Artificial intelligence-augmented bioprinting systems: Data-driven optimization and future applications in pharmacological research

The 2022 SLAS technology ten: Translating life sciences innovation.

Leveraging three-dimensional (3D) bioprinting in the fields of tissue engineering and regenerative medicine has rapidly accelerated progress toward the development of living tissue constructs and biomedical devices. Ongoing vigorous research has pursued the development of 3D in vitro tissue models to replicate the key aspects of human physiology by incorporating relevant cell populations and adequate environmental cues. Given their advantages of being able to intimately mimic the heterogeneity and complexity of their native counterparts, 3D in vitro models hold promise as alternatives to conventional cell cultures or animal models for translational application to model human physiology/pathology and drug screening. Research has highlighted the importance of in vitro models, and a sophisticated biomanufacturing strategy is vitally required. In particular, vascularization is critical for the prolonged survival and functional maturation of the engineered tissues, which has remained one of the major challenges in the establishment of physiologically relevant 3D in vitro models. To this end, 3D bioprinting can efficiently generate solid and reproducible vascularized tissue models with high architectural and compositional similarity to the native tissues, leading to improve the structural maturation and tissue-specific functionality. Multiple bioprinting strategies have been developed to vascularize in vitro tissues by spatially controlled patterning of vascular precursors or generating readily perfusable vascular structures. This review presents an overview of the advanced 3D bioprinting strategies for vascularized tissue model development. We present the key elements for rebuilding functional vasculature in 3D-bioprinted tissue models and discuss the recent achievements in the engineering of 3D vascularized in vitro models using 3D bioprinting. Finally, we delineate the current challenges and future outlooks of 3D bioprinting-based vascularized tissue models.

Application of three-dimensional (3D) bioprinting in anti-cancer therapy

Synchrotron microbeam radiotherapy (MRT) is an innovative cancer treatment that uses micron-sized of ultra-high dose rate spatially fractionated X-rays to effectively control cancer growth while reducing the damage to surrounding healthy tissue. However, the current pre-clinical experiments are commonly limited with the use of conventional two-dimensional cell cultures which cannot accurately model in vivo tissue environment. This study aims to propose a three-dimensional (3D) bioprinting gelatin methacryloyl (GelMA) hydrogel protocol and to characterize 3D bioprinted glioma relative to cell monolayer and spheroid models for experimental MRT using 9L rat gliosarcoma and U87 human glioma. Synchrotron broad-beam (SBB) and MRT beams were delivered to all cell models using 5, 10, and 20 Gy. 3D bioprinting enables the creation of 3D cell models that mimic in vivo conditions using bioinks, biomaterials, and cells. Synchrotron dosimetry, Monte Carlo simulation, in vitro cell viability, and fluorescence microscopy were performed to understand the relationship of the radiation dosimetry with the radiobiological response of different cancer models. Encapsulated gliomas were placed inside 3D printed human and rat phantoms to mimic scattering conditions. Results showed that MRT kills more gliomas relative to SBB for all cell models. The 3D bioprinted culture detected the spatial clustering of dead cells due to MRT high peak doses as seen in fluorescence imaging. The result of this study progresses MRT research by integrating 3D bioprinting techniques in radiobiological experiments. The study’s bioprinting protocol and results will help in reducing the use of animal experiments and possibly in clinical translation of MRT.

The existing in vitro models for antitumor drug screening have significant limitations. Many compounds that inhibit two-dimensional (2D) cultured cells do not exhibit the same pharmacological effects in vivo, thereby wasting human and material resources and time during drug development. Therefore, it is crucial to develop new models. Three-dimensional (3D) bioprinting technology has greater advantages in constructing human tissues than sandwich culture and organoid construction. We used 3D bioprinting technology to construct a 3D multicellular model of SW480 cells, tumor-associated macrophages, and endothelial cells. The biological activities of the model were evaluated by immunofluorescence, hematoxylin and eosin staining of frozen pathological sections, and transcriptome sequencing. Compared with 3D bioprinted single-cell model (3D printing-S), 3D bioprinted multicellular models (3D printing-M) showed significantly improved expression of tumor-related genes, including hub genes IL1B, FCGR2A, FCGR3A, CYBB, SPI1, CCL2, ITGAM, and ITGB2. Antitumor drug screening experiment showed that the IC50 values of 5-FU, oxaliplatin, and irinotecan in 3D printing-S group/2D culture group were 31.13 μM/12.79 μM, 26.79 μM/0.80 μM, and 16.73 μM/10.45 μM, respectively. Compared with the 3D printing-S group, 3D printing-M group was significantly more resistant to chemotherapy.

