RT Journal Article SR Electronic T1 Determination of a Degradation Constant for CYP3A4 by Direct Suppression of mRNA in a Novel Human Hepatocyte Model, HepatoPac JF Drug Metabolism and Disposition JO Drug Metab Dispos FD American Society for Pharmacology and Experimental Therapeutics SP 1307 OP 1315 DO 10.1124/dmd.115.065326 VO 43 IS 9 A1 Diane Ramsden A1 Jin Zhou A1 Donald J. Tweedie YR 2015 UL http://dmd.aspetjournals.org/content/43/9/1307.abstract AB Accurate determination of rates of de novo synthesis and degradation of cytochrome P450s (P450s) has been challenging. There is a high degree of variability in the multiple published values of turnover for specific P450s that is likely exacerbated by differences in methodologies. For CYP3A4, reported half-life values range from 10 to 140 hours. An accurate value for kdeg has been identified as a major limitation for prediction of drug interactions involving mechanism-based inhibition and/or induction. Estimation of P450 half-life from in vitro test systems, such as human hepatocytes, is complicated by differential decreased enzyme function over culture time, attenuation of the impact of enzyme loss through inclusion of glucocorticoids in media, and viability limitations over long-term culture times. HepatoPac overcomes some of these challenges by providing extended stability of enzymes (2.5 weeks in our hands). As such it is a unique tool for studying rates of enzyme degradation achieved through modulation of enzyme levels. CYP3A4 mRNA levels were rapidly depleted by >90% using either small interfering RNA or addition of interleukin-6, which allowed an estimation of the degradation rate constant for CYP3A protein over an incubation time of 96 hours. The degradation rate constant of 0.0240 ± 0.005 hour−1 was reproducible in hepatocytes from five different human donors. These donors also reflected the overall population with respect to CYP3A5 genotype. This methodology can be applied to additional enzymes and may provide a more accurate in vitro derived kdeg value for predicting clinical drug-drug interaction outcomes.