BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.pdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611_urn.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=Nayak2015.ziphttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.scihttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.pnghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000611?filename=BIOMD0000000611.xppprimaryOK200Satyaprakash NayakManually curatedBlood Coagulation DiseaseL2V4https://www.ebi.ac.uk/biomodels/BIOMD000000061126312163falseBioModelsBIOMD0000000340 biomodels.db MODEL1108260014 biomodels.db BIOMD0000000339 biomodels.db BIOMD0000000335 biomodels.db BIOMD0000000338 biomodels.dbModelsSBMLNayak2015 Blood Coagulation Network Predicting the Effects of Various Therapies on Biomarkers2015MODEL1511160000Nayak SNayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F26312163,
A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.. null, 4.
Pharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.satyaprakash.nayak@pfizer.comPfizerBIOMD0000000611A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.Nayak S S, Lee D D, Patel-Hett S S, Pittman D D DD, Martin S W SW, Heatherington A C AC, Vicini P P, Hua F FBiomarker, Biological Markers, Viral Marker, Serum Marker, Coagulation, Viral, Surrogate Endpoints, Clinical, Surrogate Endpoint, End Points, Clinical Markers, Laboratory, Immune Markers., Surrogate, Clinical Marker, Clotting, Blood, Biological Marker, Serum Markers, Endpoints, Biochemical, End Point, Endpoint, Biochemical Markers, Blood Clotting, Immunologic, Serum, Laboratory Marker, Biologic Marker, Surrogate Marker, Immune Marker, Surrogate End Points, Surrogate Markers, Laboratory Markers, Immunologic Markers, Immune, Markers, Biochemical Marker, Marker, Biological, Surrogate End Point, Viral Markers, Blood Clottings, blood clotting, Immunologic Marker, Biologic, Biologic MarkersNetworks, Factor 8 C, PP17, single-organism developmental process, determination, treatment_or_therapy, PP19, Blood, Cf2, HLA Match, Activated, A4, complex, sci, Factor Eight, Long Term, Factor VIIa, diseases, Treatment Effect, treatment outcome, {AUC}, HOW, How, diseases and disorders, gamma-Thrombin, 1110025J15Rik, Consortium, Effect, l(3)j5D5, C1orf215, 24B, Centers, treatment, average, In-house, Fresh Frozen Plasmas, Fresh Frozen, human disease, recombinant, Thrombostat, IMPROVED, stru, Matched, proteins, Blood Clotting, l(3)S053606, number of, treatment or therapy, DmelCG7693, CG10293, 4624, Computational, Coagulation Factor VIIa, Pp17, allergic reaction, Pp18, F VIII-C, l(3)j5B5, Modulated, Coagulation Factor VIII, Cephalin-Kaolin coagulation time, Had, area under the curve, Computational Technique, Network Interface, Pp19, Therapies, Homo sapiens disease, Thromboplastinogen, variability of a physical quality, Better, any therapy, P18, Thrombokinase, l(3)07551, P19, Has, Long-Term Effects, plasma, Therapy, Therapeutic Procedure, Thrombin-JMI, TX, 0904/17, Frozen Plasma, Apply, Coagulation, and Consortia, Thrombin, Activated Factor X, Difference, AUC, Longterm Effect, RCB2758, extra or missing physical or functional parts, Normalcy, Plasmas, Blood Coagulation Factor X, SZ1, Diseases, LAP18, Autoprothrombin C, Conflict, blood clotting, Blood Coagulation Factor VII, HLA Serotype Match, Lap18, dOSR1, thrombin, clotting, portion of blood plasma, In Vitro, Factor VIII Procoagulant Activity, CG7693, Factor VIII Coagulant Antigen, Factor VIII Clotting Antigen, Blood-coagulation factor VIII, Network Device, F VIII C, Factor VIIIC, Improved, Treatments, human, Factor Seven A, aPTT, disease, blood plasm, Health, DMDA, Treatment Outcome, hypersensitivity reaction disease, APTT, anon-EST:Liang-2.39, beta-Thrombin, activated partial thromboplastin time, in vitro, E430016J11Rik, time, Fresh, other 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Possess, Thrombase, HLA Allele Match, data, In Vitro Route of Administration, clone 2.39, Matching, Proteins, disorders, Factor 8, lag, responsivity., medical condition, qkr, l(3)S090417, impact-a, LGMD2C, mereological quality, development, Frozen Plasmas, Have, Thrombinar, Applied, KH93F, chemical analysis, Long Term Effects, Protein, Blood Clottings, condition, Factor X, Computation, STE20-like/SPAK, effect, any_therapy, Modulating, who, HyattC, Plasma, hypersensitivity, Pr22, hypersensitive, Treated, Coagulation Factor Xa, DMDA1, Therapeutic Method, Normality, Op18, Match, portion of plasma, Who/How, Factor Ten A, beta, Longterm Effects, Protein Gene Products, Gene Proteins, Activated Factor VII, Differential, therapy, FII, PR22, Therapeutic, Outcome of Therapy, concentration, OP18, cardinality, SCARMD2, Factor VII, qkr[93F], Treatment, assay, response, Factor 