Explaining Ethnic Variability of Transporter Substrate Pharmacokinetics in Healthy Asian and Caucasian Subjects with Allele Frequencies of OATP1B1 and BCRP: A Mechanistic Modeling Analysis

Physiologically based pharmacokinetic (PBPK) modeling of pitavastatin in relation to SLCO1B1 genetic polymorphism

Pitavastatin, a potent 3-hydroxymethylglutaryl coenzyme A reductase inhibitor, is indicated for the treatment of hypercholesterolemia and mixed dyslipidemia. Hepatic uptake of pitavastatin is predominantly occupied by the organic anion transporting polypeptide 1B1 (OATP1B1) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) gene, which is a polymorphic gene that encodes OATP1B1. SLCO1B1 genetic polymorphism significantly alters the pharmacokinetics of pitavastatin. This study aimed to establish the physiologically based pharmacokinetic (PBPK) model to predict pitavastatin pharmacokinetics according to SLCO1B1 genetic polymorphism. PK-Sim® version 10.0 was used to establish the whole-body PBPK model of pitavastatin. Our pharmacogenomic data and a total of 27 clinical pharmacokinetic data with different dose administration and demographic properties were used to develop and validate the model, respectively. Physicochemical properties and disposition characteristics of pitavastatin were acquired from previously reported data or optimized to capture the plasma concentration-time profiles in different SLCO1B1 diplotypes. Model evaluation was performed by comparing the predicted pharmacokinetic parameters and profiles to the observed data. Predicted plasma concentration-time profiles were visually similar to the observed profiles in the non-genotyped populations and different SLCO1B1 diplotypes. All fold error values for AUC and Cmax were included in the two fold range of observed values. Thus, the PBPK model of pitavastatin in different SLCO1B1 diplotypes was properly established. The present study can be useful to individualize the dose administration strategy of pitavastatin in individuals with various ages, races, and SLCO1B1 diplotypes.

Pharmacokinetics and Bioequivalence of 2 Safinamide Tablets in Healthy Chinese Volunteers Under Fasting and Fed Conditions

To assess the bioequivalence of a generic safinamide tablet (test) vs a brand-name safinamide tablet (reference) and effects of food on the pharmacokinetics of safinamide in healthy Chinese subjects, a single-center, single-dose, randomized, open-label, 2-preparation, 2-period study with a 15-day washout period was undertaken. A total of 56 healthy subjects were recruited in this study (fasting, n = 28; fed, n = 28). A single dose of a 100-mg test or reference safinamide tablet was administered to each subject in a randomized sequence. Blood samples were obtained at 5 minutes before drug administration and during the 120 hours after dosing. The safinamide concentration in plasma was determined by high-performance liquid chromatography-tandem mass spectrometry. Pharmacokinetic parameters were analyzed with noncompartmental methods. Safety was also monitored. The major pharmacokinetic parameters including maximum plasma drug concentration, area under plasma concentration-time curve (AUC) from zero to time t (AUC_0-t ), and AUC from time 0 to infinity (AUC_0-∞ ) were similar between the test and reference tablets under fasting and fed conditions. The 90% CIs of the test/reference ratios of log-transformed maximum plasma drug concentration, AUC from zero to time t, and AUC from time 0 to infinity all fell within the equivalence interval (80.0%-125%) whether under fasting condition or fed condition. In conclusion, the 2 formulations of safinamide tablets were bioequivalent and well tolerated under both fasting and fed conditions in healthy Chinese volunteers. High-fat food delayed the absorption of safinamide but did not affect the final bioavailability.

Inter-ethnic differences in pharmacokinetics-is there more that unites than divides?

