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1,2,3-Triazole-containing Nifedipine analogues offer the opportunity to increase biostability, bioavailability, efficacy and binding selectivity to target receptors. Here, we applied a computer-aided rational design for identifying new Nifedipine analogues containing a 1,2,3-triazole moiety. First, a new chemical library of 796 derivatives combining the 1,4-dihydropyridine (DHP) fragment and 1,2,3-triazole moiety was generated. Second, to reduce the library size, the library was pre-filtered using two 3D-pharmacophore models with different complexity, which allowed us to gradually reduce the chemical space, ending up with 26 hit candidates. Molecular docking calculations against the rCav1.1 receptor allowed the identification of eight derivatives 5a-h, characterized by the binding affinity towards the rCav1.1 receptor of the same level as approved Nifedipine-like drugs. Next, our molecular docking results were used to guide and optimize the retrosynthetic approaches for new analogues of Nifedipine as promising antihypertensive agents. So, a retrosynthetic approach for Nifedipine analogues with a 1,2,3-triazole ring in position 4 was proposed. Finally, eight analogues 5a-h determined by molecular docking calculations were synthesized using the suggested retrosynthetic approach. The aim of this study is to identify new Nifedipine analogues using a computer-aided drug design and a retrosynthetic approach. Materials and methods. The organic synthesis of new Nifedipine analogues containing a 1,2,3-triazole moiety. Computer-aided drug design of new DHP derivatives using pharmacophore screening and molecular docking calculations. Results. Molecular docking of new Nifedipine analogues made it possible to estimate the binding affinity of new Nifedipine derivatives to the rCav1.1 receptor. Pharmacophore screening of a chemical library of analogues, consisting of 796 derivatives, allowed gradually reducing the chemical space and obtaining 26 candidates with high affinity to the rCav1.1 receptor. Using the method of molecular docking, eight hits 5a-h were identified, and the synthesis of the recommended compounds was proposed and performed. Conclusions. The results of molecular docking showed that Nifedipine analogues 5a-h are characterized by binding affinity to the rCav1.1 receptor at the same level as approved Nifedipine-like drugs. Pharmacophore screening and molecular docking calculations indicate key features of the ligand-receptor interaction that can guide and optimize the synthesis of new Nifedipine analogues as promising new antihypertensive agents. A retrosynthetic approach was proposed, and the recommended compounds were synthesized [ABSTRACT FROM AUTHOR]

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

GABAA receptors, members of the pentameric ligand-gated ion channel superfamily, are widely expressed in the central nervous system and mediate a broad range of pharmaco-toxicological effects including bidirectional changes to seizure threshold. Thus, detection of GABAA receptor-mediated seizure liabilities is a big, partly unmet need in early preclinical drug development. This is in part due to the plethora of allosteric binding sites that are present on different subtypes of GABAA receptors and the critical lack of screening methods that detect interactions with any of these sites. To improve in silico screening methods, we assembled an inventory of allosteric binding sites based on structural data. Pharmacophore models representing several of the binding sites were constructed. These models from the NeuroDeRisk IL Profiler were used for in silico screening of a compiled collection of drugs with known GABAA receptor interactions to generate testable hypotheses. Amoxapine was one of the hits identified and subjected to an array of in vitro assays to examine molecular and cellular effects on neuronal excitability and in vivo locomotor pattern changes in zebrafish larvae. An additional level of analysis for our compound collection is provided by pharmacovigilance alerts using FAERS data. Inspired by the Adverse Outcome Pathway framework, we postulate several candidate pathways leading from specific binding sites to acute seizure induction. The whole workflow can be utilized for any compound collection and should inform about GABAA receptor-mediated seizure risks more comprehensively compared to standard displacement screens, as it rests chiefly on functional data. [ABSTRACT FROM AUTHOR]