Machine learning predicts 3D printing performance of over 900 drug delivery systems

Smart laser Sintering: Deep Learning-Powered powder bed fusion 3D printing in precision medicine

Medicines remain ineffective for over 50% of patients due to conventional mass production methods with fixed drug dosages. Three-dimensional (3D) printing, specifically selective laser sintering (SLS), offers a potential solution to this challenge, allowing the manufacturing of small, personalized batches of medication. Despite its simplicity and suitability for upscaling to large-scale production, SLS was not designed for pharmaceutical manufacturing and necessitates a time-consuming, trial-and-error adaptation process. In response, this study introduces a deep learning model trained on a variety of features to identify the best feature set to represent drugs and polymeric materials for the prediction of the printability of drug-loaded formulations using SLS. The proposed model demonstrates success by achieving 90% accuracy in predicting printability. Furthermore, explainability analysis unveils materials that facilitate SLS printability, offering invaluable insights for scientists to optimize SLS formulations, which can be expanded to other disciplines. This represents the first study in the field to develop an interpretable, uncertainty-optimized deep learning model for predicting the printability of drug-loaded formulations. This paves the way for accelerating formulation development, propelling us into a future of personalized medicine with unprecedented manufacturing precision.

Extrusion-based 3D printing for development of complex capsular systems for advanced drug delivery

This review explores the feasibility of extrusion-based 3D printing techniques for producing complex dosage forms (such as capsular shells/devices) that provide controlled drug release and targeted delivery. The current discussion explores how extrusion-based 3D printing techniques, particularly Fused Deposition Modelling (FDM) and Pressure-Assisted Modelling (PAM), offer significant advantages in fabricating such complex dosage forms. This technology enables the fabrication of single-, dual-, or multi-compartment capsular systems with customized designs/geometry of the capsular shell to achieve delayed, sustained, or pulsatile drug release. The impact of customized design/geometry on the biopharmaceutical performances of loaded therapeutics is comprehensively discussed. The potential of 3D printing techniques for different specialized drug delivery purposes like gastric floating, implants, suppositories, and printfills are also addressed. This technique has the potential to significantly improve the therapeutic outcomes, and patient adherence to medication regimens, and pave the way for personalized medicine.

3D printed dosage forms, where are we headed?

INTRODUCTION

3D Printing (3DP) is an innovative fabrication technology that has gained enormous popularity through its paradigm shifts in manufacturing in several disciplines, including healthcare. In this past decade, we have witnessed the impact of 3DP in drug product development. Almost 8 years after the first USFDA approval of the 3D printed tablet Levetiracetam (Spritam), the interest in 3DP for drug products is high. However, regulatory agencies have often questioned its large-scale industrial practicability, and 3DP drug approval/guidelines are yet to be streamlined.

AREAS COVERED

In this review, major technologies involved with the fabrication of drug products are introduced along with the prospects of upcoming technologies, including AI (Artificial Intelligence). We have touched upon regulatory updates and discussed the burning limitations, which require immediate focus, illuminating status, and future perspectives on the near future of 3DP in the pharmaceutical field.

EXPERT OPINION

3DP offers significant advantages in rapid prototyping for drug products, which could be beneficial for personalizing patient-based pharmaceutical dispensing. It seems inevitable that the coming decades will be marked by exponential growth in personalization, and 3DP could be a paradigm-shifting asset for pharmaceutical professionals.

Geometry-Driven Fabrication of Mini-Tablets via 3D Printing: Correlating Release Kinetics with Polyhedral Shapes

The aim of this study was to fabricate mini-tablets of polyhedrons containing theophylline using a fused deposition modeling (FDM) 3D printer, and to evaluate the correlation between release kinetics models and their geometric shapes. The filaments containing theophylline, hydroxypropyl cellulose (HPC), and EUDRAGIT RS PO (EU) could be obtained with a consistent thickness through pre-drying before hot melt extrusion (HME). Mini-tablets of polyhedrons ranging from tetrahedron to icosahedron were 3D-printed using the same formulation of the filament, ensuring equal volumes. The release kinetics models derived from dissolution tests of the polyhedrons, along with calculations for various physical parameters (edge, SA: surface area, SA/W: surface area/weight, SA/V: surface area/volume), revealed that the correlation between the Higuchi model and the SA/V was the highest (R2 = 0.995). It was confirmed that using 3D- printing for the development of personalized or pediatric drug products allows for the adjustment of drug dosage by modifying the size or shape of the drug while maintaining or controlling the same release profile.

