Synthesis, anti-parasitic activity and QSAR study of a new library of polysubstituted tetrahydronaphtho[1,2-b]azepines

Seven-membered N-heterocycles as approved drugs and promising leads in medicinal chemistry as well as the metal-free domino access to their scaffolds

Azepanes or azepines are structural motifs of many drugs, drug candidates and evaluated lead compounds. Even though compounds having N-heterocyclic 7-membered rings are often found in nature (e.g. alkaloids), the natural compounds of this group are rather rare as approved therapeutics. Thus, recently studied and approved azepane or azepine-congeners predominantly consist of semi-synthetically or synthetically-obtained scaffolds. In this review a comparison of approved drugs and recently investigated leads was proposed taking into regard their structural aspects (stereochemistry), biological activities, pharmacokinetic properties and confirmed molecular targets. The 7-membered N-heterocycles reveal a wide range of biological activities, not only against CNS diseases, but also as e.g. antibacterial, anticancer, antiviral, antiparasitic and against allergy agents. As most of the approved or investigated potential drugs or lead structures, belonging to 7-membered N-heterocycles, are synthetic scaffolds, this report also reveals different and efficient metal-free cascade approaches useful to synthesize both simple azepane or azepine-containing congeners and those of oligocyclic structures. Stereochemistry of azepane/azepine fused systems, in view of biological data and binding with the targets, is discussed. Apart from the approved drugs, we compare advances in SAR studies of 7-membered N-heterocycles (mainly from 2018 to 2023), whereas the related synthetic part concerning various domino strategies is focused on the last ten years.

Synthesis of 1,4-epoxy-2-aryltetrahydro-1-benzazepines via rhodium(III)-catalyzed C-H allylation/intramolecular 1,3-dipolar cycloaddition

Herein, we report a new synthetic route to 1,4-epoxy-2-aryltetrahydro-1-benzazepine derivatives with high efficiency, namely the Rh(III)-catalyzed C-H allylation of nitrones with allyl precursors, followed by subsequent intramolecular 1,3-dipolar cycloaddition, to deliver the title compounds. This reaction is regio- and stereo-selective, generating the cis-isomer with a broad substrate scope and good functional group tolerance.

Machine Learning-Based Prediction of Drug-Induced Hepatotoxicity: An OvA-QSTR Approach

Drug-induced hepatotoxicity, also known as drug-induced liver injury (DILI), is among the possible adverse effects of pharmacotherapy. This clinical condition is accepted as one of the factors leading to patient mortality and morbidity. The LiverTox database was built by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) to predict potential liver damage from medications and take appropriate precautions. The database has classified medicines into seven risk categories (A, B, C, D, E, E*, and X) to avoid medicine-induced liver toxicity. The hepatic damage risk decreases from group A to group E. This study did not include the E* and X classes because they contained unverified and unknown data groups. Our study aims to predict potential liver damage of new drug molecules without using experimental animals. We predict which of the LiverTox risk category drugs with unknown liver toxicity potential will fall into using our one-vs-all quantitative structure-toxicity relationship (OvA-QSTR) model. Our dataset, consisting of 678 organic drug molecules from different pharmacological classes, was collected from LiverTox. The OvA-QSTR models implemented by Bayesian Network (BayesNet) performed well based on the selected descriptors, with the precision-recall curve (PRC) areas ranging from 0.718 to 0.869. Our OvA-QSTR models provide a reliable premarketing risk evaluation of pharmaceutical-induced liver damage potential and offer predictions for different risk levels in DILI.

Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Chagas is a neglected tropical disease caused by the parasite Trypanosoma cruzi with no effective treatment in all its forms. There is a need to find more effective therapeutic alternatives with reduced toxicity. In this contribution, multiple linear regression models were used to identify the molecular descriptors that best describe the inhibitory activity of 52 fenarimol analogues against Trypanosoma cruzi. The topological, physicochemical, thermodynamic, electronic, and charge descriptors were evaluated to cover a wide range of properties that frequently encode biological activity. A model with high predictive value was obtained based on geometrical descriptors and descriptors encoding hydrophobicity and London dispersion forces as necessary for the inhibition of Trypanosoma cruzi-CYP51. Docking methodology was implemented to evaluate molecular interactions in silico. The virtual screening results in this study can be used for rational design of new analogues with improved activity against Chagas disease.