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Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network. | LitMetric

AI Article Synopsis

  • Quinidine is an antiarrhythmic drug that inhibits CYP2D6 and P-glycoprotein, making it important for studying drug-drug interactions (DDIs), but it's also affected by interactions with CYP3A4 and P-gp since it's a substrate of these proteins.
  • A physiologically-based pharmacokinetic (PBPK) model of quinidine was developed to analyze how it absorbs, distributes, metabolizes, and is excreted in the body, also involving its metabolite, 3-hydroxyquinidine.
  • The model demonstrated high accuracy, successfully simulating complex DDI scenarios with over 90% of predicted outcomes aligning closely with actual clinical data, and it will be made

Article Abstract

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10431052PMC
http://dx.doi.org/10.1002/psp4.12981DOI Listing

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