Bacterial isolates from positive paired venous catheter and peripheral blood cultures taken during parenteral nutrition were the same species but different strains: A case report

OBJECTIVE

The same microbial species isolated from blood simultaneously drawn from a central venous catheter hub and a peripheral vein (paired blood cultures) during parenteral nutrition may be assumed to represent the same strain. This case report provides an example of this assumption being incorrect along with a comparator example of it being correct. This has implications for interpretation of differential time to positivity and differential quantitative blood cultures during investigation of suspected intraluminal intravascular catheter or cannula bloodstream infection.

CASE DESCRIPTION

Two patients ages ≥18 y prescribed parenteral nutrition each had positive paired blood cultures that had been taken for suspected catheter bloodstream infection because of temperature spikes ≥38°C. The paired Staphylococcus epidermidis isolates from the first patient and the paired Enterococcus faecium isolates from the second patient were each tested beyond routine clinical care to establish if they could be different strains. The central and peripheral isolates of Staphylococcus epidermidis from the first patient were different strains based on hospital-reported antibiograms, genomic DNA profiles, thermograms, and weaker growth and different sizes of colonies of the central strain compared with the peripheral strain. There were no such differences for the isolates of Enterococcus faecium from the second patient.

RESULTS

The central and peripheral isolates of Staphylococcus epidermidis from the first patient were different strains based on hospital-reported antibiograms, genomic DNA profiles, thermograms, and weaker growth and different sizes of colonies of the central strain compared with the peripheral strain. There were no such differences for the isolates of Enterococcus faecium from the second patient.

CONCLUSION

This case report indicates consideration should be given to reporting whether bacteria have been identified at either species or strain level if differential time to positivity or differential quantitative blood cultures are used to define catheter or cannula bloodstream infection.

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.

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.

The potential for isothermal microcalorimetry to detect venous catheter infection isolates and establish antibiograms

OBJECTIVES

Because bloodstream infection and venous catheter (or cannula) bloodstream infection are associated with high morbidity and cost, early identification and treatment are important. Isothermal microcalorimetry can detect microbial growth using thermal power (heat flow), essentially in real time. The aim of this study was to examine the potential of this technique in clinical practice.

METHODS

Thermal power of wild-type bacteria (Escherichia coli, Staphylococcus epidermidis, Klebsiella pneumoniae, and Enterococcus faecium) isolated from blood cultures of adult inpatients receiving parenteral nutrition in routine clinical practice was measured at 37°C every 10s using a Thermometric 2277 instrument. Temporal patterns of heat flow were used to detect the presence of bacteria, differentiate between them, and test their antibiotic sensitivity. Within and between batch reproducibility (% coefficient of variation [%CV]) was also established.

RESULTS

Isothermal microcalorimetry always correctly detected the absence or presence of wild-type bacteria. Thermograms differed distinctly between species. Key thermographic features, such as peak heights, timing of peak heights, and interval between peak heights, were highly reproducible within each species (within-batch %CV usually about ≤1%, although between-batch %CV was usually higher). The antibiotic sensitivities (tested only for S. epidermidis and K. pneumoniae) confirmed the results obtained from the hospital laboratory.

CONCLUSIONS

Isothermal microcalorimetry is a promising and highly reproducible real-time measurement technique with potential application to the investigation, species identification, and targeted antibiotic treatment of bloodstream infection and venous catheter (or cannula) bloodstream infection.

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.

Polymorphic transitions in flufenamic acid-trehalose composites

The combination of poorly-soluble drugs with small molecule co-formers to generate amorphous solid dispersions (ASDs) has great potential to improve dissolution rate and kinetic solubility, and thus increase the bioavailability of these active ingredients. However, such ASDs are known to be unstable and to crystallise upon storage or heating. In this work, we explore the crystallisation of flufenamic acid (FFA) from ASDs prepared with trehalose. FFA-trehalose mixtures were prepared at a range of w/w composition ratios, heated to melting and crash cooled to form ASDs. They were then subject to a further heat/cool cycle, which was monitored by simultaneous differential scanning calorimetry - X-ray diffraction to observe the phase changes occurring. These varied with the composition of the blend. Upon short-term storage, formulations with low trehalose contents (FFA:trehalose 5:1 w/w) recrystallised into form I FFA, while higher trehalose contents crystallised to FFA form IV. When heated, all FFA trehalose combinations ultimately recrystallised into form I before melting. Upon a second cooling cycle, systems with low trehalose content (FFA:trehalose 5:1 w/w) recrystallised into form IV, while higher trehalose contents led to FFA form I. It is thus clear that even with a single excipient it is possible to control the crystallisation pathway through judicious choice of the formulation parameters.

