000171209 001__ 171209 000171209 005__ 20260515163945.0 000171209 0247_ $$2doi$$a10.1016/j.compbiomed.2026.111715 000171209 0248_ $$2sideral$$a149278 000171209 037__ $$aART-2026-149278 000171209 041__ $$aeng 000171209 100__ $$aCaballero, Ricardo$$uUniversidad de Zaragoza 000171209 245__ $$aA personalized mechanobiology-driven multiscale model of atherosclerosis 000171209 260__ $$c2026 000171209 5060_ $$aAccess copy available to the general public$$fUnrestricted 000171209 5203_ $$aAtherosclerosis is a chronic inflammatory and metabolic disease primarily driven by systemic lipid imbalances, with plaque localization and progression further modulated by local hemodynamic and cellular factors within the arterial wall. Here we present a validation study of a hybrid multiscale model that couples computational fluid dynamics (CFD), mass-transport-driven low-density lipoprotein (LDL) maps, and an agent-based model (ABM) of cell behavior to predict coronary plaque initiation and progression. Validation employed adult minipigs carrying a low-density lipoprotein receptor (LDLR) mutation—an established preclinical analogue of human hypercholesterolemia—using longitudinal in vivo imaging data collected within the BIOCCORA study, with 1-year follow-up capturing plaque initiation and evolution. By linking wall shear stress (WSS)-dependent LDL filtration with cytokine-guided smooth muscle cell (SMC) activity, the model mechanistically reconstructs the plaque microenvironment rather than fitting outcomes post hoc. Tested on four imaging-derived porcine coronary arteries tracked over time, the model anticipates where and how fast plaques grow and how lipid pools evolve across cross-sections, showing strong concordance with experiments. These results position hybrid multiscale in silico models as promising predictors for disease progression and could aid in future treatment decision-making. 000171209 536__ $$9nfo:eu-repo/grantAgreement/ES/AEI/PID2022-140219OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN PRE2020-095671 000171209 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 000171209 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000171209 700__ $$0(orcid)0000-0002-8375-0354$$aMartínez, Miguel Ángel$$uUniversidad de Zaragoza 000171209 700__ $$aWentzel, Jolanda J. 000171209 700__ $$aAkyildiz, Ali C. 000171209 700__ $$0(orcid)0000-0002-0664-5024$$aPeña, Estefanía$$uUniversidad de Zaragoza 000171209 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000171209 773__ $$g210 (2026), 111715 [19 pp.]$$pComput. biol. med.$$tComputers in biology and medicine$$x0010-4825 000171209 8564_ $$s6380797$$uhttps://zaguan.unizar.es/record/171209/files/texto_completo.pdf$$yVersión publicada 000171209 8564_ $$s2705707$$uhttps://zaguan.unizar.es/record/171209/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000171209 909CO $$ooai:zaguan.unizar.es:171209$$particulos$$pdriver 000171209 951__ $$a2026-05-15-14:55:07 000171209 980__ $$aARTICLE