Online resources for the prediction of biological activity of organic compounds

In Silico Investigation of Some Compounds from the N-Butanol Extract of Centaurea tougourensis Boiss. & Reut

Bioinformatics as a newly emerging discipline is considered nowadays a reference to characterize the physicochemical and pharmacological properties of the actual biocompounds contained in plants, which has helped the pharmaceutical industry a lot in the drug development process. In this study, a bioinformatics approach known as in silico was performed to predict, for the first time, the physicochemical properties, ADMET profile, pharmacological capacities, cytotoxicity, and nervous system macromolecular targets, as well as the gene expression profiles, of four compounds recently identified from Centaurea tougourensis via the gas chromatography–mass spectrometry (GC–MS) approach. Thus, four compounds were tested from the n-butanol (n-BuOH) extract of this plant, named, respectively, Acridin-9-amine, 1,2,3,4-tetrahydro-5,7-dimethyl- (compound 1), 3-[2,3-Dihydro-2,2-dimethylbenzofuran-7-yl]-5-methoxy-1,3,4-oxadiazol-2(3H)-one (compound 2), 9,9-Dimethoxybicyclo[3.3.1]nona-2,4-dione (compound 3), and 3-[3-Bromophenyl]-7-chloro-3,4-dihydro-10-hydroxy-1,9(2H,10H)-acridinedione (compound 4). The insilico investigation revealed that the four tested compounds could be a good candidate to regulate the expression of key genes and may also exert significant cytotoxic effects against several tumor celllines. In addition, these compounds could also be effective in the treatment of some diseases related to diabetes, skin pathologies, cardiovascular, and central nervous system disorders. The bioactive compounds of plant remain the best alternative in the context of the drug discovery and development process.

Antitumor Profile of Carbon-Bridged Steroids (CBS) and Triterpenoids

This review focuses on the rare group of carbon-bridged steroids (CBS) and triterpenoids found in various natural sources such as green, yellow-green, and red algae, marine sponges, soft corals, ascidians, starfish, and other marine invertebrates. In addition, this group of rare lipids is found in amoebas, fungi, fungal endophytes, and plants. For convenience, the presented CBS and triterpenoids are divided into four groups, which include: (a) CBS and triterpenoids containing a cyclopropane group; (b) CBS and triterpenoids with cyclopropane ring in the side chain; (c) CBS and triterpenoids containing a cyclobutane group; (d) CBS and triterpenoids containing cyclopentane, cyclohexane or cycloheptane moieties. For the comparative characterization of the antitumor profile, we have added several semi- and synthetic CBS and triterpenoids, with various additional rings, to identify possible promising sources for pharmacologists and the pharmaceutical industry. About 300 CBS and triterpenoids are presented in this review, which demonstrate a wide range of biological activities, but the most pronounced antitumor profile. The review summarizes biological activities both determined experimentally and estimated using the well-known PASS software. According to the data obtained, two-thirds of CBS and triterpenoids show moderate activity levels with a confidence level of 70 to 90%; however, one third of these lipids demonstrate strong antitumor activity with a confidence level exceeding 90%. Several CBS and triterpenoids, from different lipid groups, demonstrate selective action on different types of tumor cells such as renal cancer, sarcoma, pancreatic cancer, prostate cancer, lymphocytic leukemia, myeloid leukemia, liver cancer, and genitourinary cancer with varying degrees of confidence. In addition, the review presents graphical images of the antitumor profile of both individual CBS and triterpenoids groups and individual compounds.

Characterisation, pathogenicity and hydrolytic enzyme profiling of selected Fusarium species and their inhibition by novel coumarins

Three Fusarium species isolated locally were characterised by the amplification of their rDNA ITS region, host specificity, and hydrolytic enzyme production. The strains were identified as Fusarium pseudoanthophilum, which is being reported for the first time in South Africa, as well as F. foetens and F. fujikuroi. All the three strains were capable of infecting vegetables such as tomatoes, bell and cayenne peppers, belonging to the Solanaceae family. The Fusarium strains also showed significant production of cell wall degrading enzymes in vitro, such as amylase, cellulase, xylanase, and polygalacturonase, thus highlighting the possibilities of these enzymes as pathogenic factors. Subsequently, the strains were discovered to be susceptible to three halogenated coumarins. The most effective of the tested coumarins, 6-bromo3-2,2-dibromoacetyl-2H-chromen-2-one, showed MIC values of 0.125, 0.0625 and 0.125 mg/ml against F. foetens, F. pseudoanthophilum and F. fujikuroi, respectively. The antifungal potentials of the halogenated coumarins were confirmed in silico through PASS analysis, toxicity prediction and docking studies. Findings from this study demonstrate the use of these coumarins as potential control agents against the Fusarium species and other pathogenic fungi in general.

