Combinatorial Chemistry & High Throughput Screening - Volume 15, Issue 9, 2012

Due to the numerous challenges in hit identification from random RNAi screening, we have examined current practices with a discovery of a variety of methodologies employed and published in many reports; majority of them, unfortunately, do not address the minimum associated criteria for hit nomination, as this could potentially have been the cause or may well be the explanation as to the lack of confirmation and follow up studies, currently facing the RNAi field. Overall, we find that these criteria or parameters are not well defined, in most cases arbitrary in nature, and hence rendering it extremely difficult to judge the quality of and confidence in nominated hits across published studies. For this purpose, we have developed a simple method to score actives independent of assay readout; and provide, for the first time, a homogenous platform enabling cross-comparison of active gene lists resulting from different RNAi screening technologies. Here, we report on our recently developed method dedicated to RNAi data output analysis referred to as the BDA method applicable to both arrayed and pooled RNAi technologies; wherein the concerns pertaining to inconsistent hit nomination and off-target silencing in conjugation with minimal activity criteria to identify a high value target are addressed. In this report, a combined hit rate per gene, called “H score”, is introduced and defined. The H score provides a very useful tool for stringent active gene nomination, gene list comparison across multiple studies, prioritization of hits, and evaluation of the quality of the nominated gene hits.

Silica nanoparticles were synthesized from rice husk ash at room temperature by sonochemical method. The feeding rate of percipiteting agent and time of sonication were investigated. The nanostructure of the synthesized powder was realized by the FE-SEM photomicrograph, FT-IR spectroscopy, XRD and XRF analyses. These analytical observations have revealed that the nano-sized amorphous silica particles are formed and they are spheroidal in shape. The average particle size of the silica powders is found to be around 50 nm. The as-synthesized silica nanoparticles were subsequently modified with chlorosulfonic acid and prepared silica sulfuric acid nanoparticles, which were employed as an efficient catalyst for the acylation of alcohols and phenols with acetic anhydride in excellent yields under solvent-free conditions at room temperature. This reported method is simple, mild, and environmentally viable and catalyst can be simply recovered and reused over 9 times without any significant loss of its catalytic activity.

Congenital Insensitivity to Pain (CIP) is a loss of function mutation resulting in a truncated NaV1.7 protein, suggesting a pivotal role in pain signaling and rendering it an important pharmaceutical target for multiple pain conditions. The structural homology in the NaV-channel family makes it challenging to design effective analgesic compounds without inducing for example cardiotoxicity or seizure liabilities. An additional approach to structural isoform selectivity is to identify compounds with use- or state-dependent profiles, i.e. inhibition efficacy based on the gating of the ion channel. In general nerve cells in damaged or inflamed tissue are more depolarized and electrically active compared to healthy nerve cells in for instance the heart. This observation has led to the design of two types of screening protocols emulating the voltage condition of peripheral neurons or cardiac tissue. The two voltage protocols have been developed to identify both use- and state-dependent antagonists. In this paper we describe an attempt to merge the two different protocols into one to increase screening efficacy, while retaining relevant state- and use-dependent pharmacology. The new protocol is constructed of two stimulation pulses and a slow voltage ramp for simultaneous assessment of resting and state-dependent block. By comparing all protocols we show that the new protocol indeed filter compounds for statedependence and increase the prediction power of selecting use-dependent compounds.

Autophagy is an evolutionally conserved process in cells for cleaning abnormal proteins and organelles in a lysosome dependent manner. Growing studies have shown that defects or induced autophagy contributes to many diseases including aging, neurodegeneration, pathogen infection, and cancer. However, the precise involvement of autophagy in health and disease remains controversial because the theories are built on limited assays and chemical modulators, indicating that the role of autophagy in diseases may require further verification. Many food and drug administration (FDA) approved drugs modulate autophagy signaling, suggesting that modulation of autophagy with pharmacological agonists or antagonists provides a potential therapy for autophagy-related diseases. This suggestion raises an attractive issue on drug discovery for exploring chemical modulators of autophagy. High throughput screening (HTS) is becoming a powerful tool for drug discovery that may accelerate screening specific autophagy modulators to clarify the role of autophagy in diseases. Herein, this review lays out current autophagy assays to specifically measure autophagy components such as LC3 (mammalian homologue of yeast Atg8) and Atg4. These assays are feasible or successful for HTS with certain chemical libraries, which might be informative for this intensively growing field as research tools and hopefully developing new drugs for autophagy-related diseases.

Optimization of the experimental conditions of a novel HPLC method for determination of the impurity levels with ziprasidone (in bulk substance and pharmaceutical dosage forms) was performed with use of Multi-Layer Perceptron (MLP) Artificial Neural Networks (ANN) and Response Surface Plots. The obtained experimental conditions were further used to test a set of 20 reversed-phase columns for their selectivity towards ziprasidone components by use of the principal component analysis (PCA) and hierarchical clustering analysis (HCA). The obtained HPLC retention times of ziprasidone and its impurities (Imp I-V) along with the computed molecular parameters of the examined compounds were further used in the Quantitative Structure Retention Relationship (QSRR) study. The performed QSRR study has selected the LogDpH 1.5, LogDpH 2.5, LogDpH 4.0, LogP, MS, and SAS parameters as descriptors of the chromatographic behavior of ziprasidone components. The developed QSRR model can be very useful in the tR prediction for the ziprasidone derivatives (impurities, degradation products, and metabolites). As the performed LC-MS study of the test solution has confirmed that the unknown impurity (tR: 11.270 min) in the test solution is the TS1, one from two candidates predicted by QSRR (TS1 and TS5), the high prediction potential of the created QSRR models has been proved.

N-alkyl 2-[(2-oxo-2-aryl ethyl)amino] benzamide derivatives have been synthesized in high yields. This involves three-component reaction of isatoic anhydride, primary amines in the presence of 2-bromoacethophenone derivatives. The reaction workup is simple and the products can be easily separated from the reaction mixture.

Virtual screening (VS) is becoming an increasingly important approach for identifying and selecting biologically active molecules against specific pharmaceutically relevant targets. Compared to conventional high throughput screening techniques, in silico screening is fast and inexpensive, and is increasing in popularity in early-stage drug discovery endeavours. This paper reviews and discusses recent trends and developments in three-dimensional (3D) receptor-based and ligand-based VS methodologies. First, we describe the concept of accessible chemical space and its exploration. We then describe 3D structural ligand-based VS techniques, hybrid approaches, and new approaches to exploit additional knowledge that can now be found in large chemogenomic databases. We also briefly discuss some potential issues relating to pharmacokinetics, toxicity profiling, target identification and validation, inverse docking, scaffold-hopping and drug re-purposing. We propose that the best way to advance the state of the art in 3D VS is to integrate complementary strategies in a single drug discovery pipeline, rather than to focus only on theoretical or computational improvements of individual techniques. Two recent 3D VS case studies concerning the LXR-β receptor and the CCR5/CXCR4 HIV co-receptors are presented as examples which implement some of the complementary methods and strategies that are reviewed here.

Prediction of promiscuous coupling between G-protein coupling receptors (GPCRs) and various G-proteins is one of the challenges for current research. In this article, a novel method that based on the optimization of the coupling regions is proposed. The cross validation results demonstrate that it can achieve an average accuracy of over 80% for promiscuous coupling prediction. The results further suggest that the optimized coupling regions may be important for the processes of signal transduction pathways.