Customizable orodispersible films: Inkjet printing and data matrix encoding for personalized hydrocortisone dosing

The aim of this study was to exploit the versatility of inkjet printing to develop flexible doses of drug-loaded orodispersible films that encoded information in a data matrix pattern, and to introduce a specialised data matrix-generator software specifically focused on the healthcare sector. Pharma-inks (drug-loaded inks) containing hydrocortisone (HC) were developed and characterised based on their rheological properties and drug content. Different strategies were investigated to improve HC solubility: formation of β-cyclodextrin complexes, Soluplus® based micelles, and the use of co-solvent systems. The software automatically adapted the data matrix size and identified the number of layers for printing. HC content deposited in each film layer was measured, and it was found that the proportion of co-solvent used directly affected the drug solubility and simultaneously played a role in the modification of the viscosity and surface tension of the inks. The formation of β-cyclodextrin complexes improved the drug quantity deposited in each layer. On the contrary, micelle-based inks were not suitable for printing. Orodispersible films containing flexible and low doses of personalised HC were successfully prepared, and the development of a code generator software oriented to medical use provided an additional, innovative, and revolutionary advantage to personalised medicine safety and accessibility.

The colon targeting efficacies of mesalazine medications and their impacts on the gut microbiome

Successful treatment of ulcerative colitis (UC) is highly dependent on several parameters, including dosing regimen and the ability to deliver drugs to the disease site. In this study two strategies for delivering mesalazine (5-aminosalicylic acid, 5-ASA) to the colon were compared in an advanced in vitro model of the human gastrointestinal (GI) tract, the SHIME® system. Herein, a prodrug strategy employing bacteria-mediated drug release (sulfasalazine, Azulfidine®) was evaluated alongside a formulation strategy that utilised pH and bacteria-mediated release (5-ASA, Octasa® 1600 mg). SHIME® experiments were performed simulating both the GI physiology and colonic microbiota under healthy and inflammatory bowel disease (IBD) conditions, to study the impact of the disease state and ileal pH variability on colonic 5-ASA delivery. In addition, the effects of the products on the colonic microbiome were investigated by monitoring bacterial growth and metabolites. Results demonstrated that both the prodrug and formulation approaches resulted in a similar percentage of 5-ASA recovery under healthy conditions. On the contrary, during experiments simulating the GI physiology and microbiome of IBD patients (the target population) the formulation strategy resulted in a higher proportion of 5-ASA delivery to the colonic region as compared to the prodrug approach (P < 0.0001). Interestingly, the two products had distinct effects on the synthesis of key bacterial metabolites, such as lactate and short chain fatty acids, which varied according to disease state and ileal pH variability. Further, both 5-ASA and sulfasalazine significantly reduced the growth of the faecal microbiota sourced from six healthy humans. The findings support that the approach selected for colonic drug delivery could significantly influence the effectiveness of UC treatment, and highlight that drugs licensed for UC may differentially impact the growth and functioning of the colonic microbiota.

Poly(D,l-lactide-co-glycolide) particles are metabolised by the gut microbiome and elevate short chain fatty acids

The production of short chain fatty acids (SCFAs) by the colonic microbiome has numerous benefits for human health, including maintenance of epithelial barrier function, suppression of colitis, and protection against carcinogenesis. Despite the therapeutic potential, there is currently no optimal approach for elevating the colonic microbiome's synthesis of SCFAs. In this study, poly(D,l-lactide-co-glycolide) (PLGA) was investigated for this application, as it was hypothesised that the colonic microbiota would metabolise PLGA to its lactate monomers, which would promote the resident microbiota's synthesis of SCFAs. Two grades of spray dried PLGA, alongside a lactate bolus control, were screened in an advanced model of the human colon, known as the M-SHIME® system. Whilst the high molecular weight (Mw) grade of PLGA was stable in the presence of the microbiota sourced from three healthy humans, the low Mw PLGA (PLGA 2) was found to be metabolised. This microbial degradation led to sustained release of lactate over 48 h and increased concentrations of the SCFAs propionate and butyrate. Further, microbial synthesis of harmful ammonium was significantly reduced compared to untreated controls. Interestingly, both types of PLGA was found to influence the composition of the luminal and mucosal microbiota in a donor-specific manner. An in vitro model of an inflamed colonic epithelium also showed the polymer to affect the expression of pro- and anti-inflammatory markers, such as interleukins 8 and 10. The findings of this study reveal PLGA's sensitivity to enzymatic metabolism in the gut, which could be harnessed for therapeutic elevation of colonic SCFAs.

