Current Topics in Medicinal Chemistry - Volume 19, Issue 11, 2019

Chemical reactions that can proceed in living systems while not interfering with native biochemical processes are collectively referred to as bioorthogonal chemistry. Selectivity, efficiency, and aqueous compatibility are three significant characteristics of an ideal bioorthogonal reaction. To date, the specialized bioorthogonal reactions that have been reported include: Cu (I)-catalyzed [3 + 2] azido– alkyne cycloadditions (CuAAC), strain-promoted [3 + 2] azide–alkyne cycloadditions (SPAAC), Staudinger ligation, photo-click 1,3-dipolar cycloadditions, strain-promoted alkyne-nitrone cycloadditions (SPANC), transition metal catalysis (TMC), and inverse electron demand Diels–Alder (IEDDA). These reactions are divided into two subtypes, 1) bond-formation reactions (e.g. CuAAC, SPAAC, photo-click cycloadditions, SPANC), which can be conventionally applied in the chemical biology field for target identification, protein-specific modifications and others; and 2) bond-release reactions (e.g. Staudinger ligation, TMC, and IEDDA), which are emerging as powerful approaches for the study of protein activation and drug discovery. Over the past decade, bioorthogonal chemistry has enabled important compound design features in targeted drug discovery and has expanded biological knowledge on intractable targets. Research groups have also focused on the discovery of reactions with improved biocompatibility and increased reaction rates, which will undoubtably prove essential for future therapeutic development. Herein, we highlight two significant applications of bioorthogonal chemistry to drug discovery, which are tumor-targeted prodrug delivery and activation, and self-assembly of bifunctional molecules. The relevant challenges and opportunities are also discussed.

Background: Hepatitis C is a disease that constitutes a serious global health problem, is often asymptomatic and difficult to diagnose and about 60-80% of infected patients develop chronic diseases over time. As there is no vaccine against hepatitis C virus (HCV), developing new cheap treatments is a big challenge. Objective: The search for new drugs from natural products has been outstanding in recent years. The aim of this study was to combine structure-based and ligand-based virtual screening (VS) techniques to select potentially active molecules against four HCV target proteins from in-house secondary metabolite dataset (SistematX). Materials and Methods: From the ChEMBL database, we selected four sets of 1199, 355, 290 and 237chemical structures with inhibitory activity against different targets of HCV to create random forest models with an accuracy value higher than 82% for cross-validation and test sets. Afterward, a ligandbased virtual screen of the entire 1848 secondary metabolites database stored in SistematX was performed. In addition, a structure-based virtual screening was also performed for the same set of secondary metabolites using molecular docking. Results: Finally, using consensus analyses approach combining ligand-based and structure-based VS, three alkaloids were selected as potential anti-HCV compounds. Conclusion: The selected structures are a starting point for further studies in order to develop new anti- HCV compounds based on natural products.

Background: PIM-1 is a kinase which has been related to the oncogenic processes like cell survival, proliferation, and multidrug resistance (MDR). This kinase is known for its ability to phosphorylate the main extrusion pump (ABCB1) related to the MDR phenotype. Objective: In the present work, we tested a new mechanistic insight on the AZD1208 (PIM-1 specific inhibitor) under interaction with chemotherapy agents such as Daunorubicin (DNR) and Vincristine (VCR). Materials and Methods: In order to verify a potential cytotoxic effect based on pharmacological synergism, two MDR cell lines were used: Lucena (resistant to VCR) and FEPS (resistant to DNR), both derived from the K562 non-MDR cell line, by MTT analyses. The activity of Pgp was ascertained by measuring accumulation and the directional flux of Rh123. Furthermore, we performed a molecular docking simulation to delve into the molecular mechanism of PIM-1 alone, and combined with chemotherapeutic agents (VCR and DNR). Results: Our in vitro results have shown that AZD1208 alone decreases cell viability of MDR cells. However, co-exposure of AZD1208 and DNR or VCR reverses this effect. When we analyzed the ABCB1 activity AZD1208 alone was not able to affect the pump extrusion. Differently, co-exposure of AZD1208 and DNR or VCR impaired ABCB1 activity, which could be explained by compensatory expression of abcb1 or other extrusion pumps not analyzed here. Docking analysis showed that AZD1208 is capable of performing hydrophobic interactions with PIM-1 ATP- binding-site residues with stronger interaction-based negative free energy (FEB, kcal/mol) than the ATP itself, mimicking an ATP-competitive inhibitory pattern of interaction. On the same way, VCR and DNR may theoretically interact at the same biophysical environment of AZD1208 and also compete with ATP by the PIM-1 active site. These evidences suggest that AZD1208 may induce pharmacodynamic interaction with VCR and DNR, weakening its cytotoxic potential in the ATP-binding site from PIM-1 observed in the in vitro experiments. Conclusion: Finally, the current results could have a pre-clinical relevance potential in the rational polypharmacology strategies to prevent multiple-drugs resistance in human leukemia cancer therapy.

