Sibylle Neuhoff

Accounting for Transporters in Renal Clearance: Towards a Mechanistic Kidney Model (Mech KiM)

The impact of transporters in modulating the disposition of drugs in the liver and their passage across the gut wall has received much more attention than their role in renal excretion, despite the fact that 25–30 % of drugs are cleared predominantly by renal clearance and renal transporters contribute significantly to this process. Thus there is a need to improve the ability to predict changes in renal clearance arising from genetic variability, the impact of disease and interactions related to renal transporters. Such changes may also influence the accumulation of xenobiotics within the kidney cell leading to nephrotoxicity. Attempts to develop mechanistic, physiologically based models of renal drug elimination have been limited. This chapter outlines the features and application of a new model (Mech KiM) that links drug characteristics to knowledge of renal physiology in predicting the contributions of glomerular filtration, active and passive secretion, active and passive reabsorption and metabolism to renal elimination.

Metformin and cimetidine: Physiologically based pharmacokinetic modelling to investigate transporter mediated drug–drug interactions

Metformin is used as a probe for OCT2 mediated transport when investigating possible DDIs with new chemical entities. The aim of the current study was to investigate the ability of physiologically-based pharmacokinetic (PBPK) models to simulate the effects of OCT and MATE inhibition by cimetidine on metformin kinetics. PBPK models were developed, incorporating mechanistic kidney and liver sub-models for metformin (OCT and MATE substrate) and a mechanistic kidney sub-model for cimetidine. The models were used to simulate inhibition of the MATE1, MATE2-K, OCT1 and OCT2 mediated transport of metformin by cimetidine. Assuming competitive inhibition and using cimetidine Ki values determined in vitro, the predicted metformin AUC ratio was 1.0 compared to an observed value of 1.46. The observed AUC ratio could only be recovered with this model when the cimetidine Ki for OCT2 was decreased 1000-fold or the Kis for both OCT1 and OCT2 were decreased 500-fold. An alternative description of metformin renal transport by OCT1 and OCT2, incorporating electrochemical modulation of the rate of metformin uptake together with 8-18-fold decreases in cimetidine Kis for OCTs and MATEs, allowed recovery of the extent of the observed effect of cimetidine on metformin AUC. While the final PBPK model has limitations, it demonstrates the benefit of allowing for the complexities of passive permeability combined with active cellular uptake modulated by an electrochemical gradient and active efflux.

Application of permeability-limited physiologically-based pharmacokinetic models: part II - prediction of P-glycoprotein mediated drug-drug interactions with digoxin.

Digoxin is the recommended substrate for assessment of P-glycoprotein (P-gp)-mediated drug-drug interactions (DDIs) in vivo. The overall aim of our study was to investigate the inhibitory potential of both verapamil and norverapamil on the P-gp-mediated efflux of digoxin in both gut and liver. Therefore, a physiologically-based pharmacokinetic (PBPK) model for verapamil and its primary metabolite was developed and validated through the recovery of observed clinical plasma concentration data for both moieties and the reported interaction with midazolam, albeit a cytochrome P450 3A4-mediated DDI. The validated inhibitor model was then used in conjunction with the model developed previously for digoxin. The range of values obtained for the 10 trials indicated that increases in area under the plasma concentration-time curve (AUC) profiles and maximum plasma concentration observed (Cmax ) values of digoxin following administration of verapamil were more comparable with in vivo observations, when P-gp inhibition by the metabolite, norverapamil, was considered as well. The predicted decrease in AUC and Cmax values of digoxin following administration of rifampicin because of P-gp induction was 1.57- (range: 1.42-1.77) and 1.62-fold (range: 1.53-1.70), which were reasonably consistent with observed values of 1.4- and 2.2-fold, respectively. This study demonstrates the application of permeability-limited models of absorption and distribution within a PBPK framework together with relevant in vitro data on transporters to assess the clinical impact of modulated P-gp-mediated efflux by drugs in development.

In Vitro–In Vivo Extrapolation Scaling Factors for Intestinal P-Glycoprotein and Breast Cancer Resistance Protein: Part I: A Cross-Laboratory Comparison of Transporter-Protein Abundances and Relative Expression Factors in Human Intestine and Caco-2 Cells