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.

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.

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.

Unraveling the Complex Solid-State Phase Transition Behavior of 1-Iodoadamantane, a Material for Which Ostensibly Identical Crystals Undergo Different Transformation Pathways

Phase transitions in crystalline molecular solids have important implications in the fundamental understanding of materials properties and in the development of materials applications. Herein, we report the solid-state phase transition behavior of 1-iodoadamantane (1-IA) investigated using a multi-technique strategy [synchrotron powder X-ray diffraction (XRD), single-crystal XRD, solid-state NMR, and differential scanning calorimetry (DSC)], which reveals complex phase transition behavior on cooling from ambient temperature to ca. 123 K and on subsequent heating to the melting temperature (348 K). Starting from the known phase of 1-IA at ambient temperature (phase A), three low-temperature phases are identified (phases B, C, and D); the crystal structures of phases B and C are reported, together with a re-determination of the structure of phase A. Remarkably, single-crystal XRD shows that some individual crystals of phase A transform to phase B, while other crystals of phase A transform instead to phase C. Results (from powder XRD and DSC) on cooling a powder sample of phase A are fully consistent with this behavior while also revealing an additional transformation pathway from phase A to phase D. Thus, on cooling, a powder sample of phase A transforms partially to phase C (at 229 K), partially to phase D (at 226 K) and partially to phase B (at 211 K). During the cooling process, each of the phases B, C, and D is formed directly from phase A, and no transformations are observed between phases B, C, and D. On heating the resulting triphasic powder sample of phases B, C, and D from 123 K, phase B transforms to phase D (at 211 K), followed by the transformation of phase D to phase C (at 255 K), and finally, phase C transforms to phase A (at 284 K). From these observations, it is apparent that different crystals of phase A, which are ostensibly identical at the level of information revealed by XRD, must actually differ in other aspects that significantly influence their low-temperature phase transition pathways. This unusual behavior will stimulate future studies to gain deeper insights into the specific properties that control the phase transition pathways in individual crystals of this material.

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

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

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.

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.

Monitoring Polymorphic Phase Transitions in Flufenamic Acid Amorphous Solid Dispersions Using Hyphenated X-ray Diffraction-Differential Scanning Calorimetry

Flufenamic acid (FFA) is a highly polymorphic drug molecule with nine crystal structures reported in the Cambridge Structural Database. This study explores the use of synchrotron X-ray powder diffraction combined with differential scanning calorimetry to study crystallization and polymorphic phase transitions upon heating FFA–polymer amorphous solid dispersions (ASDs). Ethyl cellulose (EC, 4 cp) and hydroxypropylmethylcellulose (HPMC) grades with different viscosities and substitution patterns were used to prepare dispersions with FFA at 5:1, 2:1, 1:1, and 1:5 w/w drug/polymer ratios by quench cooling. We employed a 6 cp HPMC 2910 material and two HPMC 2208 samples at 4000 and 100 000 cp. Hyphenated X-ray diffraction (XRD)–differential scanning calorimetry (DSC) studies show that the 6 and 100 000 cp HPMCs and 4 cp EC polymers can stabilize FFA form IV by inhibiting the transition to form I during heating. It appears that the polymers stabilize FFA in both amorphous and metastable forms via a combination of intermolecular interactions and viscosity effects. Increasing the polymer content of the ASD also inhibits polymorphic transitions, with drug/polymer ratios of 1:5 w/w resulting in FFA remaining amorphous during heating. The comparison of FFA ASDs prepared with different samples of HPMCs and ECs suggests that the chemical substitution of the polymer (HPMC 2208 has 19–24% methoxy groups and 4–12% hydroxypropyl groups, while HPMC 2910 has 28–30% methoxy groups and 7–12% hydroxypropyl groups) plays a more significant role in directing polymorphic transitions than the viscosity. A previously unreported polymorph of FFA was also noted during heating but its structure could not be determined.

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.

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.

