Volumetric printing and non-destructive drug quantification of water-soluble supramolecular hydrogels.

Vat photopolymerisation 3D printing is being actively explored for manufacturing personalised medicines due to its high dimensional accuracy and lack of heat application. However, several challenges have hindered its clinical translation, including the inadequate printing speeds, the lack of resins that give soluble matrices, and the need for non-destructive quality control measures. In this study, for the first time, a rapid approach to producing water-soluble vat photopolymerised matrices and a means of non-destructively verifying their drug content were investigated. Volumetric printing, a novel form of vat photopolymerisation, was used to fabricate personalised warfarin-loaded 3D-printed tablets (printlets). Eight different formulations containing varying amounts of warfarin (0.5-6.0% w/w) were used to print two different sized torus-shaped printlets within 6.5 to 11.1 s. Nuclear magnetic resonance (NMR) spectroscopy revealed the presence of only trace amounts of unreacted acrylate monomers, suggesting that the photopolymerisation reaction had occurred to near completion. All printlets completely solubilised and released their entire drug load within 2.5 to 7 h. NIR spectroscopy (NIRS) was used to non-destructively verify the dose of warfarin loaded into the vat photopolymerised printlets. The partial least square regression model built showed strong linearity (R2 = 0.980), and high accuracy in predicting the drug loading of the test sample (RMSEP = 0.205%). Therefore, this study advances pharmaceutical vat photopolymerisation by demonstrating the feasibility of producing water-soluble printlets via volumetric printing and quantifying the drug load of vat photopolymerised printlets with NIRS.

Fabrication of 3D printed mutable drug delivery devices: a comparative study of volumetric and digital light processing printing.

Mutable devices and dosage forms have the capacity to dynamically transform dimensionally, morphologically and mechanically upon exposure to non-mechanical external triggers. By leveraging these controllable transformations, these systems can be used as minimally invasive alternatives to implants and residence devices, foregoing the need for complex surgeries or endoscopies. 4D printing, the fabrication of 3D-printed structures that evolve their shape, properties, or functionality in response to stimuli over time, allows the production of such devices. This study explores the potential of volumetric printing, a novel vat photopolymerisation technology capable of ultra-rapid printing speeds, by comparing its performance against established digital light processing (DLP) printing in fabricating hydrogel-based drug-eluting devices. Six hydrogel formulations consisting of 2-(acryloyloxy)ethyl]trimethylammonium chloride solution, lithium phenyl-2,4,6-trimethylbenzoylphosphinate, varying molecular weights of the crosslinking monomer, poly(ethylene glycol) diacrylate, and paracetamol as a model drug were prepared for both vat photopolymerisation technologies. Comprehensive studies were conducted to investigate the swelling and water sorption profiles, drug release kinetics, and physicochemical properties of each formulation. Expandable drug-eluting 4D devices were successfully fabricated within 7.5 s using volumetric printing and were shown to display equivalent drug release kinetics to prints created using DLP printing, demonstrating drug release, swelling, and water sorption properties equivalent to or better than those of DLP-printed devices. The reported findings shed light on the advantages and limitations of each technology for creating these dynamic drug delivery systems and provides a direct comparison between the two technologies, while highlighting the promising potential of volumetric printing and further expanding the growing repertoire of pharmaceutical printing.

Advancing non-destructive analysis of 3D printed medicines.

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

Inkjet drug printing onto contact lenses: Deposition optimisation and non-invasive dose verification.

Inkjet printing has the potential to advance the treatment of eye diseases by printing drugs on demand onto contact lenses for localised delivery and personalised dosing, while near-infrared (NIR) spectroscopy can further be used as a quality control method for quantifying the drug but has yet to be demonstrated with contact lenses. In this study, a glaucoma therapy drug, timolol maleate, was successfully printed onto contact lenses using a modified commercial inkjet printer. The drug-loaded ink prepared for the printer was designed to match the properties of commercial ink, whilst having maximal drug loading and avoiding ocular inflammation. This setup demonstrated personalised drug dosing by printing multiple passes. Light transmittance was found to be unaffected by drug loading on the contact lens. A novel dissolution model was built, and in vitro dissolution studies showed drug release over at least 3 h, significantly longer than eye drops. NIR was used as an external validation method to accurately quantify the drug dose. Overall, the combination of inkjet printing and NIR represent a novel method for point-of-care personalisation and quantification of drug-loaded contact lenses.