AI-driven design of customized 3D-printed multi-layer capsules with controlled drug release profiles for personalized medicine

Personalized medicine aims to effectively and efficiently provide customized drugs that cater to diverse populations, which is a significant yet challenging task. Recently, the integration of artificial intelligence (AI) and three-dimensional (3D) printing technology has transformed the medical field, and was expected to facilitate the efficient design and development of customized drugs through the synergy of their respective advantages. In this study, we present an innovative method that combines AI and 3D printing technology to design and fabricate customized capsules. Initially, we discretized and encoded the geometry of the capsule, simulated the dissolution process of the capsule with classical drug dissolution model, and verified it by experiments. Subsequently, we employed a genetic algorithm to explore the capsule geometric structure space and generate a complex multi-layer structure that satisfies the target drug release profiles, including stepwise release and zero-order release. Finally, Two model drugs, isoniazid and acetaminophen, were selected and fused deposition modeling (FDM) 3D printing technology was utilized to precisely print the AI-designed capsule. The reliability of the method was verified by comparing the in vitro release curve of the printed capsules with the target curve, and the f2 value was more than 50. Notably, accurate and autonomous design of the drug release curve was achieved mainly by changing the geometry of the capsule. This approach is expected to be applied to different drug needs and facilitate the development of customized oral dosage forms.

Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters

Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with R2 = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (i.e., polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Inkjet Printing of Pharmaceuticals

Inkjet printing (IJP) is an additive manufacturing process that selectively deposits ink materials, layer-by-layer, to create 3D objects or 2D patterns with precise control over their structure and composition. This technology has emerged as an attractive and versatile approach to address the ever-evolving demands of personalized medicine in the healthcare industry. Although originally developed for nonhealthcare applications, IJP harnesses the potential of pharma-inks, which are meticulously formulated inks containing drugs and pharmaceutical excipients. Delving into the formulation and components of pharma-inks, the key to precise and adaptable material deposition enabled by IJP is unraveled. The review extends its focus to substrate materials, including paper, films, foams, lenses, and 3D-printed materials, showcasing their diverse advantages, while exploring a wide spectrum of therapeutic applications. Additionally, the potential benefits of hardware and software improvements, along with artificial intelligence integration, are discussed to enhance IJP's precision and efficiency. Embracing these advancements, IJP holds immense potential to reshape traditional medicine manufacturing processes, ushering in an era of medical precision. However, further exploration and optimization are needed to fully utilize IJP's healthcare capabilities. As researchers push the boundaries of IJP, the vision of patient-specific treatment is on the horizon of becoming a tangible reality.

Correlating mechanical and rheological filament properties to processability and quality of 3D printed tablets using multiple linear regression

Filament formulation for FDM is a challenging and time-consuming process. Several pharmaceutical polymers are not feedable on their own. Due to inadequate filament formulation, 3D printed tablets can also exhibit poor uniformity of tablet attributes. To better understand filament formulation process, 23 filaments were prepared with the polymer mixing approach. To yield processable filaments, brittle and pliable polymers were combined. A 20 % addition of a pliable polymer to a brittle one resulted in filament processability and vice versa. Predictive statistical models for filament processability and uniformity of tablet attributes were established based on the mechanical and rheological properties of filaments. 15 input variables were correlated to 9 responses, which represent filament processability and tablet properties, by using multiple linear regression approach. Filament stiffness, assessed by indentation, and its square term were the only variables that determined the filament's feedability. However, the resulting model is equipment-specific since different feeding mechanism exert different forces on the filaments. Additional models with good predictive power (R2pred > 0.50) were established for tablet width uniformity, drug release uniformity, tablet disintegration time uniformity and occurrence of disintegration, which are equipment-independent outputs. Therefore, the obtained model outcomes could be used in other research endeavours.

Rising role of 3D-printing in delivery of therapeutics for infectious disease

Modern drug delivery to tackle infectious disease has drawn close to personalizing medicine for specific patient populations. Challenges include antibiotic-resistant infections, healthcare associated infections, and customizing treatments for local patient populations. Recently, 3D-printing has become a facilitator for the development of personalized pharmaceutic drug delivery systems. With a variety of manufacturing techniques, 3D-printing offers advantages in drug delivery development for controlled, fine-tuned release and platforms for different routes of administration. This review summarizes 3D-printing techniques in pharmaceutics and drug delivery focusing on treating infectious diseases, and discusses the influence of 3D-printing design considerations on drug delivery platforms targeting these diseases. Additionally, applications of 3D-printing in infectious diseases are summarized, with the goal to provide insight into how future delivery innovations may benefit from 3D-printing to address the global challenges in infectious disease.

