# Predicting collagen turnover for tendon repair across diverse loading environments

> **NIH NIH P20** · CLEMSON UNIVERSITY · 2020 · $186,591

## Abstract

SUMMARY 
Millions of Americans currently have some degree of tendon tear that leaves the tissue with abnormal collagen 
quantity and alignment, and reduced mechanical properties. Collagen remodeling depends on mechanical 
loading, and therefore therapeutic interventions to restore normal tendon structure can have varied effects across 
diverse loading environments, i.e. patient-specific geometries, motions, or injury severities. Our ultimate, long- 
term objective is to design therapies tailored specifically for tendons across different loading environments. 
Collagen remodeling is governed by a complex system of interactions between matrix proteins, matrix 
metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), degradation products, and growth 
factors, with mechanical loading affecting many of these interactions. Herein, we propose to experimentally 
elucidate unknown mechano-sensitivities of the collagen-MMP-growth factor network, and develop a 
computational model that integrates the multi-faceted network interactions as a tool for screening potential 
therapeutic interventions. Specifically, we aim to 1) test the effect of tensile loading on MMP-specific degradation 
of collagens by subjecting collagen I and III gels to various levels of strain with or without the addition of tendon- 
relevant MMPs, 2) test the hypothesis that tensile loading can release active TGFβ from collagenous matrix by 
subjecting collagen I and III gels to various levels of strain and measuring levels of latent and active TGFβ in the 
gels and media, and 3) build and test (ex vivo and in vivo) a computational model of load-dependent tendon 
matrix turnover that captures collagens, MMPs, TIMPs, degradation products, and TGFβ interactions as a 
system of ordinary differential equations. Collectively, these aims will immediately impact the field's basic 
knowledge of loading effects on matrix turnover and also produce the first large-scale model of the collagen- 
MMP-growth factor network, immediately impacting the field's ability to prospectively design therapeutic 
interventions (e.g., physical therapy regimens, MMP- or TIMP-targeting drugs, etc.) to control tendon matrix 
content and alignment given any specific loading and geometry. Such predictive capability will greatly support 
the proposed COBRE focus of patient-specific modeling for virtual human trials.

## Key facts

- **NIH application ID:** 10007934
- **Project number:** 5P20GM121342-03
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** William James Richardson
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $186,591
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007934

## Citation

> US National Institutes of Health, RePORTER application 10007934, Predicting collagen turnover for tendon repair across diverse loading environments (5P20GM121342-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10007934. Licensed CC0.

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