for Human and Clinical Genetics of the Leiden University Medical Center

computational drug design: a rapidly changing field that plays an increasingly important role in medicinal chemistry. Since the publication of the first glossary in 1997, substantial changes have occurred in both medicinal chemistry and computational drug design. This has resulted in the use of many new terms and the consequent necessity to update the previous glossary. For this purpose a Working Party of eight experts was assembled. They produced explanatory definitions of more than 150 new and revised terms. IUPAC  International Union of Pure and Applied Chemistry, Glossary of Terms used in Computational Drug Design, Part II,  2015

Computer-Assisted Drug Design CADD:
Involves all computer- assisted techniques used to discover, design and optimize biologically active compounds with a putative use as drugs.  IUPAC Computational   
Broader term: 
drug design  Related terms: Cheminformatics

data integration: The term "data integration" is used generically within the industry for describing disparate situations. Consequently, considerable confusion results regarding the best practices for solving specific, data integration problems. There are a number of markedly different approaches to data integration, each with its own strengths and weaknesses, and many different technologies are available for each approach. All data integration efforts are initiated to support particular research objectives. Although they are aimed toward the same strategic goal, they can differ substantially in the specific problems that they are trying to solve, in the scale of the integration, and in the types of data that are integrated. The strategies and technologies that best apply to address specific objectives are unlikely to be the same.  Key Trends Influencing Informatics Initiatives in Life Science Companies: An Interview with Eric Meyers and Jack Pollard of 3rd Millennium, CHI's GenomeLink 29.2   
Related terms: data mining - integrating, data reduction methods; Information management & interpretation interoperability;  Omes & -omics integromics

de novo design: The design of bioactive compounds by incremental construction of a ligand model within a model of the receptor or enzyme active site, the structure of which is known from X-ray or NMR data. IUPAC Medicinal Chemistry

Deep Sequencing and Single Cell Analysis for Antibody Discovery Technologies and Best Practices for Applying Repertoire Analysis in the Discovery of Therapeutic Proteins JANUARY 23-24,  2020 San Diego CA The rapid adoption of deep sequencing and single B cell analysis has given discovery scientists an extraordinary view into human and animal immune repertoires that is now informing all aspects of biopharmaceutical R&D. This dynamic field is bringing together the disciplines of immunology, structural and computational biology, informatics and microfluidics to offer previously unimaginable perspectives that will drive discovery of the next generation of biologic drugs

drug design: Includes not only ligand design, but also pharmacokinetics (Pharmacogenomics) toxicity, which are mostly beyond the possibilities of structure- and/ or computer- aided design. Nevertheless, appropriate chemometric (Chemoinformatics) tools, including experimental design and multivariate statistics, can be of value in the planning and evaluation of pharmacokinetic and toxicological experiments and results. Drug design is most often used instead of the correct term  "ligand design”.  IUPAC Computational 

The molecular designing of drugs for specific purposes (such as DNA- binding, enzyme inhibition, anti- cancer efficacy, etc.) based on knowledge of molecular properties such as activity of functional groups, molecular geometry, and electronic structure, and also on information cataloged on analogous molecules. Drug design is generally computer- assisted molecular modeling and does not include pharmacokinetics, dosage analysis, or drug administration analysis.  MeSH, 1989

The phrase "drug design" is to some extent a misnomer. What is really meant by drug design is ligand design (i.e., design of a small molecule that will bind tightly to its target).[3] Although modeling techniques for prediction of binding affinity are reasonably successful, there are many other properties, such as bioavailability, metabolic half-life, lack of side effects, etc., that first must be optimized before a ligand can become a safe and efficacious drug. These other characteristics are often difficult to optimize using rational drug design techniques.  … Typically a drug target is a key molecule involved in a particular metabolic or signaling pathway that is specific to a disease condition or pathology or to the infectivity or survival of a microbialpathogen. Some approaches attempt to inhibit the functioning of the pathway in the diseased state by causing a key molecule to stop functioning. Drugs may be designed that bind to the active region and inhibit this key molecule. Another approach may be to enhance the normal pathway by promoting specific molecules in the normal pathways that may have been affected in the diseased state. In addition, these drugs should also be designed so as not to affect any other important "off-target" molecules or antitargets that may be similar in appearance to the target molecule, since drug interactions with off-target molecules may lead to undesirable side effects. Sequence homology is often used to identify such risks. Wikipedia Nov 4 2013
See also biological target

