Storgaardhawley6419

This technique is practically applicable to process analytical technology in pharmaceutical manufacturing.Particle swelling is a crucial component in the disintegration of a pharmaceutical tablet. The swelling of particles in a tablet creates stress inside the tablet and thereby pushes apart adjoining particles, eventually causing the tablet to break-up. This work focused on quantifying the swelling of single particles to identify the swelling-limited mechanisms in a particle, i.e. diffusion- or absorption capacity-limited. This was studied for three different disintegrants (sodium starch glycolate/SSG, croscarmellose sodium/CCS, and low-substituted hydroxypropyl cellulose/L-HPC) and five grades of microcrystalline cellulose (MCC) using an optical microscope coupled with a bespoke flow cell and utilising a single particle swelling model. Fundamental swelling characteristics, such as diffusion coefficient, maximum liquid absorption ratio and swelling capacity (maximum swelling of a particle) were determined for each material. The results clearly highlighted the different swelling behaviour for the various materials, where CCS has the highest diffusion coefficient with 739.70 μm2/s and SSG has the highest maximum absorption ratio of 10.04 g/g. For the disintegrants, the swelling performance of SSG is diffusion-limited, whereas it is absorption capacity-limited for CCS. L-HPC is both diffusion- and absorption capacity-limited. This work also reveals an anisotropic, particle facet dependant, swelling behaviour, which is particularly strong for the liquid uptake ability of two MCC grades (PH101 and PH102) and for the absorption capacity of CCS. Having a better understanding of swelling characteristics of single particles will contribute to improving the rational design of a formulation for oral solid dosage forms.Paediatric oral formulations need to be improved. This is an indisputable fact that has gain attention from the regulators, the medical staff, and researchers. The lack of adequate medicines developed for children, resulted in several off-label and unlicensed prescriptions, increasing the risks of adverse drug reactions. When formulating a paediatric medicine, it is necessary to consider the product acceptability determined by the characteristics of both product and user (Gerrard et al., 2019). In the last decades, the regulators have issued guidelines to facilitate the development of medicines specialized for children. The use of oral solid dosage forms instead of liquid dosage forms has been preferred due to advantages, e.g., increase stability and shelf-life. However, palatability and size are common difficulties in solid forms. Many aspects need to be considered when developing a new oral paediatric formulation, although, palatability is recognized as a common reason for non-compliance among children. There are many methods that can be used to improve palatability; however, innovative approaches are still needed. In this review, an overview on oral paediatric formulations with emphasis on their palatability is given. Some of the most innovative approaches are discussed, for example, the use of crystal engineering to improve drug palatability, the development of candy-like pharmaceutical forms, and the use of 3D printing to develop personalized medicines for children.Recently, it has been discovered that the PEG layer on nanoparticle surface can create steric hindrance, preventing efficient cellular uptake of PEGylated nanoparticles. Thus, it would be ideal to have a nanoparticle system that sheds the PEG layer upon reaching the tumor microenvironment. Hypoxia, which is a hallmark of cancerous tumors, can be used as a trigger to shed the PEG layer from the nanoparticle surface. In this study, a hypoxia-sensitive PEG-azobenzene-PEI-DOPE (PAPD) construct, with an azobenzene group as a hypoxia-sensitive moiety, was prepared. The feasibility of co-delivering Doxorubicin (Dox) and anti-P-gp siRNA (siPgp) using the PAPD nanoparticles was evaluated in monolayers of the Adriamycin-resistant human ovarian cancer cell line, A2780 ADR, and in 3D spheroids of the multidrug-resistant human breast cancer cell line, MCF7 ADR. Under hypoxic conditions, the PAPD nanoparticles showed up to a 60% increase in cellular uptake by monolayers and a significantly greater tumor penetration in a spheroid model. siPgp, when delivered using PAPD nanoparticles, showed up to a 60% P-gp downregulation under hypoxic conditions. The combination of siPgp and Dox delivered using PAPD nanoparticles led to an 80% cytotoxicity in cell monolayers and 20% cytotoxicity in spheroids under hypoxic conditions. In this research, a novel hypoxia-sensitive nanoparticle system was developed that demonstrated improved delivery of an encapsulated cargo and augmented cytotoxicity on multidrug-resistant cancer cells under hypoxic conditions.

Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O

-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.

A retrospective study of 181 MRI studies (mean age 58±13years, mean OS 497±354days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). Ridaforolimus price 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction.

The optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2±1.7 and C-index of 90.0±0.3.

VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.

VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.

To develop and evaluate a deep adversarial learning-based image reconstruction approach for rapid and efficient MR parameter mapping.

The proposed method provides an image reconstruction framework by combining the end-to-end convolutional neural network (CNN) mapping, adversarial learning, and MR physical models. The CNN performs direct image-to-parameter mapping by transforming a series of undersampled images directly into MR parameter maps. Adversarial learning is used to improve image sharpness and enable better texture restoration during the image-to-parameter conversion. An additional pathway concerning the MR signal model is added between the estimated parameter maps and undersampled k-space data to ensure the data consistency during network training. The proposed framework was evaluated on T

mapping of the brain and the knee at an acceleration rate R=8 and was compared with other state-of-the-art reconstruction methods. Global and regional quantitative assessments were performed to demonstrate th quantitative MR parameters.

The proposed framework by incorporating the efficient end-to-end CNN mapping, adversarial learning, and physical model enforced data consistency is a promising approach for rapid and efficient reconstruction of quantitative MR parameters.

The products of the lysine biosynthesis pathway, meso-diaminopimelate and lysine, are essential for bacterial survival. This paper focuses on the structural and mechanistic characterization of 4-hydroxy-tetrahydrodipicolinate reductase (DapB), which is one of the enzymes from the lysine biosynthesis pathway. DapB catalyzes the conversion of (2S, 4S)-4-hydroxy-2,3,4,5-tetrahydrodipicolinate (HTPA) to 2,3,4,5-tetrahydrodipicolinate in an NADH/NADPH dependent reaction. Genes coding for DapBs were identified as essential for many pathogenic bacteria, and therefore DapB is an interesting new target for the development of antibiotics.

We have combined experimental and computational approaches to provide novel insights into mechanism of the DapB catalyzed reaction.

Structures of DapBs originating from Mycobacterium tuberculosis and Vibrio vulnificus in complexes with NAD

, NADP

, as well as with inhibitors, were determined and described. The structures determined by us, as well as currently available structures of DapBs from other bacterial species, were compared and used to elucidate a mechanism of reaction catalyzed by this group of enzymes. Several different computational methods were used to provide a detailed description of a plausible reaction mechanism.

This is the first report presenting the detailed mechanism of reaction catalyzed by DapB.

Structural data in combination with information on the reaction mechanism provide a background for development of DapB inhibitors, including transition-state analogues.

Structural data in combination with information on the reaction mechanism provide a background for development of DapB inhibitors, including transition-state analogues.