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A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury.

Li NY, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y - PLoS ONE (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

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.

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Empirical and model-predicted inflammatory and wound healing responses to acute phonotrauma in a single human subject (Subject 3) following spontaneous speech (Panels A–C), voice rest (Panels D–F) and resonant voice treatment conditions (Panels G–I).Panels A, D and G display empirical and predicted trajectories of IL-1β. Panels B, E and H show empirical and predicted trajectories of TNF-α. Panels C, F and I show empirical and predicted trajectories of IL-10. Inflammatory marker concentrations are in pg/ml. The grey bars represent the mean of the simulated data, and the error bars represent standard deviations in the simulated data. The dark circles represent the input data for the first three time-points (baseline, post-loading, 4-hr post treatment onset), obtained from human laryngeal secretion data. The empty circles represent the validation data at the 24-hr time point from the human laryngeal secretion data. B: baseline; PL: post vocal loading; 4hrPRx: following a 4-hr treatment. Note that human validation data for Days 2–5 have not yet been generated.
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pone-0002789-g001: Empirical and model-predicted inflammatory and wound healing responses to acute phonotrauma in a single human subject (Subject 3) following spontaneous speech (Panels A–C), voice rest (Panels D–F) and resonant voice treatment conditions (Panels G–I).Panels A, D and G display empirical and predicted trajectories of IL-1β. Panels B, E and H show empirical and predicted trajectories of TNF-α. Panels C, F and I show empirical and predicted trajectories of IL-10. Inflammatory marker concentrations are in pg/ml. The grey bars represent the mean of the simulated data, and the error bars represent standard deviations in the simulated data. The dark circles represent the input data for the first three time-points (baseline, post-loading, 4-hr post treatment onset), obtained from human laryngeal secretion data. The empty circles represent the validation data at the 24-hr time point from the human laryngeal secretion data. B: baseline; PL: post vocal loading; 4hrPRx: following a 4-hr treatment. Note that human validation data for Days 2–5 have not yet been generated.

Mentions: The premise of mathematical models involves experimental validation and feedback between the models and experiments. The details of our model calibration and validation are described in Materials and Methods. In brief, we first calibrated the ABM using data from a base cohort (Subjects 1, 2 and 3), using their mediator levels in laryngeal fluid at baseline, immediately after phonotrauma induction, and following a 4-hr treatment that involved either voice rest, “resonant voice” exercises, or spontaneous speech) (Figures 1–2, dark circles) [35]. The calibrated ABM was run 10 times for the full cohort of 7 subjects, up to 5 simulated days post baseline under the condition of (1) each subject's actual treatment group and (2) hypothetical randomization to either of the other two treatment groups, i.e., hypothetical treatments, using each subject's baseline mediator profile. Thus, a large simulation data set of subject-specific mediator trajectories (3 treatment conditions×10 runs×7 subjects = 210 runs) was generated for evaluating benefits of behavioral treatments for acute phonotrauma.


A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury.

Li NY, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y - PLoS ONE (2008)

Empirical and model-predicted inflammatory and wound healing responses to acute phonotrauma in a single human subject (Subject 3) following spontaneous speech (Panels A–C), voice rest (Panels D–F) and resonant voice treatment conditions (Panels G–I).Panels A, D and G display empirical and predicted trajectories of IL-1β. Panels B, E and H show empirical and predicted trajectories of TNF-α. Panels C, F and I show empirical and predicted trajectories of IL-10. Inflammatory marker concentrations are in pg/ml. The grey bars represent the mean of the simulated data, and the error bars represent standard deviations in the simulated data. The dark circles represent the input data for the first three time-points (baseline, post-loading, 4-hr post treatment onset), obtained from human laryngeal secretion data. The empty circles represent the validation data at the 24-hr time point from the human laryngeal secretion data. B: baseline; PL: post vocal loading; 4hrPRx: following a 4-hr treatment. Note that human validation data for Days 2–5 have not yet been generated.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2481293&req=5

pone-0002789-g001: Empirical and model-predicted inflammatory and wound healing responses to acute phonotrauma in a single human subject (Subject 3) following spontaneous speech (Panels A–C), voice rest (Panels D–F) and resonant voice treatment conditions (Panels G–I).Panels A, D and G display empirical and predicted trajectories of IL-1β. Panels B, E and H show empirical and predicted trajectories of TNF-α. Panels C, F and I show empirical and predicted trajectories of IL-10. Inflammatory marker concentrations are in pg/ml. The grey bars represent the mean of the simulated data, and the error bars represent standard deviations in the simulated data. The dark circles represent the input data for the first three time-points (baseline, post-loading, 4-hr post treatment onset), obtained from human laryngeal secretion data. The empty circles represent the validation data at the 24-hr time point from the human laryngeal secretion data. B: baseline; PL: post vocal loading; 4hrPRx: following a 4-hr treatment. Note that human validation data for Days 2–5 have not yet been generated.
Mentions: The premise of mathematical models involves experimental validation and feedback between the models and experiments. The details of our model calibration and validation are described in Materials and Methods. In brief, we first calibrated the ABM using data from a base cohort (Subjects 1, 2 and 3), using their mediator levels in laryngeal fluid at baseline, immediately after phonotrauma induction, and following a 4-hr treatment that involved either voice rest, “resonant voice” exercises, or spontaneous speech) (Figures 1–2, dark circles) [35]. The calibrated ABM was run 10 times for the full cohort of 7 subjects, up to 5 simulated days post baseline under the condition of (1) each subject's actual treatment group and (2) hypothetical randomization to either of the other two treatment groups, i.e., hypothetical treatments, using each subject's baseline mediator profile. Thus, a large simulation data set of subject-specific mediator trajectories (3 treatment conditions×10 runs×7 subjects = 210 runs) was generated for evaluating benefits of behavioral treatments for acute phonotrauma.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.

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.

Show MeSH
Related in: MedlinePlus