Evaluation and Quantitative Prediction of Renal Transporter‐Mediated Drug‐Drug Interactions

Abstract

With numerous drugs cleared renally, inhibition of uptake transporters localized on the basolateral membrane of renal proximal tubule cells, eg, organic anion transporters (OATs) and organic cation transporters (OCTs), may lead to clinically meaningful drug-drug interactions (DDIs). Additionally, clinical evidence for the possible involvement of efflux transporters, such as P-glycoprotein (P-gp) and multidrug and toxin extrusion protein 1/2-K (MATE1/2-K), in the renal DDIs is emerging. Herein, we review recent progress regarding mechanistic understanding of transporter-mediated renal DDIs as well as the quantitative predictability of renal DDIs using static and physiologically based pharmacokinetic (PBPK) models. Generally, clinical DDI data suggest that the magnitude of plasma exposure changes attributable to renal DDIs is less than 2-fold, unlike the DDIs associated with inhibition of cytochrome P-450s and/or hepatic uptake transporters. It is concluded that although there is a need for risk assessment early in drug development, current available data imply that safety concerns related to the renal DDIs are generally low. Nevertheless, consideration must be given to the therapeutic index of the victim drug and potential risk in a specific patient population (eg, renal impairment). Finally, in vitro transporter data and clinical pharmacokinetic parameters obtained from the first-in-human studies have proven useful in support of quantitative prediction of DDIs associated with inhibition of renal secretory transporters, OATs or OCTs.

Protective effect of cilastatin against diclofenac‐induced nephrotoxicity through interaction with diclofenac acyl glucuronide via organic anion transporters

“…Additionally, the maximum clinical plasma concentrations of cilastatin are documented as 88 μg·ml −1 (Balfour et al, ; Norrby et al, ). Taking into account plasma protein binding of approximately 40%, the peak level of free cilastatin in human was calculated as approximately 140 μM, which was high enough to induce an in vivo drug–drug interaction mediated by OATs based on FDA criteria ( C max /IC 50 > 0.1; Feng & Varma, ). This means that in vivo interactions between cilastatin and diclofenac need a third participant, diclofenac acyl glucuronide, the major metabolite of diclofenac in vivo.…”

The impact of CYP2B6 polymorphisms on the interactions of efavirenz with lumefantrine: Implications for paediatric antimalarial therapy

“…Due to the complexity and ethical issues of recruitment of paediatrics into complex DDI studies in HIV-infected malaria subjects, population-based physiologically-based pharmacokinetic (PBPK) modelling can be used to explore the potential risk of DDIs in adults (Feng and Varma, 2016;Johansson et al, 2016;Olafuyi et al, 2017a) and paediatric populations (Johnson et al, 2014;Olafuyi et al, 2017b;Salem et al, 2013a;Salem et al, 2013b). The benefit of this approach is both the ability to model population variability in physiology (Jamei et al, 2009a;Jamei et al, 2009b;Jamei et al, 2009c;Olafuyi et al, 2017a, b), but to also specifically develop a modelling approach that is tailored towards a specific geographical population group of interest rather than a standard healthy (Caucasian) adult male.…”

Role of Transporters in Drug Development

“…Feng and Varma provide an overview of the PBPK modeling-based approaches and outline some challenges and knowledge gaps for predicting renal transporter-based DDIs. 36 Pan et al provide a concise literature review of PBPK modeling to evaluate the contributions of various transporters and summarize regulatory submissions in which PBPK modeling was used to evaluate the role of intestinal, hepatic, and renal transporters. 37 The importance of assessing intracellular drug concentrations for predicting DDIs and the associated challenges of quantifying intracellular concentrations are widely recognized.…”

“…Further, the review provides insight into how ECCS can be used to quantitatively predict the magnitude of DDIs due to “enzyme‐transporter” interplay. Feng and Varma provide an overview of the PBPK modeling‐based approaches and outline some challenges and knowledge gaps for predicting renal transporter‐based DDIs . Pan et al provide a concise literature review of PBPK modeling to evaluate the contributions of various transporters and summarize regulatory submissions in which PBPK modeling was used to evaluate the role of intestinal, hepatic, and renal transporters …”