Search Results (4)

Pain is one of the most common symptoms experienced by patients. The use of current analgesics is limited by low efficacy and important side effects. Transient receptor potential vanilloid-1 (TRPV1) is a non-selective cation channel, activated by capsaicin, heat, low pH or pro-inflammatory agents. Since TRPV1 is a potential target for the development of novel analgesics due to its distribution and function, we aimed to develop an in silico drug repositioning framework to predict potential TRPV1 ligands among approved drugs as candidates for treating various types of pain. Structures of known TRPV1 agonists and antagonists were retrieved from ChEMBL databases and three datasets were established: agonists, antagonists and inactive molecules (pIC50 or pEC50 < 5 M). Structures of candidates for repurposing were retrieved from the DrugBank database. The curated active/inactive datasets were used to build and validate ligand-based predictive models using Bemis–Murcko structural scaffolds, plain ring systems, flexophore similarities and molecular descriptors. Further, molecular docking studies were performed on both active and inactive conformations of the TRPV1 channel to predict the binding affinities of repurposing candidates. Variables obtained from calculated scaffold-based activity scores, molecular descriptors criteria and molecular docking were used to build a multi-class neural network as an integrated machine learning algorithm to predict TRPV1 antagonists and agonists. The proposed predictive model had a higher accuracy for classifying TRPV1 agonists than antagonists, the ROC AUC values being 0.980 for predicting agonists, 0.972 for antagonists and 0.952 for inactive molecules. After screening the approved drugs with the validated algorithm, repaglinide (antidiabetic) and agomelatine (antidepressant) emerged as potential TRPV1 antagonists, and protokylol (bronchodilator) as an agonist. Further studies are required to confirm the predicted activity on TRPV1 and to assess the candidates’ efficacy in alleviating pain. Full article

The current work presents an objective overview of the impact of one important heterocyclic structure, the pyrazole ring, in the development of anti-proliferative drugs. A set of 1551 pyrazole derivatives were extracted from the National Cancer Institute (NCI) database, together with their growth inhibition effects (GI%) on the NCI’s panel of 60 cancer cell lines. The structures of these derivatives were analyzed based on the compounds’ averages of GI% values across NCI-60 cell lines and the averages of the values for the outlier cells. The distribution and the architecture of the Bemis–Murcko skeletons were analyzed, highlighting the impact of certain scaffold structures on the anti-proliferative effect’s potency and selectivity. The drug-likeness, chemical reactivity and promiscuity risks of the compounds were predicted using AMDETlab. The pyrazole ring proved to be a versatile scaffold for the design of anticancer drugs if properly substituted and if connected with other cyclic structures. The 1,3-diphenyl-pyrazole emerged as a useful scaffold for potent and targeted anticancer candidates. Full article

Analogue series play a key role in drug discovery. They arise naturally in lead optimization efforts where analogues are explored based on one or a few core structures. However, it is much harder to accurately identify and extract pairs or series of analogue molecules in large compound databases with no predefined core structures. This methodological review outlines the most common and recent methodological developments to automatically identify analogue series in large libraries. Initial approaches focused on using predefined rules to extract scaffold structures, such as the popular Bemis–Murcko scaffold. Later on, the matched molecular pair concept led to efficient algorithms to identify similar compounds sharing a common core structure by exploring many putative scaffolds for each compound. Further developments of these ideas yielded, on the one hand, approaches for hierarchical scaffold decomposition and, on the other hand, algorithms for the extraction of analogue series based on single-site modifications (so-called matched molecular series) by exploring potential scaffold structures based on systematic molecule fragmentation. Eventually, further development of these approaches resulted in methods for extracting analogue series defined by a single core structure with several substitution sites that allow convenient representations, such as R-group tables. These methods enable the efficient analysis of large data sets with hundreds of thousands or even millions of compounds and have spawned many related methodological developments. Full article

Bacterial sortases are cysteine transpeptidases that regulate the covalent linkage of several surface protein virulence factors in Gram-positive bacteria. Virulence factors play significant roles in adhesion, invasion of host tissues, biofilm formation and immune evasion, mediating the bacterial pathogenesis and infectivity. Therefore, sortases are emerging as important targets for the design of new anti-infective agents. We employed a computational study, based on structure derived descriptors and molecular fingerprints, in order to develop simple classification methods which could allow predicting low active or high active SrtA inhibitors. Our results indicate that a highly active SrtA inhibitor has a molecular weight ranging between 180 and 600, contains one up to four nitrogen atoms, up to three oxygen atoms and under 18 hydrogen atoms. Also the hydrogen acceptor number and the molecular flexibility, as assessed by the number of rotatable bounds, have emerged as the most relevant descriptors for SrtA affinity. The Bemis-Murcko scaffolding revealed favoured scaffolds as containing at least two ring structures bonded directly or merged in a condensed cycle. This data represent a valuable tool for identifying new potent SrtA inhibitors, potential anti-virulence agents targeted against Gram-positive bacteria, including multiresistant strains. Full article