Literature DB >> 22621802

Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4.

Howard J Burt1, Henry Pertinez, Carolina Säll, Claire Collins, Ruth Hyland, J Brian Houston, Aleksandra Galetin.   

Abstract

A progress curve method for assessing time-dependent inhibition of CYP3A4 is based on simultaneous quantification of probe substrate metabolite and inhibitor concentrations during the experiment. Therefore, it may overcome some of the issues associated with the traditional two-step method and estimation of inactivation rate (k(inact)) and irreversible inhibition (K(I)) constants. In the current study, seven time-dependent inhibitors were investigated using a progress curve method and recombinant CYP3A4. A novel mechanistic modeling approach was applied to determine inhibition parameters using both inhibitor and probe metabolite data. Progress curves generated for clarithromycin, erythromycin, diltiazem, and N-desmethyldiltiazem were described well by the mechanistic mechanism-based inhibition (MBI) model. In contrast, mibefradil, ritonavir, and verapamil required extension of the model and inclusion of competitive inhibition term for the metabolite. In addition, this analysis indicated that verapamil itself causes minimal MBI, and the formation of inhibitory metabolites was responsible for the irreversible loss of CYP3A4 activity. The k(inact) and K(I) estimates determined in the current study were compared with literature data generated using the conventional two-step method. In the current study, the inactivation efficiency (k(inact)/K(I)) for clarithromycin, ritonavir, and erythromycin were up to 7-fold higher, whereas k(inact)/K(I) for mibefradil, N-desmethyldiltiazem, and diltiazem were, on average, 2- to 4.8-fold lower than previously reported estimates. Use of human liver microsomes instead of recombinant CYP3A4 resulted in 5-fold lower k(inact)/K(I) for erythromycin. In conclusion, the progress curve method has shown a greater mechanistic insight when determining kinetic parameters for MBI in addition to providing a more comprehensive experimental protocol.

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Year:  2012        PMID: 22621802     DOI: 10.1124/dmd.112.046078

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  7 in total

1.  A numerical method for analysis of in vitro time-dependent inhibition data. Part 1. Theoretical considerations.

Authors:  Swati Nagar; Jeffrey P Jones; Ken Korzekwa
Journal:  Drug Metab Dispos       Date:  2014-06-17       Impact factor: 3.922

2.  A numerical method for analysis of in vitro time-dependent inhibition data. Part 2. Application to experimental data.

Authors:  Ken Korzekwa; Donald Tweedie; Upendra A Argikar; Andrea Whitcher-Johnstone; Leslie Bell; Shari Bickford; Swati Nagar
Journal:  Drug Metab Dispos       Date:  2014-06-17       Impact factor: 3.922

3.  Numerical Analysis of Time-Dependent Inhibition by MDMA.

Authors:  John T Rodgers; Jeffrey P Jones
Journal:  Drug Metab Dispos       Date:  2019-10-22       Impact factor: 3.922

4.  Improved Predictions of Drug-Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A.

Authors:  Jaydeep Yadav; Ken Korzekwa; Swati Nagar
Journal:  Mol Pharm       Date:  2018-04-10       Impact factor: 4.939

Review 5.  Time-dependent enzyme inactivation: Numerical analyses of in vitro data and prediction of drug-drug interactions.

Authors:  Jaydeep Yadav; Erickson Paragas; Ken Korzekwa; Swati Nagar
Journal:  Pharmacol Ther       Date:  2019-12-11       Impact factor: 12.310

6.  Stereoselective inhibition of CYP2C19 and CYP3A4 by fluoxetine and its metabolite: implications for risk assessment of multiple time-dependent inhibitor systems.

Authors:  Justin D Lutz; Brooke M VandenBrink; Katipudi N Babu; Wendel L Nelson; Kent L Kunze; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2013-06-19       Impact factor: 3.922

7.  Identification of Human Alanine-Glyoxylate Aminotransferase Ligands as Pharmacological Chaperones for Variants Associated with Primary Hyperoxaluria Type 1.

Authors:  Silvia Grottelli; Giannamaria Annunziato; Gioena Pampalone; Marco Pieroni; Mirco Dindo; Francesca Ferlenghi; Gabriele Costantino; Barbara Cellini
Journal:  J Med Chem       Date:  2022-07-13       Impact factor: 8.039

  7 in total

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