Rescue of mitochondrial function in parkin-mutant Fibroblasts using drug loaded PMPC-PDPA polymersomes and tubular polymersomes

Mutations in parkin cause autosomal recessive Parkinsonism and mitochondrial defects. A recent drug screen identified a class of steroid-like hydrophobic compounds able to rescue mitochondrial function in parkin-mutant fibroblasts. Whilst these possess therapeutic potential, the size and high hydrophobicity of some may limit their ability to penetrate the blood-brain barrier from systemic circulation, something that could be improved by novel drug formulations. In the present study, the steroid-like compounds Ursolic Acid (UA) and Ursocholanic Acid (UCA) were successfully encapsulated within nanoscopic polymersomes formed by poly(2-(methacryloyloxy)ethyl phosphorylcholine)–poly(2-di-isopropylamino)ethyl methacrylate) (PMPC-PDPA) and separated into spherical and tubular morphologies to assess the effects of nanoparticle mediated delivery on drug efficacy. Following incubation with either morphology, parkin-mutant fibroblasts demonstrated time and concentration dependent increases in intracellular ATP levels, resembling those resulting from treatment with nascent UA and UCA formulated in 0.1% DMSO, as used in the original drug screen. Empty PMPC-PDPA polymersomes did not alter physiological measures related to mitochondrial function or induce cytotoxicity. In combination with other techniques such as ligand functionalisation, PMPC-PDPA nanoparticles of well-defined morphology may prove a promising platform for tailoring the pharmacokinetic profile and organ specific bio-distribution of highly hydrophobic compounds

A Novel Neurotrophic Drug for Cognitive Enhancement and Alzheimer's Disease

Currently, the major drug discovery paradigm for neurodegenerative diseases is based upon high affinity ligands for single disease-specific targets. For Alzheimer's disease (AD), the focus is the amyloid beta peptide (Aß) that mediates familial Alzheimer's disease pathology. However, given that age is the greatest risk factor for AD, we explored an alternative drug discovery scheme that is based upon efficacy in multiple cell culture models of age-associated pathologies rather than exclusively amyloid metabolism. Using this approach, we identified an exceptionally potent, orally active, neurotrophic molecule that facilitates memory in normal rodents, and prevents the loss of synaptic proteins and cognitive decline in a transgenic AD mouse model

Prediction of bioconcentration factors in fish and invertebrates using machine learning

© 2018 The Authors The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23–0.73 and 0.34–1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.Biotechnology and Biological Sciences Research Council (BBSRC) CASE industrial scholarship scheme (Reference BB/K501177/1), iNVERTOX project (Reference BB/P005187/1) and AstraZeneca Global SHE research programme. This work was additionally supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001999), the UK Medical Research Council (FC001999), and the Wellcome Trust (FC001999)

HPLC-based activity profiling for GABAA receptor modulators from the traditional Chinese herbal drug Kushen (Sophora flavescens root)

An EtOAc extract from the roots of Sophora flavescens (Kushen) potentiated γ -aminobutyric acid (GABA)-induced chloride influx in Xenopus oocytes transiently expressing GABA(A) receptors with subunit composition, α(1)β(2)γ(2S). HPLC-based activity profiling of the extract led to the identification of 8-lavandulyl flavonoids, kushenol I, sophoraflavanone G, (–)-kurarinone, and kuraridine as GABA(A) receptor modulators. In addition, a series of inactive structurally related flavonoids were characterized. Among these, kushenol Y (4) was identified as a new natural product. The 8-lavandulyl flavonoids are first representatives of a novel scaffold for the target