Anti-inflammatory action of new hybrid N-acyl-[1,2]dithiolo-[3,4-c]quinoline-1-thione.

Most of pharmaceutical agents display a number of biological activities. It is obvious that testing even one compound for thousands of biological activities is not practically possible. A computer-aided prediction is therefore the method of choice in this case to select the most promising bioassays for particular compounds. Using the PASS Online software, we determined the probable anti-inflammatory action of the 12 new hybrid dithioloquinolinethiones derivatives. Chemical similarity search in the World-Wide Approved Drugs (WWAD) and DrugBank databases did not reveal close structural analogues with the anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in the carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds (2a, 3a-3k except for 3j) varied between 52.97% and 68.74%, being higher than the reference drug indomethacin (47%). The most active compounds appeared to be 3h (68.74%), 3k (66.91%) and 3b (63.74%) followed by 3e (61.50%). Thus, based on the in silico predictions a novel class of anti-inflammatory agents was discovered.

Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv.

Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.

Computer-aided discovery of pleiotropic effects: Anti-inflammatory action of dithioloquinolinethiones as a case study.

Most of pharmaceutical agents exhibit several or even many biological activities. It is clear that testing even one compound for thousands of biological activities is a practically not feasible task. Therefore, computer-aided prediction is the method-of-the-choice to select the most promising bioassays for particular compounds. Using PASS Online software, we determined the likely anti-inflammatory action of the 13 dithioloquinolinethione derivatives with antimicrobial activities. Chemical similarity search in the Cortellis Drug Discovery Intelligence database did not reveal close structural analogues with anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds was comparable with or higher than the reference drug Indomethacin. Thus, based on the in silico predictions, novel class of the anti-inflammatory agents was discovered.

Antibacterial activity of griseofulvin analogues as an example of drug repurposing.

Griseofulvin is a well-known antifungal drug that was launched in 1962 by Merck & Co. for the treatment of dermatophyte infections. However, according to predictions using the Way2Drug computational drug repurposing platform, it may also have antibacterial activity. As no confirmation of this prediction was found in the published literature, this study estimated in-silico antibacterial activity for 42 griseofulvin derivatives. Antibacterial activity was predicted for 33 of the 42 compounds, which led to the conclusion that this activity might be considered as typical for this chemical series. Therefore, experimental testing of antibacterial activity was performed on a panel of Gram-positive and Gram-negative micro-organisms. Antibacterial activity was evaluated using the microdilution method detecting the minimal inhibitory concentration (MIC) and the minimal bactericidal concentration (MBC). The tested compounds exhibited potent antibacterial activity against all the studied bacteria, with MIC and MBC values ranging from 0.0037 to 0.04 mg/mL and from 0.01 to 0.16 mg/mL, respectively. Activity was 2.5-12 times greater than that of ampicillin and 2-8 times greater than that of streptomycin, which were used as the reference drugs. Similarity analysis for all 42 compounds with the (approximately) 470,000 drug-like compounds indexed in the Clarivate Analytics Integrity database confirmed the significant novelty of the antibacterial activity for the compounds from this chemical class. Therefore, this study demonstrated that by using computer-aided prediction of biological activity spectra for a particular chemical series, it is possible to identify typical biological activities which may be used for discovery of new applications (e.g. drug repurposing).

[Discovering new antiretroviral compounds in "Big Data" chemical space of the SAVI library]. / Poisk novykh antiretrovirusnykh soedinenii v khimicheskom prostranstve "bol'shikh dannykh" biblioteki SAVI.

Despite significant advances in the application of highly active antiretroviral therapy, the development of new drugs for the treatment of HIV infection remains an important task because the existing drugs do not provide a complete cure, cause serious side effects and lead to the emergence of resistance. In 2015, a consortium of American and European scientists and specialists launched a project to create the SAVI (Synthetically Accessible Virtual Inventory) library. Its 2016 version of over 283 million structures of new easily synthesizable organic molecules, each annotated with a proposed synthetic route, were generated in silico for the purpose of searching for safer and more potent pharmacological substances. We have developed an algorithm for comparing large chemical databases (DB) based on the representation of structural formulas in SMILES codes, and evaluated the possibility of detecting new antiretroviral compounds in the SAVI database. After analyzing the intersection of SAVI with 97 million structures of the PubChem database, we found that only a small part of the SAVI (~0.015%) is represented in PubChem, which indicates a significant novelty of this virtual library. However, among those structures, 632 compounds tested for anti-HIV activity were detected, 41 of which had the desired activity. Thus, our studies for the first time demonstrated that SAVI is a promising source for the search for new anti-HIV compounds.

[Quality control study of engineered skin tissue]. / Metod otsenki kachestva rekonstruirovannogo épidermisa.

OMERO service was used to annotate the cell line HaCaT microscope images by two independent expert groups. The images were obtained in the course of developing tissue-engineered epithelium which consisted of several layers of the keratinocytes. Evaluation of expert opinions was performed by calculation of specificity, sensitivity and accuracy. The best convergence of opinions (91%) was achieved for the confluence of the cell monolayers. Accuracy 70% was observed in determining the extent of cell differentiation after 10 days of incubation. The paper illustrates the usefulness of OMERO service for dynamic cross-validation of quality in the development and standardization of cell preparations.

Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery.

Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

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.