An iterative process involving drug discovery, lead optimization and chemical synthesis with the aim of maximizing functional activity and minimizing adverse effects. 
Narrower terms: rational drug design, structure- based drug design, molecular design;  Related terms: 3D-QSAR, QSAR, Computer Aided Molecular Design, Computer Assisted Drug Design CADD, Computer Assisted Molecular Modeling CAMD, de novo design  See also structure-based drug design Related terms: 3D QSAR, QSAR Algorithms, Data & information management

drug ontology: Integrating Pharmacokinetics Knowledge into a Drug Ontology As an Extension to Support Pharmacogenomics, CG Chute, MD, DrPH,1 JS Carter,2 MS Tuttle,2 M Haber,3 and SH Brown, MS, MD4 Integrating Pharmacokinetics Knowledge into a Drug Ontology As an Extension to Support Pharmacogenomics, CG Chute, MD, DrPH,1 JS Carter,2 MS Tuttle,2 M Haber,3 and SH Brown, MS, MD4 AMIA Annu Symp Proc. 2003; 2003: 170–174.   

eScience Lab: The eScience Lab team are focused on research and development around a set of tools designed for data driven and computational research. The tools support the coming together of people, data and methods in a particular research area; this is also known as an e-laboratory. These e-Laboratories or e-Labs support domains as diverse as systems biology, social science, music, astronomy, multimedia and chemistry. The tools have been adopted by a large number of projects and institutions. Was myGrid

in silico: In a white paper I wrote for the European Commission in 1988 I advocated the funding of genome programs, and in particular the use of computers. In this endeavour I coined "in silico" following "in vitro" and "in vivo" I think that the first public use of the word is in the following paper: A. Danchin, C. Médigue, O. Gascuel, H. Soldano, A. Hénaut, From data banks to data bases. Res. Microbiol. (1991) 142: 913- 916.  You can find a developed account of this story in my book The Delphic Boat, Harvard University Press, 2003 personal communication Antoine Danchin, Institute Pasteur, 2003

Literally "in the computer".   Narrower terms: in silico biology, in silico modeling, in silico proteomics, in silico screening, in silico target discovery; Cell biology virtual cells in silico; Related terms: Chemoinformatics rules of five 

in silico biology: journal for the advancement of computational models and simulations applied to complex biological phenomena. We publish peer-reviewed leading-edge biological, biomedical and biotechnological research in which computer-based (i.e., "in silico") modeling and analysis tools are developed and utilized to predict and elucidate dynamics of biological systems, their design and control, and their evolution.  Scope Note In Silico Biology, IOS Press   Related terms: in silico, virtual cells 

in silico modeling: Modeling of biological pathways and other biological processes for drug discovery and development. Given the enormous increase in genetic and molecular data, such models will continue to improve and are predicted to become an essential tool for evaluating hypotheses, with only the more promising ones being subjected to empirical testing. 

in silico screening: See also virtual screening  
in silico
transcriptomics:  Omes & -omics

Integrated Pharma Informatics & Data Science March 11-13  2019 • San Francisco, CA Program |  Every facet of healthcare and medicine now generates and has access to enormous amounts of data from across sources, organizations, and the world; the pharmaceutical industry plays a key role in driving informatics for translational research and precision medicine. The 11th Annual Integrated Pharma Informatics program will discuss the challenges related to integrating, analyzing, and interpreting data from clinical trials, sequencing, electronic health records, and wearables. We will discuss informatics strategy for entire organizations, from business goals to infrastructure and storage projects. Special attention will be paid to artificial intelligence, machine learning, natural language processing, and how companies are integrating these tools into their informatics infrastructure. We will take a close look at how informatics is driving translational and clinical research projects with a focus on data standardization and integration.
Integrated Pharma Informatics & Data Science

