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Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.

ABSTRACT: Hydrogel ink formulations based on rheology additives are becoming increasingly popular as they enable 3-dimensional (3D) printing of non-printable but biologically relevant materials. Despite the widespread use, a generalized understanding of how these hydrogel formulations become printable is still missing, mainly due to their variety and diversity. Employing an interpretable machine learning approach allows the authors to explain the process of rendering printability through bulk rheological indices, with no bias toward the composition of formulations and the type of rheology additives. Based on an extensive library of rheological data and printability scores for 180 different formulations, 13 critical rheological measures that describe the printability of hydrogel formulations, are identified. Using advanced statistical methods, it is demonstrated that even though unique criteria to predict printability on a global scale are highly unlikely, the accretive and collaborative nature of rheological measures provides a qualitative and physically interpretable guideline for designing new printable materials.

SUBMITTER: Nadernezhad A

PROVIDER: S-EPMC9561784 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.

Nadernezhad Ali A Groll Jürgen J

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20220825 29

Hydrogel ink formulations based on rheology additives are becoming increasingly popular as they enable 3-dimensional (3D) printing of non-printable but biologically relevant materials. Despite the widespread use, a generalized understanding of how these hydrogel formulations become printable is still missing, mainly due to their variety and diversity. Employing an interpretable machine learning approach allows the authors to explain the process of rendering printability through bulk rheological ...[more]

PMID: 36008135

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Project description:Extrusion three-dimensional (3D) bioprinting is a promising technology with many applications in the biomedical and tissue engineering fields. One of the key limitations for the widespread use of this technology is the narrow window of printability that results from the need to have bioinks with rheological properties that allow the extrusion of continuous filaments while maintaining high cell viability within the materials during and after printing. In this work, we use Carbopol (CBP) as rheology modifier for extrusion printing of biomaterials that are typically nonextrudable or present low printability. We show that low concentrations of CBP can introduce the desired rheological properties for a wide range of formulations, allowing the use of polymers with different cross-linking mechanisms and the introduction of additives and cells. To explore the opportunities and limitations of CBP as a rheology modifier, we used ink formulations based on poly(ethylene glycol)diacrylate with extrusion 3D printing to produce soft, yet stable, hydrogels with tunable mechanical properties. Cell-laden constructs made with such inks presented high viability for cells seeded on top of cross-linked materials and cells incorporated within the bioink during printing, showing that the materials are noncytotoxic and the printed structures do not degrade for up to 14 days. To our knowledge, this is the first report of the use of CBP-containing bioinks to 3D-print complex cell-laden structures that are stable for days and present high cell viability. The use of CBP to obtain highly printable inks can accelerate the evolution of extrusion 3D bioprinting by guaranteeing the required rheological properties and expanding the number of materials that can be successfully printed. This will allow researchers to develop and optimize new bioinks focusing on the biochemical, cellular, and mechanical requirements of the targeted applications rather than the rheology needed to achieve good printability.

Dataset Information

Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.

Publications

Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.

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