Towards the Experimentally-Informed In Silico Nozzle Design Optimization for Extrusion-Based Bioprinting of Shear-Thinning Hydrogels

Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications

The role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification.

Extrusion bioprinting: meeting the promise of human tissue biofabrication?

Extrusion is the most popular bioprinting platform. Predictions of human tissue and whole-organ printing have been made for the technology. However, after decades of development, extruded constructs lack the essential microscale resolution and heterogeneity observed in most human tissues. Extrusion bioprinting has had little clinical impact with the majority of research directed away from the tissues most needed by patients. The distance between promise and reality is a result of technology hype and inherent design flaws that limit the shape, scale and survival of extruded features. By more widely adopting resolution innovations and softening its ambitions the biofabrication field could define a future for extrusion bioprinting that more closely aligns with its capabilities.

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Understanding the cellular dynamics, engineering perspectives and translation prospects in bioprinting epithelial tissues

The epithelium is one of the important tissues in the body as it plays a crucial barrier role serving as a gateway into and out of the body. Most organs in the body contain an epithelial tissue component, where the tightly connected, organ-specific epithelial cells organize into cysts, invaginations, or tubules, thereby performing distinct to endocrine or exocrine secretory functions. Despite the significance of epithelium, engineering functional epithelium in vitro has remained a challenge due to it is special architecture, heterotypic composition of epithelial tissues, and most importantly, difficulty in attaining the apico-basal and planar polarity of epithelial cells. Bioprinting has brought a paradigm shift in fabricating such apico-basal polarized tissues. In this review, we provide an overview of epithelial tissues and provide insights on recapitulating their cellular arrangement and polarization to achieve epithelial function. We describe the different bioprinting techniques that have been successful in engineering polarized epithelium, which can serve as in vitro models for understanding homeostasis and studying diseased conditions. We also discuss the different attempts that have been investigated to study these 3D bioprinted engineered epithelium for preclinical use. Finally, we highlight the challenges and the opportunities that need to be addressed for translation of 3D bioprinted epithelial tissues towards paving way for personalized healthcare in the future.

AI for biofabrication

Biofabrication is an advanced technology that holds great promise for constructing highly biomimeticin vitrothree-dimensional human organs. Such technology would help address the issues of immune rejection and organ donor shortage in organ transplantation, aiding doctors in formulating personalized treatments for clinical patients and replacing animal experiments. Biofabrication typically involves the interdisciplinary application of biology, materials science, mechanical engineering, and medicine to generate large amounts of data and correlations that require processing and analysis. Artificial intelligence (AI), with its excellent capabilities in big data processing and analysis, can play a crucial role in handling and processing interdisciplinary data and relationships and in better integrating and applying them in biofabrication. In recent years, the development of the semiconductor and integrated circuit industries has propelled the rapid advancement of computer processing power. An AI program can learn and iterate multiple times within a short period, thereby gaining strong automation capabilities for a specific research content or issue. To date, numerous AI programs have been applied to various processes around biofabrication, such as extracting biological information, designing and optimizing structures, intelligent cell sorting, optimizing biomaterials and processes, real-time monitoring and evaluation of models, accelerating the transformation and development of these technologies, and even changing traditional research patterns. This article reviews and summarizes the significant changes and advancements brought about by AI in biofabrication, and discusses its future application value and direction.

Micromixer driven by bubble-induced acoustic microstreaming for multi-ink 3D bioprinting

Recently, the 3D printing of cell-laden hydrogel structures, known as bioprinting, has received increasing attention owing to advances in tissue engineering and drug screening. However, a micromixing technology that efficiently mixes viscous bioinks under mild conditions is needed. Therefore, this study presents a novel method for achieving homogeneous mixing of multiple inks in 3D bioprinting through acoustic stimulation. This technique involves generating an acoustic microstream through bubble oscillations inside a 3D bioprinting nozzle. We determined the optimal hole design for trapping a bubble, hole arrangement, and voltage for efficient mixing, resulting in a four-fold increase in mixing efficiency compared to a single bubble arrangement. Subsequently, we propose a nozzle design for efficient mixing during bioprinting. The proposed nozzle design enabled the successful printing of line structures with a uniform mixture of different viscous bioinks, achieving a mixing efficiency of over 80% for mixing 0.5-1.0 wt% sodium alginate aqueous solutions. Additionally, acoustic stimulation had no adverse effects on cell viability, maintaining a high cell viability of 88% after extrusion. This study presents the first use of a bubble micromixer in 3D bioprinting, demonstrating gentle yet effective multi-ink mixing. We believe this approach will broaden 3D printing applications, particularly for constructing functional structures in 3D bioprinting.