LIMS Laboratory Information Management Systems:
A basic LIMS is a passive bookkeeping system designed to keep track of laboratory processes. It records the procedures that have been applied to each sample, when a procedure was run, the machine or instrument that was used, and who (e.g., which technician) did the work or was responsible for it. It also records any run-specific parameters of the procedure, and the results if any. In addition, a LIMS typically handles necessary administrative functions, such as inventory management, monitoring of quality measures, resource planning for instruments and personnel, and reporting.  Related terms: robotic systems, robotics, sample prep, Assays & Screening

medicinal systems biology: This review will focus on the development of a novel "chemical genetic/ genomic approach" that uses small molecules to "probe and identify" the function of genes in specific biological processes or pathways in human cells. Due to the close relationship of small molecules with drugs, these systematic and integrative studies will lead to the "medicinal systems biology approach" which is critical to "formulate and modulate" complex biological (disease) networks by small molecules (drugs) in human bio-systems. TK Kim, Chemical genomics and medicinal systems biology: chemical control of genomic networks in human systems biology for innovative medicine, J Biochem Mol Biol. 37(1): 53- 58, Jan 31, 2004    Related term: Bioinformatics systems biology

molecular informatics:  interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation.  Molecular Informatics Journal, Scope Note, Wiley

molecular mimicry: The process in which structural properties of an introduced molecule imitate or simulate molecules of the host. Direct mimicry of a molecule enables a viral protein to bind directly to a normal substrate as a substitute for the homologous normal ligand. Immunologic molecular mimicry generally refers to what can be described as antigenic mimicry and is defined by the properties of antibodies raised against various facets of epitopes on the viral protein. MeSH from Immunology Letters 28 (2): 91- 99 May 1991 

myGrid: now the e-Science Lab

NeuroCommons: The NeuroCommons project seeks to make all scientific research materials - research articles, knowledge bases, research data, physical materials - as available and as usable as they can be. We do this by fostering practices that render information in a form that promotes uniform access by computational agents - sometimes called "interoperability".

pharmaceutical bioinformatics:  a new discipline in the area of the genomics revolution. It is central to biomedicine with application in areas like pharmacy, medicine, biology and medicinal chemistry. The genomics revolution has given high throughput methods for massive gene sequencing, chemical synthesis and biological testing. This creates oceans of new information. Pharmaceutical bioinformatics is all about how to use all the new information effectively.  University of Uppsala, Pharmaceutical Bioinformatics

Track 8: Pharmaceutical R&D Informatics
Pharmaceutical R&D Informatics
Strategic Initiatives in Collaboration, Data Science, and Technologies  2019 April 17-18 Boston MA  There is no end in sight for the amount of data we can generate in pharmaceutical R&D – the amount of clinical, translational, genomic, and electronic health data we generate and collect necessitates effective strategies and infrastructure for managing, integrating, and analyzing these data for better decision making. As new tools and technologies – from digital biomarkers to artificial intelligence – we must ensure that our data is not only of high quality, but also correct and consistent. Explores real-world projects and strategies for integrating and analyzing complex data sets that are driving R&D and precision medicine.

pharmacometrics:   an emerging science defined as the science that quantifies drug, disease and trial information to aid efficient drug development and/or regulatory decisions. Drug models describe the relationship between exposure (or pharmacokinetics), response (or pharmacodynamics) for both desired and undesired effects, and individual patient characteristics. Disease models describe the relationship between biomarkers and clinical outcomes, time course of disease and placebo effects. The trial models describe the inclusion/exclusion criteria, patient dis-continuation and adherence. Typical focus of Pharmacometrics has been on drug models, also referred to by terms such as: concentration-effect, dose-response, PKPD relationships. These Pharmacometric analyses are designed, conducted and presented in the context of drug development, therapeutic and regulatory decisions.  The single-most important strength of such analyses is its ability to integrate knowledge across the development program and compounds, and biology. US FDA, CDER, Pharmacometrics at FDA, CDER
FDA, CDER Selected Pharmacometrics Reviews, Guidances and Presentations, 2018

