Current Topics in Medicinal Chemistry - Volume 21, Issue 7, 2021

Even after one year of its first outbreak reported in China, the coronavirus disease 2019 (COVID-19) pandemic is still sweeping the World, causing serious infections and claiming more fatalities. COVID-19 is caused by the novel coronavirus SARS-CoV-2, which belongs to the genus Betacoronavirus (β-CoVs), which is of greatest clinical importance since it contains many other viruses that cause respiratory disease in humans, including OC43, HKU1, SARS-CoV, and MERS. The spike (S) glycoprotein of β-CoVs is a key virulence factor in determining disease pathogenesis and host tropism, and it also mediates virus binding to the host’s receptors to allow viral entry into host cells, i.e., the first step in virus lifecycle. Viral entry inhibitors are considered promising putative drugs for COVID-19. Herein, we mined the biomedical literature for viral entry inhibitors of other coronaviruses, with special emphasis on β-CoVs entry inhibitors. We also outlined the structural features of SARS-CoV-2 S protein and how it differs from other β-CoVs to better understand the structural determinants of S protein binding to its human receptor (ACE2). This review highlighted several promising viral entry inhibitors as potential treatments for COVID-19.

Background: Molecular phylogenetic algorithms frequently disagree with the approaches considering reproductive compatibility and morphological criteria for species delimitation. The question stems if the resulting species boundaries from molecular, reproductive and/or morphological data are definitively not reconcilable; or if the existing phylogenetic methods are not sensitive enough to agree morphological and genetic variation in species delimitation. Objective: We propose DISTATIS as an integrative framework to combine alignment-based (AB) and alignment-free (AF) distance matrices from ITS2 sequences/structures to shed light whether Gelasinospora and Neurospora are sister but independent genera. Methods: We aimed at addressing this standing issue by harmonizing genus-specific classification based on their ascospore morphology and ITS2 molecular data. To validate our proposal, three phylogenetic approaches: i) traditional alignment-based, ii) alignment-free and iii) novel distance integrative (DI)-based were comparatively evaluated on a set of Gelasinospora and Neurospora species. All considered species have been extensively characterized at both the morphological and reproductive levels and there are known incongruences between their ascospore morphology and molecular data that hampers genus-specific delimitation. Results: Traditional AB phylogenetic analyses fail at resolving the Gelasinospora and Neurospora genera into independent monophyletic clades following ascospore morphology criteria. In contrast, AF and DI approaches produced phylogenetic trees that could properly delimit the expected monophyletic clades. Conclusion: The DI approach outperformed the AF one in the sense that it could also divide the Neurospora species according to their reproduction mode.

Background: Saponin metabolism shows high structural variability due to the diversity of aglycones and glycosylations (Gly). Although they represent a potential source of drug design, their metabolism remains misunderstood yet due to insufficient investments in analytical methods. Aims: Bibliographic structural data offer a wide field for extensive statistical analysis, highlighting mechanistic orders governing metabolic diversity. This work presents an original simulation method based on simplex rule for highlighting regulatory mechanisms of metabolism from categorical structural data. Methods: Simulation was applied on a set of 231 saponins of the Caryophyllaceae plant family initially affiliated to four aglycone types: gypsogenin (Gyp), quillaic acid (QA), gypsogenic acid (GA), and 16-OH-gypsogenic acid (16-OH-GA). Molecules were initially characterized by relative glycosylation levels of different carbons. Simplex approach was applied by combining saponins of the four aglycone groups using a complete set of N gradual weightings between structural groups. In silico combinations were applied by randomly sampling representative saponins from the four groups conforming to their weights given by mixture design. Gly profiles of sampled saponins were averaged to calculate a barycentric molecular profile for each mixture. With N mixtures, N barycentric molecules were iteratively calculated by bootstrap, leading to smoothed data from which Gly trends between carbons were highlighted. Results: Sequential, competing and cooperative Gly trends were highlighted according to the types of aglycones, attached saccharides and positions of substituted carbons. Such various conditional Gly trends seemed to be linked to multiple factors, including steric effects, regio-selectivity, enzymatic specificity and enzymatic promiscuity. These simulated results could be helpfully useful in chemical synthesis and drug design. Conclusion: These simulated results could usefully help for chemical syntheses and drug design.

