Genotype‐sensitive reversible and time‐dependent CYP2D6 inhibition in human liver microsomes

Abstract

Cytochrome P450 (CYP) 2D6 metabolizes a wide range of xenobiotics and is characterized by a huge interindividual variability. A recent clinical study highlighted differential magnitude of CYP inhibition as a function of CYP2D6 genotype. The aim of this study was to investigate the effect of CYP2D6 genotype on the inhibition of dextromethorphan O-demethylation by duloxetine and paroxetine in human liver microsomes (HLMs). The study focused on genotypes defined by the combination of two fully functional alleles (activity score 2, AS 2, n = 6), of one fully functional and one reduced allele (activity score 1.5, AS 1.5, n = 4) and of one fully functional and one non-functional allele (activity score 1, AS 1, n = 6), which all predict extensive metabolizer phenotype. Kinetic experiments showed that maximal reaction velocity was affected by CYP2D6 genotype, with a decrease in 33% of V in AS 1 HLMs compared to AS 2 (P = 0.06). No difference in inhibition parameters K , K and k was observed neither with the competitive inhibitor duloxetine nor with the time-dependent inhibitor paroxetine. Among the genotypes tested, we found no difference in absolute CYP2D6 microsomal levels with ELISA immunoquantification. Therefore, our results suggest that genotype-sensitive magnitude of drug-drug interactions recently observed in vivo is likely to be due to differential amounts of functional enzymes at the microsomal level rather than to a difference in inhibition potencies across genotypes, which motivates for further quantitative proteomic investigations of functional and variant CYP2D6 alleles.

Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice

“…So, genetic polymorphisms in drug-metabolizing enzymes can affect the occurrence of phenoconversion induced by drug inhibitors. As reported by Storelli et al, differences in CYP2D6 inhibition observed in vitro with paroxetine (mechanism-based inhibitor) or duloxetine (competitive inhibitor) across CYP2D6 genotypes were not related to their inhibition parameters but likely due to a differential level of functional enzymes as a function of the CYP2D6 genotype [57,58].…”

Physiologically‐Based Pharmacokinetic Modeling for the Prediction of CYP2D6‐Mediated Gene–Drug–Drug Interactions

“…In PMs, the CYP2D6 protein is absent and therefore the CYP2D6 abundance of PM subjects is set to zero. However, immunologic and proteomic studies failed to demonstrate a difference in the amounts of CYP2D6 between activity scores 0.5 to 2, which seems to be mostly related to the high interindividual variability, even within a same genotype. Therefore, there was no evidence supporting a change of functional CYP2D6 abundance for non‐PM genotypes, and we rather decided to incorporate the effect of genetic polymorphisms on CYP2D6 enzyme kinetics data (intrinsic clearance (CLint) or maximal reaction velocity (V max )).…”

“…The optimized model predicted well CYP1A2 contribution in hepatic clearance, as verified with a DDI trial with the potent inhibitor fluvoxamine in male smokers . Duloxetine has been shown to increase the exposure of the CYP2D6 substrates desipramine and tolterodine up to 2.9‐fold with a 60 mg twice‐daily regimen, which could not be predicted by the bottom‐up approach in Simcyp using experimental Ki values in the range of 2–4 μM . Instead, this value was optimized from in vivo DDI data with desipramine and verified with a set of independent DDI data with desipramine, metoprolol, and tolterodine .…”

“…20 Duloxetine has been shown to increase the exposure of the CYP2D6 substrates desipramine and tolterodine up to 2.9-fold with a 60 mg twice-daily regimen, 18,23,24 which could not be predicted by the bottom-up approach in Simcyp using experimental Ki values in the range of 2-4 μM. 17,22 Instead, this value was optimized from in vivo DDI data with desipramine 18 and verified with a set of independent DDI data with desipramine, metoprolol, and tolterodine. [23][24][25] To note, the approach of using in vivo optimized rather than in vitro obtained Ki values for the modeling of DDIs was similarly performed previously with fluvoxamine and ciprofloxacine, two CYP1A2 inhibitors for which experimental Ki values resulted in the underprediction of DDIs.…”

“…To that end, the population used was healthy volunteers in which the frequency of CYP1A2 ultrarapid metabolizers (UMs) was set to 100% (CYP1A2 abundance 94 pmol/mg of microsomal proteins). Because experimental CYP2D6 competitive inhibition constant (Ki CYP2D6 ) values (2–4 μM) resulted in a null DDI prediction, an optimized competitive inhibition constant (Ki) value of 0.005 μM was input using sensitivity analysis with available clinical data with desipramine as a victim drug and verified with three independent DDI trials with desipramine, tolterodine, and metoprolol ( Table ).…”