Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P‐glycoprotein–cytochrome P450 3A4 dual substrates

Pan, Xian; Yamazaki, Shinji; Zhang, Mian; Reddy, Venkatesh Pilla

doi:10.1002/psp4.12717

Cited by 13 publications

(18 citation statements)

References 41 publications

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“…Hence, it is challenging to build suitable mechanistic models for DDIs involving transporter regulation due to induction/suppression. Besides an empirical model that uses a fixed relative scalar, [111][112][113] the turnover model that was established for induction/suppression modeling of CYPs and UGTs, 114 can be applied to transporters, 67 because transporters are regulated through mechanisms likely similar to CYP enzymes (Figure 1). In vivo transporter levels are governed by the rates of de novo protein synthesis and degradation, defined as the protein turnover half-life.…”

Section: Pharmacokinetic Modeling For Transporter Induction and Suppr...mentioning

confidence: 99%

Regulation of Drug Transport Proteins—From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Brouwer

Evers

Hayden

et al. 2022

Clin Pharma and Therapeutics

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Membrane transport proteins are involved in the absorption, disposition, efficacy, and/or toxicity of many drugs. Numerous mechanisms (e.g., nuclear receptors, epigenetic gene regulation, microRNAs, alternative splicing, post-translational modifications, and trafficking) regulate transport protein levels, localization, and function. Various factors associated with disease, medications, and dietary constituents, for example, may alter the regulation and activity of transport proteins in the intestine, liver, kidneys, brain, lungs, placenta, and other important sites, such as tumor tissue. This white paper reviews key mechanisms and regulatory factors that alter the function of clinically relevant transport proteins involved in drug disposition. Current considerations with in vitro and in vivo models that are used to investigate transporter regulation are discussed, including strengths, limitations, and the inherent challenges in predicting the impact of changes due to regulation of one transporter on compensatory pathways and overall drug disposition. In addition, translation and scaling of in vitro observations to in vivo outcomes are considered. The importance of incorporating altered transporter regulation in modeling and simulation approaches to predict the clinical impact on drug disposition is also discussed. Regulation of transporters is highly complex and, therefore, identification of knowledge gaps will aid in directing future research to expand our understanding of clinically relevant molecular mechanisms of transporter regulation. This information is critical to the development of tools and approaches to improve therapeutic outcomes by predicting more accurately the impact of regulation-mediated changes in transporter function on drug disposition and response.Transport proteins of the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies are widely recognized as key determinants of the absorption, distribution, and excretion of many endogenous compounds and xenobiotics, including drugs, bile acids, hormones, and nutrients, which may thereby directly or indirectly impact medication efficacy and/or safety. Not surprisingly, intersubject variability in drug response due to transporters has been attributed to altered transporter function in organs, such as the intestine, liver, and kidneys. 1,2 Mechanisms of alterations in drug transporter activity have focused primarily on (i) differences in gene expression and/ or protein abundance due to genetic polymorphisms; and (ii) drug-drug interactions (DDIs) predominantly involving direct

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Section: Pharmacokinetic Modeling For Transporter Induction and Suppr...mentioning

confidence: 99%

Regulation of Drug Transport Proteins—From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Brouwer

Evers

Hayden

et al. 2022

Clin Pharma and Therapeutics

Self Cite

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“…Underprediction of DDIs with the rifampicin model along with lack of confidence in the predictability and reliable validation of the models were cited as reasons for not acceptability of modeling results. Nevertheless, it is important to note that the field of PBPK modeling of induction DDIs continues to advance, with notable recent progress made in the development and qualification of a model of rifampicin‐mediated induction, also incorporating P‐gp induction 89,90 . As the fidelity of PBPK models continues to grow, we trust that confidence in their application in lieu of clinical DDI studies should increase for suitable contexts of use.…”

Section: Future Perspectivesmentioning

confidence: 99%

Alternatives to rifampicin: A review and perspectives on the choice of strong CYP3A inducers for clinical drug–drug interaction studies

