Molecular Imprinting Strategies for Tissue Engineering Applications: A Review

Tissue Engineering (TE) represents a promising solution to fabricate engineered constructs able to restore tissue damage after implantation. In the classic TE approach, biomaterials are used alongside growth factors to create a scaffolding structure that supports cells during the construct maturation. A current challenge in TE is the creation of engineered constructs able to mimic the complex microenvironment found in the natural tissue, so as to promote and guide cell migration, proliferation, and differentiation. In this context, the introduction inside the scaffold of molecularly imprinted polymers (MIPs)-synthetic receptors able to reversibly bind to biomolecules-holds great promise to enhance the scaffold-cell interaction. In this review, we analyze the main strategies that have been used for MIP design and fabrication with a particular focus on biomedical research. Furthermore, to highlight the potential of MIPs for scaffold-based TE, we present recent examples on how MIPs have been used in TE to introduce biophysical cues as well as for drug delivery and sequestering

A computational analysis of a novel therapeutic approach combining an advanced medicinal therapeutic device and a fracture fixation assembly for the treatment of osteoporotic fractures: Effects of physiological loading, interface conditions, and fracture fixation materials

: The occurrence of periprosthetic femoral fractures (PFF) has increased in people with osteoporosis due to decreased bone density, poor bone quality, and stress shielding from prosthetic implants. PFF treatment in the elderly is a genuine concern for orthopaedic surgeons as no effective solution currently exists. Therefore, the goal of this study was to determine whether the design of a novel advanced medicinal therapeutic device (AMTD) manufactured from a polymeric blend in combination with a fracture fixation plate in the femur is capable of withstanding physiological loads without failure during the bone regenerative process. This was achieved by developing a finite element (FE) model of the AMTD together with a fracture fixation assembly, and a femur with an implanted femoral stem. The response of both normal and osteoporotic bone was investigated by implementing their respective material properties in the model. Physiological loading simulating the peak load during standing, walking, and stair climbing was investigated. The results showed that the fixation assembly was the prime load bearing component for this configuration of devices. Within the fixation assembly, the bone screws were found to have the highest stresses in the fixation assembly for all the loading conditions. Whereas the stresses within the AMTD were significantly below the maximum yield strength of the device's polymeric blend material. Furthermore, this study also investigated the performance of different fixation assembly materials and found Ti-6Al-4V to be the optimal material choice from those included in this study

Effect of Uniaxial Compression Frequency on Osteogenic Cell Responses in Dynamic 3D Cultures

The application of mechanical stimulation on bone tissue engineering constructs aims to mimic the native dynamic nature of bone. Although many attempts have been made to evaluate the effect of applied mechanical stimuli on osteogenic differentiation, the conditions that govern this process have not yet been fully explored. In this study, pre-osteoblastic cells were seeded on PLLA/PCL/PHBV (90/5/5 wt.%) polymeric blend scaffolds. The constructs were subjected every day to cyclic uniaxial compression for 40 min at a displacement of 400 μm, using three frequency values, 0.5, 1, and 1.5 Hz, for up to 21 days, and their osteogenic response was compared to that of static cultures. Finite element simulation was performed to validate the scaffold design and the loading direction, and to assure that cells inside the scaffolds would be subjected to significant levels of strain during stimulation. None of the applied loading conditions negatively affected the cell viability. The alkaline phosphatase activity data indicated significantly higher values at all dynamic conditions compared to the static ones at day 7, with the highest response being observed at 0.5 Hz. Collagen and calcium production were significantly increased compared to static controls. These results indicate that all of the examined frequencies substantially promoted the osteogenic capacity

