An engineering control system paradigm for quantitative understanding of hemostasis

Date
2016
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University of Delaware
Abstract
Hemostasis is an essential physiological process whose proper function relies on the rapid formation of a blood clot in order to minimize blood loss after a vascular injury. To that end, effective hemostasis is a result of the regulation of a delicate balance between two opposite pathological conditions: hemophilia and thrombophilia. When the hemostatic response is too weak, as in the case of hemophilia, blood clot formation is delayed, causing excessive bleeding; while overactive hemostasis could lead to thrombosis where a blood vessel is completely occluded by a blood clot, a common manifestation of thrombophilia. Understanding the hemostatic process is of significant importance because of the acute danger associated with hemostatic disorders. In particular, they can lead to heart attack and stroke, two major causes of death in the world. The complete process of hemostasis consists of numerous components organized into two mutually interacting sub-processes known as primary and secondary hemostasis. To date, quantitative modeling studies of hemostasis have been restricted to a few individual components in isolation. Since in actuality these components never function in isolation, such studies provide only limited understanding of the individual components in question, not of the entire complex process. Obtaining a useful, holistic and high fidelity representation of the complete system requires a more comprehensive approach that adequately captures the full characteristics of all components and their interactions. However, because of intrinsic system complexity, a traditional modeling approach will generate an overly complex, computationally intractable, and hard-to-validate model. In this dissertation, in recognition of the defining characteristic of hemostasis as an automatic physiological control system, an engineering control system framework was used to provide a modular structure for organizing the overwhelming amount of information in hemostasis. In the case of primary hemostasis, mathematical expressions, specifically algebraic equations and ordinary differential equations, were developed to represent the biological events associated with each primary hemostasis module: biological equivalents of a sensor, controller, and actuator. These modules were then connected to form a mathematical model of primary hemostasis. An existing ordinary differential equation model of secondary hemostasis was updated and adapted to include additional recent biological findings on secondary hemostasis. The primary and secondary hemostasis models were modified to incorporate the interactions between the two subsystems before they were connected to form a comprehensive mathematical representation of hemostasis. This comprehensive model was used to simulate various physiological and pathological test cases, generating results consistent with experimental observations and known characteristics. We subsequently used the comprehensive model to study select disease conditions representative of one of the four categories of hemostatic disorders: hyperactive primary hemostasis, deficient primary hemostasis, hyperactive secondary hemostasis, and deficient secondary hemostasis. This exercise enabled us to identify new potential treatment targets that we recommend for future experimental investigations, but which include targets that are already being used as the basis for current clinical treatment strategies for hemophilia A and hemophilia B, demonstrating the effectiveness and predictive power of the comprehensive model. Finally, we carried out an experimental and simulation case study involving an actual patient with low platelet count due to immune thrombocytopenic purpura, a pathology that represents a deficiency in the primary hemostasis controller and the actuator. Using an existing model customized to describe the drug dose-response relationship in the patient, a weekly treatment frequency was found to be capable of achieving a stable platelet count, while biweekly treatment regimen always resulted in oscillations due to the pharmacological properties of the drug.
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