Computational mechanobiology model evaluating healing of postoperative cavities following breast-conserving surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through Gaussian process surrogates. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Computational Mechanobiology Model Evaluating Healing of Postoperative Cavities Following Breast-Conserving Surgery.

Breast cancer is the most commonly diagnosed cancer type worldwide. Given high survivorship, increased focus has been placed on long-term treatment outcomes and patient quality of life. While breast-conserving surgery (BCS) is the preferred treatment strategy for early-stage breast cancer, anticipated healing and breast deformation (cosmetic) outcomes weigh heavily on surgeon and patient selection between BCS and more aggressive mastectomy procedures. Unfortunately, surgical outcomes following BCS are difficult to predict, owing to the complexity of the tissue repair process and significant patient-to-patient variability. To overcome this challenge, we developed a predictive computational mechanobiological model that simulates breast healing and deformation following BCS. The coupled biochemical-biomechanical model incorporates multi-scale cell and tissue mechanics, including collagen deposition and remodeling, collagen-dependent cell migration and contractility, and tissue plastic deformation. Available human clinical data evaluating cavity contraction and histopathological data from an experimental porcine lumpectomy study were used for model calibration. The computational model was successfully fit to data by optimizing biochemical and mechanobiological parameters through the Gaussian Process. The calibrated model was then applied to define key mechanobiological parameters and relationships influencing healing and breast deformation outcomes. Variability in patient characteristics including cavity-to-breast volume percentage and breast composition were further evaluated to determine effects on cavity contraction and breast cosmetic outcomes, with simulation outcomes aligning well with previously reported human studies. The proposed model has the potential to assist surgeons and their patients in developing and discussing individualized treatment plans that lead to more satisfying post-surgical outcomes and improved quality of life.

Mechanobiological wound model for improved design and evaluation of collagen dermal replacement scaffolds.

Skin wounds are among the most common and costly medical problems experienced. Despite the myriad of treatment options, such wounds continue to lead to displeasing cosmetic outcomes and also carry a high burden of loss-of-function, scarring, contraction, or nonhealing. As a result, the need exists for new therapeutic options that rapidly and reliably restore skin cosmesis and function. Here we present a new mechanobiological computational model to further the design and evaluation of next-generation regenerative dermal scaffolds fabricated from polymerizable collagen. A Bayesian framework, along with microstructure and mechanical property data from engineered dermal scaffolds and autograft skin, were used to calibrate constitutive models for collagen density, fiber alignment and dispersion, and stiffness. A chemo-bio-mechanical finite element model including collagen, cells, and representative cytokine signaling was adapted to simulate no-fill, dermal scaffold, and autograft skin outcomes observed in a preclinical animal model of full-thickness skin wounds, with a focus on permanent contraction, collagen realignment, and cellularization. Finite element model simulations demonstrated wound cellularization and contraction behavior that was similar to that observed experimentally. A sensitivity analysis suggested collagen fiber stiffness and density are important scaffold design features for predictably controlling wound contraction. Finally, prospective simulations indicated that scaffolds with increased fiber dispersion (isotropy) exhibited reduced and more uniform wound contraction while supporting cell infiltration. By capturing the link between multi-scale scaffold biomechanics and cell-scaffold mechanochemical interactions, simulated healing outcomes aligned well with preclinical animal model data. STATEMENT OF SIGNIFICANCE: Skin wounds continue to be a significant burden to patients, physicians, and the healthcare system. Advancing the mechanistic understanding of the wound healing process, including multi-scale mechanobiological interactions amongst cells, the collagen scaffolding, and signaling molecules, will aide in the design of new skin restoration therapies. This work represents the first step towards integrating mechanobiology-based computational tools with in vitro and in vivo preclinical testing data for improving the design and evaluation of custom-fabricated collagen scaffolds for dermal replacement. Such an approach has potential to expedite development of new and more effective skin restoration therapies as well as improve patient-centered wound treatment.

Design and biofabrication of dermal regeneration scaffolds: role of oligomeric collagen fibril density and architecture.

Aim: To evaluate dermal regeneration scaffolds custom-fabricated from fibril-forming oligomeric collagen where the total content and spatial gradient of collagen fibrils was specified. Materials & methods: Microstructural and mechanical features were verified by electron microscopy and tensile testing. The ability of dermal scaffolds to induce regeneration of rat full-thickness skin wounds was determined and compared with no fill control, autograft skin and a commercial collagen dressing. Results: Increasing fibril content of oligomer scaffolds inhibited wound contraction and decreased myofibroblast marker expression. Cellular and vascular infiltration of scaffolds over the 14-day period varied with the graded density and orientation of fibrils. Conclusion: Fibril content, spatial gradient and orientation are important collagen scaffold design considerations for promoting vascularization and dermal regeneration while reducing wound contraction.