Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

Regulation of Nrf2 and NF-κB activities may contribute to the anti-inflammatory mechanism of xylopic acid

Xylopic acid (XA) is a kaurene diterpene which naturally exists in African plants such as Xylopia aethiopica. It has been established to exhibit acute and chronic anti-inflammatory activities from our earlier studies. This current work sets out to shed light on the potential molecular target(s) of xylopic acid. Selection of investigated targets (NF-κB, Nrf2 and PTP1B) was based on an unbiased approach, using the SPiDER in silico prediction tool, and a candidate approach, examining well-known anti-inflammatory targets. Reporter gene assays were used to test for altered NF-κB and Nrf2 activities in transfected HEK or CHO cells, respectively, and immunoblot and flow cytometric analyses examined protein expression of the Nrf2/NF-kB target genes HO-1 and VCAM-1 in HUVEC. An effect of XA on PTP1B activity assay was studied using an in vitro enzyme assay with recombinant human enzyme and pNPP as substrate as well as by looking at insulin receptor phosphorylation in HepG2 cells. XA at 30 µM significantly (p < 0.001) inhibited the NF-κB-dependent reporter gene expression and enhanced activation of Nrf2 in a concentration-dependent manner when compared to the control. XA also marginally increased HO-1 protein expression levels while expression of VCAM-1 was reduced to 70% in XA-treated endothelial cells. However, XA did not show any sign of inhibition of PTP1B or a related phosphatase. Our findings suggest that the anti-inflammatory mechanism of XA entails the inhibitory effect on NF-κB and an increased activity of Nrf2, accompanied by increased expression of HO-1 and reduced expression of VCAM-1.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Synthesis of 2-Oxo(thio)-N,4-diaryl-1,2,3,4-tetrahydropyrimidine-5-carbothioamides

It was found that 2-acylthioacetamides enter Biginelli reaction with aromatic aldehydes and urea /thiourea forming 2-oxo(thio)-N,4-diaryl-1,2,3,4-tetrahydropyrimidine-5-carbothioamides. Under the influence of K3[Fe(CN)6] in alkaline environment, the expected 2-oxo(thio)-N,4-diaryl-5-(benzothiazole-2'-yl)-1,2,3,4-tetrahydropyrimidines were not been received while 2-oxo(thio)-N,4-diaryl-1,2,3,4-tetrahydropyrimidine-5-carbamides were the products of interaction.

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.

Industrial applications of in silico ADMET

Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery

Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection.