Physiologically Based Pharmacokinetic Modeling Characterizes the Drug‐Drug Interaction Between Saxagliptin and Rifampicin in Patients With Renal Impairment

Wu, Wanhong; Lin, Rongfang; Ke, Meng; Ye, Lingling; Lin, Cuihong

doi:10.1002/jcph.2223

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Similar efforts have been undertaken with renally impaired populations. For example, DDI modeling with GastroPlus ® suggest the DDI between saxagliptin and rifampin is slightly attenuated in patients with severe renal disease (Wu et al, 2023a). While the effects of hepatic and renal impairment on potential DDI appear relatively minor for the aforementioned drugs, additional efforts are needed to accurately capture changes in enzyme and transporter levels as well as blood flows in order to more accurately inform these PBPK models.…”

Section: Pbpk Modeling Of Ddi In Special Populationsmentioning

confidence: 99%

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions

Foti

2024

Drug Metab Dispos

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Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules.

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Section: Pbpk Modeling Of Ddi In Special Populationsmentioning

confidence: 99%

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions

Foti

2024

Drug Metab Dispos

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“…Key applications include assessing the impact of drug–drug interactions on pharmacokinetics and optimizing dosage regimens [ 13 ]. While previous PBPK model studies have attempted to predict drug interactions of saxagliptin associated with CYP3A4, it is crucial to note that these studies require actual clinical DDI results for modeling [ 14 , 15 ]. Our research involved validating a PBPK model using experimental data to predict drug interactions in rats.…”

Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

Lee,

Yoon,

Maeng

et al. 2024

Pharmaceutics

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The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug–drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague–Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

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Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective

Reddy,

Cabalu,

de Zwart

et al. 2024

Clin Pharma and Therapeutics

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Physiologically‐based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug–drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well‐characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, fm,CYP3A4, and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of fm,CYP3A4 (0.086–1.0), Fg (0.11–1.0) and hepatic availability (0.09–0.96) were included. Predictions were most often accurate for compounds that are not P‐gp substrates or that are P‐gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first‐order absorption model might be more appropriate for a P‐gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P‐gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.

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Physiologically Based Pharmacokinetic Modeling Characterizes the Drug‐Drug Interaction Between Saxagliptin and Rifampicin in Patients With Renal Impairment

Cited by 6 publications

References 27 publications

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective

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