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Dabrafenib is a novel small molecule tyrosine kinase inhibitor (TKI) which is used to treat metastatic melanoma. The aim of this research was to survey the effects of dabrafenib on human UDP-glucuronosyltransferases (UGTs) and to evaluate the risk of drug-drug interactions (DDIs). The formation rates for 4-methylumbelliferone (4-MU) glucuronide and trifluoperazine-glucuronide in 12 recombinant human UGT isoforms with or without dabrafenib were measured and HPLC was used to investigate the inhibitory effects of dabrafenib on UGTs. Inhibition kinetic studies were also conducted. In vitro-in vivo extrapolation approaches were further used to predict the risk of DDI potentials of dabrafenib via inhibition of UGTs. Our data indicated that dabrafenib had a broad inhibitory effect on 4-MU glucuronidation by inhibiting the activities of UGTs, especially on UGT1A1, UGT1A7, UGT1A8, and UGT1A9, and dabrafenib could increase the area under the curve of co-administered drugs. Dabrafenib is a strong inhibitor of several UGTs and the co-administration of dabrafenib with drugs primarily metabolized by UGT1A1, 1A7, 1A8 or 1A9 may induce potential DDIs., (© 2021 John Wiley & Sons, Ltd.)

Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required. In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)