Plasma concentrations of pemafibrate with co-administered drugs predicted by physiologically based pharmacokinetic modeling in virtual populations with renal/hepatic impairment

Mechanistic Account of Distinct Change in Organic Anion Transporting Polypeptide 1B (OATP1B) Substrate Pharmacokinetics during OATP1B-Mediated Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling

The role of transporters in drug clearance is widely acknowledged, directly and indirectly by facilitating tissue/enzyme exposure. Through the latter, transporters also affect volume of distribution. Drug-drug interactions (DDIs) involving organic anion transporting polypeptides (OATPs) 1B1/1B3 and SLCO1B1 pharmacogenetics lead to altered pharmacokinetics of OATP1B substrates; however, several factors may confound direct interpretation of pharmacokinetic parameters from these clinical studies using noncompartmental analysis (NCA). A review of clinical data herein indicates a single dose of OATP1B inhibitor rifampin almost never leads to increased substrate half-life but often a decrease and that most clinical OATP1B substrates are CYP3A4 substrates and/or undergo enterohepatic cycling (EHC). Using hypothetically simple OATP1B substrate physiologically based pharmacokinetic (PBPK) models, simulated effect of rifampin differed from specific OATP1B inhibition due to short rifampin half-life causing dissipation of OATP1B inhibition over time combined with CYP3A4 induction. Calculated using simulated tissue data, volume of distribution indeed decreased with OATP1B inhibition and was expectedly limited to the contribution of liver volume. However, an apparent and counterintuitive effect of rifampin on volume greater than that on clearance resulted for CYP3A4 substrates using NCA. The effect of OATP1B inhibition and rifampin on OATP1B substrate models incorporating EHC plus or minus renal clearance was distinct compared with simpler models. Using PBPK models incorporating reversible lactone metabolism for clinical OATP1B substrates atorvastatin and pitavastatin, DDIs reporting decreased half-life with rifampin were reproduced. These simulations provide an explanation for the distinct change in OATP1B substrate pharmacokinetics observed in clinical studies, including changes in volume of distribution and additional mechanisms. SIGNIFICANCE STATEMENT: Transporters are involved in drug clearance and volume of distribution, and distinct changes in OATP1B substrate pharmacokinetics are observed with OATP1B inhibitor rifampin. Using hypothetical and validated PBPK models and simulations, this study addresses the limitations of single-dose rifampin and complicated clinical OATP1B substrate disposition in evaluating the pharmacokinetic parameters of OATP1B substrates during rifampin drug-drug interactions (DDIs). These models account for change in volume of distribution and identify additional mechanisms underlying apparent pharmacokinetic changes in OATP1B DDIs.

Predicting transporter mediated drug-drug interactions via static and dynamic physiologically based pharmacokinetic modeling: A comprehensive insight on where we are now and the way forward

The greater utilization and acceptance of physiologically-based pharmacokinetic (PBPK) modeling to evaluate the potential metabolic drug-drug interactions is evident by the plethora of literature, guidance's, and regulatory dossiers available in the literature. In contrast, it is not widely used to predict transporter-mediated DDI (tDDI). This is attributed to the unavailability of accurate transporter tissue expression levels, the absence of accurate in vitro to in vivo extrapolations (IVIVE), enzyme-transporter interplay, and a lack of specific probe substrates. Additionally, poor understanding of the inhibition/induction mechanisms coupled with the inability to determine unbound concentrations at the interaction site made tDDI assessment challenging. Despite these challenges, continuous improvements in IVIVE approaches enabled accurate tDDI predictions. Furthermore, the necessity of extrapolating tDDI's to special (pediatrics, pregnant, geriatrics) and diseased (renal, hepatic impaired) populations is gaining impetus and is encouraged by regulatory authorities. This review aims to visit the current state-of-the-art and summarizes contemporary knowledge on tDDI predictions. The current understanding and ability of static and dynamic PBPK models to predict tDDI are portrayed in detail. Peer-reviewed transporter abundance data in special and diseased populations from recent publications were compiled, enabling direct input into modeling tools for accurate tDDI predictions. A compilation of regulatory guidance's for tDDI's assessment and success stories from regulatory submissions are presented. Future perspectives and challenges of predicting tDDI in terms of in vitro system considerations, endogenous biomarkers, the use of empirical scaling factors, enzyme-transporter interplay, and acceptance criteria for model validation to meet the regulatory expectations were discussed.

Clinical Implications of Altered Drug Transporter Abundance/Function and PBPK Modeling in Specific Populations: An ITC Perspective

The role of membrane transporters on pharmacokinetics (PKs), drug-drug interactions (DDIs), pharmacodynamics (PDs), and toxicity of drugs has been broadly recognized. However, our knowledge of modulation of transporter expression and/or function in the diseased patient population or specific populations, such as pediatrics or pregnancy, is still emerging. This white paper highlights recent advances in studying the changes in transporter expression and activity in various diseases (i.e., renal and hepatic impairment and cancer) and some specific populations (i.e., pediatrics and pregnancy) with the focus on clinical implications. Proposed alterations in transporter abundance and/or activity in diseased and specific populations are based on (i) quantitative transporter proteomic data and relative abundance in specific populations vs. healthy adults, (ii) clinical PKs, and emerging transporter biomarker and/or pharmacogenomic data, and (iii) physiologically-based pharmacokinetic modeling and simulation. The potential for altered PK, PD, and toxicity in these populations needs to be considered for drugs and their active metabolites in which transporter-mediated uptake/efflux is a major contributor to their absorption, distribution, and elimination pathways and/or associated DDI risk. In addition to best practices, this white paper discusses current challenges and knowledge gaps to study and quantitatively predict the effects of modulation in transporter activity in these populations, together with the perspectives from the International Transporter Consortium (ITC) on future directions.