Simultaneous fabrication of multiple tablets within seconds using tomographic volumetric 3D printing.

3D printing is driving a shift in patient care away from a generalised model and towards personalised treatments. To complement fast-paced clinical environments, 3D printing technologies must provide sufficiently high throughputs for them to be feasibly implemented. Volumetric printing is an emerging 3D printing technology that affords such speeds, being capable of producing entire objects within seconds. In this study, for the first time, rotatory volumetric printing was used to simultaneously produce two torus- or cylinder-shaped paracetamol-loaded Printlets (3D printed tablets). Six resin formulations comprising paracetamol as the model drug, poly(ethylene glycol) diacrylate (PEGDA) 575 or 700 as photoreactive monomers, water and PEG 300 as non-reactive diluents, and lithium phenyl-2,4,6-trimethylbenzoylphosphinate (LAP) as the photoinitiator were investigated. Two printlets were successfully printed in 12 to 32 s and exhibited sustained drug release profiles. These results support the use of rotary volumetric printing for efficient and effective manufacturing of various personalised medicines at the same time. With the speed and precision it affords, rotatory volumetric printing has the potential to become one of the most promising alternative manufacturing technologies in the pharmaceutical industry.

Supramolecular chemistry enables vat photopolymerization 3D printing of novel water-soluble tablets.

Vat photopolymerization has garnered interest from pharmaceutical researchers for the fabrication of personalised medicines, especially for drugs that require high precision dosing or are heat labile. However, the 3D printed structures created thus far have been insoluble, limiting printable dosage forms to sustained-release systems or drug-eluting medical devices which do not require dissolution of the printed matrix. Resins that produce water-soluble structures will enable more versatile drug release profiles and expand potential applications. To achieve this, instead of employing cross-linking chemistry to fabricate matrices, supramolecular chemistry may be used to impart dynamic interaction between polymer chains. In this study, water-soluble drug-loaded printlets (3D printed tablets) are fabricated via digital light processing (DLP) 3DP for the first time. Six formulations with varying ratios of an electrolyte acrylate monomer, [2-(acryloyloxy)ethyl]trimethylammonium chloride (TMAEA), and a co-monomer, 1-vinyl-2-pyrrolidone (NVP), were prepared to produce paracetamol-loaded printlets. 1H NMR spectroscopy analysis confirmed the integration of TMAEA and NVP in the polymer, and residual TMAEA monomers were found to be present only in trace amounts (0.71 - 1.37 %w/w). The apparent molecular mass of the photopolymerised polymer was found to exceed 300,000 Da with hydrodynamic radii of 15 - 20 nm, estimated based on 1H DOSY NMR measurements The loaded paracetamol was completely released from the printlets between 45 minutes to 5 hours. In vivo single-dose acute toxicity studies in rats suggest that the printlets did not cause any tissue damage. The findings reported in this study represent a significant step towards the adoption of vat photopolymerization-based 3DP to produce personalised medicines.

A case study on decentralized manufacturing of 3D printed medicines.

Pharmaceutical 3D printing (3DP) is one of the emerging enabling technologies of personalised medicines as it affords the ability to fabricate highly versatile dosage forms. In the past 2 years, national medicines regulatory authorities have held consultations with external stakeholders to adapt regulatory frameworks to embrace point-of-care manufacturing. The proposed concept of decentralized manufacturing (DM) involves the provision of feedstock intermediates (pharma-inks) prepared by pharmaceutical companies to DM sites for manufacturing into the final medicine. In this study, we examine the feasibility of this model, with respect to both manufacturing and quality control. Efavirenz-loaded granulates (0-35%w/w) were produced by a manufacturing partner and shipped to a 3DP site in a different country. Direct powder extrusion (DPE) 3DP was subsequently used to prepare printlets (3D printed tablets), with mass ranging 266-371 mg. All printlets released more than 80% drug load within the first 60 min of the in vitro drug release test. An in-line near-infrared spectroscopy system was used as a process analytical technology (PAT) to quantify the printlets' drug load. Calibration models were developed using partial least squares regression, which showed excellent linearity (R2 = 0.9833) and accuracy (RMSE = 1.0662). Overall, this work is the first to report the use of an in-line NIR system to perform real-time analysis of printlets prepared using pharma-inks produced by a pharmaceutical company. By demonstrating the feasibility of the proposed distribution model through this proof-of-concept study, this work paves the way for investigation of further PAT tools for quality control in 3DP point-of-care manufacturing.

