Disrupting 3D printing of medicines with machine learning

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.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10-2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.

Trend of pharmaceuticals 3D printing in the Middle East and North Africa (MENA) region: An overview, regulatory perspective and future outlook

The traditional method of producing medicine using the "one-size fits all" model is becoming a major issue for pharmaceutical manufacturers due to its inability to produce customizable medicines for individuals' needs. Three-dimensional (3D) printing is a new disruptive technology that offers many benefits to the pharmaceutical industry by revolutionizing the way pharmaceuticals are developed and manufactured. 3D printing technology enables the on-demand production of personalized medicine with tailored dosage, shape and release characteristics. Despite the lack of clear regulatory guidance, there is substantial interest in adopting 3D printing technology in the large-scale manufacturing of medicine. This review aims to evaluate the research efforts of 3D printing technology in the Middle East and North Africa (MENA) region, with a particular emphasis on pharmaceutical research and development. Our analysis indicates an upsurge in the overall research activity of 3D printing technology but there is limited progress in pharmaceuticals research and development. While the MENA region still lags, there is evidence of the regional interest in expanding the 3D printing technology applications in different sectors including pharmaceuticals. 3D printing holds great promise for pharmaceutical development within the MENA region and its advancement will require a strong collaboration between academic researchers and industry partners in parallel with drafting detailed guidelines from regulatory authorities.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

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.

3D printing processes in precise drug delivery for personalized medicine

With the advent of personalized medicine, the drug delivery system will be changed significantly. The development of personalized medicine needs the support of many technologies, among which three-dimensional printing (3DP) technology is a novel formulation-preparing process that creates 3D objects by depositing printing materials layer-by-layer based on the computer-aided design method. Compared with traditional pharmaceutical processes, 3DP produces complex drug combinations, personalized dosage, and flexible shape and structure of dosage forms (DFs) on demand. In the future, personalized 3DP drugs may supplement and even replace their traditional counterpart. We systematically introduce the applications of 3DP technologies in the pharmaceutical industry and summarize the virtues and shortcomings of each technique. The release behaviors and control mechanisms of the pharmaceutical DFs with desired structures are also analyzed. Finally, the benefits, challenges, and prospects of 3DP technology to the pharmaceutical industry are discussed.

The role of artificial intelligence in generating original scientific research

Artificial intelligence (AI) is a revolutionary technology that is finding wide application across numerous sectors. Large language models (LLMs) are an emerging subset technology of AI and have been developed to communicate using human languages. At their core, LLMs are trained with vast amounts of information extracted from the internet, including text and images. Their ability to create human-like, expert text in almost any subject means they are increasingly being used as an aid to presentation, particularly in scientific writing. However, we wondered whether LLMs could go further, generating original scientific research and preparing the results for publication. We taskedGPT-4, an LLM, to write an original pharmaceutics manuscript, on a topic that is itself novel. It was able to conceive a research hypothesis, define an experimental protocol, produce photo-realistic images of 3D printed tablets, generate believable analytical data from a range of instruments and write a convincing publication-ready manuscript with evidence of critical interpretation. The model achieved all this is less than 1 h. Moreover, the generated data were multi-modal in nature, including thermal analyses, vibrational spectroscopy and dissolution testing, demonstrating multi-disciplinary expertise in the LLM. One area in which the model failed, however, was in referencing to the literature. Since the generated experimental results appeared believable though, we suggest that LLMs could certainly play a role in scientific research but with human input, interpretation and data validation. We discuss the potential benefits and current bottlenecks for realising this ambition here.

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.

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.

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.

3D screen printing technology enables fabrication of oral drug dosage forms with freely tailorable release profiles

3D printing offers new opportunities to customize oral dosage forms of pharmaceuticals for different patient populations, improving patient safety, care, and compliance. Although several notable 3D print technologies have been developed, such as inkjet printing, powder-based printing, selective laser sintering (SLS) printing, and fused deposition modelling (FDM), among others, their capacity is often limited by the number of printing heads. 3D screen-printing (3DSP) is based on a classic flatbed screen printing that is widely used in industrial applications for technical applications. 3DSP can build up thousands of units per screen simultaneously, enabling mass customization of pharmaceuticals. Here, we use 3DSP to investigate two novel paste formulations: immediate-release (IR) and extended-release (ER) using Paracetamol (acetaminophen) as the active pharmaceutical ingredient (API). Both disk-shaped and donut-shaped tablets were fabricated using one or both pastes to design drug delivery systems (DDS) with tailored API release profiles. The size and mass of the produced tablets demonstrated high uniformity. Characterization of the tablets physical properties, such as breaking force (25-39 N) and friability (0.002-0.237%), adhering to Ph. Eur (10th edition). Finally, drug release tests with a phosphate buffer at pH 5.8 showed Paracetamol release depended on the IR- and ER paste materials and their respective compartment size of the composite DDS, which can be readily varied using 3DSP. This work further demonstrates the potential of 3DSP to manufacture complex oral dosage forms exhibiting custom release functionalities for mass production.

