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A mathematical model of flow-mediated coagulation identifies factor V as a modifier of thrombin generation in hemophilia A

Citation

Stobb, Michael et al. (2019), A mathematical model of flow-mediated coagulation identifies factor V as a modifier of thrombin generation in hemophilia A, v2, UC Merced Dash, Dataset, https://doi.org/10.6071/M38Q15

Abstract

Hemophilia A is a bleeding disorder categorized as severe, mild, and moderate deficiencies in factor VIII (FVIII). Within these categories the variance in bleeding severity is significant and the origins unknown. The number of parameters that could modify bleeding are so numerous that experimental approaches are not feasible for considering all possible combinations. Consequently, we turn to a mathematical model of coagulation under flow to act as a screening tool to identify parameters that are most likely to enhance thrombin generation. We performed global sensitivity analysis on 110,000 simulations that varied coagulation factor levels by 50-150% of their normal values in humans while holding FVIII levels at 1%. These simulations identified low factor V (FV) levels as the strongest candidate, with additional enhancement when combined with high prothrombin levels. This prediction was confirmed in two experimental models: Partial FV inhibition boosted fibrin deposition in flow assays performed at 100 s^-1 on collagen-tissue factor surfaces using whole blood from individuals with mild and moderate FVIII deficiencies. Low FV (≥50%) or partial FV inhibition also augmented thrombin generation in FVIII-inhibited or FVIII-deficient plasma in calibrated automated thrombography. These effects were amplified by high prothrombin levels in both experimental models. Our mathematical model suggests a mechanism in which FV and FVIII compete to bind to factor Xa to initiate thrombin generation in low FV, FVIII-deficient blood. This unexpected result was made possible by a mechanistic mathematical model, providing an example of the potential of such models in making predictions in complex biological networks.