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Cyclosporine Inhibition of Hepatic and Intestinal CYP3A4, Uptake and Efflux Transporters: Application of PBPK Modeling in the Assessment of Drug-Drug Interaction Potential

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Abstract

Purpose

To apply physiologically-based pharmacokinetic (PBPK) modeling to investigate the consequences of reduction in activity of hepatic and intestinal uptake and efflux transporters by cyclosporine and its metabolite AM1.

Methods

Inhibitory potencies of cyclosporine and AM1 against OATP1B1, OATP1B3 and OATP2B1 were investigated in HEK293 cells +/− pre-incubation. Cyclosporine PBPK model implemented in Matlab was used to assess interaction potential (+/− metabolite) against different processes (uptake, efflux and metabolism) in liver and intestine and to predict quantitatively drug-drug interaction with repaglinide.

Results

Cyclosporine and AM1 were potent inhibitors of OATP1B1 and OATP1B3, IC50 ranging from 0.019–0.093 μM following pre-incubation. Cyclosporine PBPK model predicted the highest interaction potential against liver uptake transporters, with a maximal reduction of >70% in OATP1B1 activity; the effect on hepatic efflux and metabolism was minimal. In contrast, 80–97% of intestinal P-gp and CYP3A4 activity was reduced due to the 50-fold higher cyclosporine enterocytic concentrations relative to unbound hepatic inlet. The inclusion of AM1 resulted in a minor increase in the predicted maximal reduction of OATP1B1/1B3 activity. Good predictability of cyclosporine-repaglinide DDI and the impact of dose staggering are illustrated.

Conclusions

This study highlights the application of PBPK modeling for quantitative prediction of transporter-mediated DDIs with concomitant consideration of P450 inhibition.

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Notes

  1. C, A, K and ka, refer to concentrations, amounts, transit rate- and absorption rate constants in stomach (St), duodenum (G,1) and remaining intestinal segments n (G,n); Vent and V, refer to the volumes of the enterocyte and intestinal lumen in compartment n; QGut, refers to hybrid function of blood flow and drug permeability; dissolved (dis) and undissolved (un) drug in the intestinal lumen were modeled using diffusion constant (D), particle density and radius (ρ and r); effective diffusion layer thickness (h) and drug solubility (CS). Modeling of intestinal metabolism based on in vitro clearance data failed and consequently intestinal metabolism was modeled semi-mechanistically by incorporating the term FG (Eq. 10).

Abbreviations

AM1:

mono-hydroxylated metabolite of cyclosporine A (Hawk’s nomenclature)

CsA:

cyclosporine A

CYP enzymes:

Cytochrome P450 enzymes

DDI(s):

drug-drug interaction(s)

HEK-cells:

human embryonic kidney cells

IC50 :

inhibitory constant (concentration at which 50% of total inhibitory effect is observed)

IVIVE:

in vitro-in vivo extrapolation

OATP:

organic anion transporter proteins

PBPK model:

physiologically-based pharmacokinetic model

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Acknowledgments and disclosures

The authors would like to thank Prof. Leon Aarons and Drs Kayode Ogungbenro and Henry Pertinez for useful discussions and critical review of this article prior to submission. Further, the valuable discussions with Carolina Säll regarding the repaglinide PBPK model are acknowledged.

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Correspondence to Aleksandra Galetin.

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Gertz, M., Cartwright, C.M., Hobbs, M.J. et al. Cyclosporine Inhibition of Hepatic and Intestinal CYP3A4, Uptake and Efflux Transporters: Application of PBPK Modeling in the Assessment of Drug-Drug Interaction Potential. Pharm Res 30, 761–780 (2013). https://doi.org/10.1007/s11095-012-0918-y

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