Total quality control of bioprinting processes towards clinical translation

Bioprinting has seen an exponential increase of attention in recent years from both academia and industry as a promising solution to fabricate constructs for Tissue Engineering applications, including implantation, in vitro models, and drug screening. Even though important results have been shown in literature, the field is currently suffering from a severe lack of clinical translation examples, mainly due to both technological limitations (e.g., need for new inks, need for advancements in current fabrication technologies, incorporation of vasculature inside the construct) and regulatory barriers (e.g., definition of the classification for the bioprinted product, development of bioprinting-specific standards). Considering these challenges, a major need for the bioprinting field is the implementation of quality control strategies to reduce inter-batch variability, guarantee that the final product is close to the designed model, and comply to relevant quality-related international standards. In this context, the objective of the PhD work is the formulation and development of quality control strategies for different bioprinting processes, which will be applied for the development and fabrication of a case study Advanced Therapy Medicinal Product (ATMP) device in the context of the European H2020 project GIOTTO (GA: 814410). The starting point of the doctoral project was the design and fabrication of a custom-made bioprinter, which served as the basis for the development of add-on quality control systems. In particular, the bioprinter is characterized by a high precision (5 µm) and repeatability (± 10 µm of bidirectional repeatability) in positioning thanks to the use of stepper-actuated linear stages. The robust positioning system guarantees that no errors are introduced in the scaffold production due to incorrect movements of the machine. From a software point of view, the bioprinter employs LinuxCNC, an open-source control software for numerical control machines (e.g., lathes, cutters). LinuxCNC can enable real-time control of the bioprinter positioning system, as well as the integration of new modules thanks to its software architecture. With this system, at any given time two tool-heads are in place in the bioprinter for multimaterial and multiscale fabrication. The user can select from three interchangeable deposition tool heads that can be swapped in place depending on the application: i) piston-actuated extruder, designed for highly viscous solutions; ii) thermal drop-on-demand inkjet, to pattern the scaffold with picoliter droplets of liquid material; iii) fused deposition modelling (FDM) extruder, to introduce rigid polymeric support inside the scaffold. For each tool-head, different strategies were implemented to assure consistent deposition and control over the printing process. In particular, the extruder stepper actuator was equipped with a rotary encoder that monitors the position in closed-loop feedback to compensate for under- or over-extrusion artifacts. A custom-made electronic control system was implemented for the inkjet tool-head to modify the operating voltage and so enable the patterning of custom solutions. Finally, a sensor was designed and fabricated to monitor the filament diameter for the FDM system, with the aim of keeping a constant flowrate by compensating for variation of the diameter. Then, advanced quality control solutions were developed specifically for the extrusion tool-head, since currently it represents the most used manufacturing technology in bioprinting. Firstly, an analytical model of the extrusion bioprinting process was formulated to: i) predict the printability of a given biomaterial ink using a specific bioprinting apparatus, scaffold geometry, and set of printing parameters, and ii) if the biomaterial ink is found to be printable, provide a set of optimized printing parameters to be used for experimentation. The model was developed by taking into consideration multiple aspects of the process (referred to as stages for brevity), including: 1. the extrudability, i.e., the easiness of extrusion of the given material from the syringe needle; 2. the line deposition process when printing the first layer of the scaffold; 3. the stabilization of a three-dimensional woodpile scaffold after printing to evaluate the shape fidelity of the final product. For each model stage, relevant equations were formulated by considering different constitutive equations for the biomaterial ink (i.e., Newtonian fluid, power law, Herschel Bulkley), and the equations were experimentally validated. The analytical model was then implemented in both a standalone program and web-based application to help the bioprinting scholars determine a priori the ink printability and optimize the printing parameters. Concurrently, to support the model use, a set of rheological characterization experiments (based on recognized material characterization standards) were defined to find the important material properties for the model. Building on the aforementioned results, a novel, artificial intelligence-based solution for the in-process monitoring and automatic parameter optimization was developed. Briefly, a comprehensive dataset of multiple scenarios was constructed by recording the printing process from a front view using a high-resolution webcam. Each video corresponded to a print with a different combination of relevant parameters, including layer height, flow, printing set-up (i.e., pneumatic and piston-actuated extrusion), material color. Two main errors were introduced in the dataset resulting from a non-optimal printing parameters combination, including under- (i.e., not enough material is extruded) and over-extrusion (i.e., too much material is extruded). After sampling and frame pre-processing, the resulting dataset was used to comprehensively optimize an ad-hoc convolutional neural network by considering as main requirements the high classification accuracy (around 94%) and the fast response time (around 180 ms to process 30 frames on CPU). The model was used for monitoring the printing process online to stop the print if an error occurred before completion, to save time and reduce material waste (at least 20% of the material for a print saved). Furthermore, an automatic parameter optimization system based on a series of consecutive prints with varying parameters was developed to optimize the parameters automatically, without the need for user intervention and material characterization. Altogether, the two strategies represent a comprehensive software solution for quality control of the extrusion bioprinting process. Even though quality control during manufacturing is a key requirement for clinical translation of the bioprinted product, it is also important to standardize not only the production process but also the nomenclature related to the field, to facilitate the development of shared libraries of materials, protocols, and bioprinting-specific standards. In this context, during this PhD project a novel application of Natural Language Processing (NLP) to the bioprinting literature was developed, with the goal of extracting knowledge from scientific papers. In particular, the approach is based on two main data sources, the abstracts and related author keywords, which are used to train a composite NLP model. This is based on: i) an embeddings part, which generates word vectors (i.e., dense numerical representations of a word) for an input keyword, and ii) a classifier part, to label it based on its word vector into a manufacturing technique, used material, or application of the bioprinted product. The composite model was trained and optimized in a two-stage optimization procedure to yield the best classification performance (around 90% accuracy). The annotated author keywords were then found in the abstract collection to both generate a lexicon of the bioprinting field and extract relevant information, like technology trends and the relationship between manufacturing-material-application. The proposed approach can serve as a basis for more complex NLP-related studies toward the automated analysis and standardization of the bioprinting literature. Finally, the bioprinter platform alongside the developed quality control solutions were applied for the fabrication of an ATMP device in the context of the GIOTTO project. The Device is a porous, graded structure, produced through bioprinting technologies (i..e, FDM, inkjet), which can direct the diffusion of relevant molecules towards an osteoporotic femoral fracture and so accelerate fracture healing. During the project multiple deposition technologies were used to fabricate prototypes and scaffolds at different stages of the device development. The experiments started with the assessment of the printability of different filament compositions using FDM and the determination of the optimal printing parameters for each one. The optimized set of printing parameters were used for FDM production of scaffolds (i.e., porous cylinders, 5 mm diameter, 1 mm height) for in vitro testing. The scaffold quality was characterized after printing in terms of their dimensional accuracy and overall porosity when compared to the original design. An open-mould method was developed for the FDM fabrication of scaffolds (i.e., non-porous cylinders, 2 mm diameter, 0.2 mm height) for in vivo testing. Post-printing quality assessment was performed by measuring their dimensions and evaluating their shape at an optical microscope. The results showed that the developed method could yield tight dimensional tolerances (i.e., ± 0.1 mm in both diameter and height) according to the fabrication constraints. Moreover, superparamagnetic iron oxide nano-particles (SPIONs) solutions printability was assessed through inkjet printing. The experiments helped determine the best performing solvent by evaluating the printed solutions at an optical microscope and looking at the stability of the printing process through subsequent prints. Finally, a combination of FDM and inkjet was used to demonstrate the printer ability to fabricate multimaterial and multiscale scaffolds. All in all, these experiments served as validation of both the developed bioprinting platform and the quality control strategies, showing how these solutions can be used to obtain repeatable, high quality results and so can be envisioned to move the bioprinting field to more impactful clinical applications