Vascular tissues bioprinted with smooth muscle cell-only bioinks in support baths mimic features of native coronary arteries

This study explores the bioprinting of a smooth muscle cell-only bioink into ionically crosslinked oxidized methacrylated alginate (OMA) microgel baths to create self-supporting vascular tissues. The impact of OMA microgel support bath methacrylation degree and cell-only bioink dispensing parameters on tissue formation, remodeling, structure and strength was investigated. We hypothesized that reducing dispensing tip diameter from 27 G (210μm) to 30 G (159μm) for cell-only bioink dispensing would reduce tissue wall thickness and improve the consistency of tissue dimensions while maintaining cell viability. Printing with 30 G tips resulted in decreased mean wall thickness (318.6μm) without compromising mean cell viability (94.8%). Histological analysis of cell-only smooth muscle tissues cultured for 14 d in OMA support baths exhibited decreased wall thickness using 30 G dispensing tips, which correlated with increased collagen deposition and alignment. In addition, a TUNEL assay indicated a decrease in cell death in tissues printed with thinner (30 G) dispensing tips. Mechanical testing demonstrated that tissues printed with a 30 G dispensing tip exhibit an increase in ultimate tensile strength compared to those printed with a 27 G dispensing tip. Overall, these findings highlight the importance of precise control over bioprinting parameters to generate mechanically robust tissues when using cell-only bioinks dispensed and cultured within hydrogel support baths. The ability to control print dimensions using cell-only bioinks may enable bioprinting of more complex soft tissue geometries to generatein vitrotissue models.

Computational Fluid Dynamics (CFD) Analysis of Bioprinting

Regenerative medicine has evolved with the rise of tissue engineering due to advancements in healthcare and technology. In recent years, bioprinting has been an upcoming approach to traditional tissue engineering practices, through the fabrication of functional tissue by its layer-by-layer deposition process. This overcomes challenges such as irregular cell distribution and limited cell density, and it can potentially address organ shortages, increasing transplant options. Bioprinting fully functional organs is a long stretch but the advancement is rapidly growing due to its precision and compatibility with complex geometries. Computational Fluid Dynamics (CFD), a carestone of computer-aided engineering, has been instrumental in assisting bioprinting research and development by cutting costs and saving time. CFD optimizes bioprinting by testing parameters such as shear stress, diffusivity, and cell viability, reducing repetitive experiments and aiding in material selection and bioprinter nozzle design. This review discusses the current application of CFD in bioprinting and its potential to enhance the technology that can contribute to the evolution of regenerative medicine.

Optimising Bioprinting Nozzles through Computational Modelling and Design of Experiments

3D bioprinting is a promising technique for creating artificial tissues and organs. One of the main challenges of bioprinting is cell damage, due to high pressures and tensions. During the biofabrication process, extrusion bioprinting usually results in low cell viability, typically ranging from 40% to 80%, although better printing performance with higher cell viability can be achieved by optimising the experimental design and operating conditions, with nozzle geometry being a key factor. This article presents a review of studies that have used computational fluid dynamics (CFD) to optimise nozzle geometry. They show that the optimal ranges for diameter and length are 0.2 mm to 1 mm and 8 mm to 10 mm, respectively. In addition, it is recommended that the nozzle should have an internal angle of 20 to 30 degrees, an internal coating of ethylenediaminetetraacetic acid (EDTA), and a shear stress of less than 10 kPa. In addition, a design of experiments technique to obtain an optimal 3D bioprinting configuration for a bioink is also presented. This experimental design would identify bioprinting conditions that minimise cell damage and improve the viability of the printed cells.

