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

Time course pharmacokinetic data for OATP1B1 transporter substrates available from healthy subjects in more than one ethnic group are included in this analysis. A previously published PBPK model structure incorporating hepatic active uptake (CL_act), passive diffusion (CL_pass), and biliary excretion (CL_bile) or metabolism (CL_met) [6] was used to analyze all the pharmacokinetic data in this study. Briefly, all tissues were connected with circulating blood. The compound distribution was perfusion limited in all tissues except for the liver, which was modeled as permeability limited with five sequential segments. Every segment included one pair of extracellular and intracellular sub-compartments with CL_act, CL_pass, and CL_bile or CL_met incorporated. Absorption was described empirically using two sequential compartments with first-order absorption rates (k _a) and F _a F _g (i.e., the product of the fraction of a dose absorbed and the fraction of a drug passing though the gut wall without metabolism) in each compartment. The parameter values are assumed the same for the two compartments. To simulate the enterohepatic recirculation, there are five sequential compartments, with the same first-order transit rate (k _bile), between the liver intracellular compartments and the first absorption compartment. MATLAB was used for the modeling (Mathworks, Natick, MA, USA). The schematic for the model structure is provided in Fig. S1 of the Electronic Supplementary Material (ESM).

Assuming that OATP1B1 is the major transporter responsible for CL_act, in the model, SLCO1B1 c.521TT carriers in different ethnic groups have the same CL_act, as do the c.521CC carriers. The impact of BCRP genetic variation on the pharmacokinetics is reflected through the empirical term, F _a F _g. To simplify the analysis, only wild-type (SLCO1B1 c.521TT or ABCG2 c.421CC) and homogenous mutation (SLCO1B1 c.521CC or ABCG2 c.421AA) data are included. To our knowledge, no pharmacokinetic time course data have been published with both SLCO1B1 and ABCG2 genotyped. As such, in modeling SLCO1B1-genotyped data, we have to assume that the allele frequencies of ABCG2 genotypes in these studies are the same as previously published values (Table 1). A similar approach is taken when modeling ABCG2 genotyped data, and making assumptions about SLCO1B1 frequencies [7, 8].

The values for physiological parameters (e.g., tissue weights and blood flows, Table S1 of the ESM) have been previously published [9]. The compound-specific values (e.g., plasma and liver intracellular free fraction, blood-to-plasma ratio, pKa, and LogD_7.4) are provided in Table S2 of the ESM. For non-liver tissues, the compound partition coefficients between tissue and blood are calculated using a previously published approach [10]. The renal clearances of pravastatin and rosuvastatin are fixed at 26.5 and 12.3 L h⁻¹ [11, 12], and are assumed to be the same across different populations according to a previous study [2].

The hepatic CL_act values are shared by the carriers with the same SLCO1B1 c.521T>C genotype, independent of ethnicity. Except for atorvastatin, F _a F _g values for each compound are shared by the different ethnic groups with the same ABCG2 c.421C>A genotype. BCRP activity may also affect k _a. However, except for rosuvastatin, all the groups share k _a values for each compound owing to limited data in the absorption phase. All groups assumed the same CL_pass values for each compound. Compound-specific CL_met, CL_bile, and k _bile have different values for different ethnic groups, but are assumed the same within each ethnic group independent of the SLCO1B1 or ABCG2 genotype. The impact of the ABCG2 genotype on CL_bile is considered be minimal [7, 13].

Hepatic clearance processes, k _a, and F _a F _g, are simultaneously estimated by fitting all genotyped data for a given compound. Because all the genotyped pharmacokinetic data included here are generated with oral dosing, it becomes challenging to precisely estimate both clearance and F _a F _g. As such, F _a F _g is estimated first for every compound with its ungenotyped Caucasian pharmacokinetic data following intravenous and oral dosing. To be specific, for rosuvastatin, pitavastatin, and pravastatin for which pharmacokinetic profiles are affected by biliary excretion and enterohepatic recirculation of the parent drug, ungenotyped F _a F _g is determined using the PBPK model (Fig. S2 of the ESM); while for repaglinide for which metabolism is the major elimination pathway, F _a F _g can be estimated with empirical equations, i.e., bioavailability/(1 − blood clearance/hepatic blood flow). No atorvastatin (i.e., another compound with a metabolism-driven disposition) pharmacokinetic time course data following intravenous dosing are published; thus, F _a F _g is fixed at a previously reported value of 0.24 [14]. The value of F _a F _g in ungenotyped studies is assumed to be the same as the value of F _a F _g in c.421CC groups because of the very low frequency of c.421AA in the Caucasian population (about 1%, Table 1), and very similar pharmacokinetic profiles between c.421CC and c.421CA carriers [7, 8]. Then, F _a F _g values for the c.421CC groups are fixed when estimating other parameters including F _a F _g values for the c.421AA groups.