Design and Fabrication of a Multi-Scale and Multi-Material Bioprinting System.

This thesis focuses on the design and fabrication of a printing platform for multi-material and multi-scale Bioprinting, which integrates micro-extrusion and thermal Drop-on-demand Inkjet on the same machine. The new platform will integrate with a previously built three-axis cartesian positioning system, in the scope of the BOOST project, a European funded project whose aim is to study Osteoporosis using Bioprinted bone scaffolds. The platform was designed following a series of sequential steps, by starting with the identification of the design requirements through theorical modelling and Finite Element Analysis (FEA). The values found in through these analyses were then used to (1) correctly dimension the platform, and (2) for its mechanical validation. A major focus of this thesis is on electronic prototyping and control of the platform. A whole printing utility was designed and coded with three main programming languages: Python, C++, and Matlab. The utility guides the user through all the necessary steps required to print the final construct, allowing him/her to specify the desired printing parameters. Moreover, the utility has several add-ons and error handling features to improve the user experience. Using this method, the platform was validated through a series of printing tests. Firstly, the correct combination of printing parameters was found for the given material. These parameters were then used to print a series of proof-of-concept shapes by using Inkjet printing to create a 3D ink shape inside an extruded gel support. The promising results of these printing tests show the potential of the novel printing platform for scaffold fabrication in Bioprinting

Dataset and code for 'AI-based Knowledge Extraction from the Bioprinting Literature for identifying technology trends'

Zip file containing the dataset and code for the paper 'AI-based Knowledge Extraction from the Bioprinting Literature for identifying technology trends'. The dataset is composed of: A train_data.csv file, containing all annotated keywords used for classifier training. A filt_ls.pkl file, containing the sentences used to train the embeddings model. A train.py file, to train the composite keyword annotation model. The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410

Open-source CAD-CAM simulator of the extrusion-based bioprinting process

Extrusion-based Bioprinting (EBB) represents one of the most used Bioprinting technologies among researchers, thanks to its ease of use, wide variety of available materials, and affordable cost. Even though the technique has successfully been applied in bioprinting constructs for Tissue Engineering applications, there is still no consensus on which parameters have more influence of the accuracy of bioprinted scaffolds. Moreover, the literature lacks a rapid and robust method to consistently set the printing parameters before the actual printing phase, thus minimizing the trial-and-error process. In this context, we present a mathematical model for understanding the printability of a defined structure by depositing a given biomaterial ink through a specific EBB apparatus. The model takes into account different steps of the printing process, including extrusion, line formation and scaffold stabilization over time. The model was experimentally validated and implemented in an open-source software to guide the user in setting the correct printing parameters (i.e., printing speed, layer height and flow) based on scaffold dimensions, material properties (including rheological and mechanical ones) and printer set-up. To encourage the model use, we also propose a set of experiments to extract the relevant material properties for our model, and the software is available both as a stand-alone program (available at https://github.com/CentroEPiaggio/Rheology-GUI), as well as a webpage (available at https://www.prin-vision.it/printability-assessment)