Unraveling the influence of solvent composition on Drop-on-Demand binder jet 3D printed tablets containing calcium sulfate hemihydrate

Recently, binder jet printed modular tablets were loaded with three anti-viral drugs via Drop on Demand (DoD) technology where drug solutions prepared in ethanol showed faster release than those prepared in water. During printing, water is used as a binding agent, whereas ethanol is added to maintain the porous structure of the tablets. Thus, the hypothesis is that the porosity would be controlled by manipulating the percentage of water and ethanol. In this study, Rhodamine 6G (R6G) was selected as a model drug due to its high solubility in water and ethanol, visualization function as a fluorescent dye, and potential therapeutic effects for cancer treatment. Approximately, 10 mg/ml R6G solutions were prepared with five different water-ethanol ratios (0-100, 75-25, 50-50, 75-25, 100-0). The ink solutions were printed onto blank binder jet 3D-printed tablets containing calcium sulphate hemihydrate using DoD technology. The tablets were dried at room temperature and then characterized using SEM-EDX, fluorescent microscope, TGA, XRD, FTIR, and DSC as well as in vitro release studies to investigate the impact of water-ethanol ratio on the release profile of R6G. Results indicated that the solution with higher ethanol ratio penetrated the tablets faster than the lower ethanol ratio, while the solution prepared with pure water was first accumulated onto the tablets' surface and then absorbed by the tablets. Moreover, tablets with more water content gained more weight and thickness. The EDX analysis and fluorescent microscope showed the uniform surface distribution of the drug. The SEM images revealed the difference in the tablet surface among the five formulations. Furthermore, the TGA data presents a notable increase in water loss, with XRD analysis suggesting the formation of gypsum in tablets containing elevated water content. The release study exhibited that the fastest release was from WE0-100, whereas the release rate decreases as the content of water increases. The WE0-100 releases more than 40 % drug within the first hour which is almost twice as high of the WE100-0 formulation. This DoD technology could distribute drugs onto the tablet's surface uniformly. The calcium sulfate would transform from hemihydrate to dihydrate form in the presence of water and therefore, those tablets treated with higher water content led to slower release. In conclusion, this study underscores the substantial impact of the water-ethanol ratio on drug release from binder jet printed tablets and highlights the potential of DoD technology for uniform drug distribution and controlled release.

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.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

BACKGROUND

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

OBJECTIVES

The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

METHODS

This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

RESULTS

Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

CONCLUSION

The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

Predicting pharmaceutical inkjet printing outcomes using machine learning

Inkjet printing has been extensively explored in recent years to produce personalised medicines due to its low cost and versatility. Pharmaceutical applications have ranged from orodispersible films to complex polydrug implants. However, the multi-factorial nature of the inkjet printing process makes formulation (e.g., composition, surface tension, and viscosity) and printing parameter optimization (e.g., nozzle diameter, peak voltage, and drop spacing) an empirical and time-consuming endeavour. Instead, given the wealth of publicly available data on pharmaceutical inkjet printing, there is potential for a predictive model for inkjet printing outcomes to be developed. In this study, machine learning (ML) models (random forest, multilayer perceptron, and support vector machine) to predict printability and drug dose were developed using a dataset of 687 formulations, consolidated from in-house and literature-mined data on inkjet-printed formulations. The optimized ML models predicted the printability of formulations with an accuracy of 97.22%, and predicted the quality of the prints with an accuracy of 97.14%. This study demonstrates that ML models can feasibly provide predictive insights to inkjet printing outcomes prior to formulation preparation, affording resource- and time-savings.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Exploiting Pharma 4.0 Technologies in the Non-Biological Complex Drugs Manufacturing: Innovations and Implications

The pharmaceutical industry has entered an era of transformation with the emergence of Pharma 4.0, which leverages cutting-edge technologies in manufacturing processes. These hold tremendous potential for enhancing the overall efficiency, safety, and quality of non-biological complex drugs (NBCDs), a category of pharmaceutical products that pose unique challenges due to their intricate composition and complex manufacturing requirements. This review attempts to provide insight into the application of select Pharma 4.0 technologies, namely machine learning, in silico modeling, and 3D printing, in the manufacturing process of NBCDs. Specifically, it reviews the impact of these tools on NBCDs such as liposomes, polymeric micelles, glatiramer acetate, iron carbohydrate complexes, and nanocrystals. It also addresses regulatory challenges associated with the implementation of these technologies and presents potential future perspectives, highlighting the incorporation of digital twins in this field of research as it seems to be a very promising approach, namely for the optimization of NBCDs manufacturing processes.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

A new approach to prediction riboflavin absorbance using imprinted polymer and ensemble machine learning algorithms

