Design, synthesis and pharmacological evaluation of novel vanadium-containing complexes as antidiabetic agents

In Vitro Study of Biological Activity of Tanacetum vulgare Extracts

Tanacetum vulgare is an herbaceous plant widely used in folk medicine. It is rich in phenolic acids and flavonoids, which have pharmacological and medicinal properties, such as anthelmintic, antispasmodic, tonic, antidiabetic, diuretic, and antihypertensive. This study aimed to confirm the presence of biologically active substances in Tanacetum vulgare and to determine the pharmacological spectrum of biological activity of Tanacetum vulgare extract components. When preparing Tanacetum vulgare extracts, the highest yield was observed when using the maceration method with a mixture of solvents methanol + trifluoroacetic acid (22.65 ± 0.68%). The biologically active substances in Tanacetum vulgare extract samples were determined using high-performance liquid chromatography. Biologically active substances such as luteolin-7-glucoside (550.80 mg/kg), chlorogenic acid (5945.40 mg/kg), and rosmarinic acid (661.31 mg/kg) were identified. Their structures were determined. The experiments have confirmed the antioxidant and antibacterial activities. Secondary metabolites of Tanacetum vulgare extracts have been found to have previously unknown biological activity types; experimental confirmation of their existence will advance phytochemical research and lead to the development of new drugs.

A Dioxidovanadium Complex cis-[VO2 (obz) py] Attenuates Hyperglycemia in Streptozotocin (STZ)-Induced Diabetic Male Sprague-Dawley Rats via Increased GLUT4 and Glycogen Synthase Expression in the Skeletal Muscle

Vanadium has demonstrated antihyperglycemic effects in diabetes mellitus (DM) but is, however, associated with toxicity. Therefore, new vanadium complexes envisaged to possess heightened therapeutic potency while rendering less toxicity are being explored. Accordingly, the aim of the study was to investigate the effects of a dioxidovanadium (V) complex, cis-[VO2 (obz) py], on selected glucose metabolism markers in streptozotocin (STZ)-induced diabetic rats. STZ-induced diabetic rats were treated orally with cis-[VO2 (obz) py] (10, 20, and 40 mg/kg) twice every 3rd day for 5 weeks. Blood glucose concentrations, body weight, and food and water intake were monitored weekly, for 5 weeks. Rats were then euthanized after which blood, liver, and muscle tissues were collected for biochemical analysis. The administration of dioxidovanadium complex significantly decreased blood glucose concentrations throughout the 5-week period in comparison with the diabetic control (DC). The attenuation of hyperglycemia was accompanied by an increased glycogen concentration in both liver and muscle tissues in the treated groups. Furthermore, a significant increase was observed in the expression of glucose transporter type 4 (GLUT4) in the skeletal muscle tissues and glycogen synthase in the liver tissues. These findings indicate that our vanadium complex cis-[VO2 (obz) py] may exert antihyperglycemic effects through increased glucose uptake, glycogen synthesis, and increased GLUT4 and glycogen synthase expression.

Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors

Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 μM to 26 μM for LOX, from 200 μM to 0.018 μM for COX-1, and from 210 μM to 1 μM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".

Schiff base oxovanadium complexes resist the assembly behavior of human islet amyloid polypeptide

The misfolding and fibrillation of human islet amyloid polypeptide (hIAPP) is related to the pathologic process of type II diabetes mellitus (T2DM). The inhibitors of hIAPP aggregation include aromatic organic molecules, short peptides, and metal complexes. Vanadium complexes have been applied for the treatment of diabetes since the 19th century. However, the antidiabetes mechanism remains unclear. In this work, we used four Schiff base oxidovanadium(IV) complexes, namely VO(bhbb)·H₂O (1, and ligand 1 H₂bhbb, 2-(5-bromo-2-hydroxylbenzylideneamino) benzoic acid), VO(nhbb)·H₂O (2, and lignad 2 H₂nhbb, 2-(5-nitro-2-hydroxylbenzylideneamino) benzoic acid), VO(cpmp)₂ (3, and ligand 3 Hcpmp, 4-chloro-2-(phenylimino) methyl) phenol), and VO(bpmp)₂ (4, and ligand 4 Hbpmp, 4-bromo- 2-(phenylmino) methyl) phenol) to inhibit the fibril formation of hIAPP and reduce peptide-induced cytotoxicity. Results indicated that the four Schiff base oxidovanadium complexes effectively impeded hIAPP aggregation and disaggregated mature fibrils into monomers or oligomers. These V complexes also decreased hIAPP-induced cytotoxicity. Among the four V complexes, 1 is a promising candidate metallodrug considering its inhibitory effect, disaggregation ability, regulation of peptide-induced cytotoxicity, and binding affinity to the peptide. Our research provides a new outlook for the design of oxidovanadium complexes as effective inhibitors of hIAPP against T2DM.

Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation

An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.

Ameliorative effect of an oxovanadium (IV) complex against oxidative stress and nephrotoxicity induced by cisplatin

OBJECTIVE

The present study was designed to investigate the chemoprotective efficacy of an L-cysteine-based oxovanadium (IV) complex, namely, oxovanadium (IV)-L-cysteine methyl ester complex (VC-IV) against cisplatin (CDDP)-induced renal injury in Swiss albino mice.

METHODS

CDDP was administered intraperitoneally (5 mg/kg body weight) and VC-IV was administered orally (1 mg/kg body weight) in concomitant and 7 days pre-treatment schedule.

RESULTS

CDDP-treated mice showed marked kidney damage and renal failure. Administration of VC-IV caused significant attenuation of renal oxidative stress and elevation of antioxidant status. VC-IV also significantly decreased serum levels of creatinine and blood urea nitrogen, and improved histopathological lesions. Western blot analysis of the kidneys showed that VC-IV treatment resulted in nuclear translocation of nuclear factor E2-related factor 2 (Nrf2) through modulation of cytosolic Kelch-like ECH-associated protein 1. Thus, VC-IV stimulated Nrf2-mediated activation of antioxidant response element (ARE) pathway and promoted expression of ARE-driven cytoprotective proteins, heme oxygenase 1 and NAD(P)H:quinone oxidoreductase 1, and enhanced activity of antioxidant enzymes. Interestingly, VC-IV did not alter the bioavailability and renal accumulation of CDDP in mice.

DISCUSSION

In this study, VC-IV exhibited strong nephroprotective efficacy by restoring antioxidant defense mechanisms and hence may serve as a promising chemoprotectant in cancer chemotherapy.

In vitro studies on the pleotropic antidiabetic effects of zinc oxide nanoparticles

Aim: Our earlier study demonstrated antidiabetic activity of zinc oxide nanoparticles (ZON) in diabetic rats. The present study was performed to elucidate its mechanism of antidiabetic action. Methods: Protein tyrosine phosphatase 1B, protein kinase B and hormone sensitive lipase phosphorylation; glucose transporter 4 translocation and glucose uptake; glucose 6 phosphatase, phosphoenol pyruvate carboxykinase and glucokinase expression; and pancreatic beta cell proliferation were evaluated after ZON treatment to cells. Result: ZON treatment resulted in PKB activation, protein tyrosine phosphatase 1B inactivation, increased glucose transporter 4 translocation and enhanced glucose uptake, decreased glucose 6 phosphatase and phosphoenol pyruvate carboxykinase expression, hormone sensitive lipase inactivation and pancreatic beta cell proliferation. Conclusion: To the best of our knowledge, we report for the first time, pleiotropic antidiabetic effects of ZON viz. improved insulin signaling, enhanced glucose uptake, decreased hepatic glucose output, decreased lipolysis and enhanced pancreatic beta cell mass.

Assessment of the Potential Biological Activity of Low Molecular Weight Metabolites of Freshwater Macrophytes with QSAR

The paper focuses on the assessment of the spectrum of biological activities (antineoplastic, anti-inflammatory, antifungal, and antibacterial) with PASS (Prediction of Activity Spectra for Substances) for the major components of three macrophytes widespread in the Holarctic species of freshwater, emergent macrophyte with floating leaves, Nuphar lutea (L.) Sm., and two species of submergent macrophyte groups, Ceratophyllum demersum L. and Potamogeton obtusifolius (Mert. et Koch), for the discovery of their ecological and pharmacological potential. The predicted probability of anti-inflammatory or antineoplastic activities above 0.8 was observed for twenty compounds. The same compounds were also characterized by high probability of antifungal and antibacterial activity. Six metabolites, namely, hexanal, pentadecanal, tetradecanoic acid, dibutyl phthalate, hexadecanoic acid, and manool, were a part of the major components of all three studied plants, indicating their high ecological significance and a certain universalism in their use by various species of water plants for the implementation of ecological and biochemical functions. This report underlines the role of identified compounds not only as important components in regulation of biochemical and metabolic pathways and processes in aquatic ecological systems, but also as potential pharmacological agents in the fight against different diseases.

QSAR Modeling and Prediction of Drug-Drug Interactions

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.