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Physiology-Based IVIVE Predictions of Tramadol from in Vitro Metabolism Data

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ABSTRACT

Purpose

To predict the tramadol in vivo pharmacokinetics in adults by using in vitro metabolism data and an in vitro-in vivo extrapolation (IVIVE)-linked physiologically-based pharmacokinetic (PBPK) modeling and simulation approach (Simcyp®).

Methods

Tramadol metabolism data was gathered using metabolite formation in human liver microsomes (HLM) and recombinant enzyme systems (rCYP). Hepatic intrinsic clearance (CLintH) was (i) estimated from HLM corrected for specific CYP450 contributions from a chemical inhibition assay (model 1); (ii) obtained in rCYP and corrected for specific CYP450 contributions by study-specific intersystem extrapolation factor (ISEF) values (model 2); and (iii) scaled back from in vivo observed clearance values (model 3). The model-predicted clearances of these three models were evaluated against observed clearance values in terms of relative difference of their geometric means, the fold difference of their coefficients of variation, and relative CYP2D6 contribution.

Results

Model 1 underpredicted, while model 2 overpredicted the total tramadol clearance by −27 and +22%, respectively. The CYP2D6 contribution was underestimated in both models 1 and 2. Also, the variability on the clearance of those models was slightly underpredicted. Additionally, blood-to-plasma ratio and hepatic uptake factor were identified as most influential factors in the prediction of the hepatic clearance using a sensitivity analysis.

Conclusion

IVIVE-PBPK proved to be a useful tool in combining tramadol’s low turnover in vitro metabolism data with system-specific physiological information to come up with reliable PK predictions in adults.

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Abbreviations

PBPK:

Physiologically-based pharmacokinetics

IVIVE:

In vitro-in vivo extrapolation

CLint:

Intrinsic clearance

HLM:

Human liver microsomes

rCYP:

Recombinant CYP450 enzyme systems

ISEF:

Inter-system extrapolation factor

ODT:

O-desmethyltramadol

NDT:

N-desmethyltramadol

NODT:

N,O-didesmethyltramadol

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ACKNOWLEDGMENTS AND DISCLOSURES

Karel Allegaert is supported by the Fund for Scientific Research, Flanders (Fundamental Clinical Investigatorship 1800214N).

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Authors and Affiliations

  1. Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium

    Huybrecht T’jollyn, Pieter Colin, Jan Van Bocxlaer, An Vermeulen & Koen Boussery

  2. Division of Janssen Pharmaceutica NV, Janssen Research and Development, Turnhoutseweg 30, 2340, Beerse, Belgium

    Jan Snoeys, Filip Cuyckens, An Vermeulen, Achiel Van Peer & Geert Mannens

  3. Drug Delivery and Disposition, KU Leuven Department of Pharmaceutical and Pharmacological Sciences, O&N2, Herestraat 49-box 921, 3000, Leuven, Belgium

    Pieter Annaert

  4. Department of Development and Regeneration, KU Leuven and Neonatal Intensive Care Unit, University Hospitals Leuven, 3000, Leuven, Belgium

    Karel Allegaert

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  1. Huybrecht T’jollyn
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Correspondence to Koen Boussery.

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T’jollyn, H., Snoeys, J., Colin, P. et al. Physiology-Based IVIVE Predictions of Tramadol from in Vitro Metabolism Data. Pharm Res 32, 260–274 (2015). https://doi.org/10.1007/s11095-014-1460-x

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  • DOI: https://doi.org/10.1007/s11095-014-1460-x

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