Structure determination, thermal stability and dissolution rate of δ-indomethacin

The structure solution of the δ-polymorph of indomethacin was obtained using three-dimensional electron diffraction. This form shows a significantly enhanced dissolution rate compared with the more common and better studied α- and γ-polymorphs, indicating better biopharmaceutical properties for medicinal applications. The structure was solved in non-centrosymmetric space group P2₁ and comprises two molecules in the asymmetric unit. Packing and molecule conformation closely resemble indomethacin methyl ester and indomethacin methanol solvate. Knowledge of the structure allowed the rational interpretation of spectroscopic IR and Raman data for δ-polymorph and a tentative interpretation for still unsolved indomethacin polymorphs. Finally, we observed a solid-solid transition from δ-polymorph to α-polymorph that can be driven by similarities in molecular conformation.

3D Printed Punctal Plugs for Controlled Ocular Drug Delivery

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

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

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

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.

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.

Kinetic analysis of microcalorimetric data derived from microbial growth: Basic theoretical, practical and industrial considerations

We report here a mathematical framework for the quantitative interpretation of exponential bacterial growth measured with isothermal microcalorimetry. The method allows determination of many parameters that define the exponential growth phase. To automate the analysis, we also wrote a coding program, so that the approach could be embedded in a commercial setting. As an exemplar, we apply the method to a commercial probiotic product. The outcome was that we could identify characteristic parameters of growth (including rate constant and doubling time), and hence authenticate product quality, within 15 h. This compares favourably with the current 7-10 days required for conventional microbiological assessment (to allow release of product for bottling and marketing) via plating methods. The method would lend itself to growth analysis of single and mixed bacterial cultures.

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.

Harnessing machine learning for development of microbiome therapeutics

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

Advanced machine-learning techniques in drug discovery

The popularity of machine learning (ML) across drug discovery continues to grow, yielding impressive results. As their use increases, so do their limitations become apparent. Such limitations include their need for big data, sparsity in data, and their lack of interpretability. It has also become apparent that the techniques are not truly autonomous, requiring retraining even post deployment. In this review, we detail the use of advanced techniques to circumvent these challenges, with examples drawn from drug discovery and allied disciplines. In addition, we present emerging techniques and their potential role in drug discovery. The techniques presented herein are anticipated to expand the applicability of ML in drug discovery.

Mechanistic In Situ and Ex Situ Studies of Phase Transformations in Molecular Co-Crystals

Co-crystallisation is widely explored as a route to improve the physical properties of pharmaceutical active ingredients, but little is known about the fundamental mechanisms of the process. Herein, we apply a hyphenated differential scanning calorimetry-X-ray diffraction technique to mimic the commercial hot melt extrusion process, and explore the heat-induced synthesis of a series of new co-crystals containing isonicotinamide. These comprise a 1:1 co-crystal with 4-hydroxybenzoic acid, 2:1 and 1:2 systems with 4-hydroxyphenylacetic acid and a 1:1 crystal with 3,4-dihydroxyphenylactic acid. The formation of co-crystals during heating is complex mechanistically. In addition to co-crystallisation, conversions between polymorphs of the co-former starting materials and co-crystal products are also observed. A subsequent study exploring the use of inkjet printing and milling to generate co-crystals revealed that the synthetic approach has a major effect on the co-crystal species and polymorphs produced.

I Spy with My Little Eye: A Paediatric Visual Preferences Survey of 3D Printed Tablets

3D printing (3DP) in the pharmaceutical field is a disruptive technology that allows the preparation of personalised medicines at the point of dispensing. The paediatric population presents a variety of pharmaceutical formulation challenges such as dose flexibility, patient compliance, taste masking and the fear or difficulty to swallow tablets, all factors that could be overcome using the adaptable nature of 3DP. User acceptability studies of 3D printed formulations have been previously carried out in adults; however, feedback from children themselves is essential in establishing the quality target product profile towards the development of age-appropriate medicines. The aim of this study was to investigate the preference of children for different 3D printed tablets (Printlets™) as an important precursor to patient acceptability studies. Four different 3DP technologies; digital light processing (DLP), selective laser sintering (SLS), semi-solid extrusion (SSE) and fused deposition modeling (FDM) were used to prepare placebo printlets with similar physical attributes including size and shape. A single-site, two-part survey was completed with participants aged 4-11 years to determine their preference and opinions based on visual inspection of the printlets. A total of 368 participants completed an individual open questionnaire to visually select the best and worst printlet, and 310 participants completed further non-compulsory open questions to elaborate on their choices. Overall, the DLP printlets were the most visually appealing to the children (61.7%) followed by the SLS printlets (21.2%), and with both the FDM (5.4%) and SSE (11.7%) printlets receiving the lowest scores. However, after being informed that the SSE printlets were chewable, the majority of participants changed their selection and favoured this printlet, despite their original choice, in line with children's preference towards chewable dosage forms. Participant age and sex displayed no significant differences in printlet selection. Printlet descriptions were grouped into four distinct categories; appearance, perceived taste, texture and familiarity, and were found to be equally important when creating a quality target product profile for paediatric 3D printed formulations. This study is the first to investigate children's perceptions of printlets, and the findings aim to provide guidance for further development of paediatric-appropriate medicines using different 3DP technologies.