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.

Inkjet Printing of Pharmaceuticals

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

Electroactive Polymers for On-Demand Drug Release

Conductive materials have played a significant role in advancing society into the digital era. Such materials are able to harness the power of electricity and are used to control many aspects of daily life. Conductive polymers (CPs) are an emerging group of polymers that possess metal-like conductivity yet retain desirable polymeric features, such as processability, mechanical properties, and biodegradability. Upon receiving an electrical stimulus, CPs can be tailored to achieve a number of responses, such as harvesting energy and stimulating tissue growth. The recent FDA approval of a CP-based material for a medical device has invigorated their research in healthcare. In drug delivery, CPs can act as electrical switches, drug release is achieved at a flick of a switch, thereby providing unprecedented control over drug release. In this review, recent developments in CP as electroactive polymers for voltage-stimuli responsive drug delivery systems are evaluated. The review demonstrates the distinct drug release profiles achieved by electroactive formulations, and both the precision and ease of stimuli response. This level of dynamism promises to yield "smart medicines" and warrants further research. The review concludes by providing an outlook on electroactive formulations in drug delivery and highlighting their integral roles in healthcare IoT.

Visualizing disintegration of 3D printed tablets in humans using MRI and comparison with in vitro data

Three-dimensional (3D) printing is revolutionising the way that medicines are manufactured today, paving the way towards more personalised medicine. However, there is limited in vivo data on 3D printed dosage forms, and no studies to date have been performed investigating the intestinal behaviour of these drug products in humans, hindering the complete translation of 3D printed medications into clinical practice. Furthermore, it is unknown whether conventional in vitro release tests can accurately predict the in vivo performance of 3D printed formulations in humans. In this study, selective laser sintering (SLS) 3D printing technology has been used to produce two placebo torus-shaped tablets (printlets) using different laser scanning speeds. The printlets were administered to 6 human volunteers, and in vivo disintegration times were assessed using magnetic resonance imaging (MRI). In vitro disintegration tests were performed using a standard USP disintegration apparatus, as well as an alternative method based on the use of reduced media volume and minimal agitation. Printlets fabricated at a laser scanning speed of 90 mm/s exhibited an average in vitro disintegration time of 7.2 ± 1 min (measured using the USP apparatus) and 25.5 ± 4.1 min (measured using the alternative method). In contrast, printlets manufactured at a higher laser scanning speed of 130 mm/s had an in vitro disintegration time of 2.8 ± 0.8 min (USP apparatus) and 18.8 ± 1.9 min (alternative method). When tested in humans, printlets fabricated at a laser scanning speed of 90 mm/s showed an average disintegration time of 17.3 ± 7.2 min, while those manufactured at a laser scanning speed of 130 mm/s exhibited a shorter disintegration time of 12.7 ± 6.8 min. Although the disintegration times obtained using the alternative method more closely resembled those obtained in vivo, no clear correlation was observed between the in vitro and in vivo disintegration times, highlighting the need to develop better in vitro methodology for 3D printed drug products.

3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations

The incidence of Inborn Error of Intermediary Metabolism (IEiM) diseases may be low, yet collectively, they impact approximately 6-10% of the global population, primarily affecting children. Precise treatment doses and strict adherence to prescribed diet and pharmacological treatment regimens are imperative to avert metabolic disturbances in patients. However, the existing dietary and pharmacological products suffer from poor palatability, posing challenges to patient adherence. Furthermore, frequent dose adjustments contingent on age and drug blood levels further complicate treatment. Semi-solid extrusion (SSE) 3D printing technology is currently under assessment as a pioneering method for crafting customized chewable dosage forms, surmounting the primary limitations prevalent in present therapies. This method offers a spectrum of advantages, including the flexibility to tailor patient-specific doses, excipients, and organoleptic properties. These elements are pivotal in ensuring the treatment's efficacy, safety, and adherence. This comprehensive review presents the current landscape of available dietary products, diagnostic methods, therapeutic monitoring, and the latest advancements in SSE technology. It highlights the rationale underpinning their adoption while addressing regulatory aspects imperative for their seamless integration into clinical practice.

