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

4D printing polymeric biomaterials for adaptive tissue regeneration

4D printing polymeric biomaterials can change their morphology or performance in response to stimuli from the external environment, compensating for the shortcomings of traditional 3D-printed static structures. This paper provides a systematic overview of 4D printing polymeric biomaterials for tissue regeneration and provides an in-depth discussion of the principles of these materials, including various smart properties, unique deformation mechanisms under stimulation conditions, and so on. A series of typical polymeric biomaterials and their composites are introduced from structural design and preparation methods, and their applications in tissue regeneration are discussed. Finally, the development prospect of 4D printing polymeric biomaterials is envisioned, aiming to provide innovative ideas and new perspectives for their more efficient and convenient application in tissue regeneration.

Artificial Intelligence tool for prediction of ECM mimics hydrogel formulations via click chemistry

A user-friendly machine learning (ML) predictive tool is reported for designing extracellular matrix (ECM)-mimetic hydrogels with tailored rheological properties. Developed for regenerative medicine and 3D bioprinting, the model leverages click chemistry crosslinking to fine-tune the mechanical behaviour of gelatin- and hyaluronic acid-based hydrogels. Using both experimental rheological data and synthetic datasets, our supervised ML approach accurately predicts hydrogel compositions, significantly reducing the cost and time associated with trial-and-error approach. Despite advancements in the field, existing models remain limited in their ability to mimic the ECM due to the use of non-natural polymers, reliance on a single type of biologically active macromolecule, and physical crosslinking reactions with limited tuneability. Additionally, their lack of generalizability confines them to specific formulations and demands extensive experimental data for training. This predictive platform represents a major advancement in biomaterial design, improving reproducibility, scalability, and efficiency. By integrating rational design, it accelerates tissue engineering research and expands access to customized ECM-mimetic hydrogels with tailored viscoelastic properties for biomedical applications, enabling both experts and non-experts in materials design.

Advances and Challenges in 3D Bioprinted Cancer Models: Opportunities for Personalized Medicine and Tissue Engineering

Cancer is the second leading cause of death worldwide, after cardiovascular disease, claiming not only a staggering number of lives but also causing considerable health and economic devastation, particularly in less-developed countries. Therapeutic interventions are impeded by differences in patient-to-patient responses to anti-cancer drugs. A personalized medicine approach is crucial for treating specific patient groups and includes using molecular and genetic screens to find appropriate stratifications of patients who will respond (and those who will not) to treatment regimens. However, information on which risk stratification method can be used to hone in on cancer types and patients who will be likely responders to a specific anti-cancer agent remains elusive for most cancers. Novel developments in 3D bioprinting technology have been widely applied to recreate relevant bioengineered tumor organotypic structures capable of mimicking the human tissue and microenvironment or adequate drug responses in high-throughput screening settings. Parts are autogenously printed in the form of 3D bioengineered tissues using a computer-aided design concept where multiple layers include different cell types and compatible biomaterials to build specific configurations. Patient-derived cancer and stromal cells, together with genetic material, extracellular matrix proteins, and growth factors, are used to create bioprinted cancer models that provide a possible platform for the screening of new personalized therapies in advance. Both natural and synthetic biopolymers have been used to encourage the growth of cells and biological materials in personalized tumor models/implants. These models may facilitate physiologically relevant cell-cell and cell-matrix interactions with 3D heterogeneity resembling real tumors.

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.

Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study

Determining the values of various properties for new bio-inks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted, and a database with more than 1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example. These data are difficult to deal with in terms of determining combinations of conditions to optimize the tests and analyze new options. The best model demonstrated a specificity (Sp) of 88.4% and a sensitivity (Sn) of 86.2% in the training series while achieving an Sp of 85.9% and an Sn of 80.3% in the external validation series. This model utilizes operators based on perturbation theory to analyze the complexity of the data. For comparative purposes, neural networks have been used, and very similar results have been obtained. The developed tool could easily be applied to predict the properties of bioprinting assays in silico. These findings could significantly improve the efficiency and accuracy of predictive models in bioprinting without resorting to trial-and-error tests, thereby saving time and funds. Ultimately, this tool may help pave the way for advances in personalized medicine and tissue engineering.