Inter-ethnic variability in pharmacokinetics (PK) has been attributed to several factors ranging from genetic to environmental. It is not clear how current teaching in higher education (HE) reflects what published literature suggests on this subject. This study aims to gain insights into current knowledge about inter-ethnic differences in PK based on reports from published literature and current teaching practices in HE. A systematic literature search was conducted on PubMed and Scopus to identify suitable literature to be reviewed. Insights into inter-ethnic differences in PK teaching among educators in HE and industry were determined using a questionnaire. Thirty-one percent of the studies reviewed reported inter-ethnic differences in PK, of these, 37% of authors suggested genetic polymorphism as possible explanation for the inter-ethnic differences observed. Other factors authors proposed included diet and weight differences between ethnicities. Most respondents (80%) who taught inter-ethnic difference in PK attributed inter-ethnic differences to genetic polymorphism. While genetic polymorphism is one source of variability in PK, the teaching of genetic polymorphism is better associated with interindividual variabilities rather than inter-ethnic differences in PK as there are no genes with PK implications specific to any one ethnic group. Nongenetic factors such as diet, weight, and environmental factors, should be highlighted as potential sources of interindividual variation in the PK of drugs.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

Physiologically Based Pharmacokinetic Modeling of Rosuvastatin to Predict Transporter-Mediated Drug-Drug Interactions

Purpose

To build a physiologically based pharmacokinetic (PBPK) model of the clinical OATP1B1/OATP1B3/BCRP victim drug rosuvastatin for the investigation and prediction of its transporter-mediated drug-drug interactions (DDIs).

Methods

The Rosuvastatin model was developed using the open-source PBPK software PK-Sim®, following a middle-out approach. 42 clinical studies (dosing range 0.002–80.0 mg), providing rosuvastatin plasma, urine and feces data, positron emission tomography (PET) measurements of tissue concentrations and 7 different rosuvastatin DDI studies with rifampicin, gemfibrozil and probenecid as the perpetrator drugs, were included to build and qualify the model.

Results

The carefully developed and thoroughly evaluated model adequately describes the analyzed clinical data, including blood, liver, feces and urine measurements. The processes implemented to describe the rosuvastatin pharmacokinetics and DDIs are active uptake by OATP2B1, OATP1B1/OATP1B3 and OAT3, active efflux by BCRP and Pgp, metabolism by CYP2C9 and passive glomerular filtration. The available clinical rifampicin, gemfibrozil and probenecid DDI studies were modeled using in vitro inhibition constants without adjustments. The good prediction of DDIs was demonstrated by simulated rosuvastatin plasma profiles, DDI AUC_last ratios (AUC_last during DDI/AUC_last without co-administration) and DDI C_max ratios (C_max during DDI/C_max without co-administration), with all simulated DDI ratios within 1.6-fold of the observed values.

Conclusions

A whole-body PBPK model of rosuvastatin was built and qualified for the prediction of rosuvastatin pharmacokinetics and transporter-mediated DDIs. The model is freely available in the Open Systems Pharmacology model repository, to support future investigations of rosuvastatin pharmacokinetics, rosuvastatin therapy and DDI studies during model-informed drug discovery and development (MID3).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03109-6.

ABCB1 and ABCG2 Together Limit the Distribution of ABCB1/ABCG2 Substrates to the Human Retina and the ABCG2 Single Nucleotide Polymorphism Q141K (c.421C> A) May Lead to Increased Drug Exposure

The widely expressed and poly-specific ABC transporters breast cancer resistance protein (ABCG2) and P-glycoprotein (ABCB1) are co-localized at the blood-brain barrier (BBB) and have shown to limit the brain distribution of several clinically used ABCB1/ABCG2 substrate drugs. It is currently not known to which extent these transporters, which are also expressed at the blood-retinal barrier (BRB), may limit drug distribution to the human eye and whether the ABCG2 reduced-function single-nucleotide polymorphism (SNP) Q141K (c.421C > A) has an impact on retinal drug distribution. Ten healthy male volunteers (five subjects with the c.421CC and c.421CA genotype, respectively) underwent two consecutive positron emission tomography (PET) scans after intravenous injection of the model ABCB1/ABCG2 substrate [¹¹C]tariquidar. The second PET scan was performed with concurrent intravenous infusion of unlabelled tariquidar to inhibit ABCB1 in order to specifically reveal ABCG2 function.In response to ABCB1 inhibition with unlabelled tariquidar, ABCG2 c.421C > A genotype carriers showed significant increases (as compared to the baseline scan) in retinal radiotracer influx K₁ (+62 ± 57%, p = 0.043) and volume of distribution V_T (+86 ± 131%, p = 0.043), but no significant changes were observed in subjects with the c.421C > C genotype. Our results provide the first evidence that ABCB1 and ABCG2 may together limit the distribution of systemically administered ABCB1/ABCG2 substrate drugs to the human retina. Functional redundancy between ABCB1 and ABCG2 appears to be compromised in carriers of the c.421C > A SNP who may therefore be more susceptible to transporter-mediated drug-drug interactions at the BRB than non-carriers.