Chemical Diversity of Soft Coral Steroids and Their Pharmacological Activities

The review is devoted to the chemical diversity of steroids produced by soft corals and their determined and potential activities. There are about 200 steroids that belong to different types of steroids such as secosteroids, spirosteroids, epoxy- and peroxy-steroids, steroid glycosides, halogenated steroids, polyoxygenated steroids and steroids containing sulfur or nitrogen heteroatoms. Of greatest interest is the pharmacological activity of these steroids. More than 40 steroids exhibit antitumor and related activity with a confidence level of over 90 percent. A group of 32 steroids shows anti-hypercholesterolemic activity with over 90 percent confidence. Ten steroids exhibit anti-inflammatory activity and 20 steroids can be classified as respiratory analeptic drugs. Several steroids exhibit rather rare and very specific activities. Steroids exhibit anti-osteoporotic properties and can be used to treat osteoporosis, as well as have strong anti-eczemic and anti-psoriatic properties and antispasmodic properties. Thus, this review is probably the first and exclusive to present the known as well as the potential pharmacological activities of 200 marine steroids.

[Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]

New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Synthesis and antinociceptive evaluation of bioisosteres and hybrids of naproxen, ibuprofen and paracetamol

The aim of this work was to design, synthesize and characterize the potential anti-nociceptive and anti-inflammatory activities of a new series of bioisosteres and hybrids from known non-steroidal anti-inflammatory drugs (NSAIDs). The compounds 4-(acetylamino)phenyl (2S)-2-(6-methoxy-2-naphthyl)propanoate (GUF-1) and 4-(acetylamino)phenyl 2-(R,S)-(4-isobutylphenyl)propanoate (GUF-2) were synthesized as hybrids (also known as heterodimers); whereas those named 2-(R,S)-(4-isobutylphenyl)-N-1H-tetrazol-5-ylpropanamide (GUF-3), (2S)-2-(6-methoxy-2-naphthyl)-N-1H-tetrazol-5-ylpropanamide (GUF-4), [2-(R,S)-N-hydroxy-2-[4-(2-methylpropyl)phenyl]propanamide] (GUF-5), and (2S)-N-hydroxy-2-(6-methoxy-2-naphthyl)propanamide (GUF-6) were synthesized as bioisosteres of the NSAIDs paracetamol, ibuprofen, and naproxen, respectively. All these compounds were characterized by spectroscopic and spectrometric analysis. Antinociceptive activity of GUF-1 to GUF-6 was evaluated using the formalin test in rats. Pharmacological responses of GUF-1, GUF-2 (hybrids), and GUF-5 (bioisostere) demonstrated significant antinociceptive effects; thus these compounds were assayed in an inflammation test like carrageenan-induced paw oedema in rats. Complete molecular docking of cyclooxygenase and the GUF-1 and GUF-2 hybrids showed high docking scores, compared to the reference drugs. Our data demonstrate that compounds GUF-1, GUF-2, and GUF-5 possesses antinociceptive and antiinflammatory activities resembling and improving those known for the traditional NSAIDs, paracetamol, naproxen and ibuprofen.

How good are publicly available web services that predict bioactivity profiles for drug repurposing?

Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely available computational tools are available via the Internet, which predict multitarget profiles of drug-like compounds. They are based on chemical similarity assessment (ChemProt, SuperPred, SEA, SwissTargetPrediction and TargetHunter) or machine learning methods (ChemProt and PASS). To compare their performance, this study has created two evaluation sets, consisting of (1) 50 well-known repositioned drugs and (2) 12 drugs recently patented for new indications. In the first set, sensitivity values varied from 0.64 (TarPred) to 1.00 (PASS Online) for the initial indications and from 0.64 (TarPred) to 0.98 (PASS Online) for the repurposed indications. In the second set, sensitivity values varied from 0.08 (SuperPred) to 1.00 (PASS Online) for the initial indications and from 0.00 (SuperPred) to 1.00 (PASS Online) for the repurposed indications. Thus, this analysis demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.