A Simultaneous Differential Scanning Calorimetry-X-ray Diffraction Study of Olanzapine Crystallization from Amorphous Solid Dispersions

Amorphous solid dispersions (ASDs) of class II and IV biopharmaceutics classification system drugs in water-miscible polymers are a well-recognized means of enhancing dissolution, while such dispersions in hydrophobic polymers form the basis of micro- and nanoparticulate technologies. However, drug recrystallization presents significant problems for product development, and the mechanisms and pathways involved are poorly understood. Here, we outline the use of combined differential scanning calorimetry (DSC)-synchrotron X-ray diffraction to monitor the sequential appearance of polymorphs of olanzapine (OLZ) when dispersed in a range of polymers. In a recent study (Cryst. Growth Des. 2019, 19, 2751-2757), we reported a new polymorph (form IV) of OLZ which crystallized from a spray-dried dispersion of OLZ in polyvinylpyrrolidone. Here, we extend our earlier study to explore OLZ dispersions in poly(lactide-co-glycolide) (PLGA), polylactide (PLA), and hydroxypropyl methyl cellulose acetate succinate (HPMCAS), with a view to identifying the sequence of form generation on heating each dispersion. While spray-dried OLZ results in the formation of crystalline form I, the spray-dried material with HPMCAS comprises an ASD, and forms I and IV are generated upon heating. PLGA and PLA result in a product which contains both amorphous OLZ and the dichloromethane solvate; upon heating, the amorphous material converts to forms I, II, and IV and the solvate to forms I and II. Our data show that it is possible to quantitatively assess not only the polymorph generation sequence but also the relative proportions as a function of temperature. Of particular note is that the sequence of form generation is significantly more complex than may be indicated by DSC data alone, with coincident generation of different polymorphs and complex interconversions as the material is heated. We argue that this may have implications not only for the mechanistic understanding of polymorph generation but also as an aid to identifying the range of polymorphic forms that may be produced by a single-drug molecule.

Anti-biofilm multi drug-loaded 3D printed hearing aids

Over 5% of the world's population has disabling hearing loss, which affects approximately one third of individuals over 65 years. Hearing aids are commonly used in this population group, but prolonged use of these devices may cause ear infections. We describe for the first time, the use of 3D printing to fabricate hearing aids loaded with two antibiotics, ciprofloxacin and fluocinolone acetonide. Digital light processing 3D printing was employed to manufacture hearing aids from two polymer resins, ENG hard and Flexible. The inclusion of the antibiotics did not affect the mechanical properties of the hearing aids. All multi-drug-loaded devices exhibited a hydrophilic surface, excellent blood compatibility and anti-biofilm activity against P. aeruginosa and S. aureus. Hearing aids loaded with ciprofloxacin (6% w/w) and fluocinolone acetonide (0.5% w/w) sustained drug release for more than two weeks and inhibited biofilm formation on the surface of the devices and bacteria growth in the surrounding medium. In summary, this work highlights the potential of vat photopolymerization 3D printing as a versatile manufacturing approach to fabricate high-fidelity patient-specific medical devices with anti-bacterial properties.

The potential of Streptococcus salivarius oral films in the management of dental caries: An inkjet printing approach

The use of probiotics, which can be administered in oro-dispersible films (ODFs) and have prolonged activity in the mouth, was explored. ODFs made of xylitol and containing Streptococcus salivarius were formulated using inkjet printing and tested against Streptococcus mutans - a causative organism of dental caries. The testing of the prepared ODFs involved co-incubating an ink-jetted formulation of S. salivarius and xylitol with S. mutans and monitoring the microbial growth kinetics in real-time using isothermal microcalorimetry and colony plate counts. Cell-free supernatants (CFS) of S. salivarius were also tested against S. mutans. The phosphate solubilisation potential of S. salivarius was also determined and found to be negative, an indication that the species will not deplete phosphate from teeth. From the tests, it was observed that the formulation reduced the S. mutans population from 7.9 to 5.04 Log CFU/mL post-calorimetry (approximately 3 Log reduction) which was comparable to the 99.9% reduction expected during antimicrobial activity testing. A gradual decrease in S. mutans population was also observed with increasing of CFS of S. salivarius volumes indicative of pathogen suppression. This study demonstrates that S. salivarius can be useful in managing dental caries and ODFs of S. salivarius can be formulated easily using ink-jetting for such management.