3D printed multi-drug-loaded suppositories for acute severe ulcerative colitis

Acute severe ulcerative colitis (ASUC) is a growing health burden that often requires treatment with multiple therapeutic agents. As inflammation is localised in the rectum and colon, local drug delivery using suppositories could improve therapeutic outcomes. Three-dimensional (3D) printing is a novel manufacturing tool that permits the combination of multiple drugs in personalised dosage forms, created based on each patient's disease condition. This study, for the first time, demonstrates the feasibility of producing 3D printed suppositories with two anti-inflammatory agents, budesonide and tofacitinib citrate, for the treatment of ASUC. As both drugs are poorly water-soluble, the suppositories' ability to self-emulsify was exploited to improve their performance. The suppositories were fabricated via semi-solid extrusion (SSE) 3D printing and contained tofacitinib citrate and budesonide in varying doses (10 or 5 mg; 4 or 2 mg, respectively). The suppositories displayed similar dissolution and disintegration behaviours irrespective of their drug content, demonstrating the flexibility of the technology. Overall, this study demonstrates the feasibility of using SSE 3D printing to create multi-drug suppositories for the treatment of ASUC, with the possibility of titrating the drug doses based on the disease progression.

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.

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.

Do probiotic interventions improve female unexplained infertility? A critical commentary

Disruption of women's gut and cervicovaginal microbiota has been associated with multiple gynaecological diseases such as endometriosis, polycystic ovary syndrome, non-cyclic pelvic pain and infertility. Female infertility affects 12.6% of women worldwide; its aetiology is complex and multifactorial and can be underpinned by uterine pathologies, systemic diseases and age. In addition, a new perspective has emerged on the role of the gut and vaginal microbiomes in reproductive health. Research shows that the administration of precisely selected probiotics, often in combination with prior antibiotic treatment, may facilitate the restoration of symbiotic microbiota to increase successful conception and assisted reproductive technology outcomes. However, clarity on this issue from fuller research is currently hampered by a lack of consistency and harmonization in clinical studies: various lactobacilli and bifidobacteria species have been delivered through both the oral and vaginal routes, in different dosages, for different treatment durations. This commentary explores the intricate relationship between the microbiota in the cervicovaginal area and gut of women, exploring their potential contribution to infertility. It highlights ongoing research on the use of probiotic formulations in improving pregnancy outcomes, critically examining the divergent findings in these studies, which complicate a conclusive assessment of the efficacy of these interventions.

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

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

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.

Impact of Peptide Structure on Colonic Stability and Tissue Permeability

Most marketed peptide drugs are administered parenterally due to their inherent gastrointestinal (GI) instability and poor permeability across the GI epithelium. Several molecular design techniques, such as cyclisation and D-amino acid (D-AA) substitution, have been proposed to improve oral peptide drug bioavailability. However, very few of these techniques have been translated to the clinic. In addition, little is known about how synthetic peptide design may improve stability and permeability in the colon, a key site for the treatment of inflammatory bowel disease and colorectal cancer. In this study, we investigated the impact of various cyclisation modifications and D-AA substitutions on the enzymatic stability and colonic tissue permeability of native oxytocin and 11 oxytocin-based peptides. Results showed that the disulfide bond cyclisation present in native oxytocin provided an improved stability in a human colon model compared to a linear oxytocin derivative. Chloroacetyl cyclisation increased native oxytocin stability in the colonic model at 1.5 h by 30.0%, whereas thioether and N-terminal acetylated cyclisations offered no additional protection at 1.5 h. The site and number of D-AA substitutions were found to be critical for stability, with three D-AAs at Tyr, Ile and Leu, improving native oxytocin stability at 1.5 h in both linear and cyclic structures by 58.2% and 79.1%, respectively. Substitution of three D-AAs into native cyclic oxytocin significantly increased peptide permeability across rat colonic tissue; this may be because D-AA substitution favourably altered the peptide's secondary structure. This study is the first to show how the strategic design of peptide therapeutics could enable their delivery to the colon via the oral route.

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.

