Literature DB >> 19645221

Exploratory population pharmacokinetics (e-PPK) analysis for predicting human PK using exploratory ADME data during early drug discovery research.

Kenji Tabata1, Nozomu Hamakawa, Seigo Sanoh, Shigeyuki Terashita, Toshio Teramura.   

Abstract

We have proposed a novel method by population pharmacokinetics analysis for forecasting the drug concentration time-course in humans. This method is based on the non-linear mixed effect model (NONMEM) combined with in vitro-in vivo extrapolation (IVIVE). Eleven clinically tested compounds were selected for retrospective analysis. The in vivo pharmacokinetic (pk) profiles (rats, dogs, monkeys, and humans) and in vitro ADME data [intrinsic clearance (CLint), plasma unbound fraction (fp), and blood-plasma partition ratio (Rb)] for each compound was routinely tested via a screening system to account for inter-compound differences in pk properties. When evaluating the pk parameters, the hepatic plasma flow (Qph) and plasma volume (Vp) were taken into account to compensate for differences in body size among species. All these data were used to conduct population pk (PPK) analyses under the hypothesis that all species constituted one population. The two-compartment model (ADVAN4 TRANS3) and NONMEM software were used for this analysis. The fixed effect model for total body clearance (CL) and central distribution volume (Vd) were constructed as theta(CL)Qph x Eh and theta(Vd) x Vp, respectively, where the hepatic extraction ratio Eh was calculated using the traditional dispersion model. NONMEM generates both fixed and random effects (eta). The key point of this concept was to substitute the eta values of each species (rats, dogs, and monkeys) into the human PPK model to simulate three kinds of pk profiles, compound by compound, for use as a general scaling factor. The NONMEM post hoc option was used to perform the simulation, after which the concentration vs. time courses were compared with actual clinical pk data. The true values were almost within the dynamic range. Thus, the advantage of this concept is that it can generate time-courses without the detail of drug-specific parameters, from which the elimination half time can be determined. This proposed exploratory population pharmacokinetic (e-PPK) approach is a useful and progressive tool that can be applied during the early stages of drug discovery research.

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Year:  2009        PMID: 19645221     DOI: 10.1007/BF03191160

Source DB:  PubMed          Journal:  Eur J Drug Metab Pharmacokinet        ISSN: 0378-7966            Impact factor:   2.441


  24 in total

Review 1.  Prediction of in vivo drug metabolism in the human liver from in vitro metabolism data.

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Review 2.  Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection.

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Journal:  Toxicol Lett       Date:  2003-02-18       Impact factor: 4.372

3.  The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

Authors:  Stefan S De Buck; Vikash K Sinha; Luca A Fenu; Ron A Gilissen; Claire E Mackie; Marjoleen J Nijsen
Journal:  Drug Metab Dispos       Date:  2007-01-31       Impact factor: 3.922

4.  Prediction of human pharmacokinetics using physiologically based modeling: a retrospective analysis of 26 clinically tested drugs.

Authors:  Stefan S De Buck; Vikash K Sinha; Luca A Fenu; Marjoleen J Nijsen; Claire E Mackie; Ron A H J Gilissen
Journal:  Drug Metab Dispos       Date:  2007-07-09       Impact factor: 3.922

5.  Linear correlation of the fraction of oral dose absorbed of 64 drugs between humans and rats.

Authors:  W L Chiou; A Barve
Journal:  Pharm Res       Date:  1998-11       Impact factor: 4.200

Review 6.  Physiological parameters in laboratory animals and humans.

Authors:  B Davies; T Morris
Journal:  Pharm Res       Date:  1993-07       Impact factor: 4.200

7.  Bayesian individualization of pharmacokinetics: simple implementation and comparison with non-Bayesian methods.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharm Sci       Date:  1982-12       Impact factor: 3.534

8.  Comparison of oral absorption and bioavailablity of drugs between monkey and human.

Authors:  Win L Chiou; Paul W Buehler
Journal:  Pharm Res       Date:  2002-06       Impact factor: 4.200

9.  Characterization of gastrointestinal drug absorption in cynomolgus monkeys.

Authors:  Masayuki Takahashi; Takuo Washio; Norio Suzuki; Katsuhiro Igeta; Yoshimine Fujii; Masahiro Hayashi; Yoshiyuki Shirasaka; Shinji Yamashita
Journal:  Mol Pharm       Date:  2008-02-02       Impact factor: 4.939

10.  Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: volume of distribution at steady state.

Authors:  Toshihiro Wajima; Kazuya Fukumura; Yoshitaka Yano; Takayoshi Oguma
Journal:  J Pharm Pharmacol       Date:  2003-07       Impact factor: 3.765

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