Quantitative Prediction of Repaglinide-Rifampicin Complex Drug Interactions Using Dynamic and Static Mechanistic Models: Delineating Differential CYP3A4 Induction and OATP1B1 Inhibition Potential of Rifampicin

Abstract

Repaglinide is mainly metabolized by cytochrome P450 enzymes CYP2C8 and CYP3A4, and it is also a substrate to a hepatic uptake transporter, organic anion transporting polypeptide (OATP)1B1. The purpose of this study is to predict the dosing time-dependent pharmacokinetic interactions of repaglinide with rifampicin, using mechanistic models. In vitro hepatic transport of repaglinide, characterized using sandwich-cultured human hepatocytes, and intrinsic metabolic parameters were used to build a dynamic whole-body physiologically-based pharmacokinetic (PBPK) model. The PBPK model adequately described repaglinide plasma concentration-time profiles and successfully predicted area under the plasma concentration-time curve ratios of repaglinide (within 6 25% error), dosed (staggered 0-24 hours) after rifampicin treatment when primarily considering induction of CYP3A4 and reversible inhibition of OATP1B1 by rifampicin. Further, a static mechanistic "extended net-effect" model incorporating transport and metabolic disposition parameters of repaglinide and interaction potency of rifampicin was devised. Predictions based on the static model are similar to those observed in the clinic (average error ∼19%) and to those based on the PBPK model. Both the models suggested that the combined effect of increased gut extraction and decreased hepatic uptake caused minimal repaglinide systemic exposure change when repaglinide is dosed simultaneously or 1 hour after the rifampicin dose. On the other hand, isolated induction effect as a result of temporal separation of the two drugs translated to an approximate 5-fold reduction in repaglinide systemic exposure. In conclusion, both dynamic and static mechanistic models are instrumental in delineating the quantitative contribution of transport and metabolism in the dosing time-dependent repaglinide-rifampicin interactions.

Role of Cytochrome P450 2C8 in Drug Metabolism and Interactions

“…Although in vitro studies are not fully consistent with the major in vivo role of CYP2C8 in the total metabolism of repaglinide (Gan et al, 2010;Säll et al, 2012;Varma et al, 2013Varma et al, , 2015, repaglinide seems to be very sensitive to inhibitors of CYP2C8 activity, such as gemfibrozil, trimethoprim, and clopidogrel (Niemi et al, 2003b(Niemi et al, , 2004bTornio et al, 2014). On the basis of detailed mechanistic drug-drug interaction studies with the strong CYP2C8 inactivator gemfibrozil, it has been estimated that the contribution of CYP2C8 to repaglinide (0.25 mg) metabolism is about 85%, indicating that the AUC of repaglinide can be increased up to an average of sevenfold by strong CYP2C8 inhibition (Honkalammi et al, 2012).…”

“…Rifampin (600 mg/day), given for several days, has decreased the plasma exposure to repaglinide by 31-80% depending on the time interval from the last rifampin dose to repaglinide ingestion (Table 11; Niemi et al, 2000;Hatorp et al, 2003;Bidstrup et al, 2004). The time interval affects the extent of interaction because rifampin is also a competitive inhibitor of OATP1B1, CYP2C8 and CYP3A4 (Kajosaari et al, 2005a;Varma et al, 2013). Interestingly, intake of St John's Wort for 14 days has had no significant effect of the pharmacokinetics of repaglinide (Fan et al, 2011).…”

A Perspective on the Prediction of Drug Pharmacokinetics and Disposition in Drug Research and Development

“…All these parameters are appropriately scaled to in vivo CL int values before being entered into Eq. 6 and 7 using hepatocellularity and liver weight factors (Umehara and Camenisch, 2012;Varma et al, 2013c). Success of mechanistic-based prediction of transportermediated disposition and drug-drug interactions (DDIs) depends upon: 1) the adaptation of experimental tools and study design to obtain in vitro parameters that are relevant in vivo, 2) bridging the functional and protein expression difference between in vitro and in vivo systems, and 3) understanding the uncertainty associated with the parameters obtained from in vitro tools (Barton et al, 2013).…”

“…The prospective quantitative prediction of complex DDIs is particularly challenging since these efforts require expansion of current static mechanistic models or utilization of mechanistic PBPK modeling (e.g., Simcyp, Gastroplus, PK-Sim) to quantitatively predict the magnitude of DDIs in the clinic (Rowland Yeo et al, 2010;Varma et al, 2013c).…”

Quantitative Prediction of Transporter- and Enzyme-Mediated Clinical Drug-Drug Interactions of Organic Anion-Transporting Polypeptide 1B1 Substrates Using a Mechanistic Net-Effect Model

“…To achieve this, in vitro hepatobiliary transport and metabolic rates were obtained and used, along with other input parameters, to quantitatively predict the change in systemic exposure using the proposed extended net-effect model (Varma et al, 2013b). This mechanistic model accounts for the simultaneous influence of reversible inhibition of active hepatic uptake and net effect of reversible inhibition, time-dependent inactivation, and induction of P450s in both the intestine and liver to quantitatively assess DDIs.…”