Computer -aided hydrogel synthesis for 3D bioprinting: application of Design of Experiment (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD)

Abstract

The hydrophilic polymeric network of hydrogels is a crucial element of bioinks, closely resembling the extracellular matrix and offering a supportive microenvironment vital for sustaining cell viability. Crucial rheological properties, especially viscosity and shear-thinning characteristics, are essential in influencing the printability and structural integrity of bioprinted constructions. Additionally, hydrogels must demonstrate suitable mechanical properties to support three-dimensional structures after printing and promote cellular proliferation and differentiation. This review highlights the incorporation of advanced methodologies such as Design of Experiments (DoE), Machine Learning (ML), and Computational Fluid Dynamics (CFD) in the systematic optimization of hydrogel formulations for 3D bioprinting applications. For example, DoE, specifically response surface methodology has been utilized to optimize the concentrations of essential components such as gelatin, alginate, and methylcellulose resulting in excellent extrusion rheological characteristics. Simultaneously, machine learning techniques are progressively employed to model and automate the optimization process, diminishing dependence on trialand-error experimentation and expediting bioink development. The review emphasizes the importance of a balanced strategy for improving the rheological and mechanical properties of hydrogels, which may be effectively realized through the combined use of DoE, ML and CFD approaches in 3D bioprinting.