SLCO1B1 c.521T>C genotyped rosuvastatin data in Caucasian, Chinese, and Korean populations, pitavastatin data in the Korean population, pravastatin data in the Caucasian population, atorvastatin data in Caucasian and Korean populations, and repaglinide data in Caucasian and Chinese populations are used in fitting. ABCG2 c.421C>A genotyped rosuvastatin data in Caucasian and Chinese populations, pravastatin data in a Caucasian population, and atorvastatin data in a Caucasian population are also used in fitting. Assuming linear kinetics, all data were scaled to a 10-mg dosing amount. Clinical data were digitized from published mean pharmacokinetic time courses, with references provided in Table S4 of the ESM.

Given the complexity of the model structure and the number of estimated parameters, a numerical global optimization method (i.e., differential evolution) initially optimized the parameter values. For a better understanding of the uncertainty associated with parameter estimation, the globally optimized values were fed into a Markov chain Monte Carlo (MCMC) method [15] as the starting point to approximate mean estimates and their 95% confidence intervals. Table 2 and Table S3 of the ESM show the parameter values and confidence intervals. Both differential evolution and MCMC methods have been previously implemented in MATLAB with codes published online (http://​www1.​icsi.​berkeley.​edu/​~storn/​code.​html#matl; http://​helios.​fmi.​fi/​~lainema/​mcmc/​#sec-4).

Time course pharmacokinetic profiles are simulated in c.521TT-c.421CC, c.521TT-c.421AA, c.521CC-c.421CC, and c.521CC-c.421AA groups in Caucasian, Chinese, Japanese, and Korean populations. To simplify the modeling, heterozygous mutations are not included in the current analysis. In fitting SLCO1B1 genotyped but ABCG2 ungenotyped data, c.521TT pharmacokinetics is simulated as an average of c.521TT-c.421CC and c.521TT-c.421AA weighted by the frequencies of c.421CC and c.421AA in four populations (Table 1). c.421CC and c.421CA carriers have similar pharmacokinetic profiles [7, 8], hence c.421CC frequencies in simulations are the sums of reported c.421CC and c.421CA frequencies. ABCG2 c.421CC pharmacokinetics are simulated as an average of c.521TT-c.421CC and c.521CC-c.421CC weighted by the frequencies of c.521TT and c.521CC in different populations (Table 1). c.521CC frequencies in the simulations include reported c.521CC and c.521TC frequencies, assuming the pharmacokinetics of c.521CC are close to those of c.521TC [16]. SLCO1B1 c.521CC and ABCG2 c.421AA pharmacokinetic profiles are simulated similarly.

To validate further the modeling, for pravastatin and pitavastatin, we also prospectively predict ungenotyped pharmacokinetics. Time courses for four genotyped groups (i.e., c.521TT-c.421CC, c.521TT-c.421AA, c.521CC-c.421CC, and c.521CC-c.421AA) are generated. As mentioned above, our hypothesis is that differences in pharmacokinetics among different ethnic groups is the result of different activity associated with each genotype and different allele frequencies in each ethnic group. Based on this hypothesis, to simulate ungenotyped pharmacokinetics in an ethnic population, we take the average pharmacokinetics of four genotyped ‘ethnicity-independent’ groups, but weighted by the allele frequencies of SLCO1B1c.521T>C and ABCG2 c.421C>A in that specific ethnic population. Only if both pharmacokinetics and allele frequencies in the four groups were right, could the model reproduce the ungenotyped data, with the underlying assumption that OATP1B1 and BCRP are the major determinants of pharmacokinetics for the tested compounds.

