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 were 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.

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.

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.

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.

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.

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.

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.

3D printed infliximab suppositories for rectal biologic delivery.

Infliximab is a monoclonal antibody that plays an important role in the management and treatment of chronic inflammatory bowel diseases (IBD). Due to its macromolecular structure, its delivery through the oral route is challenging, limiting its administration to only via the parenteral route. The rectal route offers an alternative way for administering infliximab, allowing it to be localised at the disease site and circumventing its passage across the alimentary canal and thus, maintaining its integrity and bioactivity. Three-dimensional (3D) printing is an advanced production technology that permits the creation of dose-flexible drug products from digital designs. The current study assessed the feasibility of utilising semi-solid extrusion 3D printing for the fabrication of infliximab-loaded suppositories for the local treatment of IBD. Various printing inks composed of Gelucire® (48/16 or 44/14) mixed with coconut oil and/or purified water were investigated. It was shown that following reconstitution in water, the infliximab solution can be directly incorporated into the printing ink of Gelucire® 48/16 and can withstand the extrusion process, resulting in well-defined suppositories. Since water content and temperature are critical for safeguarding infliximab's potency, the effect of changing the composition of the printing inks and printing parameters on infliximab's biologic efficiency was evaluated by measuring its binding capacity (i.e., the amount of infliximab that actively binds to its antigen to exert an effect). Despite drug loading assays showing that infliximab remains intact following printing, it was found that the incorporation of water in isolation results in only ∼65% binding capacity. However, when oil is added to the mixture, infliximab's binding capacity increases up to ∼85%. These promising results demonstrate that 3D printing has the potential to be exploited as a novel platform for fabricating dosage forms containing biopharmaceuticals, avoiding patients' compliance issues observed with injectables and addressing their unmet needs.

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 and 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.

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.

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.

Releasing fast and slow: Non-destructive prediction of density and drug release from SLS 3D printed tablets using NIR spectroscopy.

Selective laser sintering (SLS) 3D printing is a revolutionary 3D printing technology that has been found capable of creating drug products with varied release profiles by changing the laser scanning speed. Here, SLS 3D printed formulations (printlets) loaded with a narrow therapeutic index drug (theophylline) were produced using SLS 3D printing at varying laser scanning speeds (100-180 mm/s). The use of reflectance Fourier Transform - Near Infrared (FT-NIR) spectroscopy was evaluated as a non-destructive approach to predicting 3D printed tablet density and drug release at 2 h and 4 h. The printed drug products formulated with a higher laser speed exhibited an accelerated drug release and reduced density compared with the slower laser scanning speeds. Univariate calibration models were developed based on a baseline shift in the spectra in the third overtone region upon changing physical properties. For density prediction, the developed univariate model had high linearity (R2 value = 0.9335) and accuracy (error < 0.029 mg/mm3). For drug release prediction at 2 h and 4 h, the developed univariate models demonstrated a linear correlation (R2 values of 0.9383 and 0.9167, respectively) and accuracy (error < 4.4%). The predicted vs. actual dissolution profiles were found to be statistically similar (f2 > 50) for all of the test printlets. Overall, this article demonstrates the feasibility of SLS 3D printing to produce drug products containing a narrow therapeutic index drug across a range of drug release profiles, as well as the potential for FT-NIR spectroscopy to predict the physical characteristics of SLS 3D printed drug products (drug release and density) as a non-destructive quality control method at the point-of-care.

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.

Prediction of Solid-State Form of SLS 3D Printed Medicines Using NIR and Raman Spectroscopy.

Selective laser sintering (SLS) 3D printing is capable of revolutionising pharmaceutical manufacturing, by producing amorphous solid dispersions in a one-step manufacturing process. Here, 3D-printed formulations loaded with a model BCS class II drug (20% w/w itraconazole) and three grades of hydroxypropyl cellulose (HPC) polymer (-SSL, -SL and -L) were produced using SLS 3D printing. Interestingly, the polymers with higher molecular weights (HPC-L and -SL) were found to undergo a uniform sintering process, attributed to the better powder flow characteristics, compared with the lower molecular weight grade (HPC-SSL). XRPD analyses found that the SLS 3D printing process resulted in amorphous conversion of itraconazole for all three polymers, with HPC-SSL retaining a small amount of crystallinity on the drug product surface. The use of process analytical technologies (PAT), including near infrared (NIR) and Raman spectroscopy, was evaluated, to predict the amorphous content, qualitatively and quantitatively, within itraconazole-loaded formulations. Calibration models were developed using partial least squares (PLS) regression, which successfully predicted amorphous content across the range of 0-20% w/w. The models demonstrated excellent linearity (R2 = 0.998 and 0.998) and accuracy (RMSEP = 1.04% and 0.63%) for NIR and Raman spectroscopy models, respectively. Overall, this article demonstrates the feasibility of SLS 3D printing to produce solid dispersions containing a BCS II drug, and the potential for NIR and Raman spectroscopy to quantify amorphous content as a non-destructive quality control measure at the point-of-care.