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Prediction of Hepatic Transporter-Mediated Drug–Drug Interaction from In Vitro Data

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Transporters in Drug Development

Part of the book series: AAPS Advances in the Pharmaceutical Sciences Series ((AAPS,volume 7))

Abstract

The importance of transporter-mediated drug–drug interaction (TP-DDI) has been rapidly recognized by the recent publication of its clinical evidences and subsequent updated regulatory guidance (guideline). The methods of TP-DDI prediction are roughly divided into two approaches; static model and dynamic model. Static model with theoretically maximum unbound concentration is useful to sensitively catch the signal of DDIs, but predicted DDI risk should always be overestimated. Dynamic model fully considers the time courses of the plasma and tissue concentrations of both substrate and inhibitor drugs by the physiologically based pharmacokinetic (PBPK) model, thus accurate estimation of DDI risk can be achieved. However, the universal methods to set up model parameters based on the in vitro results with scaling factors remain to be discussed. This chapter is mainly focused on the basic theory and recent progress of the methods for TP-DDI predictions.

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Abbreviations

AUC:

Area under the concentration-time curve

BCRP:

Breast cancer resistance protein

BSP:

Bromosulfophthalein

CYP:

Cytochrome P450

DDI:

Drug–drug interaction

E217βG:

Estradiol-17β-d-glucuronide

EMA:

European Medicines Agency

FDA:

Food and Drug Administration

MRP:

Multidrug resistance-associated protein

NTCP:

Sodium taurocholate cotransporting polypeptide

OATP:

Organic anion transporting polypeptide

PBPK:

Physiologically based pharmacokinetic

PET:

Positron emission tomography

P-gp:

P-glycoprotein

TP-DDI:

Transporter-mediated drug–drug interaction

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Authors and Affiliations

  1. Department of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Kazuya Maeda

  2. Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan

    Yuichi Sugiyama

  3. Sugiyama Laboratory, RIKEN Innovation center, RIKEN Research Cluster for Innovation, RIKEN, 1-6 Suehiro-Cho, Tsurumi-ku, Yokohama, 230-0045, Japan

    Yuichi Sugiyama

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  1. Kazuya Maeda
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Correspondence to Yuichi Sugiyama .

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Editors and Affiliations

  1. The University of Tokyo Graduate School of Pharmac. Sciences, Tokyo, Japan

    Yuichi Sugiyama

  2. Pharma, Drug Transporters in ADME Department of Pharmacy, University of Copenhagen Faculty of Health and Medical Sciences, Copenhagen, Denmark

    Bente Steffansen

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Maeda, K., Sugiyama, Y. (2013). Prediction of Hepatic Transporter-Mediated Drug–Drug Interaction from In Vitro Data. In: Sugiyama, Y., Steffansen, B. (eds) Transporters in Drug Development. AAPS Advances in the Pharmaceutical Sciences Series, vol 7. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8229-1_6

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