The molecularly imprinted polymer (MIP) is useful for measuring the amount of riboflavin (vitamin B2), in various samples using UV/Vis instruments. The practical optimization of the MIP synthesis conditions has a number of drawbacks, like the need to spend money, the need to spend time, the use of the compounds that cause contamination, needing laboratory equipment and tools. Using machine learning (ML) to predict the amount of riboflavin absorbance is a creative solution to overcome the problems and shortcomings of optimizing polymer synthesis conditions. In fact, by using the model without needing real work in the laboratory, the optimum laboratory conditions are determined, and as a result the maximized absorption of the riboflavin is obtained. In this paper, MIP was synthesized for selective extraction of the riboflavin, and UV/Vis spectrophotometry was used to quantitatively measure riboflavin absorbance. Various factors affect the performance of the polymer. The effect of six important factors, including the molar ratio of the template, the molar ratio of monomer, the molar ratio of cross-linker, loading time, stirring rate, and pH, were investigated. Then, using ensemble ML algorithms, like gradient boosting (GB), extra trees (ET), random forest (RF), and Ada boost (Ada) algorithms, an accurate model was created to predict the riboflavin absorption. Also, the mutual information feature selection method was used to determine the important features. The results of using feature selection method was shown that variables such as the molar ratio of the template, the molar ratio of the monomer, and the molar ratio of the cross-linker had a high effect on riboflavin absorbance. The GB and Ada boost algorithms performed better than ET and RF algorithms. After tuning the n-estimator hyper parameter (n-estimator = 300), the GB algorithm was shown an excellent performance in predicting the absorbance of riboflavin and the maximum R²-scoring of the model was obtained at 0.965995, the minimum of the mean absolute error (MAE), and mean square error (MSE) of the model respectively were obtained -0.003711 and -0.000078. Therefore, by using the proposed model, it is possible to predict riboflavin absorbance theoretically, and with high accuracy by changing the inputs of model, and using the model instead of working in the lab saves time, money, chemical compounds, and lab ware.

Energy Consumption and Carbon Footprint of 3D Printing in Pharmaceutical Manufacture

Achieving carbon neutrality is seen as an important goal in order to mitigate the effects of climate change, as carbon dioxide is a major greenhouse gas that contributes to global warming. Many countries, cities and organizations have set targets to become carbon neutral. The pharmaceutical sector is no exception, being a major contributor of carbon emissions (emitting approximately 55% more than the automotive sector for instance) and hence is in need of strategies to reduce its environmental impact. Three-dimensional (3D) printing is an advanced pharmaceutical fabrication technology that has the potential to replace traditional manufacturing tools. Being a new technology, the environmental impact of 3D printed medicines has not been investigated, which is a barrier to its uptake by the pharmaceutical industry. Here, the energy consumption (and carbon emission) of 3D printers is considered, focusing on technologies that have successfully been demonstrated to produce solid dosage forms. The energy consumption of 6 benchtop 3D printers was measured during standby mode and printing. On standby, energy consumption ranged from 0.03 to 0.17 kWh. The energy required for producing 10 printlets ranged from 0.06 to 3.08 kWh, with printers using high temperatures consuming more energy. Carbon emissions ranged between 11.60-112.16 g CO₂ (eq) per 10 printlets, comparable with traditional tableting. Further analyses revealed that decreasing printing temperature was found to reduce the energy demand considerably, suggesting that developing formulations that are printable at lower temperatures can reduce CO₂ emissions. The study delivers key initial insights into the environmental impact of a potentially transformative manufacturing technology and provides encouraging results in demonstrating that 3D printing can deliver quality medicines without being environmentally detrimental.

Towards machine learning for hydrogel drug delivery systems

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.

Artificial intelligence for drug discovery: Resources, methods, and applications

Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.

Machine learning in additive manufacturing & Microfluidics for smarter and safer drug delivery systems

A new technological passage has emerged in the pharmaceutical field, concerning the management, application, and transfer of knowledge from humans to machines, as well as the implementation of advanced manufacturing and product optimisation processes. Machine Learning (ML) methods have been introduced to Additive Manufacturing (AM) and Microfluidics (MFs) to predict and generate learning patterns for precise fabrication of tailor-made pharmaceutical treatments. Moreover, regarding the diversity and complexity of personalised medicine, ML has been part of quality by design strategy, targeting towards the development of safe and effective drug delivery systems. The utilisation of different and novel ML techniques along with Internet of Things sensors in AM and MFs, have shown promising aspects regarding the development of well-defined automated procedures towards the production of sustainable and quality-based therapeutic systems. Thus, the effective data utilisation, prospects on a flexible and broader production of "on demand" treatments. In this study, a thorough overview has been achieved, concerning scientific achievements of the past decade, which aims to trigger the research interest on incorporating different types of ML in AM and MFs, as essential techniques for the enhancement of quality standards of customised medicinal applications, as well as the reduction of variability potency, throughout a pharmaceutical process.

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One emerging 3D printing technology is selective laser sintering (SLS), which is garnering attention for its high precision, and compatibility with a wide range of pharmaceutical materials, including low-solubility compounds. However, the full potential of SLS for medicines is yet to be realized, requiring expertise and considerable time-consuming and resource-intensive trial-and-error research. Machine learning (ML), a subset of artificial intelligence, is an in silico tool that is accomplishing remarkable breakthroughs in several sectors for its ability to make highly accurate predictions. Therefore, the present study harnessed ML to predict the printability of SLS formulations. Using a dataset of 170 formulations from 78 materials, ML models were developed from inputs that included the formulation composition and characterization data retrieved from Fourier-transformed infrared spectroscopy (FT-IR), X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC). Multiple ML models were explored, including supervised and unsupervised approaches. The results revealed that ML can achieve high accuracies, by using the formulation composition leading to a maximum F1 score of 81.9%. Using the FT-IR, XRPD and DSC data as inputs resulted in an F1 score of 84.2%, 81.3%, and 80.1%, respectively. A subsequent ML pipeline was built to combine the predictions from FT-IR, XRPD and DSC into one consensus model, where the F1 score was found to further increase to 88.9%. Therefore, it was determined for the first time that ML predictions of 3D printability benefit from multi-modal data, combining numeric, spectral, thermogram and diffraction data. The study lays the groundwork for leveraging existing characterization data for developing high-performing computational models to accelerate formulation development.