can be defined as that branch of science concerned with mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between xenobiotics and patients (human and non-human), including beneficial effects and adverse effects.[1] It is normally applied to quantify drug, disease and trial information to aid efficient drug development, regulatory decisions and rational drug treatment in patients. Pharmacometrics uses models based on pharmacologyphysiology and disease for quantitative analysis of interactions between drugs and patients. This involves  Systems pharmacology pharmacokineticspharmacodynamics and disease progression with a focus on populations and variability.  Wikipedia accessed 2018 Sept 1
Related terms: pharmacodynamics, pharmacokinetics

pre-competitive R&D information: Precompetitive R&D precludes: (a) exchanging information among competitors relating to costs, sales, profitability, prices, marketing, or distribution of any product, process, or service that is not reasonably required to conduct the research and development that is the purpose of such venture; (b) entering into any agreement or engaging in any other conduct restricting, requiring, or otherwise involving the production or marketing by any person who is a party to such venture of any product, process, or service, other than the production or marketing of proprietary information developed through such venture; and (c) entering into any agreement or engaging in any other conduct that is not reasonably required to prevent misappropriation of proprietary information contributed by any person who is a party to such venture or its results. David. Hahn, Thomas Sporleder, ADE 601 Glossary Technical Terms for Agribusiness Managers, Ohio State Univ. US* no longer on the web

May be defined as the earliest point in the drug development process at which the weight of evidence suggests that it is "reasonably likely" that the key attributes for success are present and the key causes of failure are absent. POC is multidimensional but is focused on attributes that, if not addressed, represent a threat to the success of the project in crucial areas such as safety, efficacy, pharmaceutics, and commercial and regulatory issues. The appropriate weight of evidence is assessed through the use of mathematical models and by evaluating the consequences of advancing a candidate drug that is not safe, effective, or commercially viable, vs. failing to advance a candidate that possesses these attributes. Tools for POC include biomarkers, targeted populations, pharmacokinetic (PK)/pharmacodynamic (PD) modeling, simulation, and adaptive study designs.  Proof of Concept: A PhRMA Position Paper With Recommendations for Best Practice, . Cartwright ME, et. al, Clin Pharmacol Ther. 2010 Feb 3.  Compare with  definitions in Business of biopharmaceuticals 

rational drug design:  In contrast to traditional methods of drug discovery (known as forward pharmacology), which rely on trial-and-error testing of chemical substances on cultured cells or animals, and matching the apparent effects to treatments, rational drug design (also called reverse pharmacology) begins with a hypothesis that modulation of a specific biological target may have therapeutic value. In order for a biomolecule to be selected as a drug target, two essential pieces of information are required. The first is evidence that modulation of the target will be disease modifying. This knowledge may come from, for example, disease linkage studies that show an association between mutations in the biological target and certain disease states.[15] The second is that the target is "druggable". This means that it is capable of binding to a small molecule and that its activity can be modulated by the small molecule.[16] Once a suitable target has been identified, the target is normally  cloned  and  produced and purified. The purified protein is then used to establish a screening assay. In addition, the three-dimensional structure of the target may be determined.  The search for small molecules that bind to the target is begun by screening libraries of potential drug compounds. This may be done by using the screening assay (a "wet screen"). In addition, if the structure of the target is available, a virtual screen may be performed of candidate drugs. Ideally the candidate drug compounds should be "drug-like", that is they should possess properties that are predicted to lead to oral bioavailability, adequate chemical and metabolic stability, and minimal toxic effects.[17] Several methods are available to estimate druglikeness such as Lipinski's Rule of Five and a range of scoring methods such as lipophilic efficiency.[18] Several methods for predicting drug metabolism have also been proposed in the scientific literature.[19] Due to the large number of drug properties that must be simultaneously optimized during the design process, multi-objective optimization techniques are sometimes employed.[20] Finally because of the limitations in the current methods for prediction of activity, drug design is still very much reliant on serendipity[21] and bounded rationality.[22] Wikipedia accessed 2018 Sept 10   Related terms : structure based drug design   Combinatorial Libraries & synthesis: rational library design,  computational quantum chemistry 

receptor mapping: The technique used to describe the geometric and/or electronic features of a binding site when insufficient structural data for this receptor or enzyme are available. Generally the active site cavity is defined by comparing the superposition of active to that of inactive molecules. IUPAC Medicinal Chemistry, IUPAC Compendium

