ChemSkin Reference Chemical Database for the Development of an In Vitro Skin Irritation Test

Since the animal test ban on cosmetics in the EU in 2013, alternative in vitro safety tests have been actively researched to replace in vivo animal tests. For the development and evaluation of a new test method, reference chemicals with quality in vivo data are essential to assess the predictive capacity and applicability domain. Here, we compiled a reference chemical database (ChemSkin DB) for the development and evaluation of new in vitro skin irritation tests. The first candidates were selected from 317 chemicals (source data n = 1567) searched from the literature from the last 20 years, including previous validation study reports, ECETOC, and published papers. Chemicals showing inconsistent classification or those that were commercially unavailable, difficult or dangerous to handle, prohibitively expensive, or without quality in vivo or in vitro data were removed, leaving a total of 100 chemicals. Supporting references, in vivo Draize scores, UN GHS/EU CLP classifications and commercial sources were compiled. Test results produced by the approved methods of OECD Test No. 439 were included and compared using the classification table, scatter plot, and Pearson correlation analysis to identify the false predictions and differences between in vitro skin irritation tests. These results may provide an insight into the future development of new in vitro skin irritation tests.

Smoking May Increase the Risk of COVID-19 Infection: Evidence from In Silico Analysis

Introduction: SARS-CoV2, first reported in December 2019 in Wuhan as COVID-19 causing respiratory illness, rapidly evolved into a pandemic owing to its very high infectivity. There is insufficient evidence about if and how smoking affects the risk of COVID-19 infection, and the reports on whether smoking increases or reduces the risk of respiratory infections, are contradictory. Therefore, the current study was designed to determine the effects of nicotine consumption on the infectivity of COVID-19. Methods: We performed in silico computer simulation-based study. The structures of SARS-CoV2spike ectodomain, and its receptor ACE2, were obtained from PDB. The structure of nicotine and its metabolites NNK and NNAL were obtained from the PubChem chemical database. After optimization, they were interacted using AutoDock 4.2, to see the effect of nicotine, NNK, or NNAL presence on the docking of viral spike protein to its receptor ACE2. Results: ACE2 vs spike protein interaction results were used as a control (ZDOCK score 1498.484, with four hydrogen bonds). The NNK+ACE2 vs spike protein docking formed 10 hydrogen bonds with the highest ZDOCK score of 1515.564. NNAL+ ACE2 vs spike protein interaction formed eleven hydrogen bonds with the ZDOCK score of 1499.371. Nicotine+ACE2 vs spike protein docking showed the lowest ZDOCK score of 1496.302 and formed 8 hydrogen bonds. Whereas, NNK+spike vs ACE2 interaction had a ZDOCK score of 1498.490 and formed eight hydrogen bonds. NNAL+spike vs ACE2 docking formed eleven hydrogen bonds with a ZDOCK score of 1498.482. And Nicotine+spike vs ACE2 interaction showed a ZDOCK score of 1498.488 and formed 9 hydrogen bonds. Conclusions: The binding of nicotine to either spike of virus or its receptor ACE2 is not affecting the viral docking with the receptor. But binding of NNK, a metabolite of nicotine, is facilitating the viral docking with its receptor indicating that smoking may increase the risk of COVID-19 infection.

QSAR-АНАЛІЗ БІБЛІОТЕК 4-ТІАЗОЛІДИНОН-СПОРІДНЕНИХ ГЕТЕРОЦИКЛІВ ДЛЯ ПРОГНОЗУВАННЯ ПРОТИТРИПАНОСОМНИХ ВЛАСТИВОСТЕЙ НОВИХ ПОХІДНИХ

