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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 zymogen, inhibitor, and active protein levels in NHP and 8DP on aPTT. (a) Changes in aPTT with modulation of zymogen and inhibitor levels. (b) Changes in aPTT with modulation of active proteins. The dashed broken lines indicate aPTT values when all the proteins are at their nominal levels for NHP (blue line) or 8DP (red line).
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fig05: Effects of varying zymogen, inhibitor, and active protein levels in NHP and 8DP on aPTT. (a) Changes in aPTT with modulation of zymogen and inhibitor levels. (b) Changes in aPTT with modulation of active proteins. The dashed broken lines indicate aPTT values when all the proteins are at their nominal levels for NHP (blue line) or 8DP (red line).

Mentions: Similar to the analysis for TGA, we also used the model to study the effects of varying the initial concentrations of zymogens or their active forms on aPTT (Figure5). The initial concentration of zymogens and inhibitor proteins were varied by two orders of magnitude (0.1× – 10×) from their nominal levels (Figure5, panel a) and active proteins were added at 0.05, 5, or 120 nM concentrations (Figure5, panelb) to match previous simulations for TGA response. The dashed red and blue lines indicate aPTT values when all the protein concentrations are at their nominal values for NHP and 8DP. As seen from Figure5 (panela), as expected, proteins in the intrinsic pathway and common pathway (FIX, FXI, FXII, and FX) have a larger effect on aPTT than FV and FVII, for both NHP and 8DP. Among inhibitors, ATIII affects aPTT in 8DP and NHP, but TFPI and PC have no effects in either type of plasma. Overall change in aPTT is much more pronounced in 8DP than that in the NHP, probably due to higher nominal aPTT levels in hemophilia patients.


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 zymogen, inhibitor, and active protein levels in NHP and 8DP on aPTT. (a) Changes in aPTT with modulation of zymogen and inhibitor levels. (b) Changes in aPTT with modulation of active proteins. The dashed broken lines indicate aPTT values when all the proteins are at their nominal levels for NHP (blue line) or 8DP (red line).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig05: Effects of varying zymogen, inhibitor, and active protein levels in NHP and 8DP on aPTT. (a) Changes in aPTT with modulation of zymogen and inhibitor levels. (b) Changes in aPTT with modulation of active proteins. The dashed broken lines indicate aPTT values when all the proteins are at their nominal levels for NHP (blue line) or 8DP (red line).
Mentions: Similar to the analysis for TGA, we also used the model to study the effects of varying the initial concentrations of zymogens or their active forms on aPTT (Figure5). The initial concentration of zymogens and inhibitor proteins were varied by two orders of magnitude (0.1× – 10×) from their nominal levels (Figure5, panel a) and active proteins were added at 0.05, 5, or 120 nM concentrations (Figure5, panelb) to match previous simulations for TGA response. The dashed red and blue lines indicate aPTT values when all the protein concentrations are at their nominal values for NHP and 8DP. As seen from Figure5 (panela), as expected, proteins in the intrinsic pathway and common pathway (FIX, FXI, FXII, and FX) have a larger effect on aPTT than FV and FVII, for both NHP and 8DP. Among inhibitors, ATIII affects aPTT in 8DP and NHP, but TFPI and PC have no effects in either type of plasma. Overall change in aPTT is much more pronounced in 8DP than that in the NHP, probably due to higher nominal aPTT levels in hemophilia patients.

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.