Over the past ten to fifteen years [before 1987], receptor mapping has expanded from a very minor technique, besieged by problems and limited in its approach, to one that is widespread, extended beyond receptors and applied to clinical problems and populations with modern imaging and scanning techniques. MJ Kuhar "Imaging receptors for drugs in neural tissue"  Neuropharmacology 1987 Jul. 26 (7B): 911-6  

simulations: Up until now, biomolecular simulations in drug design have been of limited use because of the short time scales, long turnaround times (implying poor sampling), the limited accuracy of simulations alluded to above, and the relatively small size of systems simulated when one wishes to account for proper inclusion of the physiological environment like membranes and solvent. Developing a new drug goes beyond finding binding compounds and must rely on good properties from the outset: activity, absorption, distribution, metabolism, excretion. Pharmacological researchers would like to predict these properties first, before one optimizes activity as conventionally done, and before analogs are made. ... When sufficient resources are available, simulations can determine the relative free energy values of drugs passing through membranes. These values are required to estimate the bioavailability of drugs. Opportunities in Molecular Biomedicine in the Era of  Teraflop Computing March 3 & 4, 1999,  Rockville, MD, NIH Resource for Macromolecular Modeling and Bioinformatics  Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana- Champaign  Molecular Biomedicine in the Era of Teraflop Computing -

soft drug design: Soft drug design represents a new approach aimed to design safer drugs with an increased therapeutic index by integrating metabolism considerations into the drug design process. Soft drugs are new therapeutic agents that undergo predictable metabolism to inactive metabolites after exerting their therapeutic effect. Hence, they are obtained by building into the molecule, in addition to the activity, the most desired way in which the molecule is to be deactivated and detoxified. Soft drug design: general principles and recent applications. Bodor N, Buchwald P. Med Res Rev. 2000 Jan;20(1): 58-101 Related term: Microarrays small molecule microarrays

Software Applications & Services 2019 April 16-18  Boston MA As data generation increases, there is a need for workflows that are reproducible across infrastructures, able to empower scientists and researchers to apply cutting-edge analysis methods. A main challenge is scientific data is not centralized or standardized and is fragmented – from instrumentation to clinical research to legacy software. The Software Applications & Services track explores how biopharma companies are utilizing software tools to leverage data platforms to advance data strategies. Themes of case studies that will be presented will focus on data analytics approaches, data methods and standards approaches, transparency, efficiency, security, and cost-effective solutions. 

structure:  In a biological or anatomical context, the term structure is associated with two distinct concepts (meanings): 1. a material object generated as a result of coordinated gene expression, which necessarily consists of parts (e.g., hemoglobin molecule, cell, heart, human body); and 2. the manner of organization or interrelation of the parts that constitute a structure specified by the first definition (i.e., the structure of a structure). Both definitions emphasize the critical need for declaring the principles according to which units of organization can be defined in order to be able to state what is 'whole' and what is 'part'. Specifying the manner in which parts interrelate must satisfy two requirements: 1. to determine the kinds of parts of which various structures may be constituted; and 2. to state the manner of spatial organization of parts by describing their boundaries, continuities and attachments, as well as their location, orientation and spatial adjacencies in terms of qualitative coordinates (in addition to the quantitative geometric coordinates, which are embedded in the Visible Human data sets). Cornelius Rosse, et. al., Visible Human, Know Thyself: The Digital Anatomist Dynamic Structural Abstraction, National Library of Medicine, US  Related terms: Cell biology, Expression Compare unstructured.

structure based design:  A design strategy for new chemical entities based on the three- dimensional (3D) structure of the target obtained by X-ray or nuclear magnetic resonance (NMR) studies, or from protein homology models. IUPAC Computational

structure-based drug design: the design and optimization of a chemical structure with the goal of identifying a compound suitable for clinical testing — a drug candidate. It is based on knowledge of the drug’s three-dimensional structure and how its shape and charge cause it to interact with its biological target, ultimately eliciting a medical effect.