Over the last 5 years the quantification of transporter-protein absolute abundances has dramatically increased in parallel to the expanded use of in vitro–in vivo extrapolation (IVIVE) and physiologically based pharmacokinetics (PBPK)-linked models, for decision-making in pharmaceutical company drug development pipelines and regulatory submissions. Although several research groups have developed laboratory-specific proteomic workflows, it is unclear if the large range of reported variability is founded on true interindividual variability or experimental variability resulting from sample preparation or the proteomic methodology used. To assess the potential for methodological bias on end-point abundance quantification, two independent laboratories, the University of Manchester (UoM) and Bertin Pharma (BPh), employing different proteomic workflows, quantified the absolute abundances of Na/K-ATPase, P-gp, and breast cancer resistance protein (BCRP) in the same set of biologic samples from human intestinal and Caco-2 cell membranes. Across all samples, P-gp abundances were significantly correlated (P = 0.04, Rs = 0.72) with a 2.4-fold higher abundance (P = 0.001) generated at UoM compared with BPh. There was a systematically higher BCRP abundance in Caco-2 cell samples quantified by BPh compared with UoM, but not in human intestinal samples. Consequently, a similar intestinal relative expression factor (REF), derived from distal jejunum and Caco-2 monolayer samples, between laboratories was found for P-gp. However, a 2-fold higher intestinal REF was generated by UoM (2.22) versus BPh (1.11). We demonstrate that differences in absolute protein abundance are evident between laboratories and they probably result from laboratory-specific methodologies relating to peptide choice.

Abundance of Hepatic Transporters in Caucasians: A Meta-Analysis

This study aimed to derive quantitative abundance values for key hepatic transporters suitable for in vitro–in vivo extrapolation within a physiologically based pharmacokinetic modeling framework. A meta-analysis was performed whereby data on abundance measurements, sample preparation methods, and donor demography were collated from the literature. To define values for a healthy Caucasian population, a subdatabase was created whereby exclusion criteria were applied to remove samples from non-Caucasian individuals, those with underlying disease, or those with subcellular fractions other than crude membrane. Where a clinically relevant active genotype was known, only samples from individuals with an extensive transporter phenotype were included. Authors were contacted directly when additional information was required. After removing duplicated samples, the weighted mean, geometric mean, standard deviation, coefficient of variation, and between-study homogeneity of transporter abundances were determined. From the complete database containing 24 transporters, suitable abundance data were available for 11 hepatic transporters from nine studies after exclusion criteria were applied. Organic anion transporting polypeptides OATP1B1 and OATP1B3 showed the highest population abundance in healthy adult Caucasians. For several transporters, the variability in abundance was reduced significantly once the exclusion criteria were applied. The highest variability was observed for OATP1B3 > OATP1B1 > multidrug resistance protein 2 > multidrug resistance gene 1. No relationship was found between transporter expression and donor age. To our knowledge, this study provides the first in-depth analysis of current quantitative abundance data for a wide range of hepatic transporters, with the aim of using these data for in vitro–in vivo extrapolation, and highlights the significance of investigating the background of tissue(s) used in quantitative transporter proteomic studies. Similar studies are now warranted for other ethnicities.

In Vitro-In Vivo Extrapolation Scaling Factors for Intestinal P-glycoprotein and Breast Cancer Resistance Protein: Part II. The Impact of Cross-Laboratory Variations of Intestinal Transporter Relative Expression Factors on Predicted Drug Disposition

Relative expression factors (REFs) are used to scale in vitro transporter kinetic data via in vitro–in vivo extrapolation linked to physiologically based pharmacokinetic (IVIVE-PBPK) models to clinical observations. Primarily two techniques to quantify transporter protein expression are available, immunoblotting and liquid chromatography–tandem mass spectrometry. Literature-collated REFs ranged from 0.4 to 5.1 and 1.1 to 90 for intestinal P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), respectively. The impact of using human jejunum–Caco-2 REFs for P-gp (REFiP-gp) and BCRP (REFiBCRP), generated from the same samples and using different proteomic methodologies from independent laboratories, on PBPK outcomes was assessed. A 5-fold decrease in REFiP-gp for a single oral dose of digoxin resulted in a 1.19- and 1.31-fold higher plasma area under the curve and Cmax, respectively. All generated REFiP-gp values led to simulated digoxin Cmax values within observed ranges; however, combining kinetic data generated from a different laboratory with the 5-fold lower REFiP-gp could not recover a digoxin-rifampicin drug-drug interaction, emphasizing the necessity to obtain transporter-specific kinetic estimates and REFs from the same in vitro system. For a theoretical BCRP compound, with absorption taking place primarily in the jejunum, a decrease in the REFiBCRP from 2.22 (University of Manchester) to 1.11 (Bertin Pharma) promoted proximal intestinal absorption while delaying tmax 1.44-fold. Laboratory-specific differences in REF may lead to different IVIVE-PBPK outcomes. To understand the mechanisms underlying projected pharmacokinetic liabilities, it is important to assess the potential impact of bias on the generation of REFs on an interindividual basis within a target population.

