Cargando…

20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model

ABSTRACT IMPACT: This work will be used to improve the design of engineered dermal replacements that can be used to treat difficult-to-heal wounds such as burns or ulcers. OBJECTIVES/GOALS: Wounds of the skin are among the most common and costly medical problems experienced. Engineered dermal replac...

Descripción completa

Detalles Bibliográficos
Autores principales: Sohutskay, David, Tepole, Adrian Buganza, Voytik-Harbin, Sherry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827680/
http://dx.doi.org/10.1017/cts.2021.517
_version_ 1784647684949278720
author Sohutskay, David
Tepole, Adrian Buganza
Voytik-Harbin, Sherry
author_facet Sohutskay, David
Tepole, Adrian Buganza
Voytik-Harbin, Sherry
author_sort Sohutskay, David
collection PubMed
description ABSTRACT IMPACT: This work will be used to improve the design of engineered dermal replacements that can be used to treat difficult-to-heal wounds such as burns or ulcers. OBJECTIVES/GOALS: Wounds of the skin are among the most common and costly medical problems experienced. Engineered dermal replacements have been developed to improve outcomes, but the optimal design features are unknown. Here we describe a hypothesis-driven study of scaffold parameters using a computational model of wound healing to simulate a variety of treatments. METHODS/STUDY POPULATION: The computational model, which was informed by animal data, was used to simulate cell, cytokine, and collagen density fields. There are reciprocal mechanobiological interactions between the cells and collagen that guide the wound healing process. We analyzed initial wound properties such as scaffold stiffness, microstructure, degradation, and wound geometry by running a one-at-a-time order-of-magnitude parameter change. We then conducted a derivative-based local sensitivity analysis for simulated experimental conditions and constructed a surrogate model of wound contraction using Gaussian process regression. RESULTS/ANTICIPATED RESULTS: We conducted finite element model simulations of scaffolds that varied in physical properties. A sensitivity analysis demonstrated that wound contraction was highly sensitive to collagen fiber stiffness and density. Wound contraction rate was also dependent on initial wound size and surface area. Collagen fiber orientation in the scaffolds affected contraction directionality and the orientation of the final wound area. A Gaussian process regression model was fit to the simulation results for use in rapid prototyping of scaffolds for design optimization. The Gaussian process model was able to reproduce the wound contracture for training and test cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work further analyzes a computational model of wounds treated with collagen scaffold dermal replacements. The hypothesis driven analysis of the model suggested several key design features of scaffolds. The model surrogate will further be used for the purposes of prediction and optimization of tissue regeneration outcomes.
format Online
Article
Text
id pubmed-8827680
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Cambridge University Press
record_format MEDLINE/PubMed
spelling pubmed-88276802022-02-28 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model Sohutskay, David Tepole, Adrian Buganza Voytik-Harbin, Sherry J Clin Transl Sci Data Science/Biostatistics/Informatics ABSTRACT IMPACT: This work will be used to improve the design of engineered dermal replacements that can be used to treat difficult-to-heal wounds such as burns or ulcers. OBJECTIVES/GOALS: Wounds of the skin are among the most common and costly medical problems experienced. Engineered dermal replacements have been developed to improve outcomes, but the optimal design features are unknown. Here we describe a hypothesis-driven study of scaffold parameters using a computational model of wound healing to simulate a variety of treatments. METHODS/STUDY POPULATION: The computational model, which was informed by animal data, was used to simulate cell, cytokine, and collagen density fields. There are reciprocal mechanobiological interactions between the cells and collagen that guide the wound healing process. We analyzed initial wound properties such as scaffold stiffness, microstructure, degradation, and wound geometry by running a one-at-a-time order-of-magnitude parameter change. We then conducted a derivative-based local sensitivity analysis for simulated experimental conditions and constructed a surrogate model of wound contraction using Gaussian process regression. RESULTS/ANTICIPATED RESULTS: We conducted finite element model simulations of scaffolds that varied in physical properties. A sensitivity analysis demonstrated that wound contraction was highly sensitive to collagen fiber stiffness and density. Wound contraction rate was also dependent on initial wound size and surface area. Collagen fiber orientation in the scaffolds affected contraction directionality and the orientation of the final wound area. A Gaussian process regression model was fit to the simulation results for use in rapid prototyping of scaffolds for design optimization. The Gaussian process model was able to reproduce the wound contracture for training and test cases. DISCUSSION/SIGNIFICANCE OF FINDINGS: This work further analyzes a computational model of wounds treated with collagen scaffold dermal replacements. The hypothesis driven analysis of the model suggested several key design features of scaffolds. The model surrogate will further be used for the purposes of prediction and optimization of tissue regeneration outcomes. Cambridge University Press 2021-03-30 /pmc/articles/PMC8827680/ http://dx.doi.org/10.1017/cts.2021.517 Text en © The Association for Clinical and Translational Science 2021 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Data Science/Biostatistics/Informatics
Sohutskay, David
Tepole, Adrian Buganza
Voytik-Harbin, Sherry
20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title_full 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title_fullStr 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title_full_unstemmed 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title_short 20184 A Hypothesis-Driven Parametric Study of a Computational Dermal Replacement Model
title_sort 20184 a hypothesis-driven parametric study of a computational dermal replacement model
topic Data Science/Biostatistics/Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8827680/
http://dx.doi.org/10.1017/cts.2021.517
work_keys_str_mv AT sohutskaydavid 20184ahypothesisdrivenparametricstudyofacomputationaldermalreplacementmodel
AT tepoleadrianbuganza 20184ahypothesisdrivenparametricstudyofacomputationaldermalreplacementmodel
AT voytikharbinsherry 20184ahypothesisdrivenparametricstudyofacomputationaldermalreplacementmodel