3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives

3D printing, also known as Additive manufacturing (AM), has emerged as a transformative technology with applications across various industries, including the medical sector. This review paper provides an overview of the current status of AM technology, its challenges, and its application in the medical industry. The paper covers the different types of AM technologies, such as fused deposition modeling, stereolithography, selective laser sintering, digital light processing, binder jetting, and electron beam melting, and their suitability for medical applications. The most commonly used biomedical materials in AM, such as plastic, metal, ceramic, composite, and bio-inks, are also viewed. The challenges of AM technology, such as material selection, accuracy, precision, regulatory compliance, cost and quality control, and standardization, are also discussed. The review also highlights the various applications of AM in the medical sector, including the production of patient-specific surgical guides, prosthetics, orthotics, and implants. Finally, the review highlights the Internet of Medical Things (IoMT) and artificial intelligence (AI) for regulatory frameworks and safety standards for 3D-printed biomedical devices. The review concludes that AM technology can transform the healthcare industry by enabling patients to access more personalized and reasonably priced treatment alternatives. Despite the challenges, integrating AI and IoMT with 3D printing technology is expected to play a vital role in the future of biomedical device applications, leading to further advancements and improvements in patient care. More research is needed to address the challenges and optimize its use for medical applications to utilize AM's potential in the medical industry fully.

The Concept of Scaffold-Guided Bone Regeneration for the Treatment of Long Bone Defects: Current Clinical Application and Future Perspective

The treatment of bone defects remains a challenging clinical problem with high reintervention rates, morbidity, and resulting significant healthcare costs. Surgical techniques are constantly evolving, but outcomes can be influenced by several parameters, including the patient's age, comorbidities, systemic disorders, the anatomical location of the defect, and the surgeon's preference and experience. The most used therapeutic modalities for the regeneration of long bone defects include distraction osteogenesis (bone transport), free vascularized fibular grafts, the Masquelet technique, allograft, and (arthroplasty with) mega-prostheses. Over the past 25 years, three-dimensional (3D) printing, a breakthrough layer-by-layer manufacturing technology that produces final parts directly from 3D model data, has taken off and transformed the treatment of bone defects by enabling personalized therapies with highly porous 3D-printed implants tailored to the patient. Therefore, to reduce the morbidities and complications associated with current treatment regimens, efforts have been made in translational research toward 3D-printed scaffolds to facilitate bone regeneration. Three-dimensional printed scaffolds should not only provide osteoconductive surfaces for cell attachment and subsequent bone formation but also provide physical support and containment of bone graft material during the regeneration process, enhancing bone ingrowth, while simultaneously, orthopaedic implants supply mechanical strength with rigid, stable external and/or internal fixation. In this perspective review, we focus on elaborating on the history of bone defect treatment methods and assessing current treatment approaches as well as recent developments, including existing evidence on the advantages and disadvantages of 3D-printed scaffolds for bone defect regeneration. Furthermore, it is evident that the regulatory framework and organization and financing of evidence-based clinical trials remains very complex, and new challenges for non-biodegradable and biodegradable 3D-printed scaffolds for bone regeneration are emerging that have not yet been sufficiently addressed, such as guideline development for specific surgical indications, clinically feasible design concepts for needed multicentre international preclinical and clinical trials, the current medico-legal status, and reimbursement. These challenges underscore the need for intensive exchange and open and honest debate among leaders in the field. This goal can be addressed in a well-planned and focused stakeholder workshop on the topic of patient-specific 3D-printed scaffolds for long bone defect regeneration, as proposed in this perspective review.

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.

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.

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.

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.

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.