Characterization of Contributing Factors to Variability in Morphine Clearance Through PBPK Modeling Implemented With OCT1 Transporter

Morphine shows large interindividual variability in its pharmacokinetics; however, the cause of this has not been fully addressed. The variability in morphine disposition is considered to be due to a combination of pharmacogenetic and physiological determinants related to morphine disposition. We previously reported the effect of organic cation transporter (OCT1) genotype on morphine disposition in pediatric patients. To further explore the underlying mechanisms for variability arising from relevant determinants, including OCT1, a physiologically based pharmacokinetic (PBPK) model of morphine was developed. The PBPK model predicted morphine concentration‐time profiles well, in both adults and children. Almost all of the observed morphine clearances in pediatric patients fell within a twofold range of median predicted values for each OCT1 genotype in each age group. This PBPK modeling approach quantitatively demonstrates that OCT1 genotype, age‐related growth, and changes in blood flow as important contributors to morphine pharmacokinetic (PK) variability.

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter‐Mediated Clearances

Rosuvastatin is a substrate of choice in clinical studies of organic anion‐transporting polypeptide (OATP)1B1‐ and OATP1B3‐associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability‐limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor‐transporter and 45 intermediate‐transporter individuals are required to achieve 80% power to discriminate the AUC0‐48h of rosuvastatin from that of the extensive‐transporter phenotype. This number was reduced to 7 poor‐transporter and 40 intermediate‐transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design.

Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer.

Aims Use of the anti‐tumour antibiotic actinomycin D is associated with development of hepatotoxicity, particularly in young children. A paucity of actinomycin D pharmacokinetic data make it challenging to develop a sound rationale for defining dosing regimens in younger patients. The study aim was to develop a physiologically based pharmacokinetic (PBPK) model using a combination of data from the literature and generated from experimental analyses. Methods Assays to determine actinomycin D Log P, blood:plasma partition ratio and ABCB1 kinetics were conducted. These data were combined with physiochemical properties sourced from the literature to generate a compound file for use within the modelling‐simulation software Simcyp (version 14 release 1). For simulation, information was taken from two datasets, one from 117 patients under the age of 21 and one from 20 patients aged 16–48. Results The final model incorporated clinical renal and biliary clearance data and an additional systemic clearance value. The mean AUC0‐26h of simulated subjects was within 1.25‐fold of the observed AUC0‐26h (84 ng h ml−1 simulated vs. 93 ng h ml−1 observed). For the younger age ranges, AUC predictions were within two‐fold of observed values, with simulated data from six of the eight age/dose ranges falling within 15% of observed data. Simulated values for actinomycin D AUC0‐26h and clearance in infants aged 0–12 months ranged from 104 to 115 ng h ml−1 and 3.5–3.8 l h−1, respectively. Conclusions The model has potential utility for prediction of actinomycin D exposure in younger patients and may help guide future dosing. However, additional independent data from neonates and infants is needed for further validation. Physiological differences between paediatric cancer patients and healthy children also need to be further characterized and incorporated into PBPK models.

Breast Cancer Resistance Protein Abundance, but Not mRNA Expression, Correlates With Estrone-3-Sulfate Transport in Caco-2

Transporter mRNA and protein expression data are used to extrapolate in vitro transporter kinetics to in vivo drug disposition predictions. Breast cancer resistance protein (BCRP) possesses broad substrate specificity; therefore, understanding BCRP expression-activity relationships are necessary for the translation to in vivo. Bidirectional transport of estrone-3-sulfate (E-3-S), a BCRP probe, was evaluated with respect to relative BCRP mRNA expression and absolute protein abundance in 10- and 29-day cultured Caco-2 cells. BCRP mRNA expression was quantified by real-time PCR against a housekeeper gene, Cyclophilin A. The BCRP protein abundance in total membrane fractions was quantified by targeted proteomics, and [(3)H]-E-3-S bidirectional transport was determined in the presence or absence of Ko143, a potent BCRP inhibitor. BCRP mRNA expression was 1.5-fold higher in 29- versus 10-day cultured cells (n = 3), whereas a 2.4-fold lower (p < 0.001) BCRP protein abundance was observed in 29- versus 10-day cultured cells (1.28 ± 0.33 and 3.06 ± 0.22 fmol/μg protein, n = 6, respectively). This correlated to a 2.45-fold lower (p < 0.01) efflux ratio for E-3-S in 29- versus 10-day cultured cells (8.97 ± 2.51 and 3.32 ± 0.66, n = 6, respectively). Caco-2 cell BCRP protein abundance, but not mRNA levels, correlates with BCRP activity, suggesting that extrapolation strategies incorporating BCRP protein abundance-activity relationships may be more successful.