Advancing oral delivery of biologics: Machine learning predicts peptide stability in the gastrointestinal tract

The oral delivery of peptide therapeutics could facilitate precision treatment of numerous gastrointestinal (GI) and systemic diseases with simple administration for patients. However, the vast majority of licensed peptide drugs are currently administered parenterally due to prohibitive peptide instability in the GI tract. As such, the development of GI-stable peptides is receiving considerable investment. This study provides researchers with the first tool to predict the GI stability of peptide therapeutics based solely on the amino acid sequence. Both unsupervised and supervised machine learning techniques were trained on literature-extracted data describing peptide stability in simulated gastric and small intestinal fluid (SGF and SIF). Based on 109 peptide incubations, classification models for SGF and SIF were developed. The best models utilized k-Nearest Neighbor (for SGF) and XGBoost (for SIF) algorithms, with accuracies of 75.1% (SGF) and 69.3% (SIF), and f1 scores of 84.5% (SGF) and 73.4% (SIF) under 5-fold cross-validation. Feature importance analysis demonstrated that peptides' lipophilicity, rigidity, and size were key determinants of stability. These models are now available to those working on the development of oral peptide therapeutics.

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

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

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

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

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.

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.

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.

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

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

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

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

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

Ileocolonic-Targeted JAK Inhibitor: A Safer and More Effective Treatment for Inflammatory Bowel Disease

Janus kinase (JAK) inhibitors, such as tofacitinib (Xeljanz) and filgotinib (Jyseleca), have been approved for treatment of ulcerative colitis with several other JAK inhibitors in late-stage clinical trials for inflammatory bowel disease (IBD). Despite their impressive efficacy, the risk of adverse effects accompanying the use of JAK inhibitors has brought the entire class under scrutiny, leading to them receiving an FDA black box warning. In this study we investigated whether ileocolonic-targeted delivery of a pan-JAK inhibitor, tofacitinib, can lead to increased tissue exposure and reduced systemic exposure compared to untargeted formulations. The stability of tofacitinib in the presence of rat colonic microbiota was first confirmed. Next, in vivo computed tomography imaging was performed in rats to determine the transit time and disintegration site of ileocolonic-targeted capsules compared to gastric release capsules. Pharmacokinetic studies demonstrated that systemic drug exposure was significantly decreased, and colonic tissue exposure increased at 10 mg/kg tofacitinib dosed in ileocolonic-targeted capsules compared to gastric release capsules and an oral solution. Finally, in a rat model of LPS-induced colonic inflammation, targeted tofacitinib capsules significantly reduced concentrations of proinflammatory interleukin 6 in colonic tissue compared to a vehicle-treated control (p = 0.0408), unlike gastric release tofacitinib capsules and orally administered dexamethasone. Overall, these results support further development of ileocolonic-targeted tofacitinib, and potentially other specific JAK inhibitors in pre-clinical and clinical development, for the treatment of IBD.

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

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

Poly(lactic acid)-hyperbranched polyglycerol nanoparticles enhance bioadhesive treatment of esophageal disease and reduce systemic drug exposure

The effective treatment of esophageal disease represents a significant unmet clinical need, as existing treatments often lead to unnecessary systemic drug exposure and suboptimal concentrations at the disease site. Here, surface-modified bioadhesive poly(lactic acid)-hyperbranched polyglycerol nanoparticles (BNPs), with an average 100-200 nm diameter, were developed for local and sustained esophageal drug delivery. BNPs showed significantly higher adhesion and permeation into ex vivo human and rat esophageal tissue than non-adhesive nanoparticles (NNPs) and had longer residence times within the rat esophagus in vivo. Incubation with human esophagus (Het-1A) cells confirmed BNPs' biocompatibility at clinically relevant concentrations. In a rat model of achalasia, nifedipine-loaded BNPs significantly enhanced esophageal drug exposure, increased therapeutic efficacy, and reduced systemic drug exposure compared to NNPs and free drug. The safety of BNPs was demonstrated by an absence of intestinal, hepatic, and splenic toxicity following administration. This study is the first to demonstrate the efficacy of BNPs for esophageal drug delivery and highlight their potential for improving the lives of patients suffering with esophageal conditions.

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.