Medically Important Alterations in Transport Function and Trafficking of ABCG2

Several polymorphisms and mutations in the human ABCG2 multidrug transporter result in reduced plasma membrane expression and/or diminished transport function. Since ABCG2 plays a pivotal role in uric acid clearance, its malfunction may lead to hyperuricemia and gout. On the other hand, ABCG2 residing in various barrier tissues is involved in the innate defense mechanisms of the body; thus, genetic alterations in ABCG2 may modify the absorption, distribution, excretion of potentially toxic endo- and exogenous substances. In turn, this can lead either to altered therapy responses or to drug-related toxic reactions. This paper reviews the various types of mutations and polymorphisms in ABCG2, as well as the ways how altered cellular processing, trafficking, and transport activity of the protein can contribute to phenotypic manifestations. In addition, the various methods used for the identification of the impairments in ABCG2 variants and the different approaches to correct these defects are overviewed.

PBPK Model of Coproporphyrin I: Evaluation of the Impact of SLCO1B1 Genotype, Ethnicity, and Sex on its Inter-Individual Variability

Coproporphyrin I (CPI) is an endogenous biomarker of OATP1B activity and associated drug-drug interactions. In this study, a minimal physiologically-based pharmacokinetic model was developed to investigate the impact of OATP1B1 genotype (c.521T>C), ethnicity, and sex on CPI pharmacokinetics and interindividual variability in its baseline. The model implemented mechanistic descriptions of CPI hepatic transport between liver blood and liver tissue and renal excretion. Key model parameters (e.g., endogenous CPI synthesis rate, and CPI hepatic uptake clearance) were estimated by fitting the model simultaneously to three independent CPI clinical datasets (plasma and urine data) obtained from white (n = 16, men and women) and Asian-Indian (n = 26, all men) subjects, with c.521 variants (TT, TC, and CC). The optimized CPI model successfully described the observed data using c.521T>C genotype, ethnicity, and sex as covariates. CPI hepatic active was 79% lower in 521CC relative to the wild type and 42% lower in Asian-Indians relative to white subjects, whereas CPI synthesis was 23% higher in male relative to female subjects. Parameter sensitivity analysis showed marginal impact of the assumption of CPI synthesis site (blood or liver), resulting in comparable recovery of plasma and urine CPI data. Lower magnitude of CPI-drug interaction was simulated in 521CC subjects, suggesting the risk of underestimation of CPI-drug interaction without prior OATP1B1 genotyping. The CPI model incorporates key covariates contributing to interindividual variability in its baseline and highlights the utility of the CPI modeling to facilitate the design of prospective clinical studies to maximize the sensitivity of this biomarker.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Influence of Drug-Drug Interactions on the Pharmacokinetics of Atorvastatin and Its Major Active Metabolite ortho-OH-Atorvastatin in Aging People Living with HIV

Background

People living with HIV (PLWH) are aging and experience age-related physiological changes and comorbidities. Atorvastatin is a widely prescribed lipid-lowering agent metabolized by cytochrome P450 (CYP) 3A4, whose hepatocyte uptake is facilitated by organic anion transporting polypeptide (OATP) 1B1/1B3. Inhibition or induction of this enzyme and hepatic transporter can increase or decrease atorvastatin exposure, respectively.

Objective

This study aimed to describe the pharmacokinetic profile of atorvastatin and its major metabolite, and to evaluate drug–drug interactions (DDIs) with antiretrovirals (ARVs).

Methods

The atorvastatin pharmacokinetic profile was best described by a two-compartment model with first-order absorption and elimination. Metabolite concentrations were described by considering both linear metabolism from atorvastatin and presystemic metabolism. The influence of demographic and clinical covariates on drug and metabolite pharmacokinetics was assessed using NONMEM^®. Model-based simulations were performed to evaluate the magnitude of DDIs with ARVs.