Scalable Biofabrication: A Perspective on the Current State and Future Potentials of Process Automation in 3D-Bioprinting Applications

Biofabrication, specifically 3D-Bioprinting, has the potential to disruptively impact a wide range of future technological developments to improve human well-being. Organs-on-Chips could enable animal-free and individualized drug development, printed organs may help to overcome non-treatable diseases as well as deficiencies in donor organs and cultured meat may solve a worldwide environmental threat in factory farming. A high degree of manual labor in the laboratory in combination with little trained personnel leads to high costs and is along with strict regulations currently often a hindrance to the commercialization of technologies that have already been well researched. This paper therefore illustrates current developments in process automation in 3D-Bioprinting and provides a perspective on how the use of proven and new automation solutions can help to overcome regulatory and technological hurdles to achieve an economically scalable production.

Recent Trends in Advanced Photoinitiators for Vat Photopolymerization 3D Printing

3D printing has revolutionized the way of manufacturing with a huge impact on various fields, in particular biomedicine. Vat photopolymerization-based 3D printing techniques such as stereolithography (SLA) and digital light processing (DLP) attracted considerable attention owing to their superior print resolution, relatively high speed, low cost and flexibility in resin material design. As one key element of the SLA/DLP resin, photoinitiators or photoinitiating systems have experienced significant development in recent years, in parallel with the exploration of 3D printing (macro)monomers. The design of new photoinitiating systems can not only offer faster 3D printing speed and enable low-energy visible light fabrication, but also can bring new functions to the 3D printed products and even generate new printing methods in combination with advanced optics. This review evaluates recent trends in the development and application of advanced photoinitiators and photoinitiating systems for vat photopolymerization 3D printing, with a wide range of small molecules, polymers and nanoassemblies involved. Personal perspectives on the current limitations and future directions are eventually provided. This article is protected by copyright. All rights reserved.

A Semi-Automated 3D-Printed Chainmail Design Algorithm with Preprogrammed Directional Functions for Hand Exoskeleton

The problem of computerising the design and development of 3D-printed chainmail with programmed directional functions provides a basis for further research, including the automation of medical devices. The scope of the present research was focused on computational optimisation of the selection of materials and shapes for 3D printing, including the design of medical devices, which constitutes a significant scientific, technical, and clinical problem. The aim of this article was to solve the scientific problem of automated or semi-automated efficient and practical design of 3D-printed chainmail with programmed directional functions (variable stiffness/elasticity depending on the direction). We demonstrate for the first time that 3D-printed particles can be arranged into single-layer chainmail with a tunable one- or two-directional bending modulus for use in a medical hand exoskeleton. In the present work, we accomplished this in two ways: based on traditional programming and based on machine learning. This paper presents the novel results of our research, including 3D printouts, providing routes toward the wider implementation of adaptive chainmails. Our research resulted in an automated or semi-automated efficient and practical 3D printed chainmail design with programmed directional functions for a wrist exoskeleton with variable stiffness/flexibility, depending on the direction. We also compared two methodologies of planning and construction: the use of traditional software and machine-learning-based software, with the latter being more efficient for more complex chainmail designs.

Artificial intelligence to bring nanomedicine to life

The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.

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.

Applications of machine learning in computational nanotechnology

Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Machine learning predicts the effect of food on orally administered medicines

Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tract in preclinical studies is challenging, poorly predictive and can produce difficult to interpret datasets. Machine learning (ML) has emerged from the computer science field and shows promise in interpreting complex datasets present in the pharmaceutical field. A ML-based approach aimed to predict the food effect based on an extensive dataset of over 311 drugs with more than 20 drug physicochemical properties, referred to as features. Machine learning techniques were tested; including logistic regression, support vector machine, k-Nearest neighbours and random forest. First a standard ML pipeline using a 80:20 split for training and testing was tried to predict no food effect, negative food effect and positive food effect, however this lead to specificities of less than 40%. To overcome this, a strategic ML pipeline was devised and three tasks were developed. Random forest achieved the strongest performance overall. High accuracies and sensitivities of 70%, 80% and 70% and specificities of 71%, 76% and 71% were achieved for classifying; (i) no food effect vs food effect, (ii) negative food vs positive food effect and (iii) no food effect vs negative food effect vs positive food effect, respectively. Feature importance using random forest ranked the features by importance for building the predictive tasks. The calculated dose number was the most important feature. Here, ML has provided an effective screening tool for predicting the food effect, with the potential to select lead compounds with no food effect, reduce the number of animal studies, and accelerate oral drug development studies.

