Multi-scale computational frameworks for predicting biomaterial-host interactions

Pushpendra Kumar Verma, Preety Preety, Pooja Rani Sharma, Pulkit Sharma, Amit Kumar Sharma

Abstract


The clinical success of biomaterials is fundamentally governed by their multifaceted interactions with the host biological system, a dynamic process spanning multiple spatial and temporal scales. Traditional empirical development paradigms are inefficient, costly, and inadequate for predicting these complex, multi-scale interactions. This research presents a unified, multi-scale computational framework that integrates molecular dynamics (MD) simulations, agent-based models (ABM), and finite element method (FEM) analyses to comprehensively predict biomaterial-host responses. To overcome the limitations of purely mechanistic modeling, we incorporate a graph neural network (GNN) trained on a high-throughput experimental dataset of hydrogel libraries, characterized for their physicochemical properties and biological performance. This AI-powered model achieved 92.3% accuracy in predicting immune compatibility, significantly outperforming traditional machine learning methods, and identified surface topography and degradation kinetics as more critical predictors than chemical composition alone. Furthermore, we developed a patient-specific digital twin framework, validated against retrospective clinical data (correlation of 0.87 for fibrosis scores) and a prospective study in genetically diverse mice, which accurately predicts individual outcomes by integrating medical imaging and patient biometrics. Experimental validation confirmed the model’s predictions, with differences of less than 5% for key properties such as compressive modulus and degradation rate. Notably, our work uncovered novel design principles for creating advanced biomaterials. We found that if a material can dynamically change its properties during the body’s natural healing process - almost as if it ”evolves” alongside the tissue - it can dramatically improve outcomes. Specifically, this approach reduced scar tissue formation by over 42% and enhanced implant integration by nearly 38% compared to traditional, static materials.

 

https://doi.org/10.70974/mat09225112


Keywords


Multi-Scale Modeling; Biomaterial-Host Interactions; Graph Neural Networks; Digital Twin; Predictive Biomaterial Design.

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References


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