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.

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.

Integrating pressure sensor control into semi-solid extrusion 3D printing to optimize medicine manufacturing.

Semi-solid extrusion (SSE) is a three-dimensional printing (3DP) process that involves the extrusion of a gel or paste-like material via a syringe-based printhead to create the desired object. In pharmaceuticals, SSE 3DP has already been used to manufacture formulations for human clinical studies. To further support its clinical adoption, the use of a pressure sensor may provide information on the printability of the feedstock material in situ and under the exact printing conditions for quality control purposes. This study aimed to integrate a pressure sensor in an SSE pharmaceutical 3D printer for both material characterization and as a process analytical technology (PAT) to monitor the printing process. In this study, three materials of different consistency were tested (soft vaseline, gel-like mass and paste-like mass) under 12 different conditions, by changing flow rate, temperature, or nozzle diameter. The use of a pressure sensor allowed, for the first time, the characterization of rheological properties of the inks, which exhibited temperature-dependent, plastic and viscoelastic behaviours. Controlling critical material attributes and 3D printing process parameters may allow a quality by design (QbD) approach to facilitate a high-fidelity 3D printing process critical for the future of personalized medicine.

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.

Optical biosensors - Illuminating the path to personalized drug dosing.

Optical biosensors are low-cost, sensitive and portable devices that are poised to revolutionize the medical industry. Healthcare monitoring has already been transformed by such devices, with notable recent applications including heart rate monitoring in smartwatches and COVID-19 lateral flow diagnostic test kits. The commercial success and impact of existing optical sensors has galvanized research in expanding its application in numerous disciplines. Drug detection and monitoring seeks to benefit from the fast-approaching wave of optical biosensors, with diverse applications ranging from illicit drug testing, clinical trials, monitoring in advanced drug delivery systems and personalized drug dosing. The latter has the potential to significantly improve patients' lives by minimizing toxicity and maximizing efficacy. To achieve this, the patient's serum drug levels must be frequently measured. Yet, the current method of obtaining such information, namely therapeutic drug monitoring (TDM), is not routinely practiced as it is invasive, expensive, time-consuming and skilled labor-intensive. Certainly, optical sensors possess the capabilities to challenge this convention. This review explores the current state of optical biosensors in personalized dosing with special emphasis on TDM, and provides an appraisal on recent strategies. The strengths and challenges of optical biosensors are critically evaluated, before concluding with perspectives on the future direction of these sensors.

3D printed tacrolimus suppositories for the treatment of ulcerative colitis.

Ulcerative colitis is a global health problem, affecting millions of individuals worldwide. As an inflammatory condition localised in the large intestine, rectal delivery of immunosuppressive therapies such as tacrolimus is a promising strategy to maximise drug concentration at the site of action whilst minimising systemic side effects. Here, for the first time, self-supporting 3D-printed tacrolimus suppositories were prepared without the aid of moulds using a pharmaceutical semi-solid extrusion (SSE) 3D printer. The suppositories were printed vertically in three different sizes using combinations of two lipid pharmaceutical excipients (Gelucire 44/14 or Gelucire 48/16) and coconut oil. Although both suppository formulations had the appropriate viscosity characteristics for printing, the Gel 44 formulation required less energy and force for extrusion compared to the Gel 48 system. The Gel 44 disintegrated more rapidly but released tacrolimus more slowly than the Gel 48 suppositories. Although the tacrolimus release profiles were significantly different, both suppository systems released more than 80% drug within 120 min. DSC and XRD analysis was inconclusive in determining the solid-state properties of the drug in the suppositories. In summary, this article reports on the fabrication of 3D printed self-supporting suppositories to deliver personalised doses of a narrow therapeutic index drug, with potential benefits for patients with ulcerative colitis.

Harnessing artificial intelligence for the next generation of 3D printed medicines.