Surface reconstruction and tissue recognition for robotic-based in situ bioprinting

In this study, an innovative approach for the surface reconstruction and mechanical evaluation of anatomical defects for robotic-based in situ bioprinting applications is presented. A touch probe was developed to be used as the end-effector of a 5 Degrees-of-Freedom robotic arm. The probe, based on the combination of a spring system and a light sensor, is able not only to reconstruct the surface but also to register the penetration depth for each contact point. The knowledge of this parameter allows the evaluation of the mechanical properties of the substrate and thus the recognition of the biological tissue. The probe was able to correctly identify the elastic moduli of silicone substrates with various shapes and stiffnesses (E = 4–23–160 kPa) and showed good agreement compared with standard uniaxial compression test. In situ bioprinting tests were performed onto meshes reconstructed with the probe using different path planning methods. Finally, the presented in situ bioprinting workflow was tested as a proof-of-concept onto an anthropomorphic phantom to completely regenerate a cranial defect. The complete knowledge of the geometry and the mechanical properties of the damaged site allows a more accurate path planning, enabling the deposition of different biomaterials for different target tissues and so the regeneration of heterogeneous anatomical defects

Robotic platform and path planning algorithm for in situ bioprinting

The aim of this work is to design a robotic bioprinting platform able to fabricate a three-dimensional structure onto irregular surfaces. With respect to the limitations of current in vitro bioprinting approach, widely used in scaffold-based tissue engineering – handling difficulty, risk of contamination, shape not matching with the defect site – this robotic bioprinter can offer an innovative solution allowing in situ bioprinting, a direct dispensing of biological materials onto and into the damaged site. The robotic platform was developed starting from the 5 degrees-of-freedom open source MOVEO robot from BCN3D. The hardware and the software of the original project were re-engineered to control the robot using LinuxCNC, a path planning algorithm was developed in Matlab®, and the end-effector was equipped with a pneumatic extruder. The algorithm automatically projects any generic printing pattern on the surface on which the scaffold will be 3D bioprinted. For each point, the algorithm calculates the joint angles to keep the end effector always perpendicular to the surface. A g-code file is then exported to Linux CNC adding parameters to control the air pressure and the printing speed. The robotic platform was tested to evaluate its performances. Resolution (~200 ​μm) and repeatability were estimated and preliminary in situ bioprinting tests were performed onto different irregular surfaces, including a physiologically relevant bone model

Promotion of In Vitro Osteogenic Activity by Melt Extrusion-Based PLLA/PCL/PHBV Scaffolds Enriched with Nano-Hydroxyapatite and Strontium Substituted Nano-Hydroxyapatite

Bone tissue engineering has emerged as a promising strategy to overcome the limitations of current treatments for bone-related disorders, but the trade-off between mechanical properties and bioactivity remains a concern for many polymeric materials. To address this need, novel polymeric blends of poly-L-lactic acid (PLLA), polycaprolactone (PCL) and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) have been explored. Blend filaments comprising PLLA/PCL/PHBV at a ratio of 90/5/5 wt% have been prepared using twin-screw extrusion. The PLLA/PCL/PHBV blends were enriched with nano-hydroxyapatite (nano-HA) and strontium-substituted nano-HA (Sr-nano-HA) to produce composite filaments. Three-dimensional scaffolds were printed by fused deposition modelling from PLLA/PCL/PHBV blend and composite filaments and evaluated mechanically and biologically for their capacity to support bone formation in vitro. The composite scaffolds had a mean porosity of 40%, mean pores of 800 µm, and an average compressive modulus of 32 MPa. Polymer blend and enriched scaffolds supported cell attachment and proliferation. The alkaline phosphatase activity and calcium production were significantly higher in composite scaffolds compared to the blends. These findings demonstrate that thermoplastic polyesters (PLLA and PCL) can be combined with polymers produced via a bacterial route (PHBV) to produce polymer blends with excellent biocompatibility, providing additional options for polymer blend optimization. The enrichment of the blend with nano-HA and Sr-nano-HA powders enhanced the osteogenic potential in vitro