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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)

Bottom Line: 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.We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

View Article: PubMed Central - PubMed

Affiliation: Pharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.

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.

No MeSH data available.


Related in: MedlinePlus

A schematic of the coagulation network. The green broken lines represent positive feedback mechanisms; red broken lines represent mechanisms that inhibit formation of thrombin, a key product of the coagulation network that catalyzes the formation of blood clots, and the solid red lines represent the activation of anticoagulant proteins in the system. The numbers alongside the arrows show the reaction number in the model. Not all the reactions in the model are shown here, to lessen the complexity of the figure. APC, active protein C; PC, protein C; TF, Tissue Factor; TFPI, Tissue Factor Pathway Inhibitor; CA, Contact Activators; F1 + 2, prothrombin fragment 1 + 2; mIIa, meizo-thrombin; Tmod, thrombomodulin; IIa, thrombin; PK, pre-Kallikrein; K, Kallikrein; ATIII, anti-thrombin III; XII, factor XII (FXIII); XI, factor XI (FXI); X, factor X (FX); IX, factor IX (FIX); VIII, factor VIII (FVIII); VII, factor VII (FVII); V, factor V (FV). For these factors the a after factor name indicates its active form.
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fig01: A schematic of the coagulation network. The green broken lines represent positive feedback mechanisms; red broken lines represent mechanisms that inhibit formation of thrombin, a key product of the coagulation network that catalyzes the formation of blood clots, and the solid red lines represent the activation of anticoagulant proteins in the system. The numbers alongside the arrows show the reaction number in the model. Not all the reactions in the model are shown here, to lessen the complexity of the figure. APC, active protein C; PC, protein C; TF, Tissue Factor; TFPI, Tissue Factor Pathway Inhibitor; CA, Contact Activators; F1 + 2, prothrombin fragment 1 + 2; mIIa, meizo-thrombin; Tmod, thrombomodulin; IIa, thrombin; PK, pre-Kallikrein; K, Kallikrein; ATIII, anti-thrombin III; XII, factor XII (FXIII); XI, factor XI (FXI); X, factor X (FX); IX, factor IX (FIX); VIII, factor VIII (FVIII); VII, factor VII (FVII); V, factor V (FV). For these factors the a after factor name indicates its active form.

Mentions: A mechanistic model of the blood coagulation network was constructed including both intrinsic and extrinsic pathways and ending at fibrin formation. Figure1 shows the schematic of the network described in our model. The detailed description of model construction and model structure, as well as a complete list of reactions used in the model, is listed in the Supplementary Information (Supplementary Information & Table of Reactions), along with the rate parameters and nonzero initial conditions for proteins. The model assumed a well-mixed system for in vitro experiments. The resulting system of ordinary differential equations was simulated using MATLAB's (MathWorks, Natick, MA; v. 2012b 64-bit) SimBiology (v. 4.2) toolbox (see Supplementary Information: Coagulation_Systems_Model_Nayak_et_al.sbproj).


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)

A schematic of the coagulation network. The green broken lines represent positive feedback mechanisms; red broken lines represent mechanisms that inhibit formation of thrombin, a key product of the coagulation network that catalyzes the formation of blood clots, and the solid red lines represent the activation of anticoagulant proteins in the system. The numbers alongside the arrows show the reaction number in the model. Not all the reactions in the model are shown here, to lessen the complexity of the figure. APC, active protein C; PC, protein C; TF, Tissue Factor; TFPI, Tissue Factor Pathway Inhibitor; CA, Contact Activators; F1 + 2, prothrombin fragment 1 + 2; mIIa, meizo-thrombin; Tmod, thrombomodulin; IIa, thrombin; PK, pre-Kallikrein; K, Kallikrein; ATIII, anti-thrombin III; XII, factor XII (FXIII); XI, factor XI (FXI); X, factor X (FX); IX, factor IX (FIX); VIII, factor VIII (FVIII); VII, factor VII (FVII); V, factor V (FV). For these factors the a after factor name indicates its active form.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544053&req=5

fig01: A schematic of the coagulation network. The green broken lines represent positive feedback mechanisms; red broken lines represent mechanisms that inhibit formation of thrombin, a key product of the coagulation network that catalyzes the formation of blood clots, and the solid red lines represent the activation of anticoagulant proteins in the system. The numbers alongside the arrows show the reaction number in the model. Not all the reactions in the model are shown here, to lessen the complexity of the figure. APC, active protein C; PC, protein C; TF, Tissue Factor; TFPI, Tissue Factor Pathway Inhibitor; CA, Contact Activators; F1 + 2, prothrombin fragment 1 + 2; mIIa, meizo-thrombin; Tmod, thrombomodulin; IIa, thrombin; PK, pre-Kallikrein; K, Kallikrein; ATIII, anti-thrombin III; XII, factor XII (FXIII); XI, factor XI (FXI); X, factor X (FX); IX, factor IX (FIX); VIII, factor VIII (FVIII); VII, factor VII (FVII); V, factor V (FV). For these factors the a after factor name indicates its active form.
Mentions: A mechanistic model of the blood coagulation network was constructed including both intrinsic and extrinsic pathways and ending at fibrin formation. Figure1 shows the schematic of the network described in our model. The detailed description of model construction and model structure, as well as a complete list of reactions used in the model, is listed in the Supplementary Information (Supplementary Information & Table of Reactions), along with the rate parameters and nonzero initial conditions for proteins. The model assumed a well-mixed system for in vitro experiments. The resulting system of ordinary differential equations was simulated using MATLAB's (MathWorks, Natick, MA; v. 2012b 64-bit) SimBiology (v. 4.2) toolbox (see Supplementary Information: Coagulation_Systems_Model_Nayak_et_al.sbproj).

Bottom Line: 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.We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

View Article: PubMed Central - PubMed

Affiliation: Pharmacometrics, Global Innovative Pharma Business (GIPB), Pfizer Inc. Cambridge, Massachusetts, USA.

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.

No MeSH data available.


Related in: MedlinePlus