Active Machine learning for formulation of precision probiotics

It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance. To reach the distal gut alive where the density of microbiota is highest, oral probiotics should be protected from harsh conditions during transit through the stomach and small intestines. At present, few probiotic formulations are designed with this delivery strategy in mind. This study employs an emerging machine learning (ML) technique, known as active ML, to predict how excipients at pharmaceutically relevant concentrations affect the intestinal proliferation of a common probiotic, Lactobacillus paracasei. Starting with a labelled dataset of just 6 bacteria-excipient interactions, active ML was able to predict the effects of a further 111 excipients using uncertainty sampling. The average certainty of the final model was 67.70% and experimental validation demonstrated that 3/4 excipient-probiotic interactions could be correctly predicted. The model can be used to enable superior probiotic delivery to maximise proliferation in vivo and marks the first use of active ML in microbiome science.

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.

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.

A customizable 3D printed device for enzymatic removal of drugs in water

The infiltration of drugs into water is a key global issue, with pharmaceuticals being detected in all nearly aqueous systems at often alarming concentrations. Pharmaceutical contamination of environmental water supplies has been shown to negatively impact ecological equilibrium and pose a risk to human health. In this study, we design and develop a novel system for the removal of drugs from water, termed as Printzyme. The device, fabricated with stereolithography (SLA) 3D printing, immobilises laccase sourced from Trametes Versicolor within a poly(ethylene glycol) diacrylate hydrogel. We show that SLA printing is a sustainable method for enzyme entrapment under mild conditions, and measure the stability of the system when exposed to extremes of pH and temperature in comparison to free laccase. When tested for its drug removal capacity, the 3D printed device substantially degraded two dissolved drugs on the European water pollution watch list. When configured in the shape of a torus, the device effectively removed 95% of diclofenac and ethinylestradiol from aqueous solution within 24 and 2 h, respectively, more efficiently than free enzyme. Being customizable and reusable, these 3D printed devices could help to efficiently tackle the world's water pollution crisis, in a flexible, easily scalable, and cost-efficient manner.

Nanoencapsulation for Probiotic Delivery

Gut microbiota dynamically participate in diverse physiological activities with direct impact on the host's health. A range of factors associated with the highly complex intestinal flora ecosystem poses challenges in regulating the homeostasis of microbiota. The consumption of live probiotic bacteria, in principle, can address these challenges and confer health benefits. In this context, one of the major problems is ensuring the survival of probiotic cells when faced with physical and chemical assaults during their intake and subsequent gastrointestinal passage to the gut. Advances in the field have focused on improving conventional encapsulation techniques in the microscale to achieve high cell viability, gastric and temperature resistance, and longer shelf lives. However, these microencapsulation approaches are known to have limitations with possible difficulties in clinical translation. In this Perspective, we present a brief overview of the current progress of different probiotic encapsulation methods and highlight the contemporary and emerging single-cell encapsulation strategies using nanocoatings for individual probiotic cells. Finally, we discuss the relative advantages of various nanoencapsulation approaches and the future trend toward developing coated probiotics with advanced features and health benefits.

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.

Influence of probiotic bacteria on gut microbiota composition and gut wall function in an in-vitro model in patients with Parkinson's disease

We report here the potential role of a 4-strain probiotic suspension for use with patients with Parkinson's disease (PD). Stool samples from a group of three patients with diagnosed PD were used to create microbiotas in an in-vitro gut model. The effects of dosing with an oral probiotic suspension (Symprove) on bacterial composition and metabolic activity in the microbiotas was evaluated over 48 h and compared with healthy controls. Additionally, the effect of probiotic dosing on epithelial tight-junction integrity, production of inflammatory markers and wound healing were evaluated in cell culture models. In general, the relative proportions of the main bacterial phyla in the microbiotas of PD patients differed from those of healthy subjects, with levels of Firmicutes raised and levels of Bacteroidetes reduced. Dosing with probiotic resulted in a change in bacterial composition in the microbiotas over a 48 h period. Several other indicators of gut health changed upon dosing with the probiotic; production of short chain fatty acids (SCFAs) and lactate was stimulated, levels of anti-inflammatory cytokines (IL-6, IL-10) increased and levels of pro-inflammatory cytokines and chemokines (MCP-1 and IL-8) decreased. Tight junction integrity was seen to improve with probiotic dosing and wound healing was seen to occur faster than a control. The data suggest that if development and/or progression of PD is influenced by gut microbiota dysbiosis then supplementation of the diet with a properly formulated probiotic may be a useful adjunct to standard treatment in clinic.

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.

