Literature DB >> 24989891

Deciding on success criteria for predictability of pharmacokinetic parameters from in vitro studies: an analysis based on in vivo observations.

Khaled Abduljalil1, Theresa Cain2, Helen Humphries2, Amin Rostami-Hodjegan2.   

Abstract

Prediction accuracy of pharmacokinetic parameters is often assessed using prediction fold error, i.e., being within 2-, 3-, or n-fold of observed values. However, published studies disagree on which fold error represents an accurate prediction. In addition, "observed data" from only one clinical study are often used as the gold standard for in vitro to in vivo extrapolation (IVIVE) studies, despite data being subject to significant interstudy variability and subjective selection from various available reports. The current study involved analysis of published systemic clearance (CL) and volume of distribution at steady state (Vss) values taken from over 200 clinical studies. These parameters were obtained for 17 different drugs after intravenous administration. Data were analyzed with emphasis on the appropriateness to use a parameter value from one particular clinical study to judge the performance of IVIVE and the ability of CL and Vss values obtained from one clinical study to "predict" the same values obtained in a different clinical study using the n-fold criteria for prediction accuracy. The twofold criteria method was of interest because it is widely used in IVIVE predictions. The analysis shows that in some cases the twofold criteria method is an unreasonable expectation when the observed data are obtained from studies with small sample size. A more reasonable approach would allow prediction criteria to include clinical study information such as sample size and the variance of the parameter of interest. A method is proposed that allows the "success" criteria to be linked to the measure of variation in the observed value.
Copyright © 2014 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2014        PMID: 24989891     DOI: 10.1124/dmd.114.058099

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


  39 in total

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Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

2.  Physiologically Based Pharmacokinetic Modelling of Hyperforin to Predict Drug Interactions with St John's Wort.

Authors:  Jeffry Adiwidjaja; Alan V Boddy; Andrew J McLachlan
Journal:  Clin Pharmacokinet       Date:  2019-07       Impact factor: 6.447

3.  A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients.

Authors:  Daniel Moj; Hugo Maas; André Schaeftlein; Nina Hanke; José David Gómez-Mantilla; Thorsten Lehr
Journal:  Clin Pharmacokinet       Date:  2019-12       Impact factor: 6.447

4.  Physiologically Based Pharmacokinetic Model of All-trans-Retinoic Acid with Application to Cancer Populations and Drug Interactions.

Authors:  Jing Jing; Cara Nelson; Jisun Paik; Yoshiyuki Shirasaka; John K Amory; Nina Isoherranen
Journal:  J Pharmacol Exp Ther       Date:  2017-03-08       Impact factor: 4.030

5.  Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs).

Authors:  Peng Duan; Ping Zhao; Lei Zhang
Journal:  Eur J Drug Metab Pharmacokinet       Date:  2017-08       Impact factor: 2.441

6.  Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions with Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes.

Authors:  Catia Marzolini; Rajith Rajoli; Manuel Battegay; Luigia Elzi; David Back; Marco Siccardi
Journal:  Clin Pharmacokinet       Date:  2017-04       Impact factor: 6.447

7.  Physiologically Based Pharmacokinetic Modeling of Fimasartan, Amlodipine, and Hydrochlorothiazide for the Investigation of Drug-Drug Interaction Potentials.

Authors:  Su-Jin Rhee; Hyun A Lee; Soyoung Lee; Eunwoo Kim; Inseung Jeon; Im-Sook Song; Kyung-Sang Yu
Journal:  Pharm Res       Date:  2018-10-15       Impact factor: 4.200

8.  In Silico Dose Prediction for Long-Acting Rilpivirine and Cabotegravir Administration to Children and Adolescents.

Authors:  Rajith K R Rajoli; David J Back; Steve Rannard; Caren Freel Meyers; Charles Flexner; Andrew Owen; Marco Siccardi
Journal:  Clin Pharmacokinet       Date:  2018-02       Impact factor: 6.447

9.  Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions.

Authors:  Weize Huang; Mariko Nakano; Jennifer Sager; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2017-08-31       Impact factor: 3.922

10.  Quantitative Assessment of Elagolix Enzyme-Transporter Interplay and Drug-Drug Interactions Using Physiologically Based Pharmacokinetic Modeling.

Authors:  Manoj S Chiney; Juki Ng; John P Gibbs; Mohamad Shebley
Journal:  Clin Pharmacokinet       Date:  2020-05       Impact factor: 6.447

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