QbD guided development of immediate release FDM-3D printed tablets with customizable API doses

The objective of this work was to develop a fused deposition modeling (FDM) 3D printed immediate release (IR) tablet with flexibility in adjusting the dose of the active pharmaceutical ingredient (API) by scaling the size of the dosage form and appropriate drug release profile steadiness to the variation of dimensions or thickness of the deposited layers throughout the printing process. Polyvinyl alcohol-based filaments with elevated API content (50% w/w) were prepared by hot melt extrusion (HME), through systematic screening of polymeric formulations with different drug loadings, and their printability was evaluated by means of mechanical characterization. For the tablet fabrication step by 3D printing (3DP), the Quality by Design (QbD) approach was implemented by employing risk management strategies and Design of Experiments (DoE). The effects of the tablet design, tablet size and the layer height settings on the drug release and the API content were investigated. Between the two proposed original tablet architectures, the honeycomb configuration was found to be a suitable candidate for the preparation of IR dosage forms with readily customizable API doses. Also, a predictive model was obtained, which assists the optimization of variables involved in the printing phase and thereby facilitates the tailoring process.

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.

Stereolithography-assisted fabrication of 3D printed polymeric film for topical berberine delivery: in-vitro, ex-vivo and in-vivo investigations

OBJECTIVES

3D printed polymeric film intended for topical delivery of berberine (BBR) was developed using stereolithography (SLA) to enhance its local concentrations. PEGDMA was utilized as photopolymerizing resin, with PEG 400 as an inert component to facilitate BBR solubilization and permeation.

METHODS

Three batches of topical films were printed by varying resin and PEG 400 compositions. In-vitro physicochemical characterizations of the 3D printed films were performed using several analytical techniques including ex-vivo drug permeation studies. In-vivo skin irritation studies were also conducted to assess the skin irritation potential.

KEY FINDINGS

Films were 3D printed according to design specifications with minimal variations. Microscopic analysis confirmed 3D architecture, while thermal and X-ray diffraction studies revealed amorphous BBR entrapment. Drug permeation study showed effective ex-vivo diffusion up to 344.32 ± 61.20 µg/cm2 after 24.0 h possessing a higher ratio of PEG 400. In-vivo skin irritation studies have suggested the non-irritant nature of printed films.

CONCLUSIONS

Results indicated the suitability of SLA 3D printing for topical application in the treatment of skin diseases. The presence of PEG 400 in the printed 3D films facilitated BBR diffusion, resulting in an improved flux in ex-vivo model and non-irritant properties in vivo.

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Over 150 drugs are currently recognised as being susceptible to metabolism or bioaccumulation (together described as depletion) by gastrointestinal microorganisms; however, the true number is likely higher. Microbial drug depletion is often variable between and within individuals, depending on their unique composition of gut microbiota. Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response. In this study, literature mining and unsupervised learning were used to curate a dataset of 455 drug-microbiota interactions. From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota. The best model, a tuned extremely randomised trees classifier, achieved performance metrics of AUROC: 75.1% ± 6.8; weighted recall: 79.2% ± 3.9; balanced accuracy: 69.0% ± 4.6; and weighted precision: 80.2% ± 3.7 when validated on 91 drugs. This machine learning model is the first of its kind and provides a rapid, reliable, and resource-friendly tool for researchers and industry professionals to screen drugs for susceptibility to depletion by gut microbiota. The recognition of drug-microbiome interactions can support successful drug development and promote better formulations and dosage regimens for patients.

Smartphone-enabled 3D printing of medicines

3D printing is a manufacturing technique that is transforming numerous industrial sectors, particularly where it is key tool in the development and fabrication of medicinees that are personalised to the individual needs of patients. Most 3D printers are relatively large, require trained operators and must be located in a pharmaceutical setting to manufacture dosage forms. In order to realise fully the potential of point-of-care manufacturing of medicines, portable printers that are easy to operate are required. Here, we report the development of a 3D printer that operates using a mobile smartphone. The printer, operating on stereolithographic principles, uses the light from the smartphone's screen to photopolymerise liquid resins and create solid structures. The shape of the printed dosage form is determined using a custom app on the smartphone. Warfarin-loaded Printlets (3D printed tablets) of various sizes and patient-centred shapes (caplet, triangle, diamond, square, pentagon, torus, and gyroid lattices) were successfully printed to a high resolution and with excellent dimensional precision using different photosensitive resins. The drug was present in an amorphous form, and the Printlets displayed sustained release characterises. The promising proof-of-concept results support the future potential of this compact, user-friendly and interconnected smartphone-based system for point-of-care manufacturing of personalised medications.

Connected healthcare: Improving patient care using digital health technologies

Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are under investigation for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery, rehabilitation, or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and opportunities for clinical adoption.

Modular design principle based on compartmental drug delivery systems

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release.

