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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.


Model optimization against TGA and aPTT data. (a–e) TGA for various concentration of FVIIa added to normal human plasma (NHP). (a) shows the experimental data, (b) shows the model simulation results of the corresponding experiment. (c–e) Comparison between experimental (black line) vs. simulation (red line) results for lag time (c), AUC (d), and peak thrombin (e). (f–j) Comparison between experimental and simulation results when FVIIa was added to FVIII-deficient plasma (8DP) in the TGA experiment. (k–o) Comparison results of varying levels of FXa added to normal plasma. (p–t) Comparison results of varying levels of FXa added to 8DP. (u) Experimental (gray bar) and simulation (red bar) aPTT results when varying FVIIa added in 8DP. (v) Experimental (gray bar) and simulation (red bar) aPTT results when varying FXa added in NHP.
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fig02: Model optimization against TGA and aPTT data. (a–e) TGA for various concentration of FVIIa added to normal human plasma (NHP). (a) shows the experimental data, (b) shows the model simulation results of the corresponding experiment. (c–e) Comparison between experimental (black line) vs. simulation (red line) results for lag time (c), AUC (d), and peak thrombin (e). (f–j) Comparison between experimental and simulation results when FVIIa was added to FVIII-deficient plasma (8DP) in the TGA experiment. (k–o) Comparison results of varying levels of FXa added to normal plasma. (p–t) Comparison results of varying levels of FXa added to 8DP. (u) Experimental (gray bar) and simulation (red bar) aPTT results when varying FVIIa added in 8DP. (v) Experimental (gray bar) and simulation (red bar) aPTT results when varying FXa added in NHP.

Mentions: Figure2 shows a comparison between experimental data and simulation results for TGA after parameter optimization. The experimental TGA data for different initial levels of FVIIa and FXa in normal human plasma (NHP) and FVIII-deficient plasma (8DP) are shown in the left-most column (circles) (Figure2, panelsa,f,k,p). The second column from the left (panelsb,g,l,q) shows the corresponding optimized simulation results (lines). Panels in the right three columns of Figure2 (panels c,h,m,r; d,i,n,s; e,j,o,t) show the comparison between TGA parameters derived experimentally (gray lines) and from simulation results (red lines) for lag time, AUC (ETP), and peak thrombin. The FVIIa or FXa dose-dependent TGA profiles from simulations match well with the in vitro experimental data. In a few cases, e.g., simulated AUC with various concentrations of FXa added to 8DP (panel s), is consistently lower than the experimental value, but the dose-dependent trend is well captured.


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)

Model optimization against TGA and aPTT data. (a–e) TGA for various concentration of FVIIa added to normal human plasma (NHP). (a) shows the experimental data, (b) shows the model simulation results of the corresponding experiment. (c–e) Comparison between experimental (black line) vs. simulation (red line) results for lag time (c), AUC (d), and peak thrombin (e). (f–j) Comparison between experimental and simulation results when FVIIa was added to FVIII-deficient plasma (8DP) in the TGA experiment. (k–o) Comparison results of varying levels of FXa added to normal plasma. (p–t) Comparison results of varying levels of FXa added to 8DP. (u) Experimental (gray bar) and simulation (red bar) aPTT results when varying FVIIa added in 8DP. (v) Experimental (gray bar) and simulation (red bar) aPTT results when varying FXa added in NHP.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
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fig02: Model optimization against TGA and aPTT data. (a–e) TGA for various concentration of FVIIa added to normal human plasma (NHP). (a) shows the experimental data, (b) shows the model simulation results of the corresponding experiment. (c–e) Comparison between experimental (black line) vs. simulation (red line) results for lag time (c), AUC (d), and peak thrombin (e). (f–j) Comparison between experimental and simulation results when FVIIa was added to FVIII-deficient plasma (8DP) in the TGA experiment. (k–o) Comparison results of varying levels of FXa added to normal plasma. (p–t) Comparison results of varying levels of FXa added to 8DP. (u) Experimental (gray bar) and simulation (red bar) aPTT results when varying FVIIa added in 8DP. (v) Experimental (gray bar) and simulation (red bar) aPTT results when varying FXa added in NHP.
Mentions: Figure2 shows a comparison between experimental data and simulation results for TGA after parameter optimization. The experimental TGA data for different initial levels of FVIIa and FXa in normal human plasma (NHP) and FVIII-deficient plasma (8DP) are shown in the left-most column (circles) (Figure2, panelsa,f,k,p). The second column from the left (panelsb,g,l,q) shows the corresponding optimized simulation results (lines). Panels in the right three columns of Figure2 (panels c,h,m,r; d,i,n,s; e,j,o,t) show the comparison between TGA parameters derived experimentally (gray lines) and from simulation results (red lines) for lag time, AUC (ETP), and peak thrombin. The FVIIa or FXa dose-dependent TGA profiles from simulations match well with the in vitro experimental data. In a few cases, e.g., simulated AUC with various concentrations of FXa added to 8DP (panel s), is consistently lower than the experimental value, but the dose-dependent trend is well captured.

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.