A Non-Nutritive Feeding Intervention Alters the Expression of Efflux Transporters in the Gastrointestinal Tract

Intestinal interactions with nutrients, xenobiotics and endogenous hormones can influence the expression of clinically relevant membrane transporters. These changes in the gastrointestinal (GI) physiology can in turn affect the absorption of numerous drug substrates. Several studies have examined the effect of food on intestinal transporters in male and female humans and animal models. However, to our knowledge no studies have investigated the influence of a non-nutritive fibre meal on intestinal efflux transporters and key sex and GI hormones. Here, we show that a fibre meal increased the acute expression of P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), and multidrug-resistance-associated protein-2 (MRP2) in small intestinal segments in both male and female Wistar rats. Enzyme-linked immunosorbent assays were used for the protein quantification of efflux transporters and hormonal plasma concentration. In male rats, the fibre meal caused the plasma concentration of the GI hormone cholecystokinin (CCK) to increase by 75% and the sex hormone testosterone to decrease by 50%, whereas, in contrast, the housing food meal caused a decrease in CCK by 32% and testosterone saw an increase of 31%. No significant changes in the hormonal concentrations, however, were seen in female rats. A deeper understanding of the modulation of efflux transporters by sex, food intake and time can improve our understanding of inter- and intra-variability in the pharmacokinetics of drug substrates.

Progesterone Metabolism by Human and Rat Hepatic and Intestinal Tissue

Following oral administration, the bioavailability of progesterone is low and highly variable. As a result, no clinically relevant, natural progesterone oral formulation is available. After oral delivery, first-pass metabolism initially occurs in the intestines; however, very little information on progesterone metabolism in this organ currently exists. The aim of this study is to investigate the contributions of liver and intestine to progesterone clearance. In the presence of NADPH, a rapid clearance of progesterone was observed in human and rat liver samples (t_1/2 2.7 and 2.72 min, respectively). The rate of progesterone depletion in intestine was statistically similar between rat and human (t_1/2 197.6 min in rat and 157.2 min in human). However, in the absence of NADPH, progesterone was depleted at a significantly lower rate in rat intestine compared to human. The roles of aldo keto reductases (AKR), xanthine oxidase (XAO) and aldehyde oxidase (AOX) in progesterone metabolism were also investigated. The rate of progesterone depletion was found to be significantly reduced by AKR1C, 1D1 and 1B1 in human liver and by AKR1B1 in human intestine. The inhibition of AOX also caused a significant reduction in progesterone degradation in human liver, whereas no change was observed in the presence of an XAO inhibitor. Understanding the kinetics of intestinal as well as liver metabolism is important for the future development of progesterone oral formulations. This novel information can inform decisions on the development of targeted formulations and help predict dosage regimens.

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.

Let's talk about sex: Differences in drug therapy in males and females

Professor Henry Higgins in My Fair Lady said, 'Why can't a woman be more like a man?' Perhaps unintended, such narration extends to the reality of current drug development. A clear sex-gap exists in pharmaceutical research spanning from preclinical studies, clinical trials to post-marketing surveillance with a bias towards males. Consequently, women experience adverse drug reactions from approved drug products more often than men. Distinct differences in pharmaceutical response across drug classes and the lack of understanding of disease pathophysiology also exists between the sexes, often leading to suboptimal drug therapy in women. This review explores the influence of sex as a biological variable in drug delivery, pharmacokinetic response and overall efficacy in the context of pharmaceutical research and practice in the clinic. Prospective recommendations are provided to guide researchers towards the consideration of sex differences in methodologies and analyses. The promotion of disaggregating data according to sex to strengthen scientific rigour, encouraging innovation through the personalisation of medicines and adopting machine learning algorithms is vital for optimised drug development in the sexes and population health equity.

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.

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.

Translating 3D printed pharmaceuticals: From hype to real-world clinical applications

Three-dimensional (3D) printing is a revolutionary technology that is disrupting pharmaceutical development by enabling the production of personalised printlets (3D printed drug products) on demand. By creating small batches of dose flexible medicines, this versatile technology offers significant advantages for clinical practice and drug development, namely the ability to personalise medicines to individual patient needs, as well as expedite drug development timelines within preclinical studies through to first-in-human (FIH) and Phase I/II clinical trials. Despite the widely demonstrated benefits of 3D printing pharmaceuticals, the clinical potential of the technology is yet to be realised. In this timely review, we provide an overview of the latest cutting-edge investigations in 3D printing pharmaceuticals in the pre-clinical and clinical arena and offer a forward-looking approach towards strategies to further aid the translation of 3D printing into the clinic.

