# Predicted Effects of Patient Variability and Notch Signaling on In Situ Vascular Tissue Engineering

## TL;DR

This study uses computational models to explore how patient differences and Notch signaling affect the success of engineered blood vessels.

## Contribution

The study introduces a computational model to identify sources of variability in vascular tissue engineering outcomes and evaluates Notch signaling manipulation.

## Key findings

- Differential inflammation, scaffold degradation, and pre-stretch are major sources of variability in TEVG outcomes.
- Immobilizing Jagged ligands on scaffolds improves some TEVG functionalities but does not reduce outcome variability significantly.
- Combining Jagged immobilization with other treatments may enhance TEVG performance under patient-specific conditions.

## Abstract

In situ vascular tissue engineering aims to create living blood vessel replacements from biodegradable scaffolds. The functionality of these tissue-engineered vascular grafts (TEVGs) has often been limited, with substantial failure rate and outcome variability. Current optimization strategies seem unable to satisfy all requirements for functional TEVGs and the key sources of outcome variability remain unclear. Here, we computationally explored potential sources of TEVG variability and effects of manipulating Notch, a key vascular signaling pathway. We simulated the evolution of a TEVG from a degradable scaffold under varying patient-specific conditions, driven by immuno-mechano-mediated growth and remodeling mechanisms including Notch. Our simulations suggest that differential inflammatory production, scaffold degradation, and scaffold axial pre-stretch are major sources of variability in TEVG outcome. Immobilizing Jagged ligands to the scaffold did not substantially reduce outcome variability in our simulations, but did improve some aspects of TEVG functionality. This intervention may therefore be beneficial in combination with other treatments that compensate for predicted negative effects. Overall, our model may advance future TEVG optimization by incorporating Notch manipulations under various patient-specific conditions.