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Abstract

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 dye-loaded 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.

  • Submitted October 26, 2011.
  • Accepted April 3, 2012.