PURPOSE: To evaluate 26 marketed oncology drugs for time-dependent inhibition (TDI) of cytochrome P450 (CYP) enzymes. Evaluate TDI-positive drugs for potential to generate reactive intermediates. Assess clinical drug-drug interaction (DDI) risk using static mechanistic models. METHODS: Human liver microsomes and CYP-specific probes were used to assess TDI in a dilution shift assay followed by generation of K(I) and k(inact). Reactive metabolite trapping studies were performed with stable label probes. Static mechanistic model was used to predict DDI risk using a 1.25-fold AUC increase as a cut-off for positive DDI. RESULTS: Negative TDI across CYPs was observed for 13/26 drugs; the rest were time-dependent inhibitors of, predominantly, CYP3A. The k(inact)/K(I) ratios for 11 kinase inhibitors ranged from 0.7 to 42.2 ml/min/μmol. Stable label trapping agent-drug conjugates were observed for ten kinase inhibitors. DDI predictions gave no false negatives, one true negative, four false positives and three true positives. The magnitude of DDI was overestimated irrespective of the inhibitor concentration selected. CONCLUSIONS: 13/26 oncology drugs investigated showed TDI potential towards CYP3A, formation of reactive metabolites was also observed. An industry standard static mechanistic model gave no false negative predictions but did not capture the modest clinical DDI potential of kinase inhibitors.
PURPOSE: To evaluate 26 marketed oncology drugs for time-dependent inhibition (TDI) of cytochrome P450 (CYP) enzymes. Evaluate TDI-positive drugs for potential to generate reactive intermediates. Assess clinical drug-drug interaction (DDI) risk using static mechanistic models. METHODS:Human liver microsomes and CYP-specific probes were used to assess TDI in a dilution shift assay followed by generation of K(I) and k(inact). Reactive metabolite trapping studies were performed with stable label probes. Static mechanistic model was used to predict DDI risk using a 1.25-fold AUC increase as a cut-off for positive DDI. RESULTS: Negative TDI across CYPs was observed for 13/26 drugs; the rest were time-dependent inhibitors of, predominantly, CYP3A. The k(inact)/K(I) ratios for 11 kinase inhibitors ranged from 0.7 to 42.2 ml/min/μmol. Stable label trapping agent-drug conjugates were observed for ten kinase inhibitors. DDI predictions gave no false negatives, one true negative, four false positives and three true positives. The magnitude of DDI was overestimated irrespective of the inhibitor concentration selected. CONCLUSIONS: 13/26 oncology drugs investigated showed TDI potential towards CYP3A, formation of reactive metabolites was also observed. An industry standard static mechanistic model gave no false negative predictions but did not capture the modest clinical DDI potential of kinase inhibitors.
Authors: Dayana Argoti; Li Liang; Abdul Conteh; Liangfu Chen; Dave Bershas; Chung-Ping Yu; Paul Vouros; Eric Yang Journal: Chem Res Toxicol Date: 2005-10 Impact factor: 3.739
Authors: Hong-Guang Xie; Alastair J J Wood; Richard B Kim; C Michael Stein; Grant R Wilkinson Journal: Pharmacogenomics Date: 2004-04 Impact factor: 2.533
Authors: Yvonne S Lin; Amy L S Dowling; Sean D Quigley; Federico M Farin; Jiong Zhang; Jatinder Lamba; Erin G Schuetz; Kenneth E Thummel Journal: Mol Pharmacol Date: 2002-07 Impact factor: 4.436
Authors: Gracia M Amaya; Rebecca Durandis; David S Bourgeois; James A Perkins; Arsany A Abouda; Kahari J Wines; Mohamed Mohamud; Samuel A Starks; R Nathan Daniels; Klarissa D Jackson Journal: Chem Res Toxicol Date: 2018-06-18 Impact factor: 3.739
Authors: Jialin Mao; Suzanne Tay; Cyrus S Khojasteh; Yuan Chen; Cornelis E C A Hop; Jane R Kenny Journal: Pharm Res Date: 2016-02-11 Impact factor: 4.200
Authors: Xiaojing Wang; Minghua Sun; Connie New; Spencer Nam; Wesley P Blackaby; Alastair J Hodges; David Nash; Mizio Matteucci; Joseph P Lyssikatos; Peter W Fan; Suzanne Tay; Jae H Chang Journal: ACS Med Chem Lett Date: 2015-07-12 Impact factor: 4.345