Does the Systemic Plasma Profile Inform the Liver Profile? Analysis Using a Physiologically Based Pharmacokinetic Model and Individual Compounds

Exploring the Pharmacokinetic Mysteries of the Liver: Application of Series Compartment Models of Hepatic Elimination

Among the basic hepatic clearance models, the dispersion model (DM) is the most physiologically sound compared with the well-stirred model and the parallel tube model. However, its application in physiologically-based pharmacokinetic (PBPK) modeling has been limited due to computational complexities. The series compartment models (SCM) of hepatic elimination that treats the liver as a cascade of well-stirred compartments connected by hepatic blood flow exhibits some mathematical similarities to the DM but is easier to operate. This work assesses the quantitative correlation between the SCM and DM and demonstrates the operation of the SCM in PBPK with the published single-dose blood and liver concentration-time data of six flow-limited compounds. The predicted liver concentrations and the estimated intrinsic clearance (CLint ) and PBPK-operative tissue-to-plasma partition coefficient (Kp ) values were shown to depend on the number of liver sub-compartments (n) and hepatic enzyme zonation in the SCM. The CLint and Kp decreased with increasing n, with more remarkable differences for drugs with higher hepatic extraction ratios. Given the same total CLint , the SCM yields a higher Kp when the liver perivenous region exhibits a lower CLint as compared with a high CLint at this region. Overall, the SCM nicely approximates the DM in characterizing hepatic elimination and offers an alternative flexible approach as well as providing some insights regarding sequential drug concentrations in the liver. SIGNIFICANCE STATEMENT: The SCM nicely approximates the DM when applied in PBPK for characterizing hepatic elimination. The number of liver sub-compartments and hepatic enzyme zonation are influencing factors for the SCM resulting in model-dependent predictions of total/internal liver concentrations and estimates of CLint and the PBPK-operative Kp . Such model-dependency may have an impact when the SCM is used for in vitro-to-in vivo extrapolation (IVIVE) and may also be relevant for PK/PD/toxicological effects when it is the driving force for such responses.

Advancing Estimation of Hepatobiliary Clearances in Physiologically Based Pharmacokinetic Models of Rosuvastatin Using Human Hepatic Concentrations

PURPOSE

To estimate hepatobiliary clearances of rosuvastatin via simultaneously fitting to reported human positron emission tomography (PET) data in the liver and gallbladder.

METHODS

A hepatobiliary model incorporating five intrinsic hepatobiliary clearances (active uptake clearance at the sinusoidal membrane, efflux clearance by passive diffusion through the sinusoidal membrane, influx clearance by passive diffusion through sinusoidal membrane, clearance of biliary excretion at the canalicular membrane, and intercompartment clearance from the intrahepatic bile duct to the gallbladder) and three compartments (liver, intrahepatic bile duct, and gallbladder) was developed to simultaneously fit rosuvastatin liver and gallbladder data from a representative subject reported by Billington et al. (1). Two liver blood supply input functions, arterial input function and dual input function (using peripheral venous as an alternative to portal vein), were assessed. Additionally, the predictive performance between the established model and four reported models trained with only systemic exposure data, was evaluated by comparing simulated liver and gallbladder profiles with observations.

RESULTS

The established hepatobiliary model well captured the kinetic profiles of rosuvastatin in the liver and gallbladder during the PET scans. Application of dual input function led to a marked underestimation of liver concentrations at the initial stage after i.v. dosing which cannot be offset by altering model parameter values. The simulated hepatobiliary profiles from three of the reported models demonstrated substantial deviation from the observed data.

CONCLUSIONS

The present study highlights the necessity of using hepatobiliary data to verify and improve the predictive performance of hepatic disposition of rosuvastatin.

Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug–drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro–in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis–Menten constant (K_m,u) of gadoxetate was 106 μM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK–IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE–MRI profiles and elimination from the liver. Therefore, in vivo rat DCE–MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE–MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

Estimation of the Effect of OAT2-Mediated Active Uptake on Meloxicam Exposure in the Human Liver

Active uptake mediated by organic anion transporter 2 (OAT2) has been previously hypothesized as a key player in hepatic disposition of its substrates. Previous studies have shown that another hepatic uptake transporter, organic anion transporting polypeptides (OATP) 1B1, significantly elevates liver concentrations of drugs transported by it. As tissue concentration typically governs pharmacodynamics, drug-drug interactions, and toxicity in the liver, it is important to understand if OAT2 functions similarly to OATP1B1 in raising liver exposure. Since this is a research problem that cannot be easily assessed in clinical studies at this time, here we estimated human liver exposure of an OAT2 substrate meloxicam using a deduction method based on physiologically based pharmacokinetic (PBPK) modeling of clinical systemic exposure data. Although in vitro data suggest that OAT2-mediated active uptake is involved in meloxicam disposition, the modeling result concludes that its unbound liver exposure is unlikely significantly different from its unbound systemic exposure. This conclusion is further supported by data and modeling from a terminal monkey study and in vitro hepatocyte studies with bovine serum albumin. Overall, based on currently available data, we do not expect that OAT2 has a strong impact on the liver exposure of meloxicam.