Application of Machine-Learning Algorithms for Better Understanding the Properties of Liquisolid Systems Prepared with Three Mesoporous Silica Based Carriers

The processing of liquisolid systems (LSS), which are considered a promising approach to improving the oral bioavailability of poorly soluble drugs, has proven challenging due to the relatively high amount of liquid phase incorporated within them. The objective of this study was to apply machine-learning tools to better understand the effects of formulation factors and/or tableting process parameters on the flowability and compaction properties of LSS with silica-based mesoporous excipients as carriers. In addition, the results of the flowability testing and dynamic compaction analysis of liquisolid admixtures were used to build data sets and develop predictive multivariate models. In the regression analysis, six different algorithms were used to model the relationship between tensile strength (TS), the target variable, and eight other input variables. The AdaBoost algorithm provided the best-fit model for predicting TS (coefficient of determination = 0.94), with ejection stress (ES), compaction pressure, and carrier type being the parameters that influenced its performance the most. The same algorithm was best for classification (precision = 0.90), depending on the type of carrier used, with detachment stress, ES, and TS as variables affecting the performance of the model. Furthermore, the formulations with Neusilin® US2 were able to maintain good flowability and satisfactory values of TS despite having a higher liquid load compared to the other two carriers.

Recent Advances in the Applications of Additive Manufacturing (3D Printing) in Drug Delivery: A Comprehensive Review

There has been a tremendous increase in the investigations of three-dimensional (3D) printing for biomedical and pharmaceutical applications, and drug delivery in particular, ever since the US FDA approved the first 3D printed medicine, SPRITAM® (levetiracetam) in 2015. Three-dimensional printing, also known as additive manufacturing, involves various manufacturing techniques like fused-deposition modeling, 3D inkjet, stereolithography, direct powder extrusion, and selective laser sintering, among other 3D printing techniques, which are based on the digitally controlled layer-by-layer deposition of materials to form various geometries of printlets. In contrast to conventional manufacturing methods, 3D printing technologies provide the unique and important opportunity for the fabrication of personalized dosage forms, which is an important aspect in addressing diverse patient medical needs. There is however the need to speed up the use of 3D printing in the biopharmaceutical industry and clinical settings, and this can be made possible through the integration of modern technologies like artificial intelligence, machine learning, and Internet of Things, into additive manufacturing. This will lead to less human involvement and expertise, independent, streamlined, and intelligent production of personalized medicines. Four-dimensional (4D) printing is another important additive manufacturing technique similar to 3D printing, but adds a 4th dimension defined as time, to the printing. This paper aims to give a detailed review of the applications and principles of operation of various 3D printing technologies in drug delivery, and the materials used in 3D printing, and highlight the challenges and opportunities of additive manufacturing, while introducing the concept of 4D printing and its pharmaceutical applications.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines

Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of their variable geometry design. The geometry design can be used to determine the surface area to volume (SA/V) ratio, which affects drug release from the dosage forms. This study investigated artificial neural networks (ANN) to predict suitable geometries for the desired dose and release profile. Filaments with 5% API load and polyvinyl alcohol were 3D printed using Fused Deposition Modeling to provide a wide variety of geometries with different dosages and SA/V ratios. These were dissolved in vitro, and the API release profiles were described mathematically. Using these data, ANN architectures were designed with the goal of predicting a suitable dosage form geometry. Poor accuracies of 68.5% in the training and 44.4% in the test settings were achieved with a classification architecture. However, the SA/V ratio could be predicted accurately with a mean squared error loss of only 0.05. This study shows that the prediction of the SA/V ratio using AI works, but not of the exact geometry. For this purpose, a global database could be built with a range of geometries to simplify the prescription process.

3D printing: Innovative solutions for patients and pharmaceutical industry

Three-dimensional (3D) printing is an emerging technology with great potential in pharmaceutical applications, providing innovative solutions for both patients and pharmaceutical industry. This technology offers precise construction of the structure of dosage forms and can benefit drug product design by providing versatile release modes to meet clinical needs and facilitating patient-centric treatment, such as personalized dosing, accommodate treatment of specific disease states or patient populations. Utilization of 3D printing also facilitates digital drug product development and manufacturing. Development of 3D printing at early clinical stages and commercial scale pharmaceutical manufacturing has substantially advanced in recent years. In this review, we discuss how 3D printing accelerates early-stage drug development, including pre-clinical research and early phase human studies, and facilitates late-stage product manufacturing as well as how the technology can benefit patients. The advantages, current status, and challenges of employing 3D printing in large scale manufacturing and personalized dosing are introduced respectively. The considerations and efforts of regulatory agencies to address 3D printing technology are also discussed.

