Current Topics in Medicinal Chemistry - Volume 17, Issue 30, 2017

After the identification of the anti-inflammatory properties of VA5-13l (2-benzyl-1- methyl-5-nitroindazolinone) in previous investigations, some of its analogous compounds were designed, synthesized and evaluated in two anti-inflammatory methods: LPS-enhanced leukocyte migration assay in zebrafish; and 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mouse ear edema. The products evaluated (3, 6, 8, 9 and 10) showed the lower values of relative leukocyte migration at 30 μM (0.14, 0.07, 0.10, 0.13 and 0.07, respectively), while in ear edema and myeloperoxidase activity methods, all the compounds reduced inflammation, only 4 and 16 yielded unsatisfactory results. The relationship linking structure and activity (SAR analysis) was determinate by using SARANEA software. The importance of the 5-Nitro group of the indazole ring for the activity was evident, and showed modest reduction when benzyl (Bn) is changed by alkyl group. A substituted Bn moiety at N2 (R) is the best substituent (5-10); nevertheless, if methylene group of Bn is deleted, the activity is affected. Also, introduction of halogen atoms mainly at positions 3 or 4 of the benzyl moiety (6 and 10) leads in general to strong activities. In fact, compounds 7 and 8 (R = 4-FBn or 4-ClBn, respectively) exhibit satisfactory results in in vivo tests and appear promising. The production of IL-6 at all doses assayed was significantly reduced, except with 16. Nonetheless, the production of TNF-α was significantly inhibited only by this chemical (16) at concentration of 50 μM. On the other hand, compound 2 was the one that mostly inhibited the expression of COX-2 and iNOS. From these results, it can be concluded that the inhibition in the release of cytokines can be one of the mechanisms of action responsible for the anti-inflammatory effect for 2-benzyl derivates while other 2-alkyl derivatives can inhibit production of NO. Therefore, nitroindazolinone chemical prototype could be an interesting structural group with anti-inflammatory purposes in the therapeutic.

Background: Complex network approach allows the representation and analysis of complex systems of interacting agents in an ordered and effective manner, thus increasing the probability of discovering significant properties of them. In the present study, we defined and built for the first time a complex network based on data obtained from Immune Epitope Database for parasitic organisms. We then considered the general topology, the node degree distribution, and the local structure (triadic census) of this network. In addition, we calculated 9 node centrality measures for observed network and reported a comparative study of the real network with three theoretical models to detect similarities or deviations from these ideal networks. Result: The results obtained corroborate the utility of the complex network approach for handling information and data mining within the database under study. Conclusion: They confirm that this type of approach can be considered a valuable tool for preliminary screening of the best experimental conditions to determine whether the amino acid sequences being studied are true epitopes or not.

Background: Protein c-Myb is a therapeutic target. Some sesquiterpene lactones suppress Myb-dependent gene expression, which results in their potential anti-cancer activity. Material & Methods: Database ChEMBL is a representative of lactones for physicochemical and physiochemical properties. Data presented for 31 natural lactones are discussed in terms of quantitative structureactivity relationships with the objective to predict inhibitors of Myb-induced gene expression. Several constitutional descriptors are related to structure-activity. α-Methylene-γ-lactone groups enhance while OH functions worsen potency. The latter feature is in agreement with the fact that the more lipophilic the lactone, the greater the cytotoxicity because of the ability to cross lipoidal biomembranes. In general, numbers of π-systems and atoms, and polarizability enhance activity. Linear and nonlinear structure-activity models are developed, between lactones of a great structural diversity, to predict inhibitors of Myb-induced gene expression. Four variables (ML, UNC, TCO+OCOR, UNC+UNA) related to ATOM show a positive correlation because of the partial anionic and H-acceptor characters of O-atom. In most, CO group is conjugated. Result and Conclusion: Term OH shows negative coefficients because of the partial cationic quality of H-atom and because OH forms H-bonds with CO, causing them to be less H-acceptor. s-trans-s-trans-Germacranolide structure is the most active. Coefficients standard errors result acceptable in almost all equations. After cross-validation, linear equations for lactones, pseudoguaianolides and germacranolides are the most predictive. Most descriptors are constitutional variables.

Quantitative Structure - Activity Relationship (QSAR) modeling has been widely used in medicinal chemistry and computational toxicology for many years. Today, as the amount of chemicals is increasing dramatically, QSAR methods have become pivotal for the purpose of handling the data, identifying a decision, and gathering useful information from data processing. The advances in this field have paved a way for numerous alternative approaches that require deep mathematics in order to enhance the learning capability of QSAR models. One of these directions is the use of Multiple Classifier Systems (MCSs) that potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while improving generalization and predictive performance. In this paper, we presented MCS as a next generation of QSAR modeling techniques and discuss the chance to mining the vast number of models already published in the literature. We systematically revisited the theoretical frameworks of MCS as well as current advances in MCS application for QSAR practice. Furthermore, we illustrated our idea by describing ensemble approaches on modeling histone deacetylase (HDACs) inhibitors. We expect that our analysis would contribute to a better understanding about MCS application and its future perspectives for improving the decision making of QSAR models.

The analytical bias introduced by most of the commonly used techniques in metabolomics considerably hinders the simultaneous detection of all metabolites present in complex biological samples. In order to solve this limitation, the combination of complementary approaches is emerging in recent years as the most suitable strategy in order to maximize metabolite coverage. This review article presents a general overview of the most important analytical techniques usually employed in metabolomics: nuclear magnetic resonance, mass spectrometry and hybrid approaches. Furthermore, we emphasize the potential of integrating various tools in the form of metabolomic multi-platforms in order to get a deeper metabolome characterization, for which a revision of the existing literature in this field is provided. This review is not intended to be exhaustive but, rather, to give a practical and concise guide to readers not familiar with analytical chemistry on the considerations to account for the proper selection of the technique to be used in a metabolomic experiment in biomedical research.

Computer-Aided Drug Designing (CADD) has gained a wide popularity among biologists and chemists as a part of interdisciplinary drug discovery approach. It plays a vital role in the discovery, design and analysis of drugs in pharmaceutical industry. It is extensively used to reduce cost, time and speed up the early stage development of biologically new active molecules. In the current review we presented a brief review of CADD, merits and demerits, DNA, protein and enzyme as targets, types of CADD: Structure Based Drug Designing (SBDD), Ligand Based Drug Designing (LBDD), Pharmacophore based drug designing (PBDD) and Fragment Based Drug Designing (FBDD), theory behind the types of CADD and their applications. The review also focuses on the in-silico pharmokinetic, pharmacodynamic and toxicity filters or predictions that play a major role in identifying the drug like molecules. Currently in pharmaceutical sciences computational tools and software are exhibiting imperative role in the different stages of drug discovery hence the review throws light on various commercial and freeware available for each step of CADD.

A rapid search in scientific publication's databases shows how the use of CRISPR-Cas genome editions' technique has considerably expanded, and its growing importance, in modern molecular biology. Just in pub-med platform, the search of the term gives more than 3000 results. Specifically, in Drug Discovery, Medicinal Chemistry and Chemical Biology in general CRISPR method may have multiple applications. Some of these applications are: resistance-selection studies of antimalarial lead organic compounds; investigation of druggability; development of animal models for chemical compounds testing, etc. In this paper, we offer a review of the most relevant scientific literature illustrated with specific examples of application of CRISPR technique to medicinal chemistry and chemical biology. We also present a general overview of the main legal and ethical trends regarding this method of genome editing.