Three Dimensional (3D) bioprinting holds great promise for tissue and organ regeneration due to its inherent capability to deposit biocompatible materials containing live cells in precise locations. Extrusion-based 3D bioprinting (EBP) method stands out for its ability to achieve a higher cell release rate, ensuring both external and internal scaffold structures. The systematic adjustment of key process parameters of EBP, including nozzle diameter, printing speed, print distance, extrusion pressure, material fraction, and viscosity allows for precise control over filament dimensions, ultimately shaping the desired scaffold porosity as per user specifications. However, managing these factors with all possible interactions simultaneously to achieve the desired filament width can be intricate and resource intensive. This study presents a novel framework designed to construct a predictive model for the filament width of 3D bioprinted scaffolds for various process parameters. A total of 157 experiments have been conducted under various combinations of process parameters and biomaterial’s weight fraction for this study purpose. A regression-based machine learning approach is employed to develop the predictive model utilizing Adj. R2, Mallow’s Cp, and Bayesian Information Criterion (BIC). Following model development, rigorous experimental validations are conducted to assess the accuracy and reliability of the model. Based on the cross-validation of randomly split test data, Adj. R2 model emerges as the highest performing machine learning model (Mean Squared Error, MSE = 0.0816) compared to Mallow’s Cp and BIC (MSE = 0.0841 and 0.0877, respectively) models. The comparative analysis results between the experimental and model’s data demonstrate that our predictive model achieves an accuracy of approximately 85% in filament width prediction. This framework presents a significant advancement in the precise control and optimization of 3D bioprinted scaffold fabrication, offering valuable insights for the advancement of tissue engineering and regenerative medicine applications.

Three-dimensional (3D) bioprinting is a computer-controlled technology that combines biological factors and bioinks to print an accurate 3D structure in a layer- by-layer fashion. 3D bioprinting is a new tissue engineering technology based on rapid prototyping and additive manufacturing technology, combined with various disciplines. In addition to the problems in in vitro culture process, the bioprinting procedure is also afflicted with a few issues: (1) difficulty in looking for the appropriate bioink to match the printing parameters to reduce cell damage and mortality; and (2) difficulty in improving the printing accuracy in the printing process. Data- driven machine learning algorithms with powerful predictive capabilities have natural advantages in behavior prediction and new model exploration. Combining machine learning algorithms with 3D bioprinting helps to find more efficient bioinks, determine printing parameters, and detect defects in the printing process. This paper introduces several machine learning algorithms in detail, summarizes the role of machine learning in additive manufacturing applications, and reviews the research progress of the combination of 3D bioprinting and machine learning in recent years, especially the improvement of bioink generation, the optimization of printing parameter, and the detection of printing defect.

More relevant human tissue models are needed to produce reliable results when studying disease mechanisms of genetic diseases and developing or testing novel drugs in cardiac tissue engineering (TE). Three-dimensional (3D) bioprinting enables physiologically relevant positioning of the cells inside the growth matrix according to the detailed digital design. Here we combined human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CMs) with methacrylated gelatin (GelMA) and collagen I-based bioink and 3D extrusion bioprinted a cardiac in vitro model for disease modeling and drug screening. Bioprinted constructs were characterized for their rheological properties, swelling behavior, degradation, as well as shape fidelity. The printed structures demonstrated good mechanical properties and high shape fidelity upon culture. Immunocytochemistry revealed elongated hiPSC-CMs growing inside the structures and the presence of the connexin 43 marker, indicating cardiac gap junctions between printed cells and tissue formation. Extensive functional analyses with calcium imaging showed normal functionality and calcium-handling properties for hiPSC-CMs. Finally, suitability of this 3D bioprinted construct for patient-specific disease modeling was demonstrated by bioprinting hiPSC-CMs from a patient carrying an inherited gene mutation causing catecholaminergic polymorphic ventricular tachycardia (CPVT). CPVT hiPSC-CMs responded to adrenaline treatment in the 3D bioprinted model in a manner that is characteristic for CPVT disease specific phenotype. Thus, the 3D bioprinted hiPSC-CM in vitro model has great potential for disease modeling and drug screening in cardiac tissue engineering.

In the 21st century, significant progress has been made in repairing damaged materials through material engineering. However, the creation of large-scale artificial materials still faces a major challenge in achieving proper vascularization. To address this issue, researchers have turned to biomaterials and three-dimensional (3D) bioprinting techniques, which allow for the combination of multiple biomaterials with improved mechanical and biological properties that mimic natural materials. Hydrogels, known for their ability to support living cells and biological components, have played a crucial role in this research. Among the recent developments, 3D bioprinting has emerged as a promising tool for constructing hybrid scaffolds. However, there are several challenges in the field of bioprinting, including the need for nanoscale biomimicry, the formulation of hydrogel blends, and the ongoing complexity of vascularizing biomaterials, which requires further research. On a positive note, 3D bioprinting offers a solution to the vascularization problem due to its precise spatial control, scalability, and reproducibility compared with traditional fabrication methods. This paper aims at examining the recent advancements in 3D bioprinting technology for creating blood vessels, vasculature, and vascularized materials. It provides a comprehensive overview of the progress made and discusses the limitations and challenges faced in current 3D bioprinting of vascularized tissues. In addition, the paper highlights the future research directions focusing on the development of 3D bioprinting techniques and bioinks for creating functional materials.

