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Nayak et al., (2015). Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.

June 2016, model of the month by Corina Dueñas Roca
Original model: BIOMD0000000611.

 

Introduction:

An essential role of systems pharmacology is to describe, validate and predict the effect of determined drugs over a biological system. This model by Nayak et. al., [1] is illustrative of a Quantitative Systems Pharmacology (QSP) model, where a well-characterized blood coagulation model is able to mathematically replicate the coagulation process, simulate a deregulated system and the effect of therapies. This model may serve as a tool to simulate disease development in the blood coagulation system by tailoring model components appropriately. Furthermore, it may be useful for clinicians and professionals in this field to identify potential biomarkers that can predict drug responses. This way, drug monitoring of treated patients may become more accurate and dynamics of existing biomarkers may be explained.

Two current biomarkers of homeostatic diseases are: the thrombin generation assay (TGA) and activated Partial Thromboplastin Time (aPTT). TGA is used for monitoring clinical effects of coagulation modulating therapies (CMT), whereas aPTT is a biomarker modulated by therapies as CMT and is used for diagnose coagulation disorders. Both clinical biomarkers represent the effectiveness of extrinsic and intrinsic pathway activation of blood coagulation. The novelty of this model is that it combines TGA and aPTT models into one: intrinsic and extrinsic pathways, and ending at fibrin formation (Figure 1). Parameter optimization facilitates the model to explain the differences between biomarker outcomes after various modulations or therapies. The model also enables to correlate those results with inter-subject variability of protein concentrations to FVIIa treatment.

Figure 2

Figure 2Comparison between experimental data and simulation results for TGA. The experimental TGA data for FVIIa and FXa in NHP and FVIII-8DP are shown in the left column. The right column shows the corresponding optimized simulation results. The dose-dependent trend is well captured by the simulation in all the cases. Figure taken from [1].

Figure 3

Figure 3Histogram of relative median sensitivity values for proteins in response to FVIIa treatment in 8DP. Simulation of small variation in the levels of coagulation proteins on TGA profile in response to FVIIa treatment in 8DP. Using TGA as the endpoint, we find a significant variation in the TGA profile when the same concentration of FVIIa was added. This histogram measures the sensitivity of proteins in response to FVIIa treatment, indicating that ATIII and TFPI are the most sensitive biomarkers. Figure taken from [1].

Figure 1

Figure 1Simplified Coagulation Network. Green broken lines represent positive feedback mechanisms; the red broken lines represent mechanisms that inhibit formation of thrombin; solid red lines represent the activation of anticoagulant proteins in the system. APC, active protein C; PC, protein C; TF, Tissue Factor; TFPI, Tissue Factor Pathway Inhibitor; CA, Contact Activators; F1 1 2, prothrombin fragment 1 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). Figure taken from [1].

Model:

The primary goal of this study is to unify already described models of TGA and aPTT [MODEL1108260014, BIOMD0000000338, BIOMD0000000339, BIOMD0000000340, BIOMD0000000335] in order to understand the complex mechanism, which explains the differences in response to determined coagulation therapy. A well-mixed system is assumed in the model [2] for in vitro experiments and it was optimized to match with in-house in vitro experimental TGA and aPTT data. Finally, an external validation was performed by comparing the prediction of this combined model with literature information related with coagulation pathologies (i.e Dieri et al., [3]). The authors propose to extend this model by adding more components (platelets and endothelial cells from the clot) to better understand the pharmacodynamic endpoints determined by this in silico.

Results:

The authors were successful in optimizing the model by fitting the parameters to the TGA and aPTT data. The trend in all the dose-dependent simulations was reproduced in the model simulation and the TGA curves obtained from the simulation in FVIIa or FXa dose-dependent match well with the in-house in vitro experimental data (Figure 2).

Secondly, the authors analyse the effects of varying initial levels of proteins on TGA profile in Normal human plasma (NHP) or FVIII deficient plasma (8DP). The effects of FXI and FXII are trivial on TGA parameters, whereas TF, ATIII and TFPI, and FXIa and FXIIa, significantly affect all the measures of TGA response in both conditions.

Other observations from the model are as follows: lag time is the most tightly regulated parameter and the hardest to change by ‘therapeutic intervention’, while peak thrombin values are most easily modulated; TGA from NHP is more sensitive to rise in FVIIa levels than the TGA from 8DP; and finally the effect of varying protein initial concentrations in FIX deficient plasma is almost the same as in 8DP.

Summarizing the effect of varying initial levels of protein on aPTT in NHP and 8DP: ATIII and proteins in the intrinsic and common pathway have a larger effect on aPTT than FV and FVII in both conditions while TFPI and PC have no effect; and the overall change in aPTT, which is more pronounced in 8DP than in the NHP, could be related with higher nominal aPTT levels in 8DP patients; FVIIIa is the most sensitive factor in 8DP while FIXa is the most sensitive factor in NHP.

Small variation in coagulation factor levels on the TGA response to rFVIIA treatment shows substantial variability in TGA. Furthermore, peak thrombin is most sensitive to changes in the initial levels of coagulation protein and lag time is least sensitive to the variation of the protein levels. Remarkably, TGA levels seem to be highly dependent on variations in ATIII and also very sensitive to changes in TFPI level(Figure 3).

Conclusions:

This optimized and validated QSP model is able to predict the patterns in TGA change due to factor depletion. Combining model observations with aPTT changes the authors conclude that the model results match well qualitatively. In order to explore whether a small variation of protein levels within the normal range can impact response to treatment of FVIIa, the authors used TGA as the endpoint in this model. The TGA profile showed a significant variation in response to FVIIa. Notably ATIII and TFPI were the most sensitive parameters, suggesting that they could predict the patient's response to drug treatment. Since this model is able to quantify the changes in each component of the network, it provides a better perspective for clinicians to choose endpoints to measure in clinical studies and provide a mechanistic explanation for the outcomes after therapy. In conclusion, this QSP model may prove useful in describing and predicting drug response for blood coagulation diseases.

Bibliographic references

  1. Nayak et al. Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers. CPT Pharmacometrics Syst Pharmacol 2015 Jul; 4(7): 396-405.
  2. Warren et al. A cautionary note on implications of the well-mixed compartment assumption as applied to mass balance models of chemical fate in flowing systems. Environ Toxicol Chem. 2009 Sep; 28(9): 1858-1865.
  3. Bell. The thrombogram is rare inherited coagulation disorders: its relation to clinical bleeding. Thromb Haemost. 2002 Oct; 88(4): 576-582.

 

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