Agent‐based model of inflammation and wound healing: insights into diabetic foot ulcer pathology and the role of transforming growth factor‐β1

Abstract

Inflammation and wound healing are inextricably linked and complex processes, and are deranged in the setting of chronic, nonhealing diabetic foot ulcers (DFU). An ideal therapy for DFU should both suppress excessive inflammation while enhancing healing. We reasoned that biological simulation would clarify mechanisms and help refine therapeutic approaches to DFU. We developed an agent-based model (ABM) capable of reproducing qualitatively much of the literature data on skin wound healing, including changes in relevant cell populations (macrophages, neutrophils, fibroblasts) and their key effector cytokines (tumor necrosis factor-alpha [TNF], interleukin [IL]-1beta, IL-10, and transforming growth factor [TGF]-beta1). In this simulation, a normal healing response results in tissue damage that first increases (due to wound-induced inflammation) and then decreases as the collagen levels increase. Studies by others suggest that diabetes and DFU are characterized by elevated TNF and reduced TGF-beta1, although which of these changes is a cause and which one is an effect is unclear. Accordingly, we simulated the genesis of DFU in two ways, either by (1) increasing the rate of TNF production fourfold or (2) by decreasing the rate of TGF-beta1 production 67% based on prior literature. Both manipulations resulted in increased inflammation (elevated neutrophils, TNF, and tissue damage) and delayed healing (reduced TGF-beta1 and collagen). Our ABM reproduced the therapeutic effect of platelet-derived growth factor/platelet releasate treatment as well as DFU debridement. We next simulated the expected effect of administering (1) a neutralizing anti-TNF antibody, (2) an agent that would increase the activation of endogenous latent TGF-beta1, or (3) latent TGF-beta1 (which has a longer half-life than active TGF-beta1), and found that these therapies would have similar effects regardless of the initial assumption of the derangement that underlies DFU (elevated TNF vs. reduced TGF-beta1). In silico methods may elucidate mechanisms of and suggest therapies for aberrant skin healing.

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

“…An ABM was created that reproduced qualitatively much of the literature data on skin wound healing, including changes in relevant cell populations (macrophages, neutrophils, and fibroblasts) and their key effector cytokines (TNF-a, IL-1b, IL-10, and TGF-b1). 38 In this simulation, a normal healing response results in tissue damage that first increases (due to wound-induced inflammation) and then decreases as the collagen levels increase. Studies by others suggest that diabetes and DFU are characterized by elevated TNF-a and reduced TGF-b1.…”

“…38 An ideal therapy for DFU should both suppress excessive inflammation and enhance healing. An ABM was created that reproduced qualitatively much of the literature data on skin wound healing, including changes in relevant cell populations (macrophages, neutrophils, and fibroblasts) and their key effector cytokines (TNF-a, IL-1b, IL-10, and TGF-b1).…”

“…the population, an ABM of DFU was used to give insights into how current therapies work (or do not) in given patients, and also to suggest novel therapies. 38 At the other end of the spectrum, an ABM of acute phonotrauma was not only able to recapitulate inflammatory mediator dynamics observed in the laryngeal secretions of human subjects, but also able to accurately predict those levels and time points beyond model calibration range.36 That model was also used to simulate different treatment strategies and pointed to a less intuitive strategy as more effective than the standard prescription of rest.36 These insights were garnered by incorporating only a small set of mediators and cell types into the model.Incorporating physically and physiologically relevant information into ABMs, such as blood flow in tissues and forces between cells, allowed for new mechanistic predictions. In the case of the former, a simple approximation of tissue blood flow differences between noninjured subjects and SCI patients, when juxtaposed on a stochastic model of inflammation and tissue injury, led to the prediction of higher propensity to ulcerate in SCI patients versus controls.…”

A computational, tissue-realistic model of pressure ulcer formation in individuals with spinal cord injury

“…In silico (simulated) clinical trials are an inexpensive and increasingly popular means of gleaning translational knowledge from computational models [55][56][57][58][59]. Accordingly, these methods were utilized to test both current and hypothetical or cutting-edge therapies for inflammation in the setting of post-SCI PU.…”