Computationally Driven Discovery in Coagulation

Abstract

Bleeding frequency and severity within clinical categories of hemophilia A are highly variable and the origin of this variation is unknown. Solving this mystery in coagulation requires the generation and analysis of large data sets comprised of experimental outputs or patient samples, both of which are subject to limited availability. In this review, we describe how a computationally driven approach bypasses such limitations by generating large synthetic patient data sets. These data sets were created with a mechanistic mathematical model, by varying the model inputs, clotting factor, and inhibitor concentrations, within normal physiological ranges. Specific mathematical metrics were chosen from the model output, used as a surrogate measure for bleeding severity, and statistically analyzed for further exploration and hypothesis generation. We highlight results from our recent study that employed this computationally driven approach to identify FV (factor V) as a key modifier of thrombin generation in mild to moderate hemophilia A, which was confirmed with complementary experimental assays. The mathematical model was used further to propose a potential mechanism for these observations whereby thrombin generation is rescued in FVIII-deficient plasma due to reduced substrate competition between FV and FVIII for FXa.

Sensitivity analysis of a reduced model of thrombosis under flow: Roles of Factor IX, Factor XI, and γ‘-Fibrin

“…Simulations give insights and help experimental design for further discovery in coagulations [ 41 ]. Recently, Link et al used the computationally driven approach to identify FV as modifier for hemophilia, further confirm it with experiment, and propose a potential mechanism.…”

Critical evaluation of kinetic schemes for coagulation

“…In recent studies, mathematical models of the coagulation cascade have been used to shed light on mechanisms leading to clotting disorders. Link et al [29,8] performed a global sensitivity analysis using the coagulation model of Kuharsky and Fogelson [30] that led to the identification of FV as a key modifier of thrombin generation among hemophilia A patients. Their results were confirmed later with additional experimental assays highlighting the value of using the numerical model to build a synthetic data set to construct and test specific hypotheses.…”

“…kinetic constants for reactions ( 7) and ( 8) reactions were modified following [28,26] IXa=VIIIa vitro experiments is frequently inappropriate since the mathematical models are constructed for specific and ideal conditions as pointed out by Link et al [12,8]. Chelle et al [14] tested the performance of five coagulation models by comparing their results under relevant clinical scenarios.…”

“…Additional modifications were done regarding the cofactor function of FVI-IIa. In silico thrombin generation parameters showed a larger pro-coagulant profile compared to in vitro data, especially for concentrations of FVIII under 1% (see Figures 2 and 4), suggesting that the numerical model overestimated the enzymatic activity of FIXa controlled by reactions (7) and (8).…”

“…These models have allowed the discovery of coagulation factor interactions [4], to study the pharmacokinetics of new drugs [5], and investigate the dynamics of thrombus formation in pathological conditions such as aortic aneurysms or infarcted left ventricles [6,7]. More recently, a computational thrombosis model was used to understand the underlying dynamics that control bleeding severity among hemophilia A patients highlighting the ability of numerical models to construct realistic synthetic data sets to overcome the absence of data in clinical practice [8].…”