Вступ. Похідні тіазолідинону та споріднених гетероциклів є джерелом нових протипаразитарних агентів, у тому числі молекул із протитрипаносомними властивостями. В актуальних наукових джерелах знайдено ряд досліджень про кількісний взаємозв’язок структура – протитрипаносомна активність, що включає різні підходи комп’ютерної хімії. Більшість досліджень належить до так званих мультитаргетних, коли до вибірки включають результати інших видів протипаразитарних активностей. Розробка нових QSAR-моделей похідних тіазолідинону з протитрипаносомними властивостями дозволить окреслити напрямки спрямованого дизайну нових протипаразитарних агентів на основі циклів тіазолу та тіазолідинону. Мета дослідження – встановити кількісний взаємозв’язок структура – протитрипаносомна активність у межах бібліотек тіазолідинонів та споріднених гетероциклів. Методи дослідження. Побудову математичних моделей на основі QSAR-аналізу здійснювали за допомогою онлайн-платформи Online Chemical Database. Результати й обговорення. Аналіз кількісного взаємозв’язку структура – протитрипаносомна активність проводили із застосуванням математичної моделі асоціативних нейронних мереж (ASNN: Associative Neural Networks) та методу регресії Random Forest (RFR: Random Forest regression) на основі вибірок, що включали похідні ізотіокумарин-3-карбонових кислот, тіопіранотіазолів і 4-тіазолідинон-імідазотіадіазолів із встановленою трипаноцидною активністю щодо Trypanosoma brucei brucei та Trypanosoma brucei gambiense. Кращу прогнозувальну здатність для групи ізотіокумарин-3-карбонових кислот і тіопірано[2,3-d][1,3]тіазол-2-онів обчислено за допомогою алгоритму Random Forest. Модель, обчислена на основі алгоритму Random Forest для групи імідазотіадіазолів, володіє найвищою прогнозувальною здатністю зі значенням R2=0,96. Висновок. На основі методів асоціативних нейронних мереж та регресії Random Forest розроблено прогностичні моделі для прогнозування протипаразитарної активності диверсифікованих похідних ­4-тіазолідинонів і подальшого фокусування напрямків оптимізації нових біологічно активних молекул із трипаноцидними властивостями.

Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods

Tyrosinase is an enzyme that plays a crucial role in the melanogenesis of humans and the browning of food products. Thus, tyrosinase inhibitors that are useful to the cosmetic and food industries are required. In this study, we have used evolutionary chemical binding similarity (ECBS) to screen a virtual chemical database for human tyrosinase, which resulted in seven potential tyrosinase inhibitors confirmed through the tyrosinase inhibition assay. The tyrosinase inhibition percentage for three of the new actives was over 90% compared to 61.9% of kojic acid. From the structural analysis through pharmacophore modeling and molecular docking with the human tyrosinase model, the pi–pi interaction of tyrosinase inhibitors with conserved His367 and the polar interactions with Asn364, Glu345, and Glu203 were found to be essential for tyrosinase–ligand interactions. The pharmacophore features and the docking models showed high consistency, revealing the possible essential binding interactions of inhibitors to human tyrosinase. We have also presented the activity cliff analysis that successfully revealed the chemical features related to substantial activity changes found in the new tyrosinase inhibitors. The newly identified inhibitors and their structure–activity relationships presented here will help to identify or design new human tyrosinase inhibitors.

ChemGenerator: a web server for generating potential ligands for specific targets

In drug discovery, one of the most important tasks is to find novel and biologically active molecules. Given that only a tip of iceberg of drugs was founded in nearly one-century’s experimental exploration, it shows great significance to use in silico methods to expand chemical database and profile drug-target linkages. In this study, a web server named ChemGenerator was proposed to generate novel activates for specific targets based on users’ input. The ChemGenerator relies on an autoencoder-based algorithm of Recurrent Neural Networks with Long Short-Term Memory by training of 7 million of molecular Simplified Molecular-Input Line-Entry System as the basic model, and further develops target guided generation by transfer learning. As results, ChemGenerator gains lower loss (<0.01) than existing reference model (0.2~0.4) and shows good performance in the case of Epidermal Growth Factor Receptor. Meanwhile, ChemGenerator is now freely accessible to the public by http://smiles.tcmobile.org. In proportion to endless molecular enumeration and time-consuming expensive experiments, this work demonstrates an efficient alternative way for the first virtual screening in drug discovery.

Natural Products Extracted from Fungal Species as New Potential Anti-Cancer Drugs: A Structure-Based Drug Repurposing Approach Targeting HDAC7

Mushrooms can be considered a valuable source of natural bioactive compounds with potential polypharmacological effects due to their proven antimicrobial, antiviral, antitumor, and antioxidant activities. In order to identify new potential anticancer compounds, an in-house chemical database of molecules extracted from both edible and non-edible fungal species was employed in a virtual screening against the isoform 7 of the Histone deacetylase (HDAC). This target is known to be implicated in different cancer processes, and in particular in both breast and ovarian tumors. In this work, we proposed the ibotenic acid as lead compound for the development of novel HDAC7 inhibitors, due to its antiproliferative activity in human breast cancer cells (MCF-7). These promising results represent the starting point for the discovery and the optimization of new HDAC7 inhibitors and highlight the interesting opportunity to apply the “drug repositioning” paradigm also to natural compounds deriving from mushrooms.

Fragment Library of Natural Products and Compound Databases for Drug Discovery

Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are still limited. Herein, we report the generation and analysis of a fragment library of natural products derived from a database with more than 400,000 compounds. We also report fragment libraries of a large food chemical database and other compound datasets of interest in drug discovery, including compound libraries relevant for COVID-19 drug discovery. The fragment libraries were characterized in terms of content and diversity.