High-Speed Fluorescence Imaging Corroborates Biological Data on the Influence of Different Nozzle Types on Cell Spray Viability and Formation

Treating severe dermal disruptions often presents significant challenges. Recent advancements have explored biological cell sprays as a promising treatment, but their success hinges on efficient cell delivery and complete wound coverage. This requires a good spray distribution with a small droplet size, high particle number, and ample surface coverage. The type of nozzle used with the spray device can impact these parameters. To evaluate the influence of different nozzles on spray characteristics, we compared air-assisted and unassisted nozzles. The unassisted nozzle displayed small particle size, high particle number, good overall coverage, high cell viability, preserved cell metabolic activity, and low cytotoxicity. Air-assisted nozzles did not perform well regarding cell viability and metabolic activity. Flow visualization analysis comparing two different unassisted nozzles using high-speed imaging (100 kHz frame rate) revealed a tulip-shaped spray pattern, indicating optimal spray distribution. High-speed imaging showed differences between the unassisted nozzles. One unassisted nozzle displayed a bi-modal distribution of the droplet diameter while the other unassisted nozzle displayed a mono-modal distribution. These findings demonstrate the critical role of nozzle selection in successful cell delivery. A high-quality, certified nozzle manufactured for human application omits the need for an air-assisted nozzle and provides a simple system to use with similar or better performance characteristics than those of an air-assisted system.

Advancing cardiac regeneration through 3D bioprinting: methods, applications, and future directions

Cardiovascular diseases (CVDs) represent a paramount global mortality concern, and their prevalence is on a relentless ascent. Despite the effectiveness of contemporary medical interventions in mitigating CVD-related fatality rates and complications, their efficacy remains curtailed by an array of limitations. These include the suboptimal efficiency of direct cell injection and an inherent disequilibrium between the demand and availability of heart transplantations. Consequently, the imperative to formulate innovative strategies for cardiac regeneration therapy becomes unmistakable. Within this context, 3D bioprinting technology emerges as a vanguard contender, occupying a pivotal niche in the realm of tissue engineering and regenerative medicine. This state-of-the-art methodology holds the potential to fabricate intricate heart tissues endowed with multifaceted structures and functionalities, thereby engendering substantial promise. By harnessing the prowess of 3D bioprinting, it becomes plausible to synthesize functional cardiac architectures seamlessly enmeshed with the host tissue, affording a viable avenue for the restitution of infarcted domains and, by extension, mitigating the onerous yoke of CVDs. In this review, we encapsulate the myriad applications of 3D bioprinting technology in the domain of heart tissue regeneration. Furthermore, we usher in the latest advancements in printing methodologies and bioinks, culminating in an exploration of the extant challenges and the vista of possibilities inherent to a diverse array of approaches.

Recent Developments in Bio-Ink Formulations Using Marine-Derived Biomaterials for Three-Dimensional (3D) Bioprinting

3D bioprinting is a disruptive, computer-aided, and additive manufacturing technology that allows the obtention, layer-by-layer, of 3D complex structures. This technology is believed to offer tremendous opportunities in several fields including biomedical, pharmaceutical, and food industries. Several bioprinting processes and bio-ink materials have emerged recently. However, there is still a pressing need to develop low-cost sustainable bio-ink materials with superior qualities (excellent mechanical, viscoelastic and thermal properties, biocompatibility, and biodegradability). Marine-derived biomaterials, including polysaccharides and proteins, represent a viable and renewable source for bio-ink formulations. Therefore, the focus of this review centers around the use of marine-derived biomaterials in the formulations of bio-ink. It starts with a general overview of 3D bioprinting processes followed by a description of the most commonly used marine-derived biomaterials for 3D bioprinting, with a special attention paid to chitosan, glycosaminoglycans, alginate, carrageenan, collagen, and gelatin. The challenges facing the application of marine-derived biomaterials in 3D bioprinting within the biomedical and pharmaceutical fields along with future directions are also discussed.

Hydrodynamic shear stress' impact on mammalian cell properties and its applications in 3D bioprinting

As an effective cell assembly method, three-dimensional bioprinting has been widely used in building organ models and tissue repair over the past decade. However, different shear stresses induced throughout the entire printing process can cause complex impacts on cell integrity, including reducing cell viability, provoking morphological changes and altering cellular functionalities. The potential effects that may occur and the conditions under which these effects manifest are not clearly understood. Here, we review systematically how different mammalian cells respond under shear stress. We enumerate available experimental apparatus, and we categorise properties that can be affected under disparate stress patterns. We also summarise cell damaging mathematical models as a predicting reference for the design of bioprinting systems. We concluded that it is essential to quantify specific cell resistance to shear stress for the optimisation of bioprinting systems. Besides, as substantial positive impacts, including inducing cell alignment and promoting cell motility, can be generated by shear stress, we suggest that we find the proper range of shear stress and actively utilise its positive influences in the development of future systems.

