267666 Multiscale Prediction of Patient-Specific Platelet Function Under Flow

Tuesday, October 30, 2012: 9:42 AM
Somerset East (Westin )
Matthew H. Flamm1, Thomas Colace1, Manash S. Chatterjee1, Hiuyan Jing2, Songtao Zhou2, Daniel Jaeger2, Lawrence F. Brass3, Talid R. Sinno1 and Scott L. Diamond1, (1)Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, (2)University of Pennsylvania, Philadelphia, PA, (3)School of Medicine, University of Pennsylvania, Philadelphia, PA

During thrombotic or hemostatic episodes, platelets bind collagen and release ADP and thromboxane A2, recruiting additional platelets to a growing deposit that distorts the flow field. Prediction of clotting function under hemodynamic conditions for an individual's platelet phenotype remains a challenge. A platelet signaling phenotype was obtained for 3 healthy donors using Pairwise Agonist Scanning (PAS), where calcium dyeloaded platelets were exposed to pairwise combinations of ADP, U46619, and convulxin to activate P2Y1/P2Y12, TP, and GPVI receptors, respectively, in the presence or absence of the IP receptor agonist, iloprost. A neural network model was trained on each donor's PAS experiment and then was embedded into a multiscale Monte Carlo simulation of donor-specific platelet deposition under flow. The simulations were directly compared to microfluidic experiments of whole blood flowing over collagen at 200 and 1000 s-1 wall shear rate. The simulations predicted the ranked order of drug sensitivity for indomethacin, aspirin, MRS-2179 (P2Y1 inhibitor), and iloprost. Consistent with measurement and simulation, one donor displayed larger clots, while another donor presented indomethacin-resistance (revealing a novel heterozygote TP-V241G mutation). In silico representations of an individual's platelet phenotype allowed prediction of blood function under flow, essential to identifying patient-specific cardiovascular risks, drug responses, and novel genotypes.

Fig. 1. Multiscale model of combinatorial platelet activation and thrombus formation under flow. Platelet agonists (blue) used individually or in pairs to activate GPVI or G-protein coupled receptors (thromboxane receptor, TP; purinergic receptors P2Y1 and P2Y12; and the prostacyclin receptor, IP) result in modulation of intracellular calcium (green) from intracellular stores distal of phospholipase C (PLC) activation or from store operated calcium entry via Stim1-Orai1 activation. Inhibitors (red) such as acetylsalicylic acid (ASA) or indomethacin inhibit cyclooxygenase-1 (COX-1). Autocrine pathways include release of TXA2 and ADP (A). A 2-layer, 8-node/4-node neural network (NN) with feedback is trained with 74 measured calcium traces to predict [Ca]i for each patient-specific platelet Pairwise Agonist Scan (B). The multiscale simulation of platelet deposition under flow requires simultaneous solution of the instantaneous velocity field over a complex and evolving platelet boundary Ω(t) by Lattice Boltzmann (LB), concentration fields of ADP and TXA2 by finite element method (FEM), individual intracellular platelet state ([Ca]i) and release reactions (R) for ADP and TXA2 by neural network, and all platelet positions and adhesion/detachment by lattice Kinetic Monte Carlo (LKMC) (C,D).

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