Impact of Direction of Transport on the Evaluation of Inhibition Potencies of Multidrug and Toxin Extrusion Protein 1 Inhibitors

Saito, Asami; Ishiguro, Naoki; Takatani, Masahito; Bister, Bojan; Kusuhara, Hiroyuki

doi:10.1124/dmd.120.000136

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…There are currently no universally accepted guidelines on how in vitro transporter assays should be performed, which likely contributes to the high variability in reported IC 50 values ( Figure ). Several proposals have been made with the aim of refining in vitro assays to improve the physiological relevance of the systems used 20–23 . Important considerations for assessing renal tDDIs risk during the drug development process to increase translational predictivity are highlighted below.…”

Section: Prediction Of Ddi Risk From In Vitro Assaysmentioning

confidence: 99%

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Challenges and Opportunities for Improved Drug–Drug Interaction Predictions for Renal OCT2 and MATE1/2‐K Transporters

Krishnan

Ramsden

Ferguson

et al. 2022

Clin Pharma and Therapeutics

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Transporters contribute to renal elimination of drugs; therefore drug disposition can be impacted if transporters are inhibited by comedicant drugs. Regulatory agencies have provided guidelines to assess potential drug–drug interaction (DDI) risk for renal organic cation transporter 2 (OCT2) and multidrug and toxin extrusion 1 and 2‐K (MATE1/2‐K) transporters. Despite this, there are challenges with translating in vitro data using currently available tools to obtain a quantitative assessment of DDI risk in the clinic. Given the high number of drugs and new molecular entities showing in vitro inhibition toward OCT2 and/or MATE1/2‐K and the lack of translation to clinically significant effects, it is reasonable to question whether the current in vitro assay design and modeling practice has led to unnecessary clinical evaluation. The aim of this review is to assess and discuss available in vitro and clinical data along with prediction models intended to provide clinical context of risk, including static models proposed by regulatory agencies and physiologically‐based pharmacokinetic models, in order to identify best practices and areas of future opportunity. This analysis highlights that different in vitro assay designs, including substrate and cell systems used, strongly influence the derived concentration of drug producing 50% inhibition values and contribute to high variability observed across laboratories. Furthermore, the lack of sensitive index substrates coupled with specific inhibitors for individual transporters necessitates the use of complex models to evaluate clinical DDI risk.

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Section: Prediction Of Ddi Risk From In Vitro Assaysmentioning

confidence: 99%

“…Several proposals have been made with the aim of refining in vitro assays to improve the physiological relevance of the systems used. [20][21][22][23] Important considerations for assessing renal tDDIs risk during the drug development process to increase translational predictivity are highlighted below. S4.…”

Section: Prediction Of Ddi Risk From In Vitro Assaysmentioning

confidence: 99%

Challenges and Opportunities for Improved Drug–Drug Interaction Predictions for Renal OCT2 and MATE1/2‐K Transporters

Krishnan

Ramsden

Ferguson

et al. 2022

Clin Pharma and Therapeutics

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“…Uptake experiments were done as described in our previous study (Saito et al, 2020). The detailed procedure of uptake experiments, radioactivity measurement and the determination of protein concentration are described in Supp.…”

Section: Uptake Experiments Using Transiently Transfected Hek293 Cellsmentioning

confidence: 99%

“…This analysis assumes bi-directional transport of MATEs depending on the concentration gradient of substrates and protons across the plasma membrane. Provided that the physiologically relevant transport direction of MATEs is efflux in the kidney, we have reported the equivalence of IC 50 determination of hMATE1-mediated uptake and efflux in vitro (Saito et al, 2020), however no investigation has been carried out to determine whether the substrate recognition is identical in both directions.…”

Section: Introductionmentioning

confidence: 99%

Impact of Direction of Transport on the Evaluation of Substrate Recognition of Mouse Multidrug and Toxin Extrusion Protein 1

Saito

Kito

Ishiguro³

et al. 2023

Drug Metab Dispos

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“…Technical pitfalls in vitro and interlaboratory variability are well‐documented, as are the complexities related to the estimation of perpetrator concentration at the active site of the SLC in question. This is further exacerbated by substrate‐dependent IC 50 values in vitro , preincubation‐dependent IC 50 , unanticipated inhibitory metabolites, and complications related to SLC genotype 18–28 . Even for well‐documented SLC probe drugs, ABC transporters may be lurking.…”

Section: Figurementioning

confidence: 99%

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

Rodrigues

2022

Clin Pharma and Therapeutics

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Solute carrier (SLC) transporters present as the loci of important drug–drug interactions (DDIs). Therefore, sponsors generate in vitro half‐maximal inhibitory concentration (IC50) data and apply regulatory agency‐guided “static” methods to assess DDI risk and the need for a formal clinical DDI study. Because such methods are conservative and high false‐positive rates are likely (e.g., DDI study triggered when liver SLC R value ≥ 1.04 and renal SLC maximal unbound plasma (Cmax,u)/IC50 ratio ≥ 0.02), investigators have attempted to deploy plasma‐ and urine‐based SLC biomarkers in phase I studies to de‐risk DDI and obviate the need for drug probe‐based studies. In this regard, it was possible to generate in‐house in vitro SLC IC50 data for various clinically (biomarker)‐qualified perpetrator drugs, under standard assay conditions, and then estimate “% inhibition” for each SLC and relate it empirically to published clinical biomarker data (area under the plasma concentration vs. time curve (AUC) ratio (AUCR, AUCinhibitor/AUCreference) and % decrease in renal clearance (ΔCLrenal)). After such a “calibration” exercise, it was determined that only compounds with high R values (> 1.5) and Cmax,u/IC50 ratios (> 0.5) are likely to significantly modulate liver (AUCR > 1.25) and renal (ΔCLrenal > 25%) biomarkers and evoke DDI risk. The % inhibition approach supports integration of liver and renal SLC data and allows one to generate pan‐SLC inhibition signatures for different test perpetrators (e.g., SLC % inhibition ranking). In turn, such signatures can guide the selection of the most appropriate individual (or combinations of) biomarkers for testing in phase I studies.

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Impact of Direction of Transport on the Evaluation of Inhibition Potencies of Multidrug and Toxin Extrusion Protein 1 Inhibitors

Cited by 7 publications

References 27 publications

Challenges and Opportunities for Improved Drug–Drug Interaction Predictions for Renal OCT2 and MATE1/2‐K Transporters

Challenges and Opportunities for Improved Drug–Drug Interaction Predictions for Renal OCT2 and MATE1/2‐K Transporters

Impact of Direction of Transport on the Evaluation of Substrate Recognition of Mouse Multidrug and Toxin Extrusion Protein 1

Reimagining the Framework Supporting the Static Analysis of Transporter Drug Interaction Risk; Integrated Use of Biomarkers to Generate Pan‐Transporter Inhibition Signatures

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