Login Register Cart 0
Generic placeholder image

Current Reviews in Clinical and Experimental Pharmacology

Editor-in-Chief

ISSN (Print): 2772-4328
ISSN (Online): 2772-4336

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Letter Article

In Silico Model for Predicting CYP2D6-Mediated Drug-Drug Interactions

Author(s): Roberto Lozano*, Alberto Frutos and Alejandro Martinez

Volume 16, Issue 2, 2021

Published on: 07 May, 2020

Page: [124 - 127] Pages: 4

DOI: 10.2174/1574884715666200507130824

Price: $65

Abstract

Background: Successful integration of in vitro into in vivo data on Drug-Drug Interaction (DDI) is dependent on the inhibitory concentration used. Obtaining plasma concentration of a drug is only readily available for a small number of drugs in clinical practice. We propose the use of a therapeutic range as a substitute for inhibitory concentration.

Objective: Because of this, we aimed to construct a linear-regression model based on the areaunder- curve of the victim drugs and the therapeutic range for a set of known inhibitors of the CYP2D6 of interest.

Methods: Correlation analysis of linear log-log regression of two main variables: The Area-Under- Curve ratio (AUCr) of the victim drugs and the therapeutic range-to-inhibition constant ratio, with data obtained from literature.

Results: Data were fitted to linear log-log regression, between the average of AUCr values and mean value of therapeutic range-to-inhibition constant ratio (TRm-to-Ki), of the inhibitory drugs.

Conclusion: According to our results, knowledge of the inhibition constant and therapeutic range (or its plasma levels if disponible) of the inhibitor would be sufficient to determine the intensity and clinical relevance of a CYP2D6-mediated DDI.

Keywords: Drug-drug interactions, cytochrome p-450, pharmacokinetics, clinical pharmacology, area-under-curve, inhibition constant, therapeutic range.

« Previous Next »
Graphical Abstract

[1]
Lozano R, Frutos A. Time course of the drug-drug interaction of acenocoumarol-miconazole. Int J Clin Pharmacol Ther 2018; 56(1): 28-30.
[PMID: 29162215]
[2]
US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER). 2017. Draft guidance for industry Available at: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM581965.pdf.
[3]
Ito K, Brown HS, Houston JB. Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. Br J Clin Pharmacol 2004; 57(4): 473-86.
[http://dx.doi.org/10.1111/j.1365-2125.2003.02041.x] [PMID: 15025746]
[4]
Regenthal R, Krueger M, Koeppel C, Preiss R. Drug levels: therapeutic and toxic serum/plasma concentrations of common drugs. J Clin Monit Comput 1999; 15(7-8): 529-44.
[http://dx.doi.org/10.1023/A:1009935116877] [PMID: 12578052]
[5]
Bertelsen KM, Venkatakrishnan K, Von Moltke LL, Obach RS, Greenblatt DJ. Apparent mechanism-based inhibition of human CYP2D6 in vitro by paroxetine: Comparison with fluoxetine and quinidine. Drug Metab Dispos 2003; 31(3): 289-93.
[http://dx.doi.org/10.1124/dmd.31.3.289] [PMID: 12584155]
[6]
Lozano R, Apesteguía A, Frutos A, Martínez A, Alcazar MA, Franco ME. Regression-based model for the evaluation of CYP2D6-mediated drug-drug interactions. Curr Top Pharmacol In Press
[7]
VandenBrink BM, Foti RS, Rock DA, Wienkers LC, Wahlstrom JL. Prediction of CYP2D6 drug interactions from in vitro data: Evidence for substrate-dependent inhibition. Drug Metab Dispos 2012; 40(1): 47-53.
[http://dx.doi.org/10.1124/dmd.111.041210] [PMID: 21976621]

Rights & Permissions Print Cite
Article Metrics
22
1
© 2024 Bentham Science Publishers | Privacy Policy