Assuming the same SLCO1B1 genotypes have the same hepatic uptake activity, and the same ABCG2 genotypes have the same F _a F _g, independent of ethnicity, the model can reasonably describe genotyped rosuvastatin pharmacokinetic time course data from Caucasian, Chinese, and Korean groups (Fig. 1). c.421CC carriers have lower F _a F _g values than c.421AA carriers (Table 2). Because the allele frequency of ABCG2 c.421C>A is much higher in Asian populations than that in Caucasian populations with the same SLCO1B1 genotype, the average Asian exposure is expected to be higher than the average Caucasian exposure. c.421CC groups seem to have faster k _a rates than c.421AA groups, although the difference between c.421CC and c.421AA is not statistically significant in Asian groups (Table S3). In addition, with the same ABCG2 c.421C>A genotypes, the Caucasian population has a slower k _a rate than Asian populations. Overall, the Caucasian population also has a faster CL_bile than the Asian groups. A brief discussion about these differences is provided in Sect. 4. The empirical bile transit rates (k _bile, representing bile flow from the liver to the gut) are similar across three populations.

Genotyped atorvastatin pharmacokinetic data (i.e., SLCO1B1 genotyped atorvastatin data in Caucasian and Korean populations, and ABCG2 genotyped atorvastatin data in the Caucasian population) are analyzed similarly to rosuvastatin data. Overall, the results (Fig. 2; Table 2) are similar to those for rosuvastatin. Caucasian and Korean populations require different F _a F _g values to describe atorvastatin data, which may be owing to the genetic variations of intestinal metabolic enzymes. Because of the lack of genotyped atorvastatin data in the absorption phase, we reduced the number of fitted parameters with assumptions that different populations share the same k _a values. However, the estimated k _a is still associated with large uncertainty.

The F _a F _g value for repaglinide is determined to be 1. As such, its pharmacokinetics is unlikely to be affected by the BCRP activity or the polymorphism of the ABCG2 gene. Not being confounded by BCRP activity, the compound is chosen to further validate our hypothesis: Asian and Caucasian participants with the same SLCO1B1 genotype have similar uptake activity. The results are as expected, that both observations and simulations have similar pharmacokinetic profiles between Caucasian and Chinese groups (Fig. 3; Table 2).

Clinical observations suggest that ABCG2 c.421C>A is not associated with pitavastatin pharmacokinetic variability [17, 18], consistent with in vitro observation that pitavastatin may not be a BCRP substrate [19]. The model is trained only with Asian SLCO1B1 c.521T>C data (Fig. 4a; Table 2), as there is no genotyped Caucasian data available. To test our hypothesis, we simulate pharmacokinetics in c.521TT and c.521CC groups, and then prospectively predict ungenotyped plasma concentrations in the Caucasian population as the average concentration of c.521TT and c.521CC groups weighted by the Caucasian c.521T>C allele frequency, with the underlying assumption that all parameter values (e.g., CL_act,521TT, CL_act,521CC, CL_pass, k _a, and other parameters listed in Table 2; Table S3) are the same between Caucasian and Korean populations. The prediction matches observed pharmacokinetics in Caucasian individuals (Fig. 4b). The ungenotyped Japanese pharmacokinetics is generated similarly. The model successfully predicts that there is a minimal difference in ungenotyped pharmacokinetics between Japanese and Caucasian populations (Fig. 4c), as shown in a previous study [5]. The difference between c.521TT and c.521CC activity and the differences in allele frequencies among populations are not enough to introduce an obvious difference in simulated pharmacokinetics.

Pravastatin was chosen to perform another prospective prediction. Parameter values are estimated by fitting genotyped Caucasian data (Fig. 5a–d). With estimated parameter values (Table 2; Table S3), we simulate pharmacokinetics in four groups: (1) c.521TT c.421CC, (2) c.521TT c.421AA, (3) c.521CC c.421CC, and (4) c.521CC c.421AA. The ungenotyped pharmacokinetics in the Japanese population is prospectively predicted as the average concentration of the four groups, with the same assumption made for pitavastatin. The prediction matches observed data in Japanese from two independent studies (Fig. 5e, f).