Butcher, K. S. | Search bioRxiv

bioRxiv2026

Patient-specific "Physical Twin" artery-on-chip platform reveals complex flow-dependent VWF mechanobiology and guides personalized antithrombotic therapy

Ischemic stroke triggered by carotid atherosclerosis remains unpredictable because the thrombosis embolization is governed by patient-specific vascular geometry and hemodynamics that cannot be recapitulated in conventional models. Here, we engineered "Physical Twin" artery-on-chip platforms that reproduce individualized carotid anatomy with humanized subendothelial matrix composition and arterial endothelial phenotype under physiological flow patterns. We then established thrombotic microenvironments following laser-induced injury. Computational fluid dynamics-guided experiments across distinct patient geometries reveal that local shear dictates a mechanobiological hierarchy: high-shear bifurcations ([~]3,000 s-{superscript 1}) produce von Willebrand factor (VWF) A1-dependent thrombi suppressible by conformationally sensitive inhibitors, whereas low-shear stenoses generate fragile aggregates where VWF-integrin IIb{beta} coupling governs embolization overgrowth. Pulsatile flow suppresses thrombotic growth independent of mean shear. Molecular dynamics simulations reveal why conformationally sensitive nanobodies (caplacizumab) outperform shear-independent aptamers (ARC1172) in high-shear bifurcation flow zones. This Physical Twin platform provides a mechanistic blueprint for geometry-informed, personalized antithrombotic therapy selection.

bioengineering↗

bioRxiv2026

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational "digital twins" for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging--a [~]100-fold improvement over manual methods--achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a "physical twin" experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm--combining automated fabrication with domain-specialized generative AI--establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

bioengineering↗