Background: Quillaja saponaria Mol. bark contains a high concentration of triterpene saponins that have been used for centuries as a cleansing, antiinflammatory and analgesic agent in Chilean folk medicine. In earlier studies, in mice, both the anti-inflammatory as well as the antinociceptive effect of the major sapogenin, quillaic acid have been demonstrated (QA). Objective: To determine the antihyperalgesic effect of QA one and seven days after itpl administration of complete Freund's adjuvant (CFA) in male mice using the hot plate test in the presence of complete Freund's adjuvant (HP/CFA) as an acute and chronic skeletal muscle pain model. Methods: The present study evaluated the antihyperalgesic activity of QA against acute and chronic skeletal muscle pain models in mice using the hot plate test in the presence of complete Freund's adjuvant (HP/CFA), at 24 h (acute assay) and 7 days (chronic assay) , with dexketoprofen (DEX) as the reference drug. Results: In acute and chronic skeletal muscle pain assays, QA at 30 mg/kg ip elicited its maximal antihyperalgesic effects (65.0% and 53.4%) at 24 h and 7 days, respectively. The maximal effect of DEX (99.0 and 94.1 at 24 h and 7 days, respectively) was induced at 100 mg/kg. Conclusion: QA and DEX elicit dose-dependent antihyperalgesic effects against acute and chronic skeletal muscle pain, but QA is more potent than DEX in the early and late periods of inflammatory pain induced by CFA.

Background: In the last decade, the harmful use of dioxin has been demonstrated in human health and in the whole environment. It is well known among scientists that 2, 3, 7, 8-tetrachloro dibenzo-p-dioxin (TCDD) is an environmental pollutant that causes endocrine disruption, which causes male reproductive toxicity. Objective: The objective of the present study was to evaluate the toxicity effect of low doses of TCDD in male CD1 mice. Materials and Methods: Three concentrations of TCDD (0.375, 0.75, 1.5 mg / kg) were analyzed and the effects on spermatozoa were evaluated 10 days after oral administration of the product. As bioindicators of TCDD toxicity, an exhaustive analysis of several spermatic parameters including motility, vitality, count, morphology and viability, flow cytometry was used to determine the affected sperm population by cytotoxicity and apoptosis. In addition, a morphometric analysis of testicles was performed. Results: The results show that the body weight of the treated animals was reduced in medium and high doses (0.75, 1.5 mg / kg) with respect to the control groups. In the groups treated with TCDD, the abnormal head of the sperm increased by 52.5% more than the control group. Significant differences in apoptosis were observed between the negative control and vehicle control, including the median dose (0.75 mg / kg). Conclusion: It is concluded that at these low doses there was an impact on the quality of the mouse sperm, adding an effect on apoptosis and cytotoxicity of sperm exposed to these doses of TCDD.

Background: Recently, some authors have defined new molecular descriptors (MDs) based on the use of the Graph Discrete Derivative, known as Graph Derivative Indices (GDI). This new approach about discrete derivatives over various elements from a graph takes as outset the formation of subgraphs. Previously, these definitions were extended into the chemical context (N-tuples) and interpreted in structural/physicalchemical terms as well as applied into the description of several endpoints, with good results. Objective: A generalization of GDIs using the definitions of Higher Order and Mixed Derivative for molecular graphs is proposed as a generalization of the previous works, allowing the generation of a new family of MDs. Methods: An extension of the previously defined GDIs is presented, and for this purpose, the concept of Higher Order Derivatives and Mixed Derivatives is introduced. These novel approaches to obtaining MDs based on the concepts of discrete derivatives (finite difference) of the molecular graphs use the elements of the hypermatrices conceived from 12 different ways (12 events) of fragmenting the molecular structures. The result of applying the higher order and mixed GDIs over any molecular structure allows finding Local Vertex Invariants (LOVIs) for atom-pairs, for atoms-pairs-pairs and so on. All new families of GDIs are implemented in a computational software denominated DIVATI (acronym for Discrete DeriVAtive Type Indices), a module of KeysFinder Framework in TOMOCOMD-CARDD system. Results: QSAR modeling of the biological activity (Log 1/K) of 31 steroids reveals that the GDIs obtained using the higher order and mixed GDIs approaches yield slightly higher performance compared to previously reported approaches based on the duplex, triplex and quadruplex matrix. In fact, the statistical parameters for models obtained with the higher-order and mixed GDI method are superior to those reported in the literature by using other 0-3D QSAR methods. Conclusion: It can be suggested that the higher-order and mixed GDIs, appear as a promissory tool in QSAR/QSPRs, similarity/dissimilarity analysis and virtual screening studies.

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.