Thiazole-Based Thiosemicarbazones: Synthesis, Cytotoxicity Evaluation and Molecular Docking Study

Introduction

Hybrid drug design has developed as a prime method for the development of novel anticancer therapies that can theoretically solve much of the pharmacokinetic disadvantages of traditional anticancer drugs. Thus a number of studies have indicated that thiazole-thiophene hybrids and their bis derivatives have important anticancer activity. Mammalian Rab7b protein is a member of the Rab GTPase protein family that controls the trafficking from endosomes to the TGN. Alteration in the Rab7b expression is implicated in differentiation of malignant cells, causing cancer.

Methods

1-(4-Methyl-2-(2-(1-(thiophen-2-yl) ethylidene) hydrazinyl) thiazol-5-yl) ethanone was used as building block for synthesis of novel series of 5-(1-(2-(thiazol-2-yl) hydrazono) ethyl) thiazole derivatives. The bioactivities of the synthesized compounds were evaluated with respect to their antitumor activities against MCF-7 tumor cells using MTT assay. Computer-aided docking protocol was performed to study the possible molecular interactions between the newly synthetic thiazole compounds and the active binding site of the target protein Rab7b. Moreover, the in silico prediction of adsorption, distribution, metabolism, excretion (ADME) and toxicity (T) properties of synthesized compounds were carried out using admetSAR tool.

Results

The results obtained showed that derivatives 9 and 11b have promising activity (IC₅₀ = 14.6 ± 0.8 and 28.3 ± 1.5 µM, respectively) compared to Cisplatin (IC₅₀ = 13.6 ± 0.9 µM). The molecular docking analysis reveals that the synthesized compounds are predicted to be fit into the binding site of the target Rab7b. In summary, the synthetic thiazole compounds 1–17 could be used as potent inhibitors as anticancer drugs.

Conclusion

Promising anticancer activity of compounds 9 and 11 compared with cisplatin reference drug suggests that these ligands may contribute as lead compounds in search of new anticancer agents to combat chemo-resistance.

Synthesis, In Silico and In Vitro Assessment of New Quinazolinones as Anticancer Agents via Potential AKT Inhibition

A series of novel quinazolinone derivatives (2–13) was synthesized and examined for their cytotoxicity to HepG2, MCF-7, and Caco-2 in an MTT assay. Among these derivatives, compounds 4 and 9 exhibited significant cytotoxic activity against Caco-2, HepG2, and MCF-7 cancer cells. Compound 4 had more significant inhibitory effects than compound 9 on Caco-2, HepG2, and MCF-7 cell lines, with IC₅₀ values of 23.31 ± 0.09, 53.29 ± 0.25, and 72.22 ± 0.14µM, respectively. The AKT pathway is one of human cancer’s most often deregulated signals. AKT is also overexpressed in human cancers such as glioma, lung, breast, ovarian, gastric, and pancreas. A molecular docking study was performed to analyze the inhibitory action of newly synthetic quinazolinone derivatives against Homo sapiens AKT1 protein. Molecular docking simulations were found to be in accordance with in vitro studies, and hence supported the biological activity. The results suggested that compounds 4 and 9 could be used as drug candidates for cancer therapy via its potential inhibition of AKT1 as described by docking study.

Dipeptidyl Peptidase-4 Is a Target Protein of Epigallocatechin-3-Gallate

Epigallocatechin-3-gallate (EGCG), a major active ingredient in green tea, has various health benefits. It affects glucose metabolism, but the mechanism is not well understood. This study aimed to identify targets of EGCG related to glucose metabolism. The core fragment of EGCG is a flavonoid. The flavonoid scaffold was used as a substructure to find proteins cocrystallized with flavonoids in the Protein Data Bank. The proteins identified were screened in PubMed for known relationships with diabetes. Dipeptidyl peptidase-4 (DPP4; PDB 5J3J) was identified following this approach. By molecular docking, the interactions of EGCG and DPP4 were assessed. To test the stability of the interactions between EGCG and DPP4, molecular dynamics simulation for 100 ns was performed using Desmond software. In vitro, the concentration of EGCG required to inhibit DPP4 activity by 50% (the IC50 value) was 28.42 μM. These data provide a theoretical basis for intervention in glucose metabolism with EGCG.

Brazilian malaria molecular targets (BraMMT): selected receptors for virtual high-throughput screening experiments

BACKGROUND

Owing to increased spending on pharmaceuticals since 2010, discussions about rising costs for the development of new medical technologies have been focused on the pharmaceutical industry. Computational techniques have been developed to reduce costs associated with new drug development. Among these techniques, virtual high-throughput screening (vHTS) can contribute to the drug discovery process by providing tools to search for new drugs with the ability to bind a specific molecular target.

OBJECTIVES

In this context, Brazilian malaria molecular targets (BraMMT) was generated to execute vHTS experiments on selected molecular targets of Plasmodium falciparum.

METHODS

In this study, 35 molecular targets of P. falciparum were built and evaluated against known antimalarial compounds.

FINDINGS

As a result, it could predict the correct molecular target of market drugs, such as artemisinin. In addition, our findings suggested a new pharmacological mechanism for quinine, which includes inhibition of falcipain-II and a potential new antimalarial candidate, clioquinol.

MAIN CONCLUSIONS

The BraMMT is available to perform vHTS experiments using OCTOPUS or Raccoon software to improve the search for new antimalarial compounds. It can be retrieved from www.drugdiscovery.com.br or download of Supplementary data.

Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs

OBJECTIVE

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin).

MATERIALS AND METHODS

During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server.

RESULTS

Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively.

CONCLUSION

Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.

Anti-inflammatory effects of royal poinciana through inhibition of toll-like receptor 4 signaling pathway

Inflammation is part of the non-specific immune response that occurs in reaction to any type of bodily injury. In some disorders the inflammatory process, which under normal conditions is self-limiting, becomes continuous and chronic inflammatory diseases develop subsequently including cardiovascular diseases, diabetes, cancer etc. Barks of Delonix regia is used traditionally in the treatment of inflammatory diseases. Therefore, in this study we evaluated the therapeutic potential of D. regia ethanol extract and its active constituent β-Elemene with special interest in inflammation model using standard in vivo anti-inflammatory models: Carrageenan-induced paw edema, Cotton pellet granuloma, and Acetic acid-induced vascular permeability. To explicate the mechanism of action for the possible anti-inflammatory activity, we determined the level of major inflammatory mediators (NO, iNOS, COX-2-dependent prostaglandin E2 or PGE2), and pro-inflammatory cytokines (TNF-a, IL-1b, IL-6, and IL-12). Additionally, we determined the toll-like receptor 4 (TLR4), Myeloid differentiation primary response gene 88 (MyD88), by mRNA expression in drug treated LPS-induced murine macrophage model. To explore the mechanism of anti-inflammatory activity, we evaluated expression of c-Jun N-terminal kinases (JNK), nuclear factor kappa-B cells (NF-kB), and NF-kB inhibitor alpha (IK-Ba). Furthermore, we determined the acute and sub-acute toxicity of D. regia extract in BALB/c mice. This study established a significant anti-inflammatory activity of D. regia extract and β-Elemene along with the inhibition of TNF-a, IL-1b, IL-6 and IL-12 expressions. Further, the expression of TLR4, NF-kBp65, MyD88, iNOS and COX-2 molecules were reduced in drug-treated groups, but not in the LPS-stimulated untreated or control groups, Thus, our results collectively indicated that the D. regia extract and β-Elemene can efficiently inhibit inflammation.

Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE)

BACKGROUND

The inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.

RESULTS

By considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.

CONCLUSIONS

These QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.

Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches

The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.

Sweet taste receptor gene variation and aspartame taste in primates and other species

Aspartame is a sweetener added to foods and beverages as a low-calorie sugar replacement. Unlike sugars, which are apparently perceived as sweet and desirable by a range of mammals, the ability to taste aspartame varies, with humans, apes, and Old World monkeys perceiving aspartame as sweet but not other primate species. To investigate whether the ability to perceive the sweetness of aspartame correlates with variations in the DNA sequence of the genes encoding sweet taste receptor proteins, T1R2 and T1R3, we sequenced these genes in 9 aspartame taster and nontaster primate species. We then compared these sequences with sequences of their orthologs in 4 other nontasters species. We identified 9 variant sites in the gene encoding T1R2 and 32 variant sites in the gene encoding T1R3 that distinguish aspartame tasters and nontasters. Molecular docking of aspartame to computer-generated models of the T1R2 + T1R3 receptor dimer suggests that species variation at a secondary, allosteric binding site in the T1R2 protein is the most likely origin of differences in perception of the sweetness of aspartame. These results identified a previously unknown site of aspartame interaction with the sweet receptor and suggest that the ability to taste aspartame might have developed during evolution to exploit a specialized food niche.