A supervised machine learning tool to predict the bactericidal efficiency of nanostructured surface

The emergence and rapid spread of multidrug-resistant bacterial strains is a growing concern of public health. Inspired by the natural bactericidal surfaces of lotus leaves and shark skin, increasing attention has been focused on the use of mechano-bactericidal methods to create surfaces with antibacterial and/or bactericidal effects. There have been several studies exploring the bactericidal effect of nanostructured surfaces under various combinations of parameters. However, the correlation and synergies between these factors still need to be clarified. Recently machine learning (ML), which enables prediction or decision-making based on data, has been used in the field of biomaterials with promising results. In this study, we explored ML in nanotechnology to investigate the antimicrobial potential of nanostructured surfaces. A dataset of nanostructured surfaces and their antimicrobial properties was built by extracting the published literature. Based on the literature review and the distribution of our dataset, 70% bactericidal efficiency was selected as a practical benchmark for our classification model that balances stringent bactericidal performance with achievable targets in diverse conditions. Subsequently, we developed an ML classification model, which demonstrated an 81% accuracy in its predictive capability. A regression model was further developed to predict the value of bactericidal efficiency for nanostructured surfaces. Feature importance analysis of the ML models suggested that nanotopographical features have a greater influence on bactericidal properties than material properties, thus providing insight into the principles of the mechano-bactericidal effect of nanostructured surfaces. Overall, this ML model tool could help researchers to effectively select and design the parameters of the surface structure prior to experimentation, thereby improving the timeliness and reducing the number of experiments and the associated costs.

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.

Recent advances and applications of artificial intelligence in 3D bioprinting

3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Evaluation of the printability of agar and hydroxypropyl methylcellulose gels as gummy formulations: Insights from rheological properties

The trial-and-error method currently used to create formulations with excellent printability demands considerable time and resources, primarily due to the increasing number of variables involved. Rheology serves as a relatively rapid and highly beneficial method for assessing materials and evaluating their effectiveness as 3D constructs. However, the data obtained can be overwhelming, especially for users lacking experience in this field. This study examined the rheological properties of formulations of agar, hydroxypropyl methylcellulose, and the model drug caffeine, alongside exploring their printability as gummy formulations. The gels' rheological properties were characterized using oscillatory and rotational experiments. The correlation between these gels' rheological properties and their printability was established, and three clusters were formed based on the rheological properties and printability of the samples using principal component analysis. Furthermore, the printability was predicted using the sample's rheological property that correlated most with printability, the phase angle δ, and the regression models resulted in an accuracy of over 80%. Although these relationships merit confirmation in later studies, this study suggests a quantitative definition of the relationship between printability and one rheological property and can be used for the development of formulations destined for extrusion 3D printing.

Enhancing quality control in bioprinting through machine learning

Bioprinting technologies have been extensively studied in literature to fabricate three-dimensional constructs for tissue engineering applications. However, very few examples are currently available on clinical trials using bioprinted products, due to a combination of technological challenges (i.e. difficulties in replicating the native tissue complexity, long printing times, limited choice of printable biomaterials) and regulatory barriers (i.e. no clear indication on the product classification in the current regulatory framework). In particular, quality control (QC) solutions are needed at different stages of the bioprinting workflow (including pre-process optimization, in-process monitoring, and post-process assessment) to guarantee a repeatable product which is functional and safe for the patient. In this context, machine learning (ML) algorithms can be envisioned as a promising solution for the automatization of the quality assessment, reducing the inter-batch variability and thus potentially accelerating the product clinical translation and commercialization. In this review, we comprehensively analyse the main solutions that are being developed in the bioprinting literature on QC enabled by ML, evaluating different models from a technical perspective, including the amount and type of data used, the algorithms, and performance measures. Finally, we give a perspective view on current challenges and future research directions on using these technologies to enhance the quality assessment in bioprinting.

3D printing of biologics-what has been accomplished to date?

Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.

Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing

Three-dimensional (3D) printing is emerging as a transformative technology for biomedical engineering. The 3D printed product can be patient-specific by allowing customizability and direct control of the architecture. The trial-and-error approach currently used for developing the composition of printable inks is time- and resource-consuming due to the increasing number of variables requiring expert knowledge. Artificial intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental data. In this paper, we constructed machine learning (ML) algorithms including decision tree, random forest (RF), and deep learning (DL) to predict the printability of biomaterials. A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed, and their printability was assessed. All ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial formulations. In particular, the RF algorithm has achieved the highest accuracy (88.1%), precision (90.6%), and F1 score (87.0%), indicating the best overall performance out of the 3 algorithms, while DL has the highest recall (87.3%). Furthermore, the ML algorithms have predicted the printability window of biomaterials to guide the ink development. The printability map generated with DL has finer granularity than other algorithms. ML has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.