Colonic drug delivery: Formulating the next generation of colon-targeted therapeutics

Colonic drug delivery can facilitate access to unique therapeutic targets and has the potential to enhance drug bioavailability whilst reducing off-target effects. Delivering drugs to the colon requires considered formulation development, as both oral and rectal dosage forms can encounter challenges if the colon's distinct physiological environment is not appreciated. As the therapeutic opportunities surrounding colonic drug delivery multiply, the success of novel pharmaceuticals lies in their design. This review provides a modern insight into the key parameters determining the effective design and development of colon-targeted medicines. Influential physiological features governing the release, dissolution, stability, and absorption of drugs in the colon are first discussed, followed by an overview of the most reliable colon-targeted formulation strategies. Finally, the most appropriate in vitro, in vivo, and in silico preclinical investigations are presented, with the goal of inspiring strategic development of new colon-targeted therapeutics.

Investigation on the use of fused deposition modeling for the production of IR dosage forms containing Timapiprant

The present work focused on evaluating the feasibility of fused deposition modeling (FDM) in the development of a dosage form containing Timapiprant (TMP), also known as CHF6532, which is a novel active molecule indicated in the potential treatment of eosinophilic asthma upon oral administration. The resulting product could be an alternative, with potential towards personalization, of immediate release (IR) tablets used in the clinical studies. Formulations based on different polymeric carriers were screened, leading to the identification of a polyvinyl alcohol-based one, which turned out acceptable for versatility in terms of active ingredient content, printability and dissolution performance (i.e. capability to meet the dissolution specification set, envisaging >80% of the drug dissolved within 30 min). Following an in-depth evaluation on the influence of TMP solid state and of the voids volume resulting from printing on dissolution, few prototypes with shapes especially devised for therapy customization were successfully printed and were compliant with the dissolution specification set.

Accelerating 3D printing of pharmaceutical products using machine learning

Three-dimensional printing (3DP) has seen growing interest within the healthcare industry for its ability to fabricate personalized medicines and medical devices. However, it may be burdened by the lengthy empirical process of formulation development. Active research in pharmaceutical 3DP has led to a wealth of data that machine learning could utilize to provide predictions of formulation outcomes. A balanced dataset is critical for optimal predictive performance of machine learning (ML) models, but data available from published literature often only include positive results. In this study, in-house and literature-mined data on hot melt extrusion (HME) and fused deposition modeling (FDM) 3DP formulations were combined to give a more balanced dataset of 1594 formulations. The optimized ML models predicted the printability and filament mechanical characteristics with an accuracy of 84%, and predicted HME and FDM processing temperatures with a mean absolute error of 5.5 °C and 8.4 °C, respectively. The performance of these ML models was better than previous iterations with a smaller and a more imbalanced dataset, highlighting the importance of providing a structured and heterogeneous dataset for optimal ML performance. The optimized models were integrated in an updated web-application, M3DISEEN, that provides predictions on filament characteristics, printability, HME and FDM processing temperatures, and drug release profiles (https://m3diseen.com/predictionsFDM/). By simulating the workflow of preparing FDM-printed pharmaceutical products, the web-application expedites the otherwise empirical process of formulation development, facilitating higher pharmaceutical 3DP research throughput.

A Review of 3D-Printing of Microneedles

Microneedles are micron-sized devices that are used for the transdermal administration of a wide range of active pharmaceutics substances with minimally invasive pain. In the past decade, various additive manufacturing technologies have been used for the fabrication of microneedles; however, they have limitations due to material compatibility and bioavailability and are time-consuming and expensive processes. Additive manufacturing (AM), which is popularly known as 3D-printing, is an innovative technology that builds three-dimensional solid objects (3D). This article provides a comprehensive review of the different 3D-printing technologies that have the potential to revolutionize the manufacturing of microneedles. The application of 3D-printed microneedles in various fields, such as drug delivery, vaccine delivery, cosmetics, therapy, tissue engineering, and diagnostics, are presented. This review also enumerates the challenges that are posed by the 3D-printing technologies, including the manufacturing cost, which limits its viability for large-scale production, the compatibility of the microneedle-based materials with human cells, and concerns around the efficient administration of large dosages of loaded microneedles. Furthermore, the optimization of microneedle design parameters and features for the best printing outcomes is of paramount interest. The Food and Drug Administration (FDA) regulatory guidelines relating to the safe use of microneedle devices are outlined. Finally, this review delineates the implementation of futuristic technologies, such as artificial intelligence algorithms, for 3D-printed microneedles and 4D-printing capabilities.

