A “middle-out” approach to human pharmacokinetic predictions for OATP substrates using physiologically-based pharmacokinetic modeling

Abstract

Physiologically based pharmacokinetic (PBPK) models provide a framework useful for generating credible human pharmacokinetic predictions from data available at the earliest, preclinical stages of pharmaceutical research. With this approach, the pharmacokinetic implications of in vitro data are contextualized via scaling according to independent physiological information. However, in many cases these models also require model-based estimation of additional empirical scaling factors (SFs) in order to accurately recapitulate known human pharmacokinetic behavior. While this practice clearly improves data characterization, the introduction of empirically derived SFs may belie the extrapolative power commonly attributed to PBPK. This is particularly true when such SFs are compound dependent and/or when there are issues with regard to identifiability. As such, when empirically-derived SFs are necessary, a critical evaluation of parameter estimation and model structure are prudent. In this study, we applied a global optimization method to support model-based estimation of a single set of empirical SFs from intravenous clinical data on seven OATP substrates within the context of a previously published PBPK model as well as a revised PBPK model. The revised model with experimentally measured unbound fraction in liver, permeability between liver compartments, and permeability limited distribution to selected tissues improved data characterization. We utilized large-sample approximation and resampling approaches to estimate confidence intervals for the revised model in support of forward predictions that reflect the derived uncertainty. This work illustrates an objective approach to estimating empirically-derived SFs, systematically refining PBPK model performance and conveying the associated confidence in subsequent forward predictions.

Physiologically Based Pharmacokinetic Prediction of Telmisartan in Human

“…A previously published intravenous PBPK model (Li et al, 2014) was used to model the in vivo data. All compartments are connected by the circulating blood system.…”

“…These in vitro clearances were physiologically scaled as described previously (Houston, 1994). Empirical SFs for OATP substrates were those estimated in the previous study, in which the active (SF act ), passive (SF pass ), metabolic (SF met ), and biliary (SF bile ) scaling factors are 55, 0.092, 0.11, and 0.019, respectively (Li et al, 2014). The product of physiologically scaled clearance and the previously derived empirical SF is the in vivo clearance applied for in vivo simulation.…”

“…The previously published PBPK model can adequately describe the human plasma elimination-phase profiles for four compounds with metabolic clearance (i.e., bosentan, fluvastatin, cerivastatin, and repaglinide) (Li et al, 2014). The HLM CL int, u, met values for these compounds are 22, 29, 76, and 128 ml/min per milligram, respectively , smaller than the value for telmisartan [395 ml/min per milligram microsomal protein with bovine serum albumin (Gill et al, 2012), 1212 ml/min per milligram microsomal protein with bovine serum albumin (this study)].…”

“…In these models, reasonable predictions for substrates of liver transporters are achieved only when empirical in vitro-to-in vivo extrapolation scaling factors (SFs) are applied. Previously, our group has developed a liver transporter PBPK model and a unified set of SFs for hepatic uptake, passive diffusion, and biliary and metabolic clearances, which adequately describe the human pharmacokinetics (PK) of seven structurally diverse organic anion-transporting polypeptide (OATP) substrates, solely on the basis of in vitro sandwich culture human hepatocytes (SCHH) and human liver microsome (HLM) data Li et al, 2014).…”

“…Telmisartan, a potent, long-acting, small-molecule antagonist of the angiotensin II type-1 receptor (Wienen et al, 2000), was chosen as the compound in this study because it is an OATP substrate with clinical intravenous infusion data not included in our previous work Li et al, 2014). It has been reported to be predominantly transported into liver by OATP1B3 (encoded by the SLCO1B3 gene) (Ishiguro et al, 2006) and metabolized by UDPglucuronosyltransferase 1A3 (Ieiri et al, 2011), with negligible biliary and renal clearance of parent (Stangier et al, 2000a).…”