{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617-biopax2.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617_url.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=manifest.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=metadata.rdf","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.sci","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617_url.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000617?filename=BIOMD0000000617.png"]},"type":"primary"},"statusCodeValue":200,"statusCode":"OK"}],"scores":null,"additional":{"submitter":["Thawfeek Varusai"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"disease":["Alzheimer's Disease"],"levelVersion":["L3V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000617"],"publication_pubmed":["25374788"],"isPrivate":["false"],"repository":["BioModels"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Walsh2014   Inhibition kinetics of DAPT on APP Cleavage"],"publication_year":["2014"],"submissionId":["MODEL1609120000"],"first_author":["Ryan Walsh"],"publication_authors":["Ryan Walsh"],"publication":["25374788,\n                            Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits γ-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.. null, 2.\n                            Department of Chemistry, Carleton University , Ottawa, ON , Canada."],"submitter_mail":["tvarusai@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000617"],"pubmed_abstract":["Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits γ-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions."],"pubmed_title":["Are improper kinetic models hampering drug development?"],"pubmed_authors":["Walsh Ryan R"],"name_synonyms":["Amyloid intracellular domain 57, APP, ABETA, Amyloid intracellular domain 59, Amyloidogenic glycoprotein, AICD-50, DmelCG42318, S-APP-alpha, E030013M08Rik, P3(40), CG17144, A4, CTFgamma, CR32097, Gamma-CTF(57), betaApp, Amyloid intracellular domain 50, cleavage., Abpp, AAA, Gamma-CTF(50), AID(59), C99, Cvap, APP-C99, CG42318, AG, Beta-amyloid protein 42, APP-C57, Adap, APP-C59, Beta-amyloid protein 40, AICD-57, AID(50), AICD-59, Cerebral vascular amyloid peptide, P3(42), PN-II, Ag, PreA4, S-APP-beta, Dmel_CG17144, CG5620, Soluble APP-beta, Alzheimer disease amyloid protein, Gamma-CTF(59), Protease nexin-II, Dmel_CG5620, C31, Alzheimer disease amyloid A4 protein homolog, N-APP, AID(57), Gamma-secretase C-terminal fragment 50, Gamma-secretase C-terminal fragment 57, Gamma-secretase C-terminal fragment 59, AD1, Soluble APP-alpha, Abeta, PN2, Beta-APP42, APPI, Beta-APP40, C80, CVAP, ABPP, C83"],"pubmed_abstract_synonyms":["Mental, eIF2C2, APP, SEL-10, hCdc4, beta-Secretase, other disease, alpha Secretase, ago, Activity, 1110001A17Rik, Procedures, Product, determination, Laboratory, Biocatalysts, l(2)k08121, AGO 2, Selective, l(2)04845, MRE20, FBXW3, FBXW6, Techniques, diseases, Method, Pharmaceutical Product, CG7439, Studies, disease or disorder, diseases and disorders, Amyloid Precursor Protein Secretase, Research Activity, beta Secretase, Laboratory Research, Technique, Priorities, Drugs, human disease, Research, ligand, gamma-Secretase, dAGO2, inhibiteur, dAGO1, study., procedures, FBW6, l(2)4845, FBW7, Fbw7, Social, Fbxo30, non-neoplastic, Study, ago2, Secretases, Enzyme, ago1, drugs, inhibidor, Social Attention, Methodological Studies, medicine, Concentration, SEL10, Pharmaceutical, dAgo1, CDC4, dAgo2, Cdc4, FBXO30, Fbx30, disorder, Homo sapiens disease, Development and Research, Dm Ago1, Research Priority, Ago2, Preparation, Ago1, SIMPLE, cdc4, Pharmaceuticals, TP53I7, FBX30, Drug