Artificial intelligence (AI) is redefining how we exist in the world. In almost every sector of society, AI is performing tasks with super-human speed and intellect; from the prediction of stock market trends to driverless vehicles, diagnosis of disease, and robotic surgery. Despite this growing success, the pharmaceutical field is yet to truly harness AI. Development and manufacture of medicines remains largely in a 'one size fits all' paradigm, in which mass-produced, identical formulations are expected to meet individual patient needs. Recently, 3D printing (3DP) has illuminated a path for on-demand production of fully customisable medicines. Due to its flexibility, pharmaceutical 3DP presents innumerable options during formulation development that generally require expert navigation. Leveraging AI within pharmaceutical 3DP removes the need for human expertise, as optimal process parameters can be accurately predicted by machine learning. AI can also be incorporated into a pharmaceutical 3DP 'Internet of Things', moving the personalised production of medicines into an intelligent, streamlined, and autonomous pipeline. Supportive infrastructure, such as The Cloud and blockchain, will also play a vital role. Crucially, these technologies will expedite the use of pharmaceutical 3DP in clinical settings and drive the global movement towards personalised medicine and Industry 4.0.

Electrochemical biosensors: a nexus for precision medicine.

Precision medicine is a field with huge potential for improving a patient's quality of life, wherein therapeutic drug monitoring (TDM) can provide actionable insights. More importantly, incorrect drug dose is a common contributor to medical errors. However, current TDM practice is time-consuming and expensive, and requires specialised technicians. One solution is to use electrochemical biosensors (ECBs), which are inexpensive, portable, and highly sensitive. In this review, we explore the potential for ECBs as a technology for on-demand drug monitoring, including microneedles, continuous monitoring, synthetic biorecognition elements, and multi-material electrodes. We also highlight emerging strategies to achieve continuous drug monitoring, and conclude by appraising recent developments and providing an outlook for the field.

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.

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.

3D printed opioid medicines with alcohol-resistant and abuse-deterrent properties.

In the past decade, prescriptions for opioid medicines have been exponentially increasing, instigating opioid abuse as a global health crisis associated with high morbidity and mortality. In particular, diversion from the intended mode of opioid administration, such as injecting and snorting the opioid, is a major problem that contributes to this epidemic. In light of this, novel formulation strategies are needed to support efforts in reducing the prevalence and risks of opioid abuse. Here, modified release tramadol printlets (3D printed tablets) with alcohol-resistant and abuse-deterrent properties were prepared by direct powder extrusion three-dimensional (3D) printing. The printlets were fabricated using two grades of hydroxypropylcellulose (HPC). Both formulations displayed strong ethanol-resistance and had moderate abuse-deterrent properties. Polyethylene oxide (PEO) was subsequently added into the formulations, which improved the printlets' resistance to physical tampering in nasal inhalation tests and delayed their dissolution in solvent extraction tests. Overall, this article reports for the first time the use of direct powder extrusion 3D printing to prepare drug products with both alcohol-resistant and abuse-deterrent properties. These results offer a novel approach for the safe and effective use of opioids that can contribute to the advantages that 3D printing provides in terms of on-demand dose personalisation.

M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines.

Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) three-dimensional printing (3DP) has made significant advancements in the field of oral drug delivery with personalized drug-loaded formulations being designed, developed and dispensed for the needs of the patient. The FDM 3DP process begins with the production of drug-loaded filaments by hot melt extrusion (HME), followed by the printing of a drug product using a FDM 3D printer. However, the optimization of the fabrication parameters is a time-consuming, empirical trial approach, requiring expert knowledge. Here, M3DISEEN, a web-based pharmaceutical software, was developed to accelerate FDM 3D printing using AI machine learning techniques (MLTs). In total, 614 drug-loaded formulations were designed from a comprehensive list of 145 different pharmaceutical excipients, 3D printed and assessed in-house. To build the predictive tool, a dataset was constructed and models were trained and tested at a ratio of 75:25. Significantly, the AI models predicted key fabrication parameters with accuracies of 76% and 67% for the printability and the filament characteristics, respectively. Furthermore, the AI models predicted the HME and FDM processing temperatures with a mean absolute error of 8.9 °C and 8.3 °C, respectively. Strikingly, the AI models achieved high levels of accuracy by solely inputting the pharmaceutical excipient trade names. Therefore, AI provides an effective holistic modeling technology and software to streamline and advance 3DP as a significant technology within drug development. M3DISEEN is available at (http://m3diseen.com/predictions/).