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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.


Effects of varying active protein levels in NHP and 8DP on TGA (lag time (a), peak thrombin (b), and AUC (c)). All the active protein concentrations were fixed at 0.05, 0.5, or 120 nM to simulate low, medium, and high concentrations of the active enzymes.
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fig04: Effects of varying active protein levels in NHP and 8DP on TGA (lag time (a), peak thrombin (b), and AUC (c)). All the active protein concentrations were fixed at 0.05, 0.5, or 120 nM to simulate low, medium, and high concentrations of the active enzymes.

Mentions: The effects of active proteins on TGA parameters in NHP and 8DP are shown in Figure4. Since most of the active proteins have zero value as initial concentrations, we could not use (0.1× – 10×) nominal levels in this analysis. Thus, we picked the same concentrations 0, 0.05, 5, or 120 nM for all the active proteins, to cover both physiological and super-physiological values while allowing a comparison of sensitivity among them. Different from their corresponding zymogens, FXIa and FXIIa showed a considerable effect on all the parameters of TGA. Similarly, opposite from FV, increasing FVa has little effect on peak thrombin and AUC, but decreases lag time. FVIIa also has a smaller effect on peak and AUC, but exerts a larger effect on lag time. Interestingly, TGA from NHP seems to be more sensitive to FVIIa level increases compared with TGA from 8DP.


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)

Effects of varying active protein levels in NHP and 8DP on TGA (lag time (a), peak thrombin (b), and AUC (c)). All the active protein concentrations were fixed at 0.05, 0.5, or 120 nM to simulate low, medium, and high concentrations of the active enzymes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig04: Effects of varying active protein levels in NHP and 8DP on TGA (lag time (a), peak thrombin (b), and AUC (c)). All the active protein concentrations were fixed at 0.05, 0.5, or 120 nM to simulate low, medium, and high concentrations of the active enzymes.
Mentions: The effects of active proteins on TGA parameters in NHP and 8DP are shown in Figure4. Since most of the active proteins have zero value as initial concentrations, we could not use (0.1× – 10×) nominal levels in this analysis. Thus, we picked the same concentrations 0, 0.05, 5, or 120 nM for all the active proteins, to cover both physiological and super-physiological values while allowing a comparison of sensitivity among them. Different from their corresponding zymogens, FXIa and FXIIa showed a considerable effect on all the parameters of TGA. Similarly, opposite from FV, increasing FVa has little effect on peak thrombin and AUC, but decreases lag time. FVIIa also has a smaller effect on peak and AUC, but exerts a larger effect on lag time. Interestingly, TGA from NHP seems to be more sensitive to FVIIa level increases compared with TGA from 8DP.

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.