To infinity and beyond: Strategies for fabricating medicines in outer space

Recent advancements in next generation spacecrafts have reignited public excitement over life beyond Earth. However, to safeguard the health and safety of humans in the hostile environment of space, innovation in pharmaceutical manufacturing and drug delivery deserves urgent attention. In this review/commentary, the current state of medicines provision in space is explored, accompanied by a forward look on the future of pharmaceutical manufacturing in outer space. The hazards associated with spaceflight, and their corresponding medical problems, are first briefly discussed. Subsequently, the infeasibility of present-day medicines provision systems for supporting deep space exploration is examined. The existing knowledge gaps on the altered clinical effects of medicines in space are evaluated, and suggestions are provided on how clinical trials in space might be conducted. An envisioned model of on-site production and delivery of medicines in space is proposed, referencing emerging technologies (e.g. Chemputing, synthetic biology, and 3D printing) being developed on Earth that may be adapted for extra-terrestrial use. This review concludes with a critical analysis on the regulatory considerations necessary to facilitate the adoption of these technologies and proposes a framework by which these may be enforced. In doing so, this commentary aims to instigate discussions on the pharmaceutical needs of deep space exploration, and strategies on how these may be met.

•

Space is a hostile environment that threatens human health and drug stability.

•

Data on the behaviour of medicines in space is critical but lacking.

•

Novel drug manufacturing and delivery strategies are needed to safeguard crewmembers’ safety.

•

Chemputing, synthetic biology, and 3D printing are examples of such emerging technologies.

•

A regulatory framework for space medicines must be implemented to assure quality.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Preparation and advanced characterization of highly drug-loaded, 3D printed orodispersible tablets containing fluconazole

Due to the possibility of designing various spatial structures, three-dimensional printing can be implemented in the production of customized medicines. Nevertheless, the use of these methods for the production of dosage forms requires further optimization, understanding, and development of printouts' quality verification mechanisms. Therefore, the goal of our work was the preparation and advanced characterization of 3D printed orodispersible tablets (ODTs) containing fluconazole, printed by the fused deposition modeling (FDM) method. We prepared and analyzed 7 printable filaments containing from 10% to 70% fluconazole, used as model API. Obtaining a FDM-printable filament with such a high API content makes our work unique. In addition, we confirmed the 12-month stability of the formulation, which, to our knowledge, is the first study of this type. Next, we printed 10 series of porous tablets containing 50 mg of API from both fresh and stored filaments containing 20 %, 40 %, or 70 % fluconazole. We confirmed the high quality and precision of the printouts using scanning electron microscopy. The detailed analysis of the tablets' disintegration process included the Pharmacopeial test, but also the surface dissolution imaging analysis (SDI) and the test simulating oral conditions performed in own-constructed apparatus. For each composition, we obtained tablets disintegrating in less than 3 min, i.e., meeting the criteria for ODTs required by the European Pharmacopeia. The filaments' storage at ambient conditions did not affect the quality of the tablets. All printed tablets released over 95% of the fluconazole within 30 min. Moreover, the printouts were stable for two weeks.

Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

The applications of Machine learning (ML) in designing dry powder for inhalation by using thin-film-freezing technology

Dry powder inhalers (DPIs) are one of the most widely used devices for treating respiratory diseases. Thin--film--freezing (TFF) is a particle engineering technology that has been demonstrated to prepare dry powder for inhalation with enhanced physicochemical properties. Aerosol performance, which is indicated by fine particle fraction (FPF) and mass median aerodynamic diameter (MMAD), is an important consideration during the product development process. However, the conventional approach for formulation development requires many trial-and-error experiments, which is both laborious and time consuming. As a state-of-the art technique, machine learning has gained more attention in pharmaceutical science and has been widely applied in different settings. In this study, we have successfully built a prediction model for aerosol performance by using both tabular data and scanning electron microscopy (SEM) images. TFF technology was used to prepare 134 dry powder formulations which were collected as a tabular dataset. After testing many machine learning models, we determined that the Random Forest (RF) model was best for FPF prediction with a mean absolute error of ± 7.251%, and artificial neural networks (ANNs) performed the best in estimating MMAD with a mean absolute error of ± 0.393 μm. In addition, a convolutional neural network was employed for SEM image classification and has demonstrated high accuracy (>83.86%) and adaptability in predicting 316 SEM images of three different drug formulations. In conclusion, the machine learning models using both tabular data and image classification were successfully established to evaluate the aerosol performance of dry powder for inhalation. These machine learning models facilitate the product development process of dry powder for inhalation manufactured by TFF technology and have the potential to significantly reduce the product development workload. The machine learning methodology can also be applied to other formulation design and development processes in the future.

Bottom-up design of hydrogels for programmable drug release

Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.

