BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.pdfhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.xpphttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.scihttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.pnghttps://www.ebi.ac.uk/biomodels/model/download/MODEL1011080004?filename=MODEL1011080004.mprimaryOK200Lukas EndlerNon-curatedL2V1https://www.ebi.ac.uk/biomodels/MODEL101108000420957118falseBioModelsSBMLModelsChang2010 Reduced Kidney FBA2010MODEL1011080004Non KineticRoger L Chang, Li Xie, Lei Xie, Philip E Bourne, Bernhard Ø PalssonRoger L Chang20957118, Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.. 9, 6. Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.lukas@ebi.ac.ukEMBL-EBIRecent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.Drug off-target effects predicted using structural analysis in the context of a metabolic network model.Chang Roger L RL, Xie Li L, Xie Lei L, Bourne Philip E PE, Palsson Bernhard Ø BØsmall, T4, DmelCG6058, T6, Kidneys, T7, FBA, Fba, 1700026K02Rik, reduced, underdeveloped, 1200002G09Rik, AI046358., BcDNA:LP07735, hypoplasia, T12, tiny, FBX3, CG6058, ald, ALD, reniculate kidneyscale tissue, Materials, Product, Esters, acetylglucosaminyltransferase-like protein, Mbp1, protein, Risk Factor Score, side effects, Pharmaceutical Development, Medical Specialties, Speciality, Risk Factors, Roles, Specialities, responsivity, GRP1, Pharmaceutical Product, Grp1, Concepts, disease of kidney, protein aggregate, myd, Correlates, imprinted and ancient gene protein, treatment, Thesaurismosis, Insurance Medicine, Man (Taxonomy), like-acetylglucosaminyltransferase, kidney disease, Biology, plant 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disease of kidney, protein aggregate, fs(1)M104, Insurance Medicine, Man (Taxonomy), kidney disease, Biology, Metabolic disease, Public Domains, hypoplasia, genetic, IBP-9, Pharmaceutical, l(2)k08110, Cholesterol, disease management, CT27014, Therapies, NET1, Medicines, METABOLISM DISORDER NOS, netA, MD - Metabolic disorders, CP529, Therapy, Specialties, gyltl1b-b, Modern, 9330129L11, Ids, Binding, MMEL2, MDDGA6, cholesterol esters, 3.1.3.48, Specialty, Plaything, Genetic Materials, Score, disease or disorder of kidney, EPH-like kinase 6, NOS, Pharmaceutic, NOV, KIAA0609, PlexA1, acetylglucosaminyltransferase-like 1A, Genetic Material, Domain, METAB DIS, gyltl1b, Factors, renal system disease, CG4063, Risk, Step, mdc1d, Toys, nov, hEK6, LARGE_HUMAN, Treatments, fs(1)Y[b], Insurance Medicines, Kidneys, Social Risk, Health, Patient, STEP, C130088N23Rik, Cistron, Eph2, inherited genetic, kidney diseases, Unspecified disorder of metabolism, DXS648E, nephropathy, disorder of kidney, Plays, MPS2, human being, YB, Gene, PLEXIN-B1, Development, Medication, Ximpact, froggy, Gyltl1a, netrin, cytohesin/GRP1, Generalized metabolic disorder, reduced, Homo sapiens, Yb, Hek6, tiny, Drug Target Prediction, CG2706, Cholesteryl Ester, Cholesterol Ester, ENSMUSG00000074119, Drugs, Site, reactivity, Generalized metabolic disorder (disorder), study, CG2916, Populations at Risk, Genetic, Ebi, EBI, renal disease, Tyrosine-protein kinase receptor EPH-2, inhibiteur, l(2)SH2 0323, Medication Development, Risk Score, LARGE, kidney disease or disorder, BPFD#36, Combining Site, Abstract, 2.7.10.1, inhibidor, Neural-specific protein-tyrosine phosphatase, L10, Clients, Etrp, SELP, Play, Social Risk Factor, Metabolic disorder, b2b970Clo, NAT1, Preparation, small, Medical Specialty, protein complex, DmelCG2706, drug, EPH tyrosine kinase 2, DXS648, inhibitor, Binding Site, Cistrons, Risk Factor Scores, disorder of metabolism NOS (disorder), Client, impact-a, presence., sep5, Cholesteryl, count in organism, IGFBP-9, Protein, Systems, Generalised metabolic disorder, renal disorder, techniques, l(2)SH0323, Data Base, antagonists, CYH1, Exhibit, scale, Thesaurismoses, DmelCG2916, CRYPTIC, CG18657, l(2)k16213, Risk Scores, Drug, Protein Gene Products, Computational Prediction of Drug-Target Interactions, Preparations, Therapeutic, EPHT2, Modern Man, DTGA2, Drug Target, Treatment, CP 529414, assay, response, RWDD5, Pharmaceutical Preparation, glycosyltransferase-like protein LARGE1, methodologyDrugs, Drug, Products, Preparations, drugs, Product, Pharmaceutic Preparations, medicine, determination, Pharmaceutical, Pharmaceutical Product, drug, chemical analysis., Pharmaceutic, assay, Preparation, Pharmaceutical Products, Pharmaceutical Preparation, predicted, PharmaceuticalsfalseChang2010_Reduced_Kidney_FBA This is the reduced kidney metabolic network described in the article Drug off-target effects predicted using structural analysis in the context of a metabolic network model. Chang RL, Xie L, Xie L, Bourne PE, Palsson BØ. PLoS Comput Biol. 2010 Sep 23;6(9):e1000938. PMID: 20957118 , DOI: 10.1371/journal.pcbi.1000938 Abstract: Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. This model was downloaded from the supplementary materials to the article. To make this file valid SBML, in some ids brackets had to be replaced, and all parameter units were changed from mmole per gDW per hour to mmole per hour. The model can be used, amongst others, with the COBRA toolbox . This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92. 2010-11-092005-01-012010-11-08MODEL101108000420957118MODEL1011080004GO:00972059606BTO:0000671