The ability of three-dimensional (3D) bioprinting to fabricate biomimetic organ and disease models has been recognised to be promising for drug discovery and development as 3D bioprinted models can better mimic human physiology compared to two-dimensional (2D) cultures and animal models. This is useful for target selection where disease models can be studied to understand disease pathophysiology and identify disease-linked compounds. Lead identification and preclinical studies also benefit from 3D bioprinting as 3D bioprinted models can be utilised in high-throughput screening (HTS) systems and to produce efficacy and safety data that closely resembles clinical observations. Although no published applications of 3D bioprinting in clinical trials were found, there are two clinical trials planning to evaluate the predictive ability of 3D bioprinted models by comparing human and model responses to the same chemotherapy. Overall, this review provides a comprehensive summary of the latest applications of 3D bioprinting in drug discovery and development.

Organ transplantation is the optimal treatment for patients with end-stage organ failure, but which faces the challenge of donor shortage. Two-dimensional cell culture and animal experiments are difficult to completely simulate the complex cellular microenvironment for drug testing. Three-dimensional (3D) bioprinting is an emerging manufacturing technology to fabricate artificial tissues and organs for transplantation and drug screening. This review first describes the bioprinting technologies used to fabricate constructs, including jetting-based, extrusion-based, vat photopolymerization-based methods and other emerging 3D bioprinting approaches. The various kinds of bioinks, cell sources, and the most recent applications of 3D bioprinting tissues and organs in transplantation and drug testing are subsequently summarized. Finally, we discuss the challenges and prospects of 3D bioprinting of tissues and organs. This review aims to facilitate the overcoming of the obstacles identified on the challenging journey towards the manufacturing and adoption of tissue and organs in transplantation and drug screening.

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Mohs Micrographic Surgery (MMS) is effective for treating common cutaneous malignancies, but complex repairs may often present challenges for reconstruction. This paper explores the potential of three-dimensional (3D) bioprinting in MMS, offering superior outcomes compared to traditional methods. 3D printing technologies show promise in advancing skin regeneration and refining surgical techniques in dermatologic surgery. A PubMed search was conducted using the following keywords: "Three-dimensional bioprinting" OR "3-D printing" AND "Mohs" OR "Mohs surgery" OR "Surgery." Peer-reviewed English articles discussing medical applications of 3D bioprinting were included, while non-peer-reviewed and non-English articles were excluded. Patients using 3D MMS models had lower anxiety scores (3.00 to 1.7, p < 0.0001) and higher knowledge assessment scores (5.59 or 93.25% correct responses), indicating better understanding of their procedure. Surgical residents using 3D models demonstrated improved proficiency in flap reconstructions (p = 0.002) and knowledge assessment (p = 0.001). Additionally, 3D printing offers personalized patient care through tailored surgical guides and anatomical models, reducing intraoperative time while enhancing surgical. Concurrently, efforts in tissue engineering and regenerative medicine are being explored as potential alternatives to address organ donor shortages, eliminating autografting needs. However, challenges like limited training and technological constraints persist. Integrating optical coherence tomography with 3D bioprinting may expedite grafting, but challenges remain in pre-printing grafts for complex cases. Regulatory and ethical considerations are paramount for patient safety, and further research is needed to understand long-term effects and cost-effectiveness. While promising, significant advancements are necessary for full utilization in MMS.

Three-dimensional (3D) bioprinting has emerged as a highly promising technology within the realms of tissue engineering and regenerative medicine. The assessment of printability is essential for ensuring the quality of bio-printed constructs and the functionality of the resultant tissues. Polymer materials, extensively utilized as bioink materials in extrusion-based bioprinting, have garnered significant attention from researchers due to the critical need for evaluating and optimizing their printability. Machine learning, a powerful data-driven technology, has attracted increasing attention in the evaluation and optimization of 3D bioprinting printability in recent years. This review provides an overview of the application of machine learning in the printability research of polymers for 3D bioprinting, encompassing the analysis of factors influencing printability (such as material and printing parameters), the development of predictive models, and the formulation of optimization strategies. Additionally, the review briefly explores the utilization of machine learning in predicting cell viability, evaluates the advanced nature and developmental potential of machine learning in 3D bioprinting, and examines the current challenges and future trends.

An improved machine learning approach for predicting granular flows