Results

Full pharmacokinetic profiles (98 atorvastatin + 62 o-OH-atorvastatin concentrations) and sparse concentrations (78 and 53 for atorvastatin and o-OH-atorvastatin, respectively) were collected in 59 PLWH. Interindividual variability was high. The coadministration of boosted ARVs decreased atorvastatin clearance by 58% and slowed down o-OH-atorvastatin formation by 88%. Atorvastatin clearance increased by 78% when coadministered with CYP3A4 inducers. Simulations revealed a 180% increase and 44% decrease in atorvastatin exposure (area under the curve) in the presence of ARVs with inhibiting and inducing properties, respectively.

Conclusion

This study showed an important interindividual variability in atorvastatin pharmacokinetics that remains largely unexplained after the inclusion of covariates. Since boosted ARVs double atorvastatin exposure, the initial dosage might be reduced by half, and titrated based on individual clinical targets.

Electronic supplementary material

The online version of this article (10.1007/s40262-020-00876-0) contains supplementary material, which is available to authorized users.

Science-based Ethnic Bridging in Drug Development; Review of Recent Precedence and Suggested Steps Forward

BACKGROUND

Exposure, safety and/or efficacy of drugs are subject to potential differences between human races or ethnicities, as acknowledged by regulatory guidance and by label texts of various, but not all approved drugs.

OBJECTIVE

The objective of the present review was to assess recent regulatory precedence on drug use and race or ethnicity, with the goal of identifying opportunities for increasing the informative value of clinical ethnic or racial bridging in drug development.

METHODS

Recently, (January 2014-July 2018) FDA approved drug product label texts and approval packages were reviewed for claims, comments and underlying data on use of the product in specific ethnic or racial groups.

RESULTS

Among the 266 FDA-approved products, no product with unambiguous race- or ethnicity specific dosing instructions was retrieved. A small majority (55%) was approved with a claim or comment on race or ethnicity, and of these, a large majority (87%) was based on population pharmacokinetic data analysis. Statements were often related to incidence of a genotype for drug metabolizing enzyme or for other risk factors, or were related to body weight. Absence of clinically relevant exposure differences were often justified in terms of exposure ratios that notably exceeded the typical 0.80-1.25 no-effect boundary.

CONCLUSIONS

Recent precedence reflected a pragmatic, descriptive approach of racial or ethnic bridging, apparently meeting current regulatory expectations, whilst not resulting in strict guidance to prescribers. We recommend further work on defining the objectives of bridging studies, as well as criteria for their design and data analysis. Regarding the latter, we recommend investigating the value of prospectively defined tests for similarity with appropriate follow-up analysis in the case where the test has failed.

Polymorphisms of the Multidrug Pump ABCG2: A Systematic Review of Their Effect on Protein Expression, Function, and Drug Pharmacokinetics

The widespread expression and polyspecificity of the multidrug ABCG2 efflux transporter make it an important determinant of the pharmacokinetics of a variety of substrate drugs. Null ABCG2 expression has been linked to the Junior blood group. Polymorphisms affecting the expression or function of ABCG2 may have clinically important roles in drug disposition and efficacy. The most well-studied single nucleotide polymorphism (SNP), Q141K (421C>A), is shown to decrease ABCG2 expression and activity, resulting in increased total drug exposure and decreased resistance to various substrates. The effect of Q141K can be rationalized by inspection of the ABCG2 structure, and the effects of this SNP on protein processing may make it a target for pharmacological intervention. The V12M SNP (34G>A) appears to improve outcomes in cancer patients treated with tyrosine kinase inhibitors, but the reasons for this are yet to be established, and this residue's role in the mechanism of the protein is unexplored by current biochemical and structural approaches. Research into the less-common polymorphisms is confined to in vitro studies, with several polymorphisms shown to decrease resistance to anticancer agents such as SN-38 and mitoxantrone. In this review, we present a systematic analysis of the effects of ABCG2 polymorphisms on ABCG2 function and drug pharmacokinetics. Where possible, we use recent structural advances to present a molecular interpretation of the effects of SNPs and indicate where we need further in vitro experiments to fully resolve how SNPs impact ABCG2 function.