Vat photopolymerization 3D printing for advanced drug delivery and medical device applications

Three-dimensional (3D) printing is transforming manufacturing paradigms within healthcare. Vat photopolymerization 3D printing technology combines the benefits of high resolution and favourable printing speed, offering a sophisticated approach to fabricate bespoke medical devices and drug delivery systems. Herein, an overview of the vat polymerization techniques, their unique applications in the fields of drug delivery and medical device fabrication, material examples and the advantages they provide within healthcare, is provided. The challenges and drawbacks presented by this technology are also discussed. It is forecast that the adoption of 3D printing could pave the way for a personalised health system, advancing from traditional treatments pathways towards digital healthcare.

Metastable crystalline phase formation in deep eutectic systems revealed by simultaneous synchrotron XRD and DSC

The phase behaviour of various deep eutectic systems was analysed using concurrent synchrotron powder X-ray diffraction and differential scanning calorimetry. Deep eutectic systems containing the pharmaceuticals metacetamol, 2-ethoxybenzamide or benzamide as binary mixtures with phenol revealed new crystalline phases melting either before or with crystals of phenol, highlighting their lower stabilities. Furthermore, in the phenol : 2-ethoxybenzamide system it was shown that multiple metastable phases can form, highlighting the potential for the separation of a hierarchy of crystal structures with differing stabilities from eutectic systems. Through these experiments, we strengthen the idea that eutectic systems can be described by understanding the formation and stabilities of metastable co-crystalline structures. These novel results lead to a deeper understanding of the structure and thermodynamics of deep eutectic solvents, with relevance for analagous systems across materials science.

3D Printing of Tunable Zero-Order Release Printlets

Zero-order release formulations are designed to release a drug at a constant rate over a prolonged time, thus reducing systemic side effects and improving patience adherence to the therapy. Such formulations are traditionally complex to manufacture, requiring multiple steps. In this work, fused deposition modeling (FDM) 3D printing was explored to prepare on-demand printlets (3D printed tablets). The design includes a prolonged release core surrounded by an insoluble shell able to provide zero-order release profiles. The effect of drug loading (10, 25, and 40% w/w paracetamol) on the mechanical and physical properties of the hot melt extruded filaments and 3D printed formulations was evaluated. Two different shell 3D designs (6 mm and 8 mm diameter apertures) together with three different core infills (100, 50, and 25%) were prepared. The formulations showed a range of zero-order release profiles spanning 16 to 48 h. The work has shown that with simple formulation design modifications, it is possible to print extended release formulations with tunable, zero-order release kinetics. Moreover, by using different infill percentages, the dose contained in the printlet can be infinitely adjusted, providing an additive manufacturing route for personalizing medicines to a patient.

Characterisation of rectal amoxicillin (RAMOX) for the treatment of pneumonia in children

Access to medicines, including their availability and affordability, is a major public health challenge worldwide. This research aimed to characterise rectal formulations containing amoxicillin for the treatment of pneumonia in children under five, as an accessible alternative to existing formulations. Lipophilic Suppocire (S-NA15) and hydrophilic polyethylene glycol (PEG; 80% PEG 1500 and 20% PEG 4000, w/w) suppositories containing 250 mg amoxicillin were prepared. Hardness, apparent viscosity, uniformity of mass, uniformity of content, disintegration and dissolution time were determined. Irritation potential was screened using a slug mucosal assay and antibacterial efficacy against Staphylococcus aureus determined by isothermal microcalorimetry. Both lipophilic and hydrophilic formulations met the European Pharmacopoeia standards for suppositories when tested in vitro. They disintegrated within 30 min with rapid amoxicillin release profiles (98.6 ± 0.9%, 94.9 ± 1.2% over 30 min, respectively). Over-encapsulation of S-NA15 suppositories with hydroxypropyl methylcellulose shells slowed drug release and improved stability over 2 months. S-NA15 suppositories were classified as non-irritant and PEG suppositories only mildly irritant. Antibacterial efficacy of formulations was equivalent to amoxicillin alone. Both PEG and over-encapsulated S-NA15 rectal formulations developed in the present work have shown promise based on pre-clinical screening, and further development is justified to develop a product with commercial potential.