Application of Quality by Design to the robust preparation of a liposomal GLA formulation by DELOS-susp method.

Fabry disease is a lysosomal storage disease arising from a deficiency of the enzyme α-galactosidase A (GLA). The enzyme deficiency results in an accumulation of glycolipids, which over time, leads to cardiovascular, cerebrovascular, and renal disease, ultimately leading to death in the fourth or fifth decade of life. Currently, lysosomal storage disorders are treated by enzyme replacement therapy (ERT) through the direct administration of the missing enzyme to the patients. In view of their advantages as drug delivery systems, liposomes are increasingly being researched and utilized in the pharmaceutical, food and cosmetic industries, but one of the main barriers to market is their scalability. Depressurization of an Expanded Liquid Organic Solution into aqueous solution (DELOS-susp) is a compressed fluid-based method that allows the reproducible and scalable production of nanovesicular systems with remarkable physicochemical characteristics, in terms of homogeneity, morphology, and particle size. The objective of this work was to optimize and reach a suitable formulation for in vivo preclinical studies by implementing a Quality by Design (QbD) approach, a methodology recommended by the FDA and the EMA to develop robust drug manufacturing and control methods, to the preparation of α-galactosidase-loaded nanoliposomes (nanoGLA) for the treatment of Fabry disease. Through a risk analysis and a Design of Experiments (DoE), we obtained the Design Space in which GLA concentration and lipid concentration were found as critical parameters for achieving a stable nanoformulation. This Design Space allowed the optimization of the process to produce a nanoformulation suitable for in vivo preclinical testing.

Inkjet drug printing onto contact lenses: Deposition optimisation and non-invasive dose verification.

Inkjet printing has the potential to advance the treatment of eye diseases by printing drugs on demand onto contact lenses for localised delivery and personalised dosing, while near-infrared (NIR) spectroscopy can further be used as a quality control method for quantifying the drug but has yet to be demonstrated with contact lenses. In this study, a glaucoma therapy drug, timolol maleate, was successfully printed onto contact lenses using a modified commercial inkjet printer. The drug-loaded ink prepared for the printer was designed to match the properties of commercial ink, whilst having maximal drug loading and avoiding ocular inflammation. This setup demonstrated personalised drug dosing by printing multiple passes. Light transmittance was found to be unaffected by drug loading on the contact lens. A novel dissolution model was built, and in vitro dissolution studies showed drug release over at least 3 h, significantly longer than eye drops. NIR was used as an external validation method to accurately quantify the drug dose. Overall, the combination of inkjet printing and NIR represent a novel method for point-of-care personalisation and quantification of drug-loaded contact lenses.

MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.

Background and purpose: Predicting tumour response would be useful for selecting patients with locally advanced rectal cancer (LARC) for organ preservation strategies. We aimed to develop and validate a prediction model for T downstaging (ypT0-2) in LARC patients after neoadjuvant chemoradiotherapy and to identify those who may benefit from consolidation chemotherapy. Materials and methods: cT3-4 LARC patients at three tertiary medical centers from January 2012 to January 2019 were retrospectively included, while a prospective cohort was recruited from June 2021 to March 2022. Eight filter (principal component analysis, least absolute shrinkage and selection operator, partial least-squares discriminant analysis, random forest)-classifier (support vector machine, logistic regression) models were established to select radiomic features. A nomogram combining radiomics and significant clinical features was developed and validated by calibration curve and decision curve analysis. Interaction test was conducted to investigate the consolidation chemotherapy benefits. Results: A total of 634 patients were included (426 in training cohort, 174 in testing cohort and 34 in prospective cohort). A radiomic prediction model using partial least-squares discriminant analysis and a support vector machine showed the best performance (AUC: 0.832 [training]; 0.763 [testing]). A nomogram combining radiomics and clinical features showed significantly better prognostic performance (AUC: 0.842 [training]; 0.809 [testing]) than the radiomic model. The model was also tested in the prospective cohort with AUC 0.727. High-probability group (score > 81.82) may have potential benefits from ≥ 4 cycles consolidation chemotherapy (OR: 4.173, 95 % CI: 0.953-18.276, p = 0.058, pinteraction = 0.021). Conclusion: We identified and validated a model based on multicenter pre-treatment radiomics to predict ypT0-2 in cT3-4 LARC patients, which may facilitate individualised treatment decision-making for organ-preservation strategies and consolidation chemotherapy.

Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy.

Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN.

Machine learning approach identifies meconium metabolites as potential biomarkers of neonatal hyperbilirubinemia.

Background: The gut microbiota plays an important role in the early stages of human life. Our previous study showed that the abundance of intestinal flora involved in galactose metabolism was altered and correlated with increased serum bilirubin levels in children with jaundice. We conducted the present study to systematically evaluate alterations in the meconium metabolome of neonates with jaundice and search for metabolic markers associated with neonatal jaundice. Methods: We included 68 neonates with neonatal hyperbilirubinemia, also known as neonatal jaundice (NJ) and 68 matched healthy controls (HC), collected meconium samples from them at birth, and performed metabolomic analysis via liquid chromatography-mass spectrometry. Results: Gut metabolites enabled clearly distinguishing the neonatal jaundice (NJ) and healthy control (HC) groups. We also identified the compositions of the gut metabolites that differed significantly between the NJ and HC groups; these differentially significant metabolites were enriched in aminyl tRNA biosynthesis; pantothenic acid and coenzyme biosynthesis; and the valine, leucine and isoleucine biosynthesis pathways. Gut branched-chain amino acid (BCAA) levels were positively correlated with serum bilirubin levels, and the area under the receiver operating characteristic curve of the random forest classifier model based on BCAAs, proline, methionine, phenylalanine and total bilirubin reached 96.9%, showing good potential for diagnostic applications. Machine learning-based causal inference analysis revealed the causal effect of BCAAs on serum total bilirubin and NJ. Conclusions: Altered gut metabolites in neonates with jaundice showed that increased BCAAs and total serum bilirubin were positively correlated. BCAAs proline, methionine, phenylalanine are potential biomarkers of NJ.

Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation.

In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.

Physicochemical properties, content, composition and partial least squares models of A. trifoliata seeds oil.

Physicochemical properties, oil content, and fatty acids (FAs) composition are key for determining the value of oil crops. The aim of this study was to illustrate the potential of exploiting A. trifoliata as an edible oil crop, and establish a rapid measurement model for the A. trifoliata seeds oil (ASO) content and composition. In 130 A. trifoliata germplasms, the highest content of ASO was 51.27%, and unsaturated fatty acids (UFAs) mainly accounted for 74-78% of ASO. The partial least squares (PLS) model based on GC-MS and near-infrared spectroscopy was well-suited for the determination of ASO and UFA content; however, the PLS model for oleic acid (OA) and linoleic acid (LA) was not effective. The acid values and peroxide values for ASO also conformed to the Chinese food safety standards. Our findings will provide new insights and guidance for the use of A. trifoliata as oil crops..

Differential associations of metabolic risk factors on cortical thickness in metabolic syndrome.

OBJECTIVE: Metabolic syndrome (MetS) refers to a cluster of risk factors for cardiovascular disease, including obesity, hypertension, dyslipidemia, and hyperglycemia. While sizable prior literature has examined associations between individual risk factors and quantitative measures of cortical thickness (CT), only very limited research has investigated such measures in MetS. Furthermore, the relative contributions of these risk factors to MetS-related effects on brain morphology have not yet been studied. The primary goal of this investigation was to examine how MetS may affect CT. A secondary goal was to explore the relative contributions of individual risk factors to regional alterations in CT, with the potential to identify risk factor combinations that may underlie structural changes. METHODS: Eighteen participants with MetS (mean age = 59.78 years) were age-matched with 18 healthy control participants (mean age = 60.50 years). CT measures were generated from T1-weighted images and groups were contrasted using whole-brain general linear modeling. A follow-up multivariate partial least squares correlation (PLS) analysis, including the full study sample with complete risk factor measurements (N = 53), was employed to examine which risk factors account for variance in group structural differences. RESULTS: Participants with MetS demonstrated significantly reduced CT in left hemisphere inferior parietal, rostral middle frontal, and lateral occipital clusters and in a right hemisphere precentral cluster. The PLS analysis revealed that waist circumference, high-density lipoprotein cholesterol (HDL-C), triglycerides, and glucose were significant contributors to reduced CT in these clusters. In contrast, diastolic blood pressure showed a significantly positive association with CT while systolic blood pressure did not emerge as a significant contributor. Age was not associated with CT. CONCLUSION: These results indicate that MetS can be associated with regionally specific reductions in CT. Importantly, a novel link between a risk factor profile comprising indices of obesity, hyperglycemia, dyslipidemia and diastolic BP and localized alterations in CT emerged. While the pathophysiological mechanisms underlying these associations remain incompletely understood, these findings may be relevant for future investigations of MetS and might have implications for treatment approaches that focus on specific risk factor profiles with the aim to reduce negative consequences on the structural integrity of the brain.

The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis.

Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, 'black sheep' among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the ß-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.

General and specific effects of early-life psychosocial adversities on adolescent grey matter volume.

Exposure to childhood adversities (CA) is associated with subsequent alterations in regional brain grey matter volume (GMV). Prior studies have focused mainly on severe neglect and maltreatment. The aim of this study was to determine in currently healthy adolescents if exposure to more common forms of CA results in reduced GMV. Effects on brain structure were investigated using voxel-based morphometry in a cross-sectional study of youth recruited from a population-based longitudinal cohort. 58 participants (mean age = 18.4) with (n = 27) or without (n = 31) CA exposure measured retrospectively from maternal interview were included in the study. Measures of recent negative life events (RNLE) recorded at 14 and 17 years, current depressive symptoms, gender, participant/parental psychiatric history, current family functioning perception and 5-HTTLPR genotype were covariates in analyses. A multivariate analysis of adversities demonstrated a general association with a widespread distributed neural network consisting of cortical midline, lateral frontal, temporal, limbic, and cerebellar regions. Univariate analyses showed more specific associations between adversity measures and regional GMV: CA specifically demonstrated reduced vermis GMV and past psychiatric history with reduced medial temporal lobe volume. In contrast RNLE aged 14 was associated with increased lateral cerebellar and anterior cingulate GMV. We conclude that exposure to moderate levels of childhood adversities occurring during childhood and early adolescence exerts effects on the developing adolescent brain. Reducing exposure to adverse social environments during early life may optimize typical brain development and reduce subsequent mental health risks in adult life.