Small-molecule inhibitors of hepatitis C virus (HCV) non-structural protein 5A (NS5A): a patent review (2010-2015).

INTRODUCTION: Non-structural 5A (NS5A) protein has achieved a considerable attention as an attractive target for the treatment of hepatitis C (HCV). A number of novel NS5A inhibitors have been reported to date. Several drugs having favorable ADME properties and mild side effects were launched into the pharmaceutical market. For instance, daclatasvir was launched in 2014, elbasvir is currently undergoing registration, ledipasvir was launched in 2014 as a fixed-dose combination with sofosbuvir (NS5B inhibitor). Areas covered: Thomson integrity database and SciFinder database were used as a valuable source to collect the patents on small-molecule NS5A inhibitors. All the structures were ranked by the date of priority. Patent holder and antiviral activity for each scaffold claimed were summarized and presented in a convenient manner. A particular focus was placed on the best-in-class bis-pyrrolidine-containing NS5A inhibitors. Expert opinion: Several first generation NS5A inhibitors have recently progressed into advanced clinical trials and showed superior efficacy in reducing viral load in infected subjects. Therapy schemes of using these agents in combination with other established antiviral drugs with complementary mechanisms of action can address the emergence of resistance and poor therapeutic outcome frequently attributed to antiviral drugs.

Computational insight into the chemical space of plant growth regulators.

An enormous technological progress has resulted in an explosive growth in the amount of biological and chemical data that is typically multivariate and tangled in structure. Therefore, several computational approaches have mainly focused on dimensionality reduction and convenient representation of high-dimensional datasets to elucidate the relationships between the observed activity (or effect) and calculated parameters commonly expressed in terms of molecular descriptors. We have collected the experimental data available in patent and scientific publications as well as specific databases for various agrochemicals. The resulting dataset was then thoroughly analyzed using Kohonen-based self-organizing technique. The overall aim of the presented study is to investigate whether the developed in silico model can be applied to predict the agrochemical activity of small molecule compounds and, at the same time, to offer further insights into the distinctive features of different agrochemical categories. The preliminary external validation with several plant growth regulators demonstrated a relatively high prediction power (67%) of the constructed model. This study is, actually, the first example of a large-scale modeling in the field of agrochemistry.