A Mechanistic Physiologically Based Pharmacokinetic-Enzyme Turnover Model Involving both Intestine and Liver to Predict CYP3A Induction-Mediated Drug–Drug Interactions

Guo, Hongjun; Liu, Can; Li, Jia; Zhang, Mian; Hu, Mengyue; Xu, Ping; Liu, Li; Liu, Xiaodong

doi:10.1002/jps.23613

Cited by 29 publications

(31 citation statements)

References 95 publications

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“…The present PBPK models reasonably described the DDI results of Pgp substrates with rifampin using Pgp‐FIs of 3–4, assuming that Pgp‐mediated efflux activity (i.e., J max ) proportionally increased with increasing Pgp abundances. Recently, several rifampin PBPK models have been reported, although Pgp induction was not accounted for in most models . One of the reported PBPK models sufficiently recovered clinical DDI results of digoxin with rifampin using the similar assumption, i.e., 3.5‐fold increase in Pgp J max .…”

Section: Discussionmentioning

confidence: 99%

Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction

Yamazaki

Costales

Lazzaro

et al. 2019

CPT Pharmacom & Syst Pharma

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Physiologically‐based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug–drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin‐mediated intestinal P‐glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis‐Menten constant (Km) to be intrinsic, we focused on the scaling factors for maximal efflux rate (Jmax) to adequately recover clinically observed results. Next, we predicted rifampin‐mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates.

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Section: Discussionmentioning

confidence: 99%

Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction

Yamazaki

Costales

Lazzaro

et al. 2019

CPT Pharmacom & Syst Pharma

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“…For example, more complex absorption models such as advanced dissolution, absorption, and metabolism (ADAM) models (Jamei et al, 2009b) and advanced compartmental absorption and transit (ACAT) models (Agoram et al, 2001) have been developed that enable the use of PBPK modeling for the simulation of food effects (Shono et al, 2009;Turner et al, 2012;Heimbach et al, 2013;Xia et al, 2013b;Patel et al, 2014;Zhang et al, 2014), the impact of drug properties on absorption kinetics (Kambayashi et al, 2013;Parrott et al, 2014), and intestinal interactions (Fenneteau et al, 2010). The development of sophisticated models that allow for the simulation of multiple inhibitors or inducers, relevant metabolites, and multiple mechanisms of interaction have permitted the prediction of complex DDIs involving enzymes, transporters, and multiple interaction mechanisms Reki c et al, 2011;Varma et al, 2012Varma et al, , 2013Dhuria et al, 2013;Gertz et al, 2013Gertz et al, , 2014Guo et al, 2013;Kudo et al, 2013;Siccardi et al, 2013;Wang et al, 2013a;Sager et al, 2014;Chen et al, 2015;Shi et al, 2015). Furthermore, the mechanistic understanding of ADME changes that occur in different age groups or disease states has improved, and consequently PBPK modeling has been used to simulate drug disposition in special populations including hepatic (Johnson et al, 2014) and renal impairment populations (Li et al, 2012;Zhao et al, 2012a;Lu et al, 2014;Sayama et al, 2014), children (Leong et al, 2012), and pregnant women (Andrew et al, 2008;Gaohua et al, 2012;…”

Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

et al. 2015

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Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

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“…Adverse effects are more likely to occur when the co-administered drugs are inducers or inhibitors of CYP450, especially CYP3A, which can be induced or inhibited by many xenobiotics, thus becoming a preferential target for DDIs and leading to therapeutic failure. Consequently, it is necessary to evaluate the potential of enzyme induction in the stage of new drug development (Guengerich, 1999;Guo et al, 2013;Tettey et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

“…Drug-drug interactions (DDIs), mediated by induction of CYP3A, may cause undesirable therapeutic efficacy of co-administrated drugs. Therefore, it is of great importance to estimate the DDIs mediated by the enzyme induction potential for a new compound during drug development (Guo et al, 2013;Tettey et al, 2001). Scarce data involving the potential effect of triacontanol as an anticancer drug on metabolic enzymes, especially on the expression levels of CYP3A protein and mRNA, have been reported.…”

Section: Introductionmentioning

confidence: 99%

Effect of triacontanol on the pharmacokinetics of docetaxel in rats associated with induction of cytochrome P450 3A1/2

et al. 2013

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Triacontanol was confirmed to have a potential anti-cancer effect, the aim was to assess whether the co-administration of triacontanol alters the exposure of docetaxel via inducing hepatic CYP3A1/2 activity. The concentration of docetaxel in rats pretreated with triacontanol for seven successive days was determined, and the expression levels of CYP3A protein and mRNA were analyzed by the western blot and real time polymerase chain reaction (RT-PCR) technique, respectively. 2. The concentrations of docetaxel in rats pretreated with triacontanol were decreased, with 61.5%, 61.9% decrease in AUC0-24h and 65.7%, 54.9% reduction in Cmax (120 and 180 mg kg(-1), respectively) compared with the control. Hepatic clearance of docetaxel was enhanced in vitro and in vivo at dosage of 120 and 180 mg kg(-1), and CYP3A activity was up-regulated by measuring the formation rate of 1-hydroxymidazolam. Triacontanol preferentially induced protein expression level of CYP3A2 in a dose-dependent manner and of CYP 3A1 at dosage of 120 and 180 mg kg(-1). The mRNA expression of CYP3A1 was moderately different with the western blot results, but the trends appeared similar. CYP3A2 mRNA level was not markedly affected by triacontanol. 3. The significant triacontanol-docetaxel interaction was largely due to the induction of CYP3A1/2, which brought useful information in the clinical therapy when the combination is administered in human.

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A Mechanistic Physiologically Based Pharmacokinetic-Enzyme Turnover Model Involving both Intestine and Liver to Predict CYP3A Induction-Mediated Drug–Drug Interactions

Cited by 29 publications

References 95 publications

Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction

Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

Effect of triacontanol on the pharmacokinetics of docetaxel in rats associated with induction of cytochrome P450 3A1/2

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