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The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...

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Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

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Modeling of a Bioengineered Immunomodulating Microenvironment for Cell Therapy.

Simone Capuani^1,2, Jocelyn Nikita Campa-Carranza^1,3, Nathanael Hernandez¹

¹Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, 77030, USA.

Advanced Healthcare Materials

|January 12, 2024

Summary

A new computational model optimizes cell encapsulation platforms for Type 1 Diabetes treatment. It balances vascularization and drug delivery to improve cell survival and minimize side effects.

Keywords:

cell encapsulation computational modeling islet transplantation local immunomodulation vascularization

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3D Microtissues for Injectable Regenerative Therapy and High-throughput Drug Screening

3D Microtissues for Injectable Regenerative Therapy and High-throughput Drug Screening

Published on: October 4, 2017

Area of Science:

Biomedical Engineering
Regenerative Medicine
Computational Biology

Background:

Cell delivery and encapsulation platforms are crucial for treating Type 1 Diabetes and other diseases.
Effective cell engraftment requires immune protection, vascularization, and oxygen supply for therapeutic cells.
Current platforms use immune barriers with indirect vascularization or direct vascularization with immune modulation.

Purpose of the Study:

To develop a broadly applicable predictive computational model for cell encapsulation strategies.
To comparatively study oxygen concentration, cell density, and spatial distribution under varying vascularization.
To validate the model's predictive capability for oxygen pressure and drug biodistribution.

Main Methods:

Development of a predictive computational model for cell encapsulation.
Comparative analysis of oxygen levels with different vascularization, cell densities, and adjuvant cells.
Validation of the model against experimental data for oxygen pressure and drug biodistribution.

Main Results:

Dense vascularization minimizes cell hypoxia and allows high cell loading.
Lower vascularization levels enhance drug localization and reduce systemic spread.
The model accurately predicts oxygen pressure and drug biodistribution in direct vascularization devices.

Conclusions:

The developed computational model is a valuable tool for optimizing cell encapsulation technologies.
The model aids in balancing therapeutic cell survival and localized drug delivery.
It supports the design of improved platforms for cell-based therapies.