Interaction, inhibitors, Products, DmelCG7439, ago1-1, Ago-1, Ago-2, Dm Ago2, DmFbw7, DmelCG15010, drug, APP Secretase, disorders, anon-WO0257455.29, anon-WO0118547.345, Research Priorities, inhibitor, Attention Focus, medical condition, Procedure, gamma Secretase, Literatures, Fbwd6, Priority, Interaction, Attention, Selective Attention, CG6671, PIG7, chemical analysis, Research Activities, Diseases, hAgo, condition, Fbxw6, Pharmaceutic, Focus of Attention, AG02, techniques, l(2)k00208, CG13452, Research and Development, antagonists, Pharmaceutic Preparations, common, alpha-Secretase, Mental Concentration, Methodological, Methodological Study, Activities, Drug, disease, Preparations, Ago, clear, hyaline, antagonists and inhibitors, AGO, ago-2, SCF[Ago], Biocatalyst, CG15010, assay, Pharmaceutical Products, Interactions, DmelCG6671, methodology, Pharmaceutical Preparation, Secretase"],"description_synonyms":["Amyloid intracellular domain 57, extent, APP, eIF2C2, ABETA, Amyloid intracellular domain 59, beta-Secretase, Public Sectors, Activity, Product, determination, Laboratory, l(2)k08121, cleavage, A4, CTFgamma, l(2)04845, Amyloid intracellular domain 50, Pharmaceutical Development, Abpp, AAA, Cvap, Techniques, CG42318, AG, Beta-amyloid protein 42, diseases, Method, Pharmaceutical Product, Beta-amyloid protein 40, CG7439, diseases and disorders, Amyloid Precursor Protein Secretase, Research Activity, Public Enterprise, AID(50), Laboratory Research, Priorities, human disease, Ag, Public Domains, procedures, FBW6, l(2)4845, FBW7, Fbw7, Alzheimer disease amyloid A4 protein homolog, Social, N-APP, ago2, Gamma-secretase C-terminal fragment 50, ago1, Methodological Studies, Gamma-secretase C-terminal fragment 57, medicine, Pharmaceutical, Gamma-secretase C-terminal fragment 59, CDC4, Cdc4, AD1, Fbx30, Abeta, Homo sapiens disease, Dm Ago1, APPI, Research Priority, Ago2, Ago1, SIMPLE, C80, cdc4, C83, DmelCG7439, ago1-1, AICD-50, Dm Ago2, S-APP-alpha, completeness, DmFbw7, DmelCG15010, APP Secretase, CG17144, anon-WO0257455.29, Research Priorities, CR32097, Attention Focus, Procedure, Gamma-CTF(57), Literatures, C99, Fbwd6, Prediction, Attention, Public Domain, PIG7, Diseases, AICD-57, Domains, Fbxw6, Pharmaceutic, AG02, Domain, Target Prediction, AICD-59, CG13452, Cerebral vascular amyloid peptide, Research and Development, common, Methodological, Alzheimer disease amyloid protein, Protease nexin-II, Dmel_CG5620, Methodological Study, Activities, disease, Sector, Ago, hyaline, antagonists and inhibitors, AGO, Proliferation, Biocatalyst, DmelCG6671, Secretase, Mental, Proliferating, SEL-10, hCdc4, other disease, alpha Secretase, Sectors, ago, DmelCG42318, 1110001A17Rik, Procedures, Biocatalysts, P3(40), AGO 2, number, Selective, Copyrights, Development, Medication, betaApp, presence, MRE20, AID(59), FBXW3, FBXW6, APP-C57, APP-C59, Studies, disease or disorder, Drug Target Prediction, beta Secretase, Enterprises, Technique, Drugs, study, PreA4, Research, ligand, proliferating, gamma-Secretase, dAGO2, Medication Development, inhibiteur, dAGO1, S-APP-beta, Dmel_CG17144, CG5620, Gamma-CTF(59), Public Enterprises, Fbxo30, non-neoplastic, Study, AID(57), Secretases, Enzyme, drugs, Abstract, inhibidor, Social Attention, Concentration, SEL10, dAgo1, dAgo2, FBXO30, disorder, Development and Research, Preparation, Enterprise, Pharmaceuticals, TP53I7, FBX30, inhibitors, Products, Amyloidogenic glycoprotein, Ago-1, Ago-2, E030013M08Rik, drug, disorders, anon-WO0118547.345, inhibitor, medical condition, gamma Secretase, Gamma-CTF(50), APP-C99, count in organism, Priority, Selective Attention, CG6671, Public, Adap, chemical analysis, Research