Current Topics in Medicinal Chemistry - Volume 19, Issue 25, 2019

Polysialic acid (polySia) is a novel glycan that posttranslationally modifies neural cell adhesion molecules (NCAMs) in mammalian cells. Up-regulation of polySia-NCAM expression or NCAM polysialylation is associated with tumor cell migration and progression in many metastatic cancers and neurocognition. It has been known that two highly homologous mammalian polysialyltransferases (polySTs), ST8Sia II (STX) and ST8Sia IV (PST), can catalyze polysialylation of NCAM, and two polybasic domains, polybasic region (PBR) and polysialyltransferase domain (PSTD) in polySTs play key roles in affecting polyST activity or NCAM polysialylation. However, the molecular mechanisms of NCAM polysialylation and cell migration are still not entirely clear. In this minireview, the recent research results about the intermolecular interactions between the PBR and NCAM, the PSTD and cytidine monophosphate-sialic acid (CMP-Sia), the PSTD and polySia, and as well as the intramolecular interaction between the PBR and the PSTD within the polyST, are summarized. Based on these cooperative interactions, we have built a novel model of NCAM polysialylation and cell migration mechanisms, which may be helpful to design and develop new polysialyltransferase inhibitors.

Stimulated by the 5-steps rule during the last decade or so, computational proteomics has achieved remarkable progresses in the following three areas: (1) protein structural class prediction; (2) protein subcellular location prediction; (3) post-translational modification (PTM) site prediction. The results obtained by these predictions are very useful not only for an in-depth study of the functions of proteins and their biological processes in a cell, but also for developing novel drugs against major diseases such as cancers, Alzheimer’s, and Parkinson’s. Moreover, since the targets to be predicted may have the multi-label feature, two sets of metrics are introduced: one is for inspecting the global prediction quality, while the other for the local prediction quality. All the predictors covered in this review have a userfriendly web-server, through which the majority of experimental scientists can easily obtain their desired data without the need to go through the complicated mathematics.

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

The enzyme L-DOPA decarboxylase (DDC), also called aromatic-L-amino-acid decarboxylase, catalyzes the biosynthesis of dopamine, serotonin, and trace amines. Its deficiency or perturbations in expression result in severe motor dysfunction or a range of neurodegenerative and psychiatric disorders. A DDC substrate, L-DOPA, combined with an inhibitor of the enzyme is still the most effective treatment for symptoms of Parkinson's disease. In this review, we provide an update regarding the structures, functions, and inhibitors of DDC, particularly with regards to the treatment of Parkinson's disease. This information will provide insight into the pharmacological treatment of Parkinson's disease.

The dried root of ginseng (Panax ginseng C. A. Meyer or Panax quinquefolius L.) is a traditional Chinese medicine widely used to manage cancer symptoms and chemotherapy side effects in Asia. The anti-cancer efficacy of ginseng is attributed mainly to the presence of saponins, which are commonly known as ginsenosides. Ginsenosides were first identified as key active ingredients in Panax ginseng and subsequently found in Panax quinquefolius, both of the same genus. To review the recent advances on anti-cancer effects of ginsenosides against breast cancer, we conducted a literature study of scientific articles published from 2010 through 2018 to date by searching the major databases including Pubmed, SciFinder, Science Direct, Springer, Google Scholar, and CNKI. A total of 50 articles authored in either English or Chinese related to the anti-breast cancer activity of ginsenosides have been reviewed, and the in vitro, in vivo, and clinical studies on ginsenosides are summarized. This review focuses on how ginsenosides exert their anti-breast cancer activities through various mechanisms of action such as modulation of cell growth, modulation of the cell cycle, modulation of cell death, inhibition of angiogenesis, inhibition of metastasis, inhibition of multidrug resistance, and cancer immunemodulation. In summary, recent advances in the evaluation of ginsenosides as therapeutic agents against breast cancer support further pre-clinical and clinical studies to treat primary and metastatic breast tumors.

Acetoin is an important four-carbon compound that has many applications in foods, chemical synthesis, cosmetics, cigarettes, soaps, and detergents. Its stereoisomer (S)-acetoin, a high-value chiral compound, can also be used to synthesize optically active drugs, which could enhance targeting properties and reduce side effects. Recently, considerable progress has been made in the development of biotechnological routes for (S)-acetoin production. In this review, various strategies for biological (S)- acetoin production are summarized, and their constraints and possible solutions are described. Furthermore, future prospects of biological production of (S)-acetoin are discussed.

Owing to their wide structural diversity and unique complexing properties, cyclodextrins (CDs) find manifold applications in drug discovery and development. The focus of this mini-review is on their uses as ‘enabling excipients’ both in the context of early drug discovery and in subsequent optimisation of drug performance. Features highlighted here include descriptions of the structures of CDs, synthetic derivatisation to fine-tune their properties, the nature of inclusion complexation of drugs within the CD cavity, methodology for the study of free and complexed hosts in the solid state and in solution, the inherent pharmacological activity of several CDs and its utility, novel CD-based drug delivery systems, and the role of CDs in drug discovery and optimisation. Illustrative examples are generally based on research reported during the last two decades. Application of CDs to the optimisation of the performance of established drugs is commonplace, but there are many opportunities for the intervention of CDs during the early stages of drug discovery, which could guide the selection of suitable candidates for development, thereby contributing to reducing the attrition rate of new molecular entities.