A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

Abstract

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains.

Computational Approach To Characterize Causative Factors and Molecular Indicators of Chronic Wound Inflammation

“…A complementary in silico approach has the unique advantage of providing focused and time-efficient integration and analysis of the available literature data, with the capability of generating experimentally testable hypotheses to expedite the investigative process. Although a number of mathematical models recently have been developed and applied to study inflammation (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33), their limited scope and focus predominantly on qualitative representations of inflammation dynamics generally restrict their ability to provide accurate interpretations of existing data sets and generate (semi)quantitative hypotheses. In this article, we introduce a kinetic, inherently quantitative computational model of inflammation whose parameters were derived directly from in vitro data.…”

Computational Modeling of Inflammation and Wound Healing

“…39 A NetLogo-based ABM of the inflammatory reaction to acute phonotrauma was generated and calibrated to individual subjects' mediator levels (from laryngeal secretions) before and after acute phonotrauma. 36 Subjects then underwent one of several treatment strategies: rest, resonant voice exercises, or spontaneous speech. The ABM was able to recapitulate observed dynamics and to accurately predict mediator levels at time points beyond those used for calibration.…”

“…After validating the behaviors observed in the models, they have been used to make predictions [36][37][38] and as a surrogate system for testing potential wound-healing strategies and products. Sensitivity analysis, a measure of which parameters account for the greatest amount of the variance in the model's output, allowed both pressure ulcer models to identify areas of high vulnerability in each model, thus suggesting mechanisms that might be particularly susceptible to interventions.…”

“…36 Subjects then underwent one of several treatment strategies: rest, resonant voice exercises, or spontaneous speech. The ABM was able to recapitulate observed dynamics and to accurately predict mediator levels at time points beyond those used for calibration.…”

Agent-Based Modeling in Systems Pharmacology

“…While systems pharmacology approaches have been successfully applied to tackle key issues in oncology, for instance, with the use of an integrative systems approach to quantify anticancer drug synergy in imatinib‐resistant chronic myeloid leukemia,25 clinical trials in oncology still have the highest failure rate in comparison to other therapeutic areas 26. In complex processes such as tumor formation, probing targetable mechanisms can be difficult owing to variability arising on multiple scales; cancerous cells adapt at genetic and molecular scales to survive in ever‐changing environments, altering cellular phenotypes and therefore treatment efficacy, as documented in studies of the hypoxic environment in tumor centers 27, 28, 29.…”