Literature DB >> 24066726

Quantitative prediction of renal transporter-mediated clinical drug-drug interactions.

Bo Feng1, Susan Hurst, Yasong Lu, Manthena V Varma, Charles J Rotter, Ayman El-Kattan, Peter Lockwood, Brian Corrigan.   

Abstract

Kidney plays a critical role in the elimination of xenobiotics. Drug-drug interactions (DDIs) via inhibition of renal organic anion (OAT) and organic cation (OCT) transporters have been observed in the clinic. This study examined the quantitative predictability of renal transporter-mediated clinical DDIs based on basic and mechanistic models. In vitro transport and clinical pharmacokinetics parameters were used to quantitatively predict DDIs of victim drugs when coadministrated with OAT or OCT inhibitors, probenecid and cimetidine, respectively. The predicted changes in renal clearance (CLr) and area under the plasma concentration-time curve (AUC) were comparable to that observed in clinical studies. With probenecid, basic modeling predicted 61% cases within 25% and 94% cases within 50% of the observed CLr changes in clinic. With cimetidine, basic modeling predicted 61% cases within 25% and 92% cases within 50% of the observed CLr changes in clinic. Additionally, the mechanistic model predicted 54% cases within 25% and 92% cases within 50% of the observed AUC changes with probenecid. Notably, the magnitude of AUC changes attributable to the renal DDIs is generally less than 2-fold, unlike the DDIs associated with inhibition of CYPs and/or hepatic uptake transporters. The models were further used to evaluate the renal DDIs of Pfizer clinical candidates/drugs, and the overall predictability demonstrates their utility in the drug discovery and development settings.

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Year:  2013        PMID: 24066726     DOI: 10.1021/mp400295c

Source DB:  PubMed          Journal:  Mol Pharm        ISSN: 1543-8384            Impact factor:   4.939


  5 in total

1.  Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

Authors:  Ayman F El-Kattan; Manthena V Varma; Stefan J Steyn; Dennis O Scott; Tristan S Maurer; Arthur Bergman
Journal:  Pharm Res       Date:  2016-09-12       Impact factor: 4.200

Review 2.  Key to Opening Kidney for In Vitro-In Vivo Extrapolation Entrance in Health and Disease: Part II: Mechanistic Models and In Vitro-In Vivo Extrapolation.

Authors:  Daniel Scotcher; Christopher Jones; Maria Posada; Aleksandra Galetin; Amin Rostami-Hodjegan
Journal:  AAPS J       Date:  2016-08-09       Impact factor: 4.009

3.  Organ Impairment-Drug-Drug Interaction Database: A Tool for Evaluating the Impact of Renal or Hepatic Impairment and Pharmacologic Inhibition on the Systemic Exposure of Drugs.

Authors:  C K Yeung; K Yoshida; M Kusama; H Zhang; I Ragueneau-Majlessi; S Argon; L Li; P Chang; C D Le; P Zhao; L Zhang; Y Sugiyama; S-M Huang
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2015-07-14

Review 4.  How drugs get into cells: tested and testable predictions to help discriminate between transporter-mediated uptake and lipoidal bilayer diffusion.

Authors:  Douglas B Kell; Stephen G Oliver
Journal:  Front Pharmacol       Date:  2014-10-31       Impact factor: 5.810

5.  Abundance of Drug Transporters in the Human Kidney Cortex as Quantified by Quantitative Targeted Proteomics.

Authors:  Bhagwat Prasad; Katherine Johnson; Sarah Billington; Caroline Lee; Git W Chung; Colin D A Brown; Edward J Kelly; Jonathan Himmelfarb; Jashvant D Unadkat
Journal:  Drug Metab Dispos       Date:  2016-09-12       Impact factor: 3.922

  5 in total

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