Real time microcalorimetric profiling of prebiotic inulin metabolism

The in vitro assessment of prebiotics involves elaborate microbiological techniques or a combination of culture techniques and molecular methods. In this study, the isothermal microcalorimeter, an instrument which can monitor the real time growth of bacteria was applied to investigate the prebiotic effect of inulin in real time. Fresh and standardized frozen faecal slurries were prepared, placed and monitored in the isothermal microcalorimeter. The faecal samples and commercial probiotic strains Lactobacillus acidophilus LA-5®, Bifidobacterium lactis BB-12® were cultured in a mixed medium of cooked meat medium (CMM) and brain heart infusion (BHI) broth with and without supplementation with inulin and monitored in the microcalorimeter. The results showed power-time (p-t) curves that were characteristic for the samples. The p-t curves of the fresh and frozen faecal samples were similar. Augmented microbial activity was observed when the faecal sample was inoculated into CMM-BHI mixed broth with significant enhancement of microbial activity detected in the presence of inulin which was reproducible. Deconvoluted p-t curves showed multiple peaks with time and intensity variance depending on presence or absence of inulin suggesting possible differences in utilization of inulin by the different groups of bacteria in the polymicrobial sample. P-t curves of the pure species did not show any significant change when inulin was supplemented into the medium likely due to the inability of the bacteria to primarily utilize inulin

Simultaneous differential scanning calorimetry – synchrotron X-ray powder diffraction : a powerful technique for physical form characterisation in pharmaceutical materials

© 2016 American Chemical Society. We report a powerful new technique: hyphenating synchrotron X-ray powder diffraction (XRD) with differential scanning calorimetry (DSC). This is achieved with a simple modification to a standard laboratory DSC instrument, in contrast to previous reports which have involved extensive and complex modifications to a DSC to mount it in the synchrotron beam. The high-energy X-rays of the synchrotron permit the recording of powder diffraction patterns in as little as 2 s, meaning that thermally induced phase changes can be accurately quantified and additional insight on the nature of phase transitions obtained. Such detailed knowledge cannot be gained from existing laboratory XRD instruments, since much longer collection times are required. We demonstrate the power of our approach with two model systems, glutaric acid and sulfathiazole, both of which show enantiotropic polymorphism. The phase transformations between the low and high temperature polymorphs are revealed to be direct solid-solid processes, and sequential refinement against the diffraction patterns obtained permits phase fractions at each temperature to be calculated and unit cell parameters to be accurately quantified as a function of temperature. The combination of XRD and DSC has further allowed us to identify mixtures of phases which appeared phase-pure by DSC

Crystallisation in printed droplets: understanding crystallisation of d-mannitol polymorphs

Crystallisation of mannitol in the confinement of inkjet printed droplets was studied; the effects of droplet size and the presence of a foreign surface were investigated in comparison to the commonly used manufacturing technique of spray drying. Only the metastable polymorphs (α and δ) were observed in printed droplets with predominance of the α polymorph with increasing the droplet size, except for the smallest droplets generated (�100 pL) where the stable β polymorph was observed. The β polymorph was also predominant in spray-dried samples, with metastable forms appearing upon the addition of a polymer (Eudragit). We propose a dependence of the crystallisation of mannitol polymorphs on either the presence or absence of a surface to favour either the stable or the metastable polymorphs. These findings expand the experimental work that could be conducted to understand the crystallisation behaviour in droplets. By proposing heterogeneous nucleation to favour the formation of the metastable polymorphs while homogeneous nucleation favouring the stable β polymorph, one might understand why the same manufacturing method such as spray drying would produce one polymorph when processed alone but another when present in a formulation. This is of great importance to the pharmaceutical industry to aid in understanding the implications of crystallisation in manufacturing processes such as spray drying

Damage limitation: comparing the impact of polymers on bleached hair, when applied within or as post-bleach treatments

Hair bleaching causes undesirable chemical and structural changes to the cortex, the most prominent process being the oxidation of the disulphide bonds of the amino acid cystine and the creation of cysteic acid. It is known that this process affects mostly the Keratin Associated Proteins (KAP) which are amorphous and sulphur-rich. A major secondary effect is the overall destabilisation of the cortex structure within which the crystalline Intermediate Filaments’ (IF) proteins are supported by KAP. An overall decrease in the proportion of ordered protein structure, reduction of mechanical strength and the denaturation temperature of hair have been used to quantify the degree of damage. The cuticle also undergoes oxidative damage during bleaching which causes reduced thickness and increased surface roughness. Mitigating and counteracting these changes in the hair surface and internal structure have been a prime objective of the haircare industry. Such action would be expected to deliver immediate sensory benefits perceivable by the consumer. This project was to compared the impact of three actives said to deliver structural benefits to bleached hair. Their impact was evaluated in two conditions: when applied with the bleaching cream (WB) and after bleaching (AT)

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

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

3D Printed Tablets (Printlets) with Braille and Moon Patterns for Visually Impaired Patients

Visual impairment and blindness affects 285 million people worldwide, resulting in a high public health burden. This study reports, for the first time, the use of three-dimensional (3D) printing to create orally disintegrating printlets (ODPs) suited for patients with visual impairment. Printlets were designed with Braille and Moon patterns on their surface, enabling patients to identify medications when taken out of their original packaging. Printlets with different shapes were fabricated to offer additional information, such as the medication indication or its dosing regimen. Despite the presence of the patterns, the printlets retained their original mechanical properties and dissolution characteristics, wherein all the printlets disintegrated within ~5 s, avoiding the need for water and facilitating self-administration of medications. Moreover, the readability of the printlets was verified by a blind person. Overall, this novel and practical approach should reduce medication errors and improve medication adherence in patients with visual impairmentThe authors thank the Engineering and Physical Sciences Research Council (EPSRC), UK, for their financial support (EP/L01646X)S

Accelerating 3D printing of pharmaceutical products using machine learning

[Abstract] 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 (R^{2} = 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