Current Topics in Medicinal Chemistry - Volume 16, Issue 30, 2016

There has been considerable interest in azole-containing compounds as promising antiinflammatory agents. Designed compounds with five-membered nitrogen-containing nuclei have demonstrated good anti-inflammatory activity, indicating their potential for the treatment of this highly irritating condition. Pyrazoles, have attracted much more attention than other azoles, however, reports on other azoles demonstrated that they were as effective as pyrazoles. This review describes the different classes of azoles designed as cyclooxygenase inhibitors and the effect of different structural modifications on their activity.

Oxazole derivatives are found to have a variety of biological activities. A large number of studies have revealed their outstanding anticancer activities. Here we review four different types of oxazole derivatives with anticancer potential reported over the last ten years. We focus our discussion on their activity, selectivity in different cancer cell lines, mechanisms of action, and their structural evolution.

Imidazo[1,2-a]pyridine has been shown to be an important biologically active moiety. This review is a compilation of the scattered output of results of the anticancer activities of the imidazo[1,2-a]pyridine system since 2001, which have been classified as inhibition of CDK, VEGFR, PI3K, EGFR, RGGT etc. along with inhibition against different tumor cell lines. Various imidazo[1,2-a]pyridine based analogues have been used as lead molecules and are now under human clinical trials. This review will help the wider scientific community in the area of drug discovery of imidazo[1,2-a]pyridines as novel anticancer agents.

The 1,3-azole motif is a common and integral feature of many clinical drugs. Due to their wide-ranging applications, the development of 1,3-azoles as therapeutic agents is an ongoing focus of medicinal chemists. This review highlights the recent approaches towards the assembly of 1,3-oxazole, 1,3-thiazole and 1,3-imidazole motifs.

Adverse drug reactions (ADRs) are the leading factors of drug attrition in drug development and post-market drug withdrawal. The identification of potential ADRs can help prevent the failure of drug discovery and improve development efficiency. Furthermore, elucidating possible ADRs for known drugs can help better understand the mechanism of drug actions and even find new indications of old drugs. Unfortunately, only the ADRs of some well-studied drugs are available and our knowledge about ADRs of available drugs is far from complete. Recently, with more structural and omics data available, some computational approaches have been developed for predicting drug ADRs. In this review, we present a survey on the recent progresses on computational methodologies that have been developed for ADR prediction based on various kinds of data, and some ADR related resources are also introduced.

Using existing drugs for new indications (drug repurposing) is an effective method not only to reduce drug development time and costs but also to develop treatments for new disease including those that are rare. In order to discover novel indications, potential target identification is a necessary step. One widely used method to identify potential targets is through molecule docking. It requires no prior information except structure inputs from both the drug and the target, and can identify potential targets for a given drug, or identify potential drugs for a specific target. Though molecular docking is popular for drug development and repurposing, challenges remain for the method. In order to improve the prediction accuracy, optimizing the target conformation, considering the solvents and adding cobinders to the system are possible solutions.

Low drug productivity has been a significant problem of the pharmaceutical industry for several decades even though numerous novel technologies were introduced during this period. Currently pharmacologic dogma, “single drug, single target, single disease”, is at the root of the lack of drug productivity. From a systems biology viewpoint, network pharmacology has been proposed to complement the established guiding pharmacologic approaches. The rationale for network pharmacology as a major component of drug discovery and development is that a disease can be caused by perturbation of the disease-causing network and a drug may be designed to interact with multiple targets for modulation of such a network from the disease status toward normal status. Therefore, network pharmacology has been applied to guide and assist in drug repositioning. Drugs exerting their therapeutic effects may directly target disease-associated proteins, but they may also modulate the pathways involved in the pathological process. In this review, we discuss the progresses and prospects in network pharmacology, focusing on drug off-targets discovery, disease-associated protein identification, and pathway analysis for elucidating relationships between drug targets and disease-associated proteins.

Small compounds constitute most of the available medicines. However, their stereophysical and stereochemical properties are relatively simple, which typically results in promiscuity in their interactions with human proteins. Such promiscuity has caused problems in our past efforts to discover and develop new drugs, but at the same time, it also brought us new opportunities to exploit the off-target interactions between small compounds and human proteins for novel or improved therapeutics, e.g. in applications of polypharmacology, drug repositioning, and rational design of drug combinations. In this direction, identifying the full profile of macromolecules that a small compound may interact with is of fundamental importance to harnessing the positive side of small compound promiscuity. This review summarizes available experimental and computational approaches that identify macromolecular targets for small compounds. The principle, application, performance, limitation and availability of these approaches are discussed.

The assessment of drug toxicity, especially hepatotoxicity, is a critical task in drug development process. To assist with early detection, various in silico approaches have been demonstrated to identify drugs with high hepatotoxicity potential. In this study, based on detailed review on previous reports on drug-induced cholestatic liver injury, we developed a network-based approach to predict cholestatic potential of chemicals using toxicogenomic data. First, the cholestasis network was constructed with 57 relevant genes and 78 connections between genes. Taking only genes in the disease network into account, the evaluation model was trained by genomic data of 17 typical chemicals associated with cholestasis and yielded the prediction accuracy at 88%. The performance of this model was further challenged by an X-permutation test and an external set of 98 chemicals. Among them, 14 chemicals were marked with high risk of inducing cholestasis by our approach. A survey of published literatures confirmed that 71.43% of our predicted chemicals have been shown to induce cholestatic liver injury experimentally and approximately 93% of them have been implicated to promote liver injury to various extents. Together, we concluded that network-based approaches greatly facilitate further understanding of molecular mechanisms for cholestatic liver injury and this concept could be easily generalized to other biological-relevant side-effect assessments.

The scaffold protein Axin plays important roles in multiple signaling pathways through mediating the formation of different signaling complexes. Axin is best known for its role as a negative regulator in Wnt/β-catenin pathway. Aberrant activation of the key components in Wnt/β-catenin pathway including Axin has been linked to a variety of human cancers. Drugs developed for this pathway are far from satisfying largely due to the limited targets especially enzymesin this pathway. Almost all Axinsignal pathways depend on a number of protein-protein interactions (PPIs) for transmitting information, making PPIs attractive targets and probably a future direction for drug discovery. Axin interacts with a plethora of proteins, positively or negatively regulating several signaling pathways that are closely involved in the pathogenesis of human disease. Studies in recent years indicated Axin as a promising target for treating Wnt-driven diseases. With the identification of more interacting partners for Axin and increased understanding about the roles of these Axin-mediated PPIs in human disease, new targets may surface from the Axin PPI networks, which may lead to the generation of new drugs and treatmentstrategies. In this review, we outline the Axin PPI networks in several pathways especially in Wnt signaling and discuss how those Axin-interacting proteins affect the respective signaling pathways through an interaction with Axin.