Structure-based drug design (or direct drug design) relies on knowledge of the three dimensional structure of the biological target obtained through methods such as x-ray crystallography or NMR spectroscopy.[34] If an experimental structure of a target is not available, it may be possible to create a homology model of the target based on the experimental structure of a related protein. Using the structure of the biological target, candidate drugs that are predicted to bind with high affinity and selectivity to the target may be designed using interactive graphics and the intuition of a medicinal chemist. Alternatively various automated computational procedures may be used to suggest new drug candidates.[35]

Current methods for structure-based drug design can be divided roughly into three main categories.[36] The first method is identification of new ligands for a given receptor by searching large databases of 3D structures of small molecules to find those fitting the binding pocket of the receptor using fast approximate docking programs. This method is known as virtual screening. A second category is de novo design of new ligands. In this method, ligand molecules are built up within the constraints of the binding pocket by assembling small pieces in a stepwise manner. These pieces can be either individual atoms or molecular fragments. The key advantage of such a method is that novel structures, not contained in any database, can be suggested.[37][38][39] A third method is the optimization of known ligands by evaluating proposed analogs within the binding cavity.[36]  Wikipedia accessed 2018 Oct 26

systems biology: Bioinformatics

top-down: A systems approach, which looks at the big picture and complexity. Genomics is essentially a top- down approach, the opposite of a bottom- up approach. Our ways of thinking have been so profoundly influenced by bottom- up, reductionist approaches that we are having to learn to think in very different ways to begin to fully exploit genomic data. Narrower term: Nanoscience & miniaturization nanofabrication- top- down

translational research: is one of the most important activities of translational medicine as it supports predictions about probable drug activities across species and is especially important when compounds with unprecedented drug targets are brought to humans for the first time. Translational research has the potential to deliver many practical benefits for patients and justify the extensive investments placed by the private and public sector in biomedical research. Translational research encompasses a complexity of scientific, financial, ethical, regulatory, legislative and practical hurdles that need to be addressed at several levels to make the process efficient. What's next in translational medicine? Littman BH, Di Mario L, Plebani M, Marincola FM. What's next in translational medicine? Clin Sci (London) 112 (4): 217- 227, Feb 2007     Related terms: Molecular Medicine  clinical proteomics   translational medicine   biomarker validation

Virtual Cell: VCell, (Virtual Cell) is a comprehensive platform for modeling cell biological systems that is built on a central database and disseminated as a web application.,  Related terms: -Omes & -omics metabolome, transcriptome

Drug discovery informatics Resources
Catalyzing Inquiry at the Interface of Computing and Biology, Edited by John C Wooley and Herbert S Lin. National Research Council (US) Committee on Frontiers at the Interface of Computing and Biology. Washington (DC): National Academies Press (US); 2005. ISBN-10: 0-309-09612-X

Glossary of data management terms, Cornell University

IUPAC International Union of Pure and Applied Chemistry, Glossary of Terms Used in Combinatorial Chemistry, D. Maclean, J. J. Baldwin, V.T. Ivanov, Y. Kato, A. Shaw, P. Schneider, and E. M.. Gordon, Pure Appl. Chem., Vol. 71, No. 12, pp. 2349-2365, 1999. 100+ definitions.
IUPAC International Union of Pure and Applied Chemistry, Compendium of Chemical Terminology: Recommendations, compiled by Alan D. McNaught and Andrew Wilkinson, Blackwell Science, 2012. "Gold Book" 6500+ definitions.
IUPAC  International Union of Pure and Applied Chemistry, Glossary of Terms used in Computational Drug Design, Part II 2015

How to look for other unfamiliar  terms

IUPAC definitions are reprinted with the permission of the International Union of Pure and Applied Chemistry. 

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