Abstract 232: Development and Validation of a Mathematical Model Representing Complex Cellular Interactions Within the Artery Wall: in Silico Approach to Test Targeted Interventions

Atherosclerotic cardiovascular diseases remain the number one cause of morbidity and mortality despite significant advances in lipid management. Complex cellular interactions occur within the artery wall requiring timely infiltration/egress of immune cells and lipoproteins within a changing inflammatory milieu that determine the progression of an atherosclerotic plaque. Lack of detailed understanding of the multiple processes involved and their potential interactions has hindered development of targeted therapies. The objective of this study was to develop a computational model of the sequential influx of immune cells in response to a trigger to permit a system-level analyses of the processes involved using ordinary differential equations (ODEs). Thioglycollate induced peritonitis was used as a model to examine the infiltration of immune cells and phenotypic polarization of macrophages in response to a stimulus. Peritoneal exudates obtained at 10 different time-points over 7 days were analyzed by FACS to determine the distribution of neutrophils, macrophages and Ly6C Hi (M1) or Ly6C Lo (M2) polarization. Weighted least squares of the different parameters were used to calibrate the model developed in our laboratory (Panel A) to simulate inflammation/repair during sepsis using the Levenberg-Marquardt algorithm. Panels B-D show the “match” of the experimental data to the calibrated model demonstrating the validity of this model. Since the ODEs are derived from a combination of known and hypothesized kinetics of the biological system, model the changes in physiological variables over time and are based on biological interactions, our calibrated mathematical model will permit the evaluation of changes in one or more targeted parameters in silico on immune cell distribution/phenotype. Efficacy of various macrophage-specific interventions (e.g., reduction in cholesterol content or inflammation) can be predicted prior to preclinical experimentation.