Cell viability prediction and optimization in extrusion-based bioprinting via neural network-based Bayesian optimization models

The fields of regenerative medicine and cancer modeling have witnessed tremendous growth in the application of 3D bioprinting. Maintaining high cell viability throughout the bioprinting process is crucial for the success of this technology, as it directly affects the accuracy of the 3D bioprinted models, the validity of experimental results, and the discovery of new therapeutic approaches. Therefore, optimizing bioprinting conditions, which include numerous variables influencing cell viability during and after the procedure, is of utmost importance to achieve desirable results. So far, these optimizations have been accomplished primarily through trial and error and repeating multiple time-consuming and costly experiments. To address this challenge, we initiated the process by creating a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed machine learning models to predict cell viability based on different bioprinting variables. The trained neural network yielded regressionR2value of 0.71 and classification accuracy of 0.86. Compared to models that have been developed so far, the performance of our models is superior and shows great prediction results. The study further introduces a novel optimization strategy that employs the Bayesian optimization model in combination with the developed regression neural network to determine the optimal combination of the selected bioprinting parameters to maximize cell viability and eliminate trial-and-error experiments. Finally, we experimentally validated the optimization model's performance.

An Innovative Biofunctional Composite Hydrogel with Enhanced Printability, Rheological Properties, and Structural Integrity for Cell Scaffold Applications

The present study was conducted to manipulate various biomaterials to find potential hydrogel formulations through three-dimensional (3D) bioprinting fabrication for tissue repair, reconstruction, or regeneration. The hydrogels were prepared using sodium alginate and gelatin combined with different concentrations of Pluronic F127 (6% (3 g), 8% (4 g), and 10% (5 g)) and were marked as AGF-6%, AGF-8%, and AGF-10%, respectively. The properties of the hydrogels were investigated using a contact angle goniometer, rheometer, and 3D bioprinter. In addition, the osteoblast-like cell line (MG-63) was used to evaluate the cell viability including hydrogels before and after 3D bioprinting. It was found that the ratio of contact angle was lowest at AGF-6%, and the rheological results were higher for all samples of AGF-6%, AGF-8%, and AGF-10% compared with the control sample. The printability indicated that the AGF-6% hydrogel possessed great potential in creating a cell scaffold with shape integrity. Moreover, the live/dead assay also presented the highest numbers of live cells before printing compared with after printing. However, the number of live cells on day 7 was higher than on day 1 before and after printing (** p < 0.01). Therefore, the combination of AGF-6% could be developed as a biofunctional hydrogel formulation for potential tissue regeneration applications.

Bioinks of Natural Biomaterials for Printing Tissues

Bioinks are inks-in other words, hydrogels-prepared from biomaterials with certain physiochemical properties together with cells to establish hierarchically complex biological 3D scaffolds through various 3D bioprinting technologies [...].

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

Three dimensional (3D) bioprinting is an emerging biofabrication technique that shows great potential in the field of tissue engineering, regenerative medicine and advanced drug delivery. Despite the current advancement of bioprinting technology, it faces several obstacles such as the challenge of optimizing the printing resolution of 3D constructs while retaining cell viability before, during, and after bioprinting. Therefore, it is of great significance to fully understand factors that influence the shape fidelity of printed structures and the performance of cells encapsulated in bioinks. This review presents a comprehensive analysis of bioprinting process parameters that influence bioink printability and cell performance, including bioink properties (composition, concentration, and component ratio), printing speed and pressure, nozzle charateristics (size, length, and geometry), and crosslinking parameters (crosslinker types, concentration, and crosslinking time). Key examples are provided to analyze how these parameters could be tailored to achieve the optimal printing resolution as well as cell performance. Finally, future prospects of bioprinting technology, including correlating process parameters to particular cell types with predefined applications, applying statistical analysis and artificial intelligence (AI)/machine learning (ML) technique in parameter screening, and optimizing 4D bioprinting process parameters, are highlighted.

