Structure-activity relationships of flavonoids as inhibitors of breast cancer resistance protein (BCRP).

Flavonoids are an interesting group of natural products ubiquitously present in human diet. Their consumption has been associated with various and differing beneficial health effects. However, several flavonoids have been reported to inhibit the breast cancer resistance protein (BCRP) encoded by the ABCG2 gene. Thus, the consumption of flavonoids with high inhibitory activity could change pharmacokinetics and drug levels of drugs that are BCRP substrates. In cancer patients receiving chemotherapy an increased intake of such flavonoids could lead to adverse effects. We investigated a structurally diverse set of flavonoids, including derivatives with a rare C-methylated structure that were isolated from plants used in traditional medicine. The flavones retusin and ayanin were found to be highly potent inhibitors of BCRP, showing only slightly less potency than Ko143, the most potent ABCG2 inhibitor known so far. The activity data were analyzed by 2D and 3D QSAR analyses and the results revealed the impact of the different substituents at the various positions of the flavonoid core on activity. Additionally, a lateral 2D QSAR analysis of data collected from the literature was performed aiming to derive more general information about the influence of distinct structural features on the inhibitory potency of flavonoids. The comparative QSAR analyses led to a consistent picture of the effects of the different substituents at various positions of the flavone backbone. The following structural features were found to contribute positively to BCRP inhibition: a hydroxyl group in position 5, double bond between position 2 and 3, and a methoxy group in position 3. The exchange of a 3-methoxy group by an OH-group acting also as a hydrogen bond donor, resulted in decrease in activity underlining the potential role of the hydrogen bond acceptor 3-OCH(3) for the interaction with BCRP.

Dibenzazecine compounds with a novel dopamine/5HT2A receptor profile and 3D-QSAR analysis.

BACKGROUND: Antipsychotics are divided into typical and atypical compounds based on clinical efficacy and side effects. The purpose of this study was to characterize in vitro a series of novel azecine-type compounds at human dopamine D1-D5 and 5HT2A receptors and to assign them to different classes according to their dopamine/5HT2A receptor profile. RESULTS: Regardless of using affinity data (pKi values at D1-D5 and 5HT2A) or selectivity data (15 log (Ki ratios)), principal component analysis with azecine-type compounds, haloperidol, and clozapine revealed three groups of dopamine/5HT2A ligands: 1) haloperidol; 2) clozapine plus four azecine-type compounds; 3) two hydroxylated dibenzazecines. Reducing the number of Ki ratios used for principal component analysis from 15 to two (the D1/D2 and D2/5HT2A Ki ratios) obtained the same three groups of compounds. The most potent dibenzazecine clustering in the same group as clozapine was the non-hydroxylated LE410 which shows a slightly different D2-like receptor profile (D2L > D3 > D4.4) than clozapine (D4.4 > D2L > D3). The monohydroxylated dibenzacezine LE404 clusters in a separate group from clozapine/LE410 and from haloperidol and shows increased D1 selectivity. CONCLUSION: In conclusion, two compounds with a novel dopamine/5HT2A receptor profile, LE404 and LE410, with some differences in their respective D1/D2 receptor affinities including a validated pharmacophore-based 3D-QSAR model for D1 antagonists are presented.

3D-QSAR with the aid of pharmacophore search and docking-based alignments for farnesyltransferase inhibitors.

Farnesyltransferase is a potential drug target for treating various types of cancers. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for a series of farnesyltransferase inhibitors were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Pharmacophore search and molecular docking methods were used for construction of the molecular alignments. While the 3D-QSAR models were created for a training set of 33 compounds, their external predictivity was proven using a test set of 12 compounds. The results provided a comprehensive insight into the relationship between the structural features and the activities of farnesyltransferase inhibitors. This investigation will facilitate optimization of the design of new potential farnesyltransferase inhibitors.