Discovery of novel 6,6-heterocycles as transient receptor potential vanilloid (TRPV1) antagonists.
ABSTRACT The transient receptor potential cation channel, subfamily V, member 1 (TRPV1) is a nonselective cation channel that can be activated by a wide range of noxious stimuli, including capsaicin, acid, and heat. Blockade of TRPV1 activation by selective antagonists is under investigation in an attempt to identify novel agents for pain treatment. The design and synthesis of a series of novel TRPV1 antagonists with a variety of different 6,6-heterocyclic cores is described, and an extensive evaluation of the pharmacological and pharmacokinetic properties of a number of these compounds is reported. For example, the 1,8-naphthyridine 52 was characterized as an orally bioavailable and brain penetrant TRPV1 antagonist. In vivo, 52 fully reversed carrageenan-induced thermal hyperalgesia (CITH) in rats and dose-dependently potently reduced complete Freund's adjuvant (CFA) induced chronic inflammatory pain after oral administration.
- SourceAvailable from: Daria Goldmann[show abstract] [hide abstract]
ABSTRACT: Publicly open databases of small compounds have become an indispensable tool for chemoinformaticians for collection and preparation of datasets suitable for drug discovery questions. Since these databases comprise compounds coming from structure-activity relationship (SAR) studies performed by different research groups, they are very diverse with respect to the biological assays used. In the present study we analyzed the applicability of a thoroughly curated dataset gathered from open sources for ligand-based studies, using the transient receptor potential vanilloid type 1 (TRPV1) as use case. Thorough curation of compounds according to the biological assay type and conditions led to a dataset of comparable bioactive chemicals. Subsequent exhaustive analysis of the obtained dataset using classification algorithms demonstrated that the models obtained in most of the cases possess reliable quality. Analysis of constantly misclassified compounds showed that they belong to local SAR series, where small changes in structure lead to different class labels. These small structural differences could not be captured by the classification algorithms. However application of the 3D alignment-independent QSAR technique GRIND for local, structurally related series overcomes this problem.Molecular Informatics 06/2013; 32(5-6):555-562. · 2.34 Impact Factor