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1. Accurate predictions of clinical transporter-mediated drug-drug interactions (DDI) from in vitro data can be challenging when compounds have poor solubility and/or high nonspecific binding. Additionally, current DDI predictions for compounds with high plasma-protein binding assume that the unbound fraction in plasma is 0.01, if the experimental value is less than 0.01 or cannot be determined. This approach may result in an overestimation of DDI risk. To overcome these challenges, it may be beneficial to conduct inhibition studies under physiologically relevant conditions. 2. Here, IC 50 values, determined in the presence of 4% bovine serum albumin approximating human plasma albumin concentrations, were successfully used to predict DDI for uptake transporters, OATP1B1/1B3, OCT1/2, OAT1/3 and MATE1/2K. 3. The IC 50 values of reference inhibitors with 4% bovine serum albumin, considered total IC 50 , were comparable to the predicted values based on nominal IC 50 values determined under protein-free conditions and unbound fraction in plasma. Calculation of R-total and C max /IC 50,total values using total plasma exposure and total IC 50 values explained the clinical DDI or absence of it for these inhibitors. 4. These results suggest that IC 50 determinations in the presence of 4% albumin can be used, in the context of clinical total exposure, to predict DDI involving uptake transporters.

Drug–drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin‐I (CP‐I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP‐I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP‐I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP‐I exposure changes. Static models to predict changes in exposure of CP‐I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP‐I area under the curve ratio (AUCR) or maximum concentration ratio (CmaxR) and AUCR of substrate drugs. In general, the CP‐I CmaxR was equal to or greater than the CP‐I AUCR. CP‐I CmaxR < 1.25 was associated with absence of OATP1B‐mediated DDIs (AUCR < 1.25) with no false negative predictions. CP‐I CmaxR < 2 was associated with weak OATP1B‐mediated DDIs (AUCR < 2). A correlation was identified between CP‐I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP‐I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP‐I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP‐I exposure that may be used to prioritize, delay, or replace clinical DDI studies. [ABSTRACT FROM AUTHOR]

On behalf of the team I am pleased to present the second annual 'novel insights into drug transporter sciences review' focused on peer-reviewed articles that were published in the year 2021. In compiling the articles for inclusion, preprints available in 2021 but officially published in 2022 were considered to be in scope. To support this review the contributing authors independently selected one or two articles that were thought to be impactful and of interest to the broader research community. A similar approach as published last year was adopted whereby key observations, methods and analysis of each paper is concisely summarized in the synopsis followed by a commentary highlighting the impact of the paper in understanding drug transporters' role in drug disposition. As the goal of this review is not to provide a comprehensive overview of each paper but rather highlight important findings that are well supported by the data, the reader is encouraged to consult the original articles for additional information. Further, and keeping in line with the goals of this review, it should be noted that all authors actively contributed by writing synopsis and commentary for individual papers and no attempt was made to standardize language or writing styles. In this way, the review article is reflective of not only the diversity of the articles but also that of the contributors. I extend my thanks to the authors for their continued support and also welcome Diane Ramsden and Pallabi Mitra as contributing authors for this issue (Table 1). Table 1. Selected articles for this review. Title Area Authors Source 1. Atorvastatin-associated rhabdomyolysis in a patient with a novel variant of the SLCO1B1 gene: a case report Polymorphism Kiage et al. J Clin Lipidol. 2022;16(1):23–27 2. Quantitation of plasma membrane drug transporters in kidney tissue and cell lines using a novel proteomic approach enabled a prospective prediction of metformin disposition Proteomics Kikuchi et al. Drug Metab Dispos. 2021;49(10):938–946 3. Proteomics-informed prediction of rosuvastatin plasma profiles in patients with a wide range of body weight Proteomics Wegler et al. Clin Pharmacol Ther. 2021;109(3):762–771 4. Performance of plasma coproporphyrin I and III as OATP1B1 biomarkers in humans Biomarker Neuvonen et al. Clin Pharmacol Ther. 2021 Dec;110(6):1622–1632 5. Identification of appropriate endogenous biomarker for risk assessment of multidrug and toxin extrusion protein-mediated drug-drug interactions in healthy volunteers Biomarker Miyake et al. Clin Pharmacol Ther. 2021;109(2):507–516 6. Population pharmacokinetic modeling and simulation to support qualification of pyridoxic acid as endogenous biomarker of OAT1/3 renal transporters Biomarker Ahmad et al. CPT Pharmacometrics Syst Pharmacol. 2021 May;10(5):467–477 7. Acute and chronic effects of rifampin on letermovir suggest transporter inhibition and induction contribute to letermovir Pharmacokinetics Induction/Biomarkers Robbins et al. Clin Pharmacol Ther. 2022;111(3):664–675 8. Exploring the use of serum-derived small extracellular vesicles as liquid biopsy to study the induction of hepatic cytochromes P450 and organic anion transporting polypeptides Induction/Biomarkers Rodrigues et al. Clin Pharmacol Ther. 2021;110(1):248–258 9. Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: assessing the induction magnitude of P-glycoprotein-cytochrome P450 3A4 dual substrates Induction Pan et al. CPT Pharmacometrics Syst Pharmacol. 2021;10:1485–1496. 10. Quantification of CYP3A and Drug transporters activity in healthy young, healthy elderly and chronic kidney disease elderly patients by a microdose cocktail approach Clinical pharmacology Rattanacheeworn et al. Front Pharmacol. 2021;12:726669. 11. Quantitative prediction of P-glycoprotein-mediated drug–drug interactions and intestinal absorption using humanized mice DDI Miyake et al. Br J Pharmacol. 2021;178(21):4335–4351. 12. Regulation of OATP1B1 function by tyrosine kinase-mediated phosphorylation Regulation Hayden et al. Clin Cancer Res. 2021;27(15):4301–4310. [ABSTRACT FROM AUTHOR]

Veliparib ( ABT-888) is largely eliminated as parent drug in human urine (70% of the dose). Renal unbound clearance exceeds glomerular filtration rate, suggesting the involvement of transporter-mediated active secretion. Clinically relevant pharmacokinetic interactions in the kidney have been associated with OAT1, OAT3, OCT2, MATE1, and MATE2 K. In the present study, interactions of veliparib with these transporters were investigated. Veliparib inhibited OAT1, OAT3, OCT2, MATE1, and MATE2 K with IC50 values of 1371, 505, 3913, 69.9, and 69.5 μM, respectively. The clinical unbound maximum plasma concentration of veliparib after single oral dose of 50 mg (0.45 μM) is manyfold lower than IC50 values for OAT1, OAT3, OCT2, MATE1, or MATE2 K. These results indicate a low potential for drug-drug interaction ( DDI) with OAT1/3, OCT2, or MATE1/2 K. Additional studies demonstrated that veliparib is a substrate of OCT2. In Oct1/ Oct2 double-knockout mice, the plasma exposure of veliparib was increased by 1.5-fold, and the renal clearance was decreased by 1.8-fold as compared with wild-type mice, demonstrating that organic cation transporters contribute to the renal elimination in vivo. In summary, the in vitro transporter data for veliparib predicts minimal potential for an OAT1/3-, OCT2-, and MATE1/2 K-mediated DDI given the clinical exposure after single oral dose of 50 mg. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association J Pharm Sci 102:4426-4432, 2013 [ABSTRACT FROM AUTHOR]