Bolleddula

Gopalakrishnan

et al. 2022

Clinical Translational Sci

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N‐Nitrosamine (NA) impurities are considered genotoxic and have gained attention due to the recall of several marketed drug products associated with higher‐than‐permitted limits of these impurities. Rifampicin is an index inducer of multiple cytochrome P450s (CYPs) including CYP2B6, 2C8, 2C9, 2C19, and 3A4/5 and an inhibitor of OATP1B transporters (single dose). Hence, rifampicin is used extensively in clinical studies to assess drug–drug interactions (DDIs). Despite NA impurities being reported in rifampicin and rifapentine above the acceptable limits, these critical anti‐infective drugs are available for therapeutic use considering their benefit–risk profile. Reports of NA impurities in rifampicin products have created uncertainty around using rifampicin in clinical DDI studies, especially in healthy volunteers. Hence, a systematic investigation through a literature search was performed to determine possible alternative index inducer(s) to rifampicin. The available strong CYP3A inducers were selected from the University of Washington DDI Database and their in vivo DDI potential assessed using the data from clinical DDI studies with sensitive CYP3A substrates. To propose potential alternative CYP3A inducers, factors including lack of genotoxic potential, adequate safety, feasibility of multiple dose administration to healthy volunteers, and robust in vivo evidence of induction of CYP3A were considered. Based on the qualifying criteria, carbamazepine, phenytoin, and lumacaftor were identified to be the most promising alternatives to rifampicin for conducting CYP3A induction DDI studies. Strengths and limitations of the proposed alternative CYP3A inducers, the magnitude of in vivo CYP3A induction, appropriate study designs for each alternative inducer, and future perspectives are presented in this paper.

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“…Because previously reported PBPK models of rifampicin did not predict P‐gp inhibition, we considered all such reports in this study. 16 , 37 , 38 Our established PBPK model of rifampicin successfully predicted P‐gp–mediated DDIs with three P‐gp substrates (digoxin, talinolol, and quinidine), each with different pharmacokinetic properties. Overall, in all 12 cases the predicted AUCRs and C max Rs met Guest's criteria.…”

Section: Discussionmentioning

confidence: 99%

“…To predict P‐gp–mediated DDIs by rifampicin, it is crucial to integrate the effects of P‐gp induction and inhibition as well as DDI effects in the intestine, liver, and kidney. Because previously reported PBPK models of rifampicin did not predict P‐gp inhibition, we considered all such reports in this study 16,37,38 . Our established PBPK model of rifampicin successfully predicted P‐gp–mediated DDIs with three P‐gp substrates (digoxin, talinolol, and quinidine), each with different pharmacokinetic properties.…”

Section: Discussionmentioning

confidence: 99%

Robust physiologically based pharmacokinetic model of rifampicin for predicting drug–drug interactions via P‐glycoprotein induction and inhibition in the intestine, liver, and kidney

Asaumi

Nunoya

Yamaura

et al. 2022

CPT Pharmacom & Syst Pharma

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P‐glycoprotein (P‐gp) is an efflux transporter that plays an important role in the pharmacokinetics of its substrate, and P‐gp activities can be altered by induction and inhibition effects of rifampicin. This study aimed to establish a physiologically based pharmacokinetic (PBPK) model of rifampicin to predict the P‐gp‐mediated drug–drug interactions (DDIs) and assess the DDI impact in the intestine, liver, and kidney. The induction and inhibition parameters of rifampicin for P‐gp were estimated using two of seven DDI cases of rifampicin and digoxin and incorporated into our previously constructed PBPK model of rifampicin. The constructed rifampicin model was verified using the remaining five DDI cases with digoxin and five DDI cases with other P‐gp substrates (talinolol and quinidine). Based on the established PBPK model, following repeated dosing of 600 mg rifampicin, the deduced net effect was an approximately threefold induction in P‐gp activities in the intestine, liver, and kidney. Furthermore, in all 12 cases the predicted area under the plasma concentration–time curve ratios of the P‐gp substrates were within the predefined acceptance criteria with various dosing regimens. Intestinal effects of P‐gp–mediated DDIs had their greatest impact on the pharmacokinetics of digoxin and talinolol, with a minimal impact on the liver and kidney. For quinidine, predicted intestinal P‐gp/cytochrome P450 3A–mediated DDIs were slightly underestimated because of the complexity of nonlinearity and transporter‐enzyme interplay. These findings demonstrate that our rifampicin model can be applicable to quantitatively predict the net impact of P‐gp induction and/or inhibition on diverse P‐gp substrates and investigate the magnitude of DDIs in each tissue.

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Unraveling pleiotropic effects of rifampicin by using physiologically based pharmacokinetic modeling: Assessing the induction magnitude of P‐glycoprotein–cytochrome P450 3A4 dual substrates

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 13 publications

References 41 publications

Regulation of Drug Transport Proteins—From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Regulation of Drug Transport Proteins—From Mechanisms to Clinical Impact: A White Paper on Behalf of the International Transporter Consortium

Alternatives to rifampicin: A review and perspectives on the choice of strong CYP3A inducers for clinical drug–drug interaction studies

Robust physiologically based pharmacokinetic model of rifampicin for predicting drug–drug interactions via P‐glycoprotein induction and inhibition in the intestine, liver, and kidney

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