Publication

Physiology-based IVIVE predictions of tramadol from in vitro metabolism data

T'jollyn, H., Snoeys, J., Colin, P., Van Bocxlaer, J., Annaert, P., Cuyckens, F., Vermeulen, A., Van Peer, A., Allegaert, K., Mannens, G. & Boussery, K., Jan-2015, In : Pharmaceutical Research. 32, 1, p. 260-74 15 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

T'jollyn, H., Snoeys, J., Colin, P., Van Bocxlaer, J., Annaert, P., Cuyckens, F., ... Boussery, K. (2015). Physiology-based IVIVE predictions of tramadol from in vitro metabolism data. Pharmaceutical Research, 32(1), 260-74. https://doi.org/10.1007/s11095-014-1460-x

Author

T'jollyn, Huybrecht ; Snoeys, Jan ; Colin, Pieter ; Van Bocxlaer, Jan ; Annaert, Pieter ; Cuyckens, Filip ; Vermeulen, An ; Van Peer, Achiel ; Allegaert, Karel ; Mannens, Geert ; Boussery, Koen. / Physiology-based IVIVE predictions of tramadol from in vitro metabolism data. In: Pharmaceutical Research. 2015 ; Vol. 32, No. 1. pp. 260-74.

Harvard

T'jollyn, H, Snoeys, J, Colin, P, Van Bocxlaer, J, Annaert, P, Cuyckens, F, Vermeulen, A, Van Peer, A, Allegaert, K, Mannens, G & Boussery, K 2015, 'Physiology-based IVIVE predictions of tramadol from in vitro metabolism data', Pharmaceutical Research, vol. 32, no. 1, pp. 260-74. https://doi.org/10.1007/s11095-014-1460-x

Standard

Physiology-based IVIVE predictions of tramadol from in vitro metabolism data. / T'jollyn, Huybrecht; Snoeys, Jan; Colin, Pieter; Van Bocxlaer, Jan; Annaert, Pieter; Cuyckens, Filip; Vermeulen, An; Van Peer, Achiel; Allegaert, Karel; Mannens, Geert; Boussery, Koen.

In: Pharmaceutical Research, Vol. 32, No. 1, 01.2015, p. 260-74.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

T'jollyn H, Snoeys J, Colin P, Van Bocxlaer J, Annaert P, Cuyckens F et al. Physiology-based IVIVE predictions of tramadol from in vitro metabolism data. Pharmaceutical Research. 2015 Jan;32(1):260-74. https://doi.org/10.1007/s11095-014-1460-x


BibTeX

@article{b792d8f4258d427cac3c6eef32cb7390,
title = "Physiology-based IVIVE predictions of tramadol from in vitro metabolism data",
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{\circledR}).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.",
keywords = "Analgesics, Opioid, Computer Simulation, Cytochrome P-450 Enzyme System, Humans, In Vitro Techniques, Metabolic Clearance Rate, Microsomes, Liver, Models, Biological, Predictive Value of Tests, Recombinant Proteins, Tissue Distribution, Tramadol",
author = "Huybrecht T'jollyn and Jan Snoeys and Pieter Colin and {Van Bocxlaer}, Jan and Pieter Annaert and Filip Cuyckens and An Vermeulen and {Van Peer}, Achiel and Karel Allegaert and Geert Mannens and Koen Boussery",
year = "2015",
month = "1",
doi = "10.1007/s11095-014-1460-x",
language = "English",
volume = "32",
pages = "260--74",
journal = "Pharmaceutical Research",
issn = "0724-8741",
publisher = "SPRINGER/PLENUM PUBLISHERS",
number = "1",

}

RIS

TY - JOUR

T1 - Physiology-based IVIVE predictions of tramadol from in vitro metabolism data

AU - T'jollyn, Huybrecht

AU - Snoeys, Jan

AU - Colin, Pieter

AU - Van Bocxlaer, Jan

AU - Annaert, Pieter

AU - Cuyckens, Filip

AU - Vermeulen, An

AU - Van Peer, Achiel

AU - Allegaert, Karel

AU - Mannens, Geert

AU - Boussery, Koen

PY - 2015/1

Y1 - 2015/1

N2 - 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.

AB - 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.

KW - Analgesics, Opioid

KW - Computer Simulation

KW - Cytochrome P-450 Enzyme System

KW - Humans

KW - In Vitro Techniques

KW - Metabolic Clearance Rate

KW - Microsomes, Liver

KW - Models, Biological

KW - Predictive Value of Tests

KW - Recombinant Proteins

KW - Tissue Distribution

KW - Tramadol

U2 - 10.1007/s11095-014-1