Role of histone deacetylases and their inhibitors in neurological diseases.

Histone deacetylases (HDACs) are zinc-dependent deacetylases that remove acetyl groups from lysine residues of histones or form protein complexes with other proteins for transcriptional repression, changing chromatin structure tightness, and inhibiting gene expression. Recent in vivo and in vitro studies have amply demonstrated the critical role of HDACs in the cell biology of the nervous system during both physiological and pathological processes and have provided new insights into the conduct of research on neurological disease targets. In addition, in vitro and in vivo studies on HDAC inhibitors show promise for the treatment of various diseases. This review summarizes the regulatory mechanisms of HDAC and the important role of its downstream targets in nervous system diseases, and summarizes the therapeutic mechanisms and efficacy of HDAC inhibitors in various nervous system diseases. Additionally, the current pharmacological situation, problems, and developmental prospects of HDAC inhibitors are described. A better understanding of the pathogenic mechanisms of HDACs in the nervous system may reveal new targets for therapeutic interventions in diseases and help to relieve healthcare pressure through preventive measures.

Application of machine learning in prediction of hydrotrope-enhanced solubilisation of indomethacin.

Systematic in-vitro studies have been conducted to determine the ability of a range of 10 potential hydrotropes to improve the apparent aqueous solubility of the poorly water soluble drug, indomethacin. Solubilisation of the drug in the presence of the hydrotropes was determined experimentally using high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection. These experimental data, together with various known and computed physicochemical properties of the hydrotropes were thereafter used in silico to train an artificial neural network (ANN) to allow for predictions of indomethacin solubilisation. The trained ANN was found to give highly accurate predictions of indomethacin solubilisation in the presence of hydrotropes and was thus shown to provide a valuable means by which hydrotrope efficacy could be screened computationally. Interrogation of the network connection weights afforded a quantitative assessment of the relative importance of the various hydrotrope physicochemical properties in determining the extent of the enhancement in indomethacin solubilisation. It is concluded that in-silico screening of drug/hydrotrope systems using artificial neural networks offers significant potential to reduce the need for extensive laboratory testing of these systems, and could thus provide an economy in terms of reduced costs and time in drug formulation development.