(243c) Patient-Specific Predictions of Thrombosis

Platelet
aggregation is an important regulator of hemostatic
function in response to a blood vessel injury.
Overactivity of platelet aggregation in
situations such as atherosclerotic plaque rupture can cause occlusion of the
vessel, while underactivity results in excessive
bleeding.  Several complex biological and
physical mechanisms contribute to the growth of a stable platelet mass at the
site of injury while limiting the final extent of growth to prevent occlusion.  A detailed model of this system would provide
insight on the coupling between biological signaling and fluid flow as well as
predict strategies for anti- or pro-thrombotic therapy.  Patient-specific models would enable
predictions of resistances to common anti-platelet therapies such as asipirin and clopidogrel.  A multiscale model
is built upon patient-specific platelet phenotyping
and is compared to patient-specific platelet deposition to collagen in a microfluidic chamber.

The
multiscale model has four major modules: lattice
Boltzmann (LB), finite element (FEM), lattice kinetic Monte Carlo (LKMC), and
neural network (NN).  The NN is trained
on a single donor's pairwise agonist scanning (PAS)
experiment that measures the rise in intracellular calcium to three main
platelet agonists: adenosine diphosphate (ADP), thromboxane A2 (TXA2), and convulxin
(CVX).  The LKMC method follows the
motion of platelets within the fluid due to diffusion and convection.  Platelet bonding is captured in LKMC through
a bonding model that links prediction of intracellular calcium from the NN to
bonding kinetics.  The concentrations of
soluble platelet agonists, which are released from activated platelets, are
tracked using FEM.  Finally, LB solves
for the velocity of the fluid around the growing platelet aggregate.  Due to the efficiency of the multiscale framework, the entire ?active zone' of the
experiment can be simulated in 2 dimensions.

The
multiscale model is directly compared to experimental
results of platelet aggregation for 3 donors with 3 antiplatelet
therapies: COX inhibition, P₂Y₁ inhibition, and prostacyclin receptor activation.  The model predicts that donor 1 has
consistently larger platelet aggregates than donors 2 or 3, which is confirmed
by experiment.  The model also correctly
predicts the donor-specific potency of antiplatelet
therapies.  In general, prostacyclin receptor activation had the largest effect on
platelet aggregation due to a decrease in collagen signaling.  P₂Y₁ inhibition and
COX inhibition both reduce propagation of the aggregate once a monolayer is formed, however P₂Y₁ inhibition has a
greater effect.  These trends are also
seen in the experimental results.  The
model also tracks the morphology of the platelet aggregate and shear rate
(force) distribution along the solid-fluid boundary.

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Figure 1. Simulation of platelet aggregation on collagen surface.  Circles ? platelets (Black ? fully unactivated, White ? fully activated).  Lines ? streamlines of the blood flow.  Background color ? ADP
concentration (TxA₂ concentration not shown).  Red bar ? collagen patch.