Innovations in Chewable Formulations: The Novelty and Applications of 3D Printing in Drug Product Design

Since their introduction, chewable dosage forms have gained traction due to their ability to facilitate swallowing, especially in paediatric, geriatric and dysphagia patients. Their benefits stretch beyond human use to also include veterinary applications, improving administration and palatability in different animal species. Despite their advantages, current chewable formulations do not account for individualised dosing and palatability preferences. In light of this, three-dimensional (3D) printing, and in particular the semi-solid extrusion technology, has been suggested as a novel manufacturing method for producing customised chewable dosage forms. This advanced approach offers flexibility for selecting patient-specific doses, excipients, and organoleptic properties, which are critical for ensuring efficacy, safety and adherence to the treatment. This review provides an overview of the latest advancements in chewable dosage forms for human and veterinary use, highlighting the motivations behind their use and covering formulation considerations, as well as regulatory aspects.

Machine Learning-Enabled Prediction of 3D-Printed Microneedle Features

Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs' biomedical applications.

Proof of concept of a predictive model of drug release from long-acting implants obtained by fused-deposition modeling

In the pharmaceutical field, there is a growing interest in manufacturing of drug delivery dosage forms adapted to the needs of a large variety of patients. 3D printing has proven to be a powerful tool allowing the adaptation of immediate drug delivery dosage forms. However, there are still few studies focusing on the adaptation of long-acting dosage forms for patient suffering of neurological diseases. In this study, paliperidone palmitate (PP) was chosen as a model drug in combination with different polymers adapted for fused-deposition modeling (FDM). The impact of different printing parameters on the release of PP were investigated. The layer thickness and the infill percentage were studied using a quality by design approach. Indeed, by defining the critical quality attributes (CQA), a proof of concept of a prediction system, and a quality control system were studied through designs of experiments (DoE). The first part of this study was dedicated to the release of PP from a fix geometry. In the second part, the prediction system was developed to require only surface and surface to volume ratio. From that point, it was possible to get rid of a fix geometry and predict the amount of PP released from complex architectures.

Paliperidone palmitate as model of heat-sensitive drug for long-acting 3D printing application

In this work, two technologies were used to prepare long-acting implantable dosage forms in the treatment of schizophrenia. Hot-melt extrusion (HME) as well as fused deposition modelling (FDM) were used concomitantly to create personalized 3D printed implants. Different formulations were prepared using an amorphous PLA as matrix polymer and different solid-state plasticizers. Paliperidone palmitate (PP), a heat sensitive drug prescribed in the treatment of schizophrenia was chosen as model drug. After extrusion, different formulations were characterized using DSC and XRD. Then, an in vitro dissolution test was carried out to discriminate the formulation allowing a sustained drug release of PP. The formulation showing a sustained drug release of the drug was 3D printed as an implantable dosage form. By modulating the infill, the release profile was related to the proper design of tailored dosage form and not solely to the solubility of the drug. Indeed, different release profiles were achieved over 90 days using only one formulation. In addition, a stability test was performed on the 3D printed implants for 3 months. The results showed the stability of the amorphous state of PP, independently of the temperature as well as the integrity of the matrix and the drug.

Advancing pharmacy and healthcare with virtual digital technologies

Digitalisation of the healthcare sector promises to revolutionise patient healthcare globally. From the different technologies, virtual tools including artificial intelligence, blockchain, virtual, and augmented reality, to name but a few, are providing significant benefits to patients and the pharmaceutical sector alike, ranging from improving access to clinicians and medicines, as well as improving real-time diagnoses and treatments. Indeed, it is envisioned that such technologies will communicate together in real-time, as well as with their physical counterparts, to create a large-scale, cyber healthcare system. Despite the significant benefits that virtual-based digital health technologies can bring to patient care, a number of challenges still remain, ranging from data security to acceptance within the healthcare sector. This review provides a timely account of the benefits and challenges of virtual health interventions, as well an outlook on how such technologies can be transitioned from research-focused towards real-world healthcare and pharmaceutical applications to transform treatment pathways for patients worldwide.

Clinical translation of advanced colonic drug delivery technologies

Targeted drug delivery to the colon offers a myriad of benefits, including treatment of local diseases, direct access to unique therapeutic targets and the potential for increasing systemic drug bioavailability and efficacy. Although a range of traditional colonic delivery technologies are available, these systems exhibit inconsistent drug release due to physiological variability between and within individuals, which may be further exacerbated by underlying disease states. In recent years, significant translational and commercial advances have been made with the introduction of new technologies that incorporate independent multi-stimuli release mechanisms (pH and/or microbiota-dependent release). Harnessing these advanced technologies offers new possibilities for drug delivery via the colon, including the delivery of biopharmaceuticals, vaccines, nutrients, and microbiome therapeutics for the treatment of both local and systemic diseases. This review details the latest advances in colonic drug delivery, with an emphasis on emerging therapeutic opportunities and clinical technology translation.