RT Journal Article SR Electronic T1 Time-Dependent Inhibition of CYP2C19 by Isoquinoline Alkaloids: In Vitro and In Silico Analysis JF Drug Metabolism and Disposition JO Drug Metab Dispos FD American Society for Pharmacology and Experimental Therapeutics SP 1891 OP 1904 DO 10.1124/dmd.115.065755 VO 43 IS 12 A1 Kaisa A. Salminen A1 Minna Rahnasto-Rilla A1 Raija Väänänen A1 Peter Imming A1 Achim Meyer A1 Aline Horling A1 Antti Poso A1 Tuomo Laitinen A1 Hannu Raunio A1 Maija Lahtela-Kakkonen YR 2015 UL http://dmd.aspetjournals.org/content/43/12/1891.abstract AB The cytochrome P450 2C19 (CYP2C19) enzyme plays an important role in the metabolism of many commonly used drugs. Relatively little is known about CYP2C19 inhibitors, including compounds of natural origin, which could inhibit CYP2C19, potentially causing clinically relevant metabolism-based drug interactions. We evaluated a series (N = 49) of structurally related plant isoquinoline alkaloids for their abilities to interact with CYP2C19 enzyme using in vitro and in silico methods. We examined several common active alkaloids found in herbal products such as apomorphine, berberine, noscapine, and papaverine, as well as the previously identified mechanism-based inactivators bulbocapnine, canadine, and protopine. The IC50 values of the alkaloids ranged from 0.11 to 210 µM, and 42 of the alkaloids were confirmed to be time-dependent inhibitors of CYP2C19. Molecular docking and three-dimensional quantitative structure-activity relationship analysis revealed key interactions of the potent inhibitors with the enzyme active site. We constructed a comparative molecular field analysis model that was able to predict the inhibitory potency of a series of independent test molecules. This study revealed that many of these isoquinoline alkaloids do have the potential to cause clinically relevant drug interactions. These results highlight the need for studying more profoundly the potential interactions between drugs and herbal products. When further refined, in silico methods can be useful in the high-throughput prediction of P450 inhibitory potential of pharmaceutical compounds.