Activities, hAgo, condition, Focus of Attention, techniques, l(2)k00208, Data Base, Drug Target Predictions, antagonists, P3(42), PN-II, Pharmaceutic Preparations, alpha-Secretase, Mental Concentration, Soluble APP-beta, C31, Drug, Computational Prediction of Drug-Target Interactions, Preparations, clear, ago-2, SCF[Ago], Soluble APP-alpha, Drug Target, CG15010, Public., PN2, Beta-APP42, assay, Beta-APP40, Pharmaceutical Products, CVAP, methodology, Pharmaceutical Preparation, ABPP"],"pubmed_title_synonyms":["Drug, Drug Target Predictions., Computational Prediction of Drug-Target Interactions, Medication Development, Prediction, Development, Medication, Drug Target Prediction, Pharmaceutical, Pharmaceutical Development, Target Prediction, Drug Target"],"additional_accession":[]},"is_claimable":false,"name":"Walsh2014 - Inhibition kinetics of DAPT on APP Cleavage","description":"\n      \n        Walsh2014 - Inhibition kinetics of DAPT on\nAPP Cleavage\n\n  This model is described in the article:\n  \n    Are improper kinetic models\n    hampering drug development?\n  \n  Walsh R.\n  PeerJ 2014; 2: e649\n  Abstract:\n  \n    Reproducibility of biological data is a significant problem\n    in research today. One potential contributor to this, which has\n    received little attention, is the over complication of enzyme\n    kinetic inhibition models. The over complication of inhibitory\n    models stems from the common use of the inhibitory term (1 +\n    [I]/Ki ), an equilibrium binding term that does not distinguish\n    between inhibitor binding and inhibitory effect. Since its\n    initial appearance in the literature, around a century ago, the\n    perceived mechanistic methods used in its production have\n    spurred countless inhibitory equations. These equations are\n    overly complex and are seldom compared to each other, which has\n    destroyed their usefulness resulting in the proliferation and\n    regulatory acceptance of simpler models such as IC50s for drug\n    characterization. However, empirical analysis of inhibitory\n    data recognizing the clear distinctions between inhibitor\n    binding and inhibitory effect can produce simple logical\n    inhibition models. In contrast to the common divergent practice\n    of generating new inhibitory models for every inhibitory\n    situation that presents itself. The empirical approach to\n    inhibition modeling presented here is broadly applicable\n    allowing easy comparison and rational analysis of drug\n    interactions. To demonstrate this, a simple kinetic model of\n    DAPT, a compound that both activates and inhibits ?-secretase\n    is examined using excel. The empirical kinetic method described\n    here provides an improved way of probing disease mechanisms,\n    expanding the investigation of possible therapeutic\n    interventions.\n  \n\n\n  This model is hosted on \n  BioModels Database\n  and identified by: \n  BIOMD0000000617.\n  To cite BioModels Database, please use: \n  BioModels Database:\n  An enhanced, curated and annotated resource for published\n  quantitative kinetic models.\n\n\n  To the extent possible under law, all copyright and related or\n  neighbouring rights to this encoded model have been dedicated to\n  the public domain worldwide. Please refer to \n  CC0\n  Public Domain Dedication for more information.\n\n\n    ","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2016-09-12"},"accession":"BIOMD0000000617","cross_references":{"sbo":["SBO:0000410"],"pubmed":["25374788"],"chebi":["CHEBI:86193"],"biomodels__db":["MODEL1609120000","BIOMD0000000617"],"go":["GO:0042987"],"taxonomy":["9606"],"uniprot":["Q9NZ42","P05067"]}}