Cynomolgus Monkey as an Emerging Animal Model to Study Drug Transporters: In Vitro, In Vivo, In Vitro-to-In Vivo Translation

Membrane transporters have been recognized as one of the key determinants of pharmacokinetics and are also known to affect the efficacy and toxicity of drugs. Both qualitatively and quantitatively, however, transporter studies conducted using human in vitro systems have not always been predictive. Consequently, researchers have used cynomolgus monkeys as a model to study drug transporters and anticipate their effects in humans. Burgeoning reports of data in the last few years necessitates a comprehensive review on the topic of drug transporters in cynomolgus monkeys that includes cell-based tools, sequence homology, tissue expression, in vitro studies, in vivo studies, and in vitro-to-in vivo extrapolation. This review highlights the state-of-the-art applications of monkey transporter models to support the evaluation of transporter-mediated drug-drug interactions, clearance predictions, and endogenous transporter biomarker identification and validation. The data demonstrate that cynomolgus monkey transporter models, when used appropriately, can be an invaluable tool to support drug discovery and development processes. Most importantly, they enable an early in vitro-to-in vivo extrapolation assessment, which provides additional context to human in vitro data. Additionally, comprehending species similarities and differences in transporter tissue expression and activity is crucial when translating monkey data to humans. The challenges and limitations when applying such models to inform decision-making must also be considered. SIGNIFICANCE STATEMENT: This paper presents a comprehensive review of currently available published reports describing cynomolgus monkey transporter models. The data indicate that Cynomolgus monkeys provide mechanistic insight regarding the role of intestinal, hepatic, and renal transporters in drug and biomarker disposition and drug interactions. The data generated with cynomolgus monkey models provide mechanistic insight into transporter-mediated drug absorption and disposition. They are valuable to human clearance prediction, drug drug interaction assessment, and endogenous biomarker development related to drug transporters.

Review article: time to revisit Child-Pugh score as the basis for predicting drug clearance in hepatic impairment

BACKGROUND

Prescription information for many drugs entering the market lacks dosage guidance for hepatic impairment. Dedicated studies for assessing the fate of drugs in hepatic impairment commonly stratify patients using Child-Pugh score. Child-Pugh is a prognostic clinical score with limitations in reflecting the liver's metabolic capacity.

AIMS

To demonstrate the need for better drug dosing approaches in hepatic impairment, summarise the current status, identify knowledge gaps related to drug kinetic parameters in hepatic impairment, propose solutions for predicting the liver disease impact on drug exposure and discuss barriers to dosing guidance in those patients.

METHODS

Relevant reports on dosage adjustment in hepatic impairment were analysed concerning the prediction of the impairment impact on drug kinetics using physiologically-based pharmacokinetic (PBPK) modelling.

RESULTS

PBPK models are suggested as a potential framework to understand drug clearance changes in hepatic impairment. Quantifying changes in abundance and activity of drug-metabolising enzymes and transporters, understanding the impact of shunting, and accounting for interindividual variations in drug absorption could help in extending the success of these models in hepatically-impaired populations. These variables might not correlate with Child-Pugh score as a whole. Therefore, new metabolic activity markers, imaging techniques and other scoring systems are proposed to either support or substitute Child-Pugh score.

CONCLUSIONS

Many physiological changes in hepatic impairment determining the fate of drugs do not necessarily correlate with Child-Pugh score. Quantifying these changes in individual patients is essential in future hepatic impairment studies. Further studies assessing Child-Pugh alternatives are recommended to allow better prediction of drug exposure.

Modelled plasma concentrations of pemafibrate with co-administered typical cytochrome P450 inhibitors clopidogrel, fluconazole or clarithromycin predicted by physiologically based pharmacokinetic modelling in virtual populations

Oral antidyslipidaemic drug pemafibrate is cleared from human plasma via hepatic uptake by organic anion transporting polypeptide (OATP) 1B1 and oxidation by cytochromes P450 (P450) 2C8, 2C9 and 3A4. The pharmacokinetic profiles of pemafibrate with virtual administrations of P450 inhibitors and/or disease interactions were generated using a physiologically based pharmacokinetic (PBPK) model previously established for co-administration of pemafibrate with OATP1B1 inhibitors. This PBPK model was validated in the current study using reported maximum pemafibrate plasma concentrations and areas under the curve from interaction studies in healthy subjects co-administered with clopidogrel (P450 2C8 inhibitor), fluconazole (P450 2C9/3A4 inhibitor) or clarithromycin (P450 3A4 inhibitor). Virtual co-administrations of pemafibrate with clopidogrel, fluconazole or clarithromycin increased the predicted plasma exposures of pemafibrate 1.4-1.7-fold, 1.2-1.4-fold and 2.9-11-fold, respectively, in subjects with or without moderate or severe renal impairment or Child-Pugh A or B liver cirrhosis. Some of the exposure-enhancing effects of clarithromycin may originate from its inhibitory potential toward OATP1B1, because the estimated effects of itraconazole (a P450 3A4 inhibitor) were only minor. Simulations using the current PBPK model in groups of virtual subjects with or without renal or hepatic impairment revealed modified pharmacokinetic profiles for pemafibrate following co-administration of typical P450 inhibitors.