The effect of enzymatic crosslink degradation on the mechanics of the anterior cruciate ligament: A hybrid multi-domain model

Fadi Al Khatib, Afif Gouissem, Armin Eilaghi, Malek Adouni

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The anterior cruciate ligament’s (ACL) mechanics is an important factor governing the ligament’s integrity and, hence, the knee joint’s response. Despite many investigations in this area, the cause and effect of injuries remain unclear or unknown. This may be due to the complexity of the direct link between macro-and micro-scale damage mechanisms. In the first part of this investigation, a three-dimensional coarse-grained model of collagen fibril (type I) was developed using a bottom-up approach to investigate deformation mechanisms under tensile testing. The output of this molecular level was used later to calibrate the parameters of a hierarchical multi-scale fibril-reinforced hyper-elastoplastic model of the ACL. Our model enabled us to determine the mechanical behavior of the ACL as a function of the basic response of the collagen molecules. Modeled elastic response and damage distribution were in good agreement with the reported measurements and computational investigations. Our results suggest that degradation of crosslink content dictates the loss of the stiffness of the fibrils and, hence, damage to the ACL. Therefore, the proposed computational frame is a promising tool that will allow new insights into the biomechanics of the ACL.

Original languageEnglish
Article number8580
JournalApplied Sciences (Switzerland)
Volume11
Issue number18
DOIs
StatePublished - Sep 2021

Keywords

  • Anterior cruciate ligament
  • Fibrils
  • Finite elements
  • Molecular dynamic
  • Tropocollagen

Funding Agency

  • Kuwait Foundation for the Advancement of Sciences

Fingerprint

Dive into the research topics of 'The effect of enzymatic crosslink degradation on the mechanics of the anterior cruciate ligament: A hybrid multi-domain model'. Together they form a unique fingerprint.

Cite this