10A, RWDD5, Hyate-Cextent, Factor 8 C, PP17, mode of action, Biological Markers, Viral Marker, pharmacodynamics, Public Sectors, Surrogate Endpoints, single-organism developmental process, determination, Laboratory, PP19, Blood, Cf2, alkylacylglycerol choline phosphotransferase activity, Activated, Biochemical, 2-b]quinoline-3, A4, Endpoint, complex, Serum, Factor Eight, composed of, carnitine palmitoyltransferase I, Long Term, Factor VIIa, (+)-camptothecin, Laboratory Markers, diseases, carnitine palmitoyltransferase activity, Biological, 1-alkyl-2-acetyl-m-glycerol:CDPcholine choline phosphotransferase activity, responsivity, diseases and disorders, gamma-Thrombin, 1110025J15Rik, Public Enterprise, Effect, C1orf215, 21, average, Fresh Frozen Plasmas, Fresh Frozen, human disease, cholinephosphotransferase activity, Thrombostat, Hua <gastropods>, Public Domains, composition, proteins, Blood Clotting, number of, DmelCG7693, 4624, Coagulation Factor VIIa, Elavl1, Pp17, allergic reaction, Pp18, F VIII-C, D-camptothecin, Immune, Coagulation Factor VIII, Markers, Cephalin-Kaolin coagulation time, Viral Markers, Pp19, Therapies, Homo sapiens disease, phosphocholine diacylglyceroltransferase activity, Thromboplastinogen, variability of a physical quality, P18, Thrombokinase, l(3)07551, P19, Long-Term Effects, plasma, cytidine diphosphocholine glyceride transferase activity, cytidine diphosphorylcholine diglyceride transferase activity, Therapy, Thrombin-JMI, Frozen Plasma, Coagulation, Viral, Surrogate Endpoint, Thrombin, Activated Factor X, completeness, DS, Longterm Effect, carnitine palmitoyltransferase-A, cis-prenyltransferase, RCB2758, Biochemical Markers, extra or missing physical or functional parts, Normalcy, Biologic Marker, Plasmas, CPT-B, Blood Coagulation Factor X, Marker, Public Domain, CPT-A, CIT, Diseases, LAP18, Domains, Autoprothrombin C, 1-alkyl-2-acetyl-sn-glycerol cholinephosphotransferase activity, blood clotting, Blood Coagulation Factor VII, Domain, Lap18, dOSR1, thrombin, clotting, portion of blood plasma, Factor VIII Procoagulant Activity, End Points, CG7693, 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lactone, compositionality, impact-a, (+)-camptothecine, Immune Marker, LGMD2C, DmelCG7149, mereological quality, development, Frozen Plasmas, Hua <eudicots>, cpt, Thrombinar, Surrogate End Point, Public, phosphorylcholine--glyceride transferase activity, chemical analysis, Long Term Effects, Protein, Blood Clottings, condition, Factor X, STE20-like/SPAK, (4S)-4-ethyl-4-hydroxy-1H-pyrano[3', ACPT, Data Base, Biologic Markers, HyattC, Plasma, hypersensitivity, Pr22, Serum Marker, hypersensitive, Coagulation Factor Xa, DMDA1, Normality, Surrogate, Op18, Endpoints, palmitoylcarnitine transferase activity, portion of plasma, CPTo, Factor Ten A, Surrogate Marker, CPTi, W91709, beta, Longterm Effects, Protein Gene Products, Gene Proteins, Activated Factor VII, Xel-1, 20(S)-camptothecine, FII, PR22, pharmacologic action, Therapeutic, concentration, OP18, cardinality, SCARMD2, structure, Factor VII, Treatment, Public., assay, response, Factor 10A, outer malonyl-CoA inhibitable carnitine palmitoyltransferase activity, L-carnitine palmitoyltransferase activity, RWDD5, Immune Markers, Hyate-CNetworks, mode of action, Biological Markers, Viral Marker, pharmacodynamics, Coagulation, Viral, Surrogate Endpoints, and Consortia, Surrogate Endpoint, Effects, Clinical Markers, Laboratory, Immune Markers., Clinical Marker, Clotting, Blood, Consortium or Network, Serum Markers, Biochemical, End Point, Endpoint, Biochemical Markers, Network, Serum, Biologic Marker, Immune Marker, Surrogate End Points, Surrogate Markers, Laboratory Markers, Marker, Biological, Surrogate End Point, Blood Clottings, blood clotting, Consortium, Effect, NCI Consortium or Network, Biologic Markers, Centers, Biomarker, Serum Marker, Clinical, End Points, Surrogate, Biological Marker, Endpoints, Network Device, Blood Clotting, Immunologic, Laboratory Marker, Surrogate Marker, Immunologic Markers, Immune, Markers, pharmacologic action, mechanism of action, Biochemical Marker, Viral Markers, Network Interface, Pharmacologies, Immunologic Marker, BiologicfalseNayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
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This model is described in the article:
Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.
Nayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F.
CPT Pharmacometrics Syst Pharmacol. 2015 Jul;4(7):396-405.
Abstract:
A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.
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2018-12-212015-12-082015-11-16BIOMD000000061126312163MODEL1511160000BIOMD0000000611GO:00075969606