Aqueous Two-Phase Enabled Low Viscosity 3D (LoV3D) Bioprinting of Living Matter

Embedded 3D bioprinting has great value for the freeform fabrication of living matter. However, embedded 3D bioprinting is currently limited to highly viscous liquid baths or liquid-like solid baths. In contrast, prior to crosslinking, most hydrogels are formulated as low-viscosity solutions and are therefore not directly compatible with bioprinting due to low shape fidelity and poor print stability. The authors here present a method to enable low-viscosity ink 3D (LoV3D) bioprinting, based on aqueous two-phase stabilization of the ink-bath interface. LoV3D allows for the printing of living constructs at high extrusion speeds (up to 1.8 m s^-1 ) with high viability due to its exceedingly low-viscosity. Moreover, LoV3D liquid/liquid interfaces offer unique advantages for fusing printed structures, creating intricate vasculature, and modifying surfaces at higher efficiencies than traditional systems. Furthermore, the low interfacial tension of LoV3D bioprinting offers unprecedented nozzle-independent control over filament diameter via large-dimension strand-thinning, which allows for the printing of an exceptionally wide range of diameters down to the width of a single cell. Overall, LoV3D bioprinting is a unique all-aqueous approach with broad material compatibility without the need for rheological ink adaption, which opens new avenues of application in cell patterning, drug screening, engineered meat, and organ fabrication.

A Micro-Scale Non-Linear Finite Element Model to Optimize the Mechanical Behavior of Bioprinted Constructs

Extrusion-based bioprinting is an enabling biofabrication technique that is used to create heterogeneous tissue constructs according to patient-specific geometries and compositions. The optimization of bioinks as per requirements for specific tissue applications is an essential exercise in ensuring clinical translation of the bioprinting technologies. Most notably, optimum hydrogel polymer concentrations are required to ensure adequate mechanical properties of bioprinted constructs without causing significant shear stresses on cells. However, experimental iterations are often tedious for optimizing the bioink properties. In this work, a nonlinear finite element modeling approach has been undertaken to determine the effect of different bioink parameters such as composition, concentration on the range of stresses being experienced by the cells in the bioprinted construct. The stress distribution of the cells at different parts of the constructs has also been modeled. It is found that both bioink chemical compositions and concentrations can substantially alter the stress effects experienced by the cells. Concentrated regions of softer cells near pore regions were found to increase stress concentrations by almost three times compared with stress generated in cells away from the pores. The study provides a method for rapid optimization of bioinks, design of bioprinted constructs, as well as toolpath plans for fabricating constructs with homogenous properties.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

Printability and Cell Viability in Extrusion-Based Bioprinting from Experimental, Computational, and Machine Learning Views

Extrusion bioprinting is an emerging technology to apply biomaterials precisely with living cells (referred to as bioink) layer by layer to create three-dimensional (3D) functional constructs for tissue engineering. Printability and cell viability are two critical issues in the extrusion bioprinting process; printability refers to the capacity to form and maintain reproducible 3D structure and cell viability characterizes the amount or percentage of survival cells during printing. Research reveals that both printability and cell viability can be affected by various parameters associated with the construct design, bioinks, and bioprinting process. This paper briefly reviews the literature with the aim to identify the affecting parameters and highlight the methods or strategies for rigorously determining or optimizing them for improved printability and cell viability. This paper presents the review and discussion mainly from experimental, computational, and machine learning (ML) views, given their promising in this field. It is envisioned that ML will be a powerful tool to advance bioprinting for tissue engineering.

3D Printing Methyl Cellulose Hydrogel Wound Dressings with Parameter Exploration Via Computational Fluid Dynamics Simulation

PURPOSE

To investigate and optimize the use of methyl cellulose in the fabrication of three-dimensional (3D) printed drug-loaded hydrogel wound dressings for the treatment of burns.

METHOD

The effects of incorporating various salts on the properties of methyl cellulose, especially the gelation temperature was investigated for methyl cellulose to undergo gelation at skin temperature (i.e., 31.7°C). The optimized methyl cellulose and salt compositions were then loaded with various drugs beneficial for the treatment of burns. Printability and cumulative release profiles for selected drugs were then obtained, which were then fitted to common release kinetic models. Computational Fluid Dynamics (CFD) simulation was also explored to investigate the relationship between printing parameters and the hydrogel filament produced during extrusion.

RESULTS

The printed hydrogels had moderate dimensional integrity, were found to be stable for up to 2 weeks and demonstrated good swelling properties. In vitro drug release studies of various drugs showed that the hydrogel was able to release various drugs within 6 h and release profiles were fitted to common in vitro drug release models, such as the Korsmeyer Peppas model and the Weibull model. While there were deviations from the actual printing process, CFD simulation was able to predict the shape of the printed structure and showed fair accuracy in determining the mass flow rate and line width of extruded hydrogels.

CONCLUSIONS

Methyl cellulose hydrogels with optimized salt composition demonstrated suitable properties for a wound dressing application, revealing its potential to be used for in situ wound dressing applications.