Introduction: This work provides statistical analyses of bibliographic data on pharmacologically evaluated flavonoids from different plant families. By opposition to structural elucidations benefitting from full data aspects, pharmacological evaluations are concerned with partial investigations resulting in sparse information. Methods: The limited data availability was overcome by extensive consideration of several small sets of pharmacologically evaluated flavonoids in several plant taxa, alternatively to the traditional intensive analysis of big dataset of a given metabolic family in a given plant taxon. Statistical analyses were carried out using correspondence analysis, cluster analysis, box plots and fisher exact test to highlight structure-structure, structure-plant and structure-activity trends. Results: Different aglycone types showed opposite trends between hydroxylation (flavonols, anthocyanidins, flavanols), and methoxylation (isoflavones, isoflavanes, neoflavones). Moreover, different carbons showed differential substitution levels in different aglycones: C3 in flavonols, C6, C8 in flavones, flavonols, C2’ in flavanones, C6’ in isoflavanes. Plant families were well differentiated by different relative occurrences of aglycones: flavones in Lamiaceae, flavanones in Rutaceae, neoflavones in Rubiaceae, flavonols in Asteraceae, isoflavones in Fabaceae. Relatively more hydroxylated flavonoids occurred in Asteraceae, Lamiaceae and Fabaceae vs. more methoxylated ones in Rutaceae and Rubiaceae. Concerning structure-activity trends, flavanols and isoflavones were relatively more concerned with anti-diabetic and anti-inflammatory activities, respectively, vs. balanced distribution of flavones. Anti-inflammatory activity showed significant association with substitution position of same chemical groups (OH, OCH3), whereas anti-diabetic activity was revealed to be mainly influenced by the type of chemical groups (positive effect of OH and glycosyls). Conclusion: These results call for regular updates and further investigations.

Recently, different authors have reported Perturbation Theory (PT) methods combined with machine learning (ML) to obtain PTML (PT + ML) models. They have applied PTML models to the study of different biological systems. Here we present one state-of-art review about the different applications of PTML models in Organic Synthesis, Medicinal Chemistry, Protein Research, and Technology. The aim of the models is to find relations between the molecular descriptors and the biological characteristics to predict key properties of new compounds. An area where the ML has been very useful is the drug discovery process. The entire process of drug discovery leads to the generation of lots of data, and it is also a costly and time-consuming process. ML comes with the opportunity of analyzing significant amounts of chemical data obtaining outcomes to find potential drug candidates.

Background: Cyclin-dependent kinase 4 (CDK4) and the human epidermal growth factor receptor 2 (HER2) are two of the most promising targets in oncology research. Thus, a series of computational approaches have been applied to the search for more potent inhibitors of these cancerrelated proteins. However, current approaches have focused on chemical analogs while predicting the inhibitory activity against only one of these targets, but never against both. Aims: We report the first perturbation model combined with machine learning (PTML) to enable the design and prediction of dual inhibitors of CDK4 and HER2. Methods: Inhibition data for CDK4 and HER2 were extracted from ChEMBL. The PTML model relied on artificial neural networks to allow the classification/prediction of molecules as active or inactive against CDK4 and/or HER2. Results: The PTML model displayed sensitivity and specificity higher than 80% in the training set. The same statistical metrics had values above 75% in the test set. We extracted several molecular fragments and estimated their quantitative contributions to the inhibitory activity against CDK4 and HER2. Guided by the physicochemical and structural interpretations of the molecular descriptors in the PTML model, we designed six molecules by assembling several fragments with positive contributions. Three of these molecules were predicted as potent dual inhibitors of CDK4 and HER2, while the other three were predicted as inhibitors of at least one of these proteins. All the molecules complied with Lipinski’s rule of five and its variants. Conclusion: The present work represents an encouraging alternative for future anticancer chemotherapies.