Using Quantitative Systems Toxicology to Investigate Observed Species Differences in CKA-Mediated Hepatotoxicity

CKA, a chemokine receptor antagonist intended for treating inflammatory conditions, produced dose-dependent hepatotoxicity in rats but advanced into the clinic where single doses of CKA up to 600 mg appeared safe in humans. Because existing toxicological platforms used during drug development are not perfectly predictive, a quantitative systems toxicology model investigated the hepatotoxic potential of CKA in humans and rats through in vitro assessments of CKA on mitochondrial respiration, oxidative stress, and bile acid transporters. DILIsym predicted that single doses of CKA caused serum ALT >3xULN in a subset of the simulated rat population, while single doses in a simulated human population did not produce serum ALT elevations. Species differences were largely attributed to differences in liver exposure, but increased sensitivity to inhibition of mitochondrial respiration in the rat also contributed. We conclude that mechanistic modeling can elucidate species differences in the hepatotoxic potential of drug candidates.

Analysis of the Change in the Blood Concentration-Time Profile Caused by Complex Drug-Drug Interactions in the Liver Considering the Enterohepatic Circulation: Examining Whether the Inhibition Constants for Uptake, Metabolism, and Biliary Excretion Can be Recovered by the Analyses Using Physiologically Based Pharmacokinetic Modeling

Hypothetical substrates undergoing transporter-mediated hepatic uptake, metabolism, and enterohepatic circulation with different rate-determining processes with a combination of inhibition constants (K_i) for hepatic uptake, metabolism, and biliary excretion processes were generated with a constant K_i for uptake and incorporated into a physiologically based pharmacokinetic model. Analyses of the kinetic model suggested that the fraction of substrates excreted in the bile to the total elimination by the liver (f_bile) can be estimated under certain conditions from kinetic analyses of their blood concentration-time profiles. Using the generated time profiles of substrates with and without coadministration of inhibitors, various pharmacokinetic parameters involving f_bile and K_i for the hepatic uptake, metabolism, and biliary excretion of drugs were back-calculated by fitting. Comparing parameters obtained with the original parameter sets by fitting, the K_i were found to be well estimated under the following conditions: the initial estimates for inhibition constants were relatively good, which corresponds to the case for obtaining reliable in vitro inhibition constants. In conclusion, the integration of top-down analyses with bottom-up estimates (experimental determination) of inhibition constants can be used to estimate in vivo inhibition constants and f_bile reliably.

Transporter-Mediated Disposition, Clinical Pharmacokinetics and Cholestatic Potential of Glyburide and Its Primary Active Metabolites

Glyburide is widely used for the treatment of type 2 diabetes. We studied the mechanisms involved in the disposition of glyburide and its pharmacologically active hydroxy metabolites M1 and M2b and evaluated their clinical pharmacokinetics and the potential role in glyburide-induced cholestasis employing physiologically based pharmacokinetic (PBPK) modeling. Transport studies of parent and metabolites in human hepatocytes and transfected cell systems imply hepatic uptake mediated by organic anion-transporting polypeptides. Metabolites are also subjected to basolateral and biliary efflux by P-glycoprotein, breast cancer resistance protein, and multidrug resistance-associated proteins, and are substrates to renal organic anion transporter 3. A PBPK model in combination with a Bayesian approach was developed considering the identified disposition mechanisms. The model reasonably described plasma concentration time profiles and urinary recoveries of glyburide and the metabolites, implying the role of multiple transport processes in their pharmacokinetics. Predicted free liver concentrations of the parent (∼30-fold) and metabolites (∼4-fold) were higher than their free plasma concentrations. Finally, all three compounds showed bile salt export pump inhibition in vitro; however, significant in vivo inhibition was not apparent for any compound on the basis of a predicted unbound liver exposure-response effect model using measured in vitro IC₅₀ values. In conclusion, this study demonstrates the important role of multiple drug transporters in the disposition of glyburide and its active metabolites, suggesting that variability in the function of these processes may lead to pharmacokinetic variability in the parent and the metabolites, potentially translating to pharmacodynamic variability.

Systems pharmacology modeling of drug-induced hyperbilirubinemia: Differentiating hepatotoxicity and inhibition of enzymes/transporters

Elevations in serum bilirubin during drug treatment may indicate global liver dysfunction and a high risk of liver failure. However, drugs also can increase serum bilirubin in the absence of hepatic injury by inhibiting specific enzymes/transporters. We constructed a mechanistic model of bilirubin disposition based on known functional polymorphisms in bilirubin metabolism/transport. Using physiologically based pharmacokinetic (PBPK) model-predicted drug exposure and enzyme/transporter inhibition constants determined in vitro, our model correctly predicted indinavir-mediated hyperbilirubinemia in humans and rats. Nelfinavir was predicted not to cause hyperbilirubinemia, consistent with clinical observations. We next examined a new drug candidate that caused both elevations in serum bilirubin and biochemical evidence of liver injury in rats. Simulations suggest that bilirubin elevation primarily resulted from inhibition of transporters rather than global liver dysfunction. We conclude that mechanistic modeling of bilirubin can help elucidate underlying mechanisms of drug-induced hyperbilirubinemia, and thereby distinguish benign from clinically important elevations in serum bilirubin.