Advances in powder bed fusion 3D printing in drug delivery and healthcare

Powder bed fusion (PBF) is a 3D printing method that selectively consolidates powders into 3D objects using a power source. PBF has various derivatives; selective laser sintering/melting, direct metal laser sintering, electron beam melting and multi-jet fusion. These technologies provide a multitude of benefits that make them well suited for the fabrication of bespoke drug-laden formulations, devices and implants. This includes their superior printing resolution and speed, and ability to produce objects without the need for secondary supports, enabling them to precisely create complex products. Herein, this review article outlines the unique applications of PBF 3D printing, including the main principles underpinning its technologies and highlighting their novel pharmaceutical and biomedical applications. The challenges and shortcomings are also considered, emphasising on their effects on the 3D printed products, whilst providing a forward-thinking view.

5-Aminolevulinic Acid as a Novel Therapeutic for Inflammatory Bowel Disease

5-Aminolevulinic acid (5-ALA) is a naturally occurring nonprotein amino acid licensed as an optical imaging agent for the treatment of gliomas. In recent years, 5-ALA has been shown to possess anti-inflammatory and immunoregulatory properties through upregulation of heme oxygenase-1 via enhancement of porphyrin, indicating that it may be beneficial for the treatment of inflammatory conditions. This study systematically examines 5-ALA for use in inflammatory bowel disease (IBD). Firstly, the ex vivo colonic stability and permeability of 5-ALA was assessed using human and mouse fluid and tissue. Secondly, the in vivo efficacy of 5-ALA, in the presence of sodium ferrous citrate, was investigated via the oral and intracolonic route in an acute DSS colitis mouse model of IBD. Results showed that 5-ALA was stable in mouse and human colon fluid, as well as in colon tissue. 5-ALA showed more tissue restricted pharmacokinetics when exposed to human colonic tissue. In vivo dosing demonstrated significantly improved colonic inflammation, increased local heme oxygenase-1 levels, and decreased concentrations of proinflammatory cytokines TNF-α, IL-6, and IL-1β in both plasma and colonic tissue. These effects were superior to that measured concurrently with established anti-inflammatory treatments, ciclosporin and 5-aminosalicylic acid (mesalazine). As such, 5-ALA represents a promising addition to the IBD armamentarium, with potential for targeted colonic delivery.

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.

Quantification of P-Glycoprotein in the Gastrointestinal Tract of Humans and Rodents: Methodology, Gut Region, Sex, and Species Matter

Intestinal efflux transporters affect the gastrointestinal processing of many drugs but further data on their intestinal expression levels are required. Relative mRNA expression and relative and absolute protein expression data of transporters are commonly measured by real-time polymerase chain reaction (RT-PCR), Western blot and mass spectrometry-based targeted proteomics techniques. All of these methods, however, have their own strengths and limitations, and therefore, validation for optimized quantification methods is needed. As such, the identification of the most appropriate technique is necessary to effectively translate preclinical findings to first-in-human trials. In this study, the mRNA expression and protein levels of the efflux transporter P-glycoprotein (P-gp) in jejunal and ileal epithelia of 30 male and female human subjects, and the duodenal, jejunal, ileal and colonic tissues in 48 Wistar rats were quantified using RT-PCR, Western blot and liquid chromatography-tandem mass spectrometry (LC-MS/MS). A similar sex difference was observed in the expression of small intestinal P-gp in humans and Wistar rats where P-gp was higher in males than females with an increasing trend from the proximal to the distal parts in both species. A strong positive linear correlation was determined between the Western blot data and LC-MS/MS data in the small intestine of humans (R² = 0.85). Conflicting results, however, were shown in rat small intestinal and colonic P-gp expression between the techniques (R² = 0.29 and 0.05, respectively). In RT-PCR and Western blot, an internal reference protein is experimentally required; here, beta-actin was used which is innately variable along the intestinal tract. Quantification via LC-MS/MS can provide data on P-gp expression without the need for an internal reference protein and consequently, can give higher confidence on the expression levels of P-gp along the intestinal tract. Overall, these findings highlight similar trends between the species and suggest that the Wistar rat is an appropriate preclinical animal model to predict the oral drug absorption of P-gp substrates in the human small intestine.