Key result
Neural network trained on pairwise agonists accurately predicts trinary platelet calcium mobilization with ~0.92 correlation.
Effect estimate: R = 0.924
A neural network trained on pairwise agonist scanning data can accurately predict complex, higher-order platelet calcium mobilization dynamics, providing a useful tool for multiscale blood clotting simulations.
May aid in silico clotting models; leaves open human validation before any clinical consideration.
Since platelet intracellular calcium mobilization [Ca(t)]i controls granule release, cyclooxygenase-1 and integrin activation, and phosphatidylserine exposure, blood clotting simulations require prediction of platelet [Ca(t)]i in response to combinatorial agonists. Pairwise Agonist Scanning (PAS) deployed all single and pairwise combinations of six agonists (ADP, convulxin, thrombin, U46619, iloprost and GSNO used at 0.1, 1, and 10xEC50; 154 conditions including a null condition) to stimulate platelet P2Y1/P2Y12 GPVI, PAR1/PAR4, TP, IP receptors, and guanylate cyclase, respectively, in Factor Xa-inhibited (250 nM apixaban), diluted platelet rich plasma that had been loaded with the calcium dye Fluo-4 NW. PAS of 10 healthy donors provided [Ca(t)]i data for training 10 neural networks (NN, 2-layer/12-nodes) per donor. Trinary stimulations were then conducted at all 0.1x and 1xEC50 doses (160 conditions) as was a sampling of 45 higher ordered combinations (four to six agonists). The NN-ensemble average was a calcium calculator that accurately predicted [Ca (t)]i beyond the single and binary training set for trinary stimulations (R = 0.924). The 160 trinary synergy scores, a normalized metric of signaling crosstalk, were also well predicted (R = 0.850) as were the calcium dynamics (R = 0.871) and high-dimensional synergy scores (R = 0.695) for the 45 higher ordered conditions. The calculator even predicted sequential addition experiments (n = 54 conditions, R = 0.921). NN-ensemble is a fast calcium calculator, ideal for multiscale clotting simulations that include spatiotemporal concentrations of ADP, collagen, thrombin, thromboxane, prostacyclin, and nitric oxide.
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Lee et al. (2015) studied Healthy human platelet calcium mobilization (n=12). Neural network ensemble trained on Pairwise Agonist Scanning (PAS) vs. Experimental in vitro measurements was evaluated on Correlation coefficient (R) of predicted vs measured calcium time traces for trinary stimulations (R = 0.924). A neural network ensemble trained on pairwise agonist scanning data accurately predicted platelet calcium mobilization in response to trinary agonist stimulations (R = 0.924).
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