3D Printed Punctal Plugs for Controlled Ocular Drug Delivery

Dry eye disease is a common ocular disorder that is characterised by tear deficiency or excessive tear evaporation. Current treatment involves the use of eye drops; however, therapeutic efficacy is limited because of poor ocular bioavailability of topically applied formulations. In this study, digital light processing (DLP) 3D printing was employed to develop dexamethasone-loaded punctal plugs. Punctal plugs with different drug loadings were fabricated using polyethylene glycol diacrylate (PEGDA) and polyethylene glycol 400 (PEG 400) to create a semi-interpenetrating network (semi-IPN). Drug-loaded punctal plugs were characterised in terms of physical characteristics (XRD and DSC), potential drug-photopolymer interactions (FTIR), drug release profile, and cytocompatibility. In vitro release kinetics of the punctal plugs were evaluated using an in-house flow rig model that mimics the subconjunctival space. The results showed sustained release of dexamethasone for up to 7 days from punctal plugs made with 20% w/w PEG 400 and 80% w/w PEGDA, while punctal plugs made with 100% PEGDA exhibited prolonged releases for more than 21 days. Herein, our study demonstrates that DLP 3D printing represents a potential manufacturing platform for fabricating personalised drug-loaded punctal plugs with extended release characteristics for ocular administration.

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

Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy trial-and-error approach in optimization. Artificial intelligence (AI) is a technology that could revolutionize pharmaceutical 3DP through analyzing large datasets. Herein, literature-mined data for developing AI machine learning (ML) models was used to predict key aspects of the 3DP formulation pipeline and in vitro dissolution properties. A total of 968 formulations were mined and assessed from 114 articles. The ML techniques explored were able to learn and provide accuracies as high as 93% for values in the filament hot melt extrusion process. In addition, ML algorithms were able to use data from the composition of the formulations with additional input features to predict the drug release of 3D printed medicines. The best prediction was obtained by an artificial neural network that was able to predict drug release times of a formulation with a mean error of ±24.29 min. In addition, the most important variables were revealed, which could be leveraged in formulation development. Thus, it was concluded that ML proved to be a suitable approach to modelling the 3D printing workflow.

Direct Powder Extrusion 3D Printing of Praziquantel to Overcome Neglected Disease Formulation Challenges in Paediatric Populations

For the last 40 years, praziquantel has been the standard treatment for schistosomiasis, a neglected parasitic disease affecting more than 250 million people worldwide. However, there is no suitable paediatric formulation on the market, leading to off-label use and the splitting of commercial tablets for adults. In this study, we use a recently available technology, direct powder extrusion (DPE) three-dimensional printing (3DP), to prepare paediatric Printlets™ (3D printed tablets) of amorphous solid dispersions of praziquantel with Kollidon^® VA 64 and surfactants (Span™ 20 or Kolliphor^® SLS). Printlets were successfully printed from both pellets and powders obtained from extrudates by hot melt extrusion (HME). In vitro dissolution studies showed a greater than four-fold increase in praziquantel release, due to the formation of amorphous solid dispersions. In vitro palatability data indicated that the printlets were in the range of praziquantel tolerability, highlighting the taste masking capabilities of this technology without the need for additional taste masking excipients. This work has demonstrated the possibility of 3D printing tablets using pellets or powder forms obtained by HME, avoiding the use of filaments in fused deposition modelling 3DP. Moreover, the main formulation hurdles of praziquantel, such as low drug solubility, inadequate taste, and high and variable dose requirements, can be overcome using this technology.

Predicting drug-microbiome interactions with machine learning

Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.

Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria

The human gut microbiome, composed of trillions of microorganisms, plays an essential role in human health. Many factors shape gut microbiome composition over the life span, including changes to diet, lifestyle, and medication use. Though not routinely tested during drug development, drugs can exert profound effects on the gut microbiome, potentially altering its functions and promoting disease. This study develops a machine learning (ML) model to predict whether drugs will impair the growth of 40 gut bacterial strains. Trained on over 18,600 drug-bacteria interactions, 13 distinct ML models are built and compared, including tree-based, ensemble, and artificial neural network techniques. Following hyperparameter tuning and multi-metric evaluation, a lead ML model is selected: a tuned extra trees algorithm with performances of AUROC: 0.857 (±0.014), recall: 0.587 (±0.063), precision: 0.800 (±0.053), and f1: 0.666 (±0.042). This model can be used by the pharmaceutical industry during drug development and could even be adapted for use in clinical settings.