Personalized, preemptive, and predictive medicine is a central goal of contemporary medical care. The central aim of the present study was to investigate the utility of mechanistic computational modeling of inflammation and healing to address personalized therapy for patients with acute phonotrauma.
Previously reported agent-based models (ABMs) of acute phonotrauma were extended with additional inflammatory mediators as well as extracellular matrix components. The models were calibrated with empirical data for a panel of biomarkers—interleukin (IL)-1β, IL-6, IL-8, IL-10, tumor necrosis factor-α and matrix metalloproteinase-8—from individual subjects following experimentally induced phonotrauma and a randomly assigned voice treatment namely voice rest, resonant voice exercise, and spontaneous speech. The models' prediction accuracy for biomarker levels was tested for a 24-hour follow-up time point.
The extended ABMs reproduced and predicted trajectories of biomarkers seen in experimental data. The simulation results also agreed qualitatively with various known aspects of inflammation and healing. Model prediction accuracy was generally better following individual-based calibration as compared to population-based calibration. Simulation results also suggested that the special form of vocal fold oscillation in resonant voice may accelerate acute vocal fold healing.
The calibration of inflammation/healing ABMs with subject-specific data appears to optimize the models' prediction accuracy for individual subjects. This translational application of biosimulation might be used to predict individual healing trajectories, the potential effects of different treatment options, and most importantly, provide new understanding of health and healing in the larynx and possibly in other organs and tissues as well.