Plant-Based Phytochemicals as Possible Alternative to Antibiotics in Combating Bacterial Drug Resistance

The unprecedented use of antibiotics that led to development of resistance affect human health worldwide. Prescription of antibiotics imprudently and irrationally in different diseases progressed with the acquisition and as such development of antibiotic resistant microbes that led to the resurgence of pathogenic strains harboring enhanced armors against existing therapeutics. Compromised the treatment regime of a broad range of antibiotics, rise in resistance has threatened human health and increased the treatment cost of diseases. Diverse on metabolic, genetic and physiological fronts, rapid progression of resistant microbes and the lack of a strategic management plan have led researchers to consider plant-derived substances (PDS) as alternative or in complementing antibiotics against the diseases. Considering the quantitative characteristics of plant constituents that attribute health beneficial effects, analytical procedures for their isolation, characterization and phytochemical testing for elucidating ethnopharmacological effects has being worked out for employment in the treatment of different diseases. With an immense potential to combat bacterial infections, PDSs such as polyphenols, alkaloids and tannins, present a great potential for use, either as antimicrobials or as antibiotic resistance modifiers. The present study focuses on the mechanisms by which PDSs help overcome the surge in resistance, approaches for screening different phytochemicals, methods employed in the identification of bioactive components and their testing and strategies that could be adopted for counteracting the lethal consequences of multidrug resistance.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

The application of molecular topology for ulcerative colitis drug discovery

INTRODUCTION

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Computer-Aided Identification of Anticonvulsant Effect of Natural Nonnutritive Sweeteners Stevioside and Rebaudioside A

Steviol glycosides are natural constituents of Stevia rebaudiana (Bert.) Bert. (Asteraceae) that have recently gained worldwide approval as nonnutritive sweeteners by the Joint Food and Agriculture Organization/World Organization Expert Committee on Food Additives. Cheminformatic tools suggested that the aglycone steviol and several of its phase I metabolites were predicted as potential anticonvulsant agents effective in the seizure animal model maximal electroshock seizure (MES) test. Thus, aqueous infusion from S. rebaudiana was tested in the MES test (mice, intraperitoneal administration), confirming dose-dependent anticonvulsant effect. Afterward, isolated stevioside and rebaudioside A were tested in the MES test, with positive results. Though drug repositioning most often focuses on known therapeutics, this article illustrates the possibilities of this strategy to find new functionalities and therapeutic indications for food constituents and natural products.

Discovery and resupply of pharmacologically active plant-derived natural products: A review

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.

Several new diverse anticonvulsant agents discovered in a virtual screening campaign aimed at novel antiepileptic drugs to treat refractory epilepsy

A virtual screening campaign was conducted in order to discover new anticonvulsant drug candidates for the treatment of refractory epilepsy. To this purpose, a topological discriminant function to identify antiMES drugs and a sequential filtering methodology to discriminate P-glycoprotein substrates and nonsubstrates were jointly applied to ZINC 5 and DrugBank databases. The virtual filters combine an ensemble of 2D classifiers and docking simulations. In the light of the results, 10 structurally diverse compounds were acquired and tested in animal models of seizure and the rotorod test. All 10 candidates showed some level of protection against MES test.

Anticonvulsant activity of artificial sweeteners: a structural link between sweet-taste receptor T1R3 and brain glutamate receptors

A virtual screening campaign based on application of a topological discriminant function capable of identifying novel anticonvulsant agents indicated several widely-used artificial sweeteners as potential anticonvulsant candidates. Acesulfame potassium, cyclamate and saccharin were tested in the Maximal Electroshock Seizure model (mice, ip), showing moderate anticonvulsant activity. We hypothesized a probable structural link between the receptor responsible of sweet taste and anticonvulsant molecular targets. Bioinformatic tools confirmed a highly significant sequence-similarity between taste-related protein T1R3 and several metabotropic glutamate receptors from different species, including glutamate receptors upregulated in epileptogenesis and certain types of epilepsy.

Inverse virtual screening of antitumor targets: pilot study on a small database of natural bioactive compounds

An inverse virtual screening in silico approach has been applied to natural bioactive molecules to screen their efficacy against proteins involved in cancer processes, with the aim of directing future experimental assays. Docking studies were performed on a panel of 126 protein targets extracted from the Protein Data Bank, to analyze their possible interactions with a small library of 43 bioactive compounds. Analysis of the molecular docking results was performed through the use of tables containing energy data organized in a matrix. The application of this approach may facilitate the prediction of the activity of unknown ligands for known targets involved in the development of cancer and could be applied to other models based on different libraries of ligands and different panels of targets.

Molecular modeling study of isoindolines as L-type Ca(2+) channel blockers by docking calculations

Two series of isoindolines 1(a-g) and 2(a-g) were found by docking calculations to be possible L-type Ca(2+) channel (LCC) blockers. The theoretical 3-D model of the outer vestibule and the selective filter of the LCC was provided by Professor Lipkind; this model consists of transmembrane segments S5 and S6 and P-loops contributed by each of four repeats (I, II, III, and IV) of Ca(v) 1.2. Therefore, two well-known LCC blockers, nifedipine 3 and (R)-ethosuccinimide 4 were also evaluated, and their binding sites on the LCC were identified and compared with those obtained for 1(a-g) and 2(a-g). Analysis of the results shows that the target compounds tested probably could be LCC blockers, since they interact with or near the glutamic acid residues Glu393, Glu736, Glu1145 and Glu1446 (the EEEE locus), which belong to the LCC selectivity region. The G values for all of the Ca(2+) channel ligands are between-10.78 and -3.67 (kcal mol(-1)), showing that LCC-1b, -1e and -1f complexes are more stable than the other compounds tested. Therefore, theoretically calculated dissociation constants K(d) (microM) were obtained for all compounds. Comparing these values reveals that compounds 1b (0.0244 microM), 1e (0.0176 microM) and 1f (0.0125 microM) exhibit more affinity for the LCC than the other compounds. This screening shows that the two series of isoindolines probably could act as LCC blockers.