Bioadditive manufacturing of hybrid tissue scaffolds for controlled release kinetics

Development of engineered tissue scaffolds with superior control over cell-protein interactions is still very much infancy. Advancing through heterogeneous multifold scaffolds with controlled release fashion enables synchronization of regenerating tissue with the release kinetics of loaded biomolecules. This might be an engineering challenge and promising approach for improved and efficient tissue regeneration. The most critical limitations: the selection of proper protein(s) incorporation, and precise control over concentration gradient and timing should be overcome. Hence, tissue scaffolds need to be fabricated in a way that proteins or growth factors should be incorporated and released in a specific spatial and temporal orientation to mimic the natural tissue regeneration process. Spatial and temporal control over heterogeneous porous tissue scaffolds can be achieved by controlling two important parameters: (i) internal architecture with enhanced fluid transport, and (ii) distribution of scaffold base material and loaded modifiers. In this research, heterogeneous tissue scaffolds are designed considering both the parameters. Firstly, the three-dimensional porous structures of the scaffold are geometrically partition into functionally uniform porosity regions and controlled spatial micro-architecture has been achieved using a functionally gradient porosity function. The bio-fabrication of the designed internal porous architecture has been performed using a single nozzle bioadditive manufacturing system. The internal architecture scheme is developed to enhance fluid transport with continuous base material deposition Next, the hybrid tissue scaffolds are modeled with varying material characteristics to mediate the release of base material and enclosed biological modifiers are proposed based on tissue engineering requirements. The hybrid scaffolds are fabricated for spatial control of biomolecules and base material to synchronize the release kinetics with tissue regeneration. A pressure-assisted multi-chamber single nozzle bioadditive manufacturing system is used to fabricate hybrid scaffolds

CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

Modeling and additive manufacturing of biomimetic heterogeneous Scaffold

In this paper, a feature-based bio-CAD modeling of three-dimensional tissue scaffolds by considering spatial distribution of biologically active materials is presented for biomanufacturing and tissue engineering applications. Proposed model is based on uniform distribution of bio-active particles in different regions of scaffold, which is constrained by geometrical and biological features. The proposed method was integrated with a recently developed method of multi-material additive manufacturing of hydrogel structures, for bio-additive manufacturing of the heterogeneous scaffolds. 3D bioprinted heterogeneous scaffolds were provided as an example for physical implementation of developed algorithm to validate the model

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Approaches to in vitro tissue regeneration with application for human disease modeling and drug development

Reliable in vitro human disease models that capture the complexity of in vivo tissue behaviors are crucial to gain mechanistic insights into human disease and enable the development of treatments that are effective across broad patient populations. The integration of stem cell technologies, tissue engineering, emerging biomaterials strategies and microfabrication processes, as well as computational and systems biology approaches, is enabling new tools to generate reliable in vitro systems to study the molecular basis of human disease and facilitate drug development. In this review, we discuss these recently developed tools and emphasize opportunities and challenges involved in combining these technologies toward regenerative science.National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant 5R01EB010246-02)National Center for Advancing Translational Sciences (U.S.) (Grant 1UH2TR000496)United States. Defense Advanced Research Projects Agency (Cooperative Agreement W911NF-12-2-0039

Improving precision of material extrusion 3D printing by in-situ monitoring and predicting 3D geometric deviation using Conditional Adversarial Networks

The field of additive manufacturing, especially 3D printing, has gained growing attention in the research and commercial sectors in recent years. Notwithstanding that the capabilities of 3D printing have moved on to enhanced resolution, higher deposition rate, and a wide variety of materials, the crucial challenge of verifying that the component manufactured is within the dimensional tolerance as designed continues to exist. Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably “certify-as-you- build” small-batch prototyping and large-batch production.In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion.1. This work developed and demonstrated an approach for layer-by-layer mapping of 3D printed parts, which can be used for validation of printed models and in situ adjustment of print parameters. This in situ metrology system scans each layer at the time of printing, providing a 3D model of the as-printed part. A high-speed optical scanning system was integrated with a Material Extrusion type 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate “certify-as-you-build” products.2. This work trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight Conditional Adversarial Networks (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within �0.05 mm, �0.10 mm, �0.15 mm of the actual measured value, respectively.3. This work developed an Iterative Forward approach to redesign the Computer-Aided- Design model by reverse engineering using the trained machine learning models, allowing for compensation of print imperfection at the design stage, in advance of the first printing. The compensation algorithms with eight different sets of different parameters were evaluated. It has been proven that the Iterative Forward approach improved the geometric deviation of the predicted profiles by making compensation to the CAD model

Machine intelligence for nerve conduit design and production

Nerve guidance conduits (NGCs) have emerged from recent advances within tissue engineering as a promising alternative to autografts for peripheral nerve repair. NGCs are tubular structures with engineered biomaterials, which guide axonal regeneration from the injured proximal nerve to the distal stump. NGC design can synergistically combine multiple properties to enhance proliferation of stem and neuronal cells, improve nerve migration, attenuate inflammation and reduce scar tissue formation. The aim of most laboratories fabricating NGCs is the development of an automated process that incorporates patient-specific features and complex tissue blueprints (e.g. neurovascular conduit) that serve as the basis for more complicated muscular and skin grafts. One of the major limitations for tissue engineering is lack of guidance for generating tissue blueprints and the absence of streamlined manufacturing processes. With the rapid expansion of machine intelligence, high dimensional image analysis, and computational scaffold design, optimized tissue templates for 3D bioprinting (3DBP) are feasible. In this review, we examine the translational challenges to peripheral nerve regeneration and where machine intelligence can innovate bottlenecks in neural tissue engineering

Integrating Experimental and Computational Approaches to Optimize 3D Bioprinting of Cancer Cells

A key feature distinguishing 3D bioprinting from other 3D cell culture techniques is its precise control over created structures. This property allows for the high-resolution fabrication of biomimetic structures with controlled structural and mechanical properties such as porosity, permeability, and stiffness. However, for bioprinting to be successful, a comprehensive understanding of cell behavior is essential, yet challenging. This includes the survivability of cells throughout the printing process, their interactions with the printed structures, and their responses to environmental cues after printing. There are numerous variables in bioprinting which influence the cell behavior, so bioprinting quality during and after the procedure. Thus, to achieve desirable results, it is necessary to consider and optimize these influential variables. So far, these optimizations have been accomplished primarily through trial and error and replicating several experiments, a procedure that is not only time-consuming but also costly. This issue motivated the development of computational techniques in the bioprinting process to more precisely predict and elucidate cells’ function within 3D printed structures during and after printing. During printing, we developed predictive machine learning models to determine the effect of different variables such as cell type, bioink formulation, printing settings parameters, and crosslinking condition on cell viability in extrusion-based bioprinting. To do this, we first created 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 regression and classification neural networks to predict cell viability based on these bioprinting variables. Compared to models that have been developed so far, the performance of our models was superior and showed great prediction results. The study further demonstrated that among the variables investigated in bioprinting, cell type, printing pressure, and crosslinker concentration, respectively, had the most significant impact on the survival of cells. Additionally, we introduced a new optimization strategy that employs the Bayesian optimization model based on the developed regression neural network to determine the optimal combination of the selected bioprinting parameters for maximizing cell viability and eliminating trial-and-error experiments. In our study, this strategy enabled us to identify the optimal crosslinking parameters, within a specified range, including those not previously explored, resulting in optimum cell viability. Finally, we experimentally validated the optimization model's performance. After printing, we developed a cellular automata model for the first time to predict and elucidate the post-printing cell behavior within the 3D bioprinted construct. To improve our model, we bioprinted a 3D construct using cell-laden hydrogel and evaluated cellular functions, including viability and proliferation, in 11 days. The results showed that our model successfully simulated the 3D bioprinted structure and captured in-vitro observations. The proposed model is beneficial for demonstrating complex cellular systems, including cellular proliferation, movement, cell interactions with the environment (e.g., extracellular microenvironment and neighboring cells), and cell aggregation within the scaffold. We also demonstrated that this computational model could predict post-printing biological functions for different initial cell numbers in bioink and different bioink formulations with gelatin and alginate without replicating several in-vitro measurements. Taken all together, this thesis introduces novel bioprinting process design strategies by presenting mathematical and computational frameworks for both during and after bioprinting. We believe such frameworks will substantially impact 3D bioprinting's future application and inspire researchers to further realize how computational methods might be utilized to advance in-vitro 3D bioprinting research

Application of Artificial Intelligence in Modern Healthcare System

Artificial intelligence (AI) has the potential of detecting significant interactions in a dataset and also it is widely used in several clinical conditions to expect the results, treat, and diagnose. Artificial intelligence (AI) is being used or trialed for a variety of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery. In this chapter, we will discuss the application of artificial intelligence (AI) in modern healthcare system and the challenges of this system in detail. Different types of artificial intelligence devices are described in this chapter with the help of working mechanism discussion. Alginate, a naturally available polymer found in the cell wall of the brown algae, is used in tissue engineering because of its biocompatibility, low cost, and easy gelation. It is composed of α-L-guluronic and β-D-manuronic acid. To improve the cell-material interaction and erratic degradation, alginate is blended with other polymers. Here, we discuss the relationship of artificial intelligence with alginate in tissue engineering fields