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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 and inhibitor levels in NHP and 8DP on TGA parameters. (a–c) The effects on varying zymogens 10-fold lower (lighter color) or higher (darker color) than the nominal values in NHP; (d–f) show the same as (a–c), but for 8DP. Asterisk denotes that the lag time cannot be accurately determined, as the TGA profile is highly suppressed such that peak thrombin is less than 1.3 nM.
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fig03: Effects of varying zymogen and inhibitor levels in NHP and 8DP on TGA parameters. (a–c) The effects on varying zymogens 10-fold lower (lighter color) or higher (darker color) than the nominal values in NHP; (d–f) show the same as (a–c), but for 8DP. Asterisk denotes that the lag time cannot be accurately determined, as the TGA profile is highly suppressed such that peak thrombin is less than 1.3 nM.

Mentions: Zymogens or inhibitor proteins were varied by two orders of magnitude (0.1× – 10×) one at a time from the nominal value. TGA was then simulated in the model with perturbed protein levels and fold changes from nominal values (Ynew/Ynominal) in TGA parameters were plotted (Figure3). As expected, FXI and FXII are predicted to have no significant effect on any TGA parameters in NHP or in 8DP. Procoagulants such as TF and anticoagulants such as ATIII and TFPI significantly affect all three measures of TGA response in both NHP and 8DP. Decreasing ATIII has the largest effect on AUC and peak; however, its impact on lag time is minimal. Similarly, decreasing PC in NHP has a larger effect on AUC and peak, but the effect on lag time is much smaller. Modulation in FVII in 8DP seems to have a greater effect on lag time, but the effect on peak thrombin and AUC seems to be muted. Based on the magnitude of change and the number of proteins that can change it, lag time is the most tightly regulated among the three parameters and therefore hardest to change by therapeutic intervention in silico, whereas peak thrombin values are most easily modulated, especially in 8DP. Full TGA profiles on varying zymogens are shown in Supplementary Figures S1–S3.


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 and inhibitor levels in NHP and 8DP on TGA parameters. (a–c) The effects on varying zymogens 10-fold lower (lighter color) or higher (darker color) than the nominal values in NHP; (d–f) show the same as (a–c), but for 8DP. Asterisk denotes that the lag time cannot be accurately determined, as the TGA profile is highly suppressed such that peak thrombin is less than 1.3 nM.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig03: Effects of varying zymogen and inhibitor levels in NHP and 8DP on TGA parameters. (a–c) The effects on varying zymogens 10-fold lower (lighter color) or higher (darker color) than the nominal values in NHP; (d–f) show the same as (a–c), but for 8DP. Asterisk denotes that the lag time cannot be accurately determined, as the TGA profile is highly suppressed such that peak thrombin is less than 1.3 nM.
Mentions: Zymogens or inhibitor proteins were varied by two orders of magnitude (0.1× – 10×) one at a time from the nominal value. TGA was then simulated in the model with perturbed protein levels and fold changes from nominal values (Ynew/Ynominal) in TGA parameters were plotted (Figure3). As expected, FXI and FXII are predicted to have no significant effect on any TGA parameters in NHP or in 8DP. Procoagulants such as TF and anticoagulants such as ATIII and TFPI significantly affect all three measures of TGA response in both NHP and 8DP. Decreasing ATIII has the largest effect on AUC and peak; however, its impact on lag time is minimal. Similarly, decreasing PC in NHP has a larger effect on AUC and peak, but the effect on lag time is much smaller. Modulation in FVII in 8DP seems to have a greater effect on lag time, but the effect on peak thrombin and AUC seems to be muted. Based on the magnitude of change and the number of proteins that can change it, lag time is the most tightly regulated among the three parameters and therefore hardest to change by therapeutic intervention in silico, whereas peak thrombin values are most easily modulated, especially in 8DP. Full TGA profiles on varying zymogens are shown in Supplementary Figures S1–S3.

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.