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Automated Robotic Dispensing Technique for Surface Guidance and Bioprinting of Cells

Automated Robotic Dispensing Technique for Surface Guidance and Bioprinting of Cells

Published on: November 18, 2016

Bioprinting: A focus on improving bioink printability and cell performance based on different process parameters.

Jiawei Wang¹, Zhengrong Cui², Mohammed Maniruzzaman¹

¹Pharmaceutical Engineering and 3D Printing (PharmE3D) Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA; Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX 78712, USA.

International Journal of Pharmaceutics

|May 6, 2023

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Optimizing three-dimensional (3D) bioprinting requires understanding how bioink properties and printing parameters affect construct fidelity and cell viability. This review analyzes key factors for enhancing 3D bioprinting performance in tissue engineering and regenerative medicine.

Area of Science:

Biotechnology
Biomaterials Science
Regenerative Medicine

Background:

Three-dimensional (3D) bioprinting is a key technology for tissue engineering, regenerative medicine, and drug delivery.
Current challenges include optimizing printing resolution and maintaining cell viability throughout the bioprinting process.
Understanding factors influencing shape fidelity and encapsulated cell performance is crucial for advancing bioprinting.

Approach:

This review comprehensively analyzes bioprinting process parameters affecting bioink printability and cell performance.
It examines bioink properties (composition, concentration, ratio), printing settings (speed, pressure), nozzle characteristics, and crosslinking parameters.
Key examples illustrate how tailoring these parameters achieves optimal printing resolution and cell outcomes.

Keywords:

Bioink Bioprinting Cell performance Printability Printing parameters

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Key Points:

Bioink composition, concentration, and component ratios significantly impact printability.
Printing speed, pressure, and nozzle geometry are critical for achieving desired resolution.
Crosslinking type, concentration, and time influence the structural integrity and cell survival within constructs.

Conclusions:

Tailoring bioprinting parameters is essential for achieving high-resolution constructs with excellent cell viability.
Future research should focus on correlating process parameters with specific cell types and applications.
Integrating artificial intelligence (AI)/machine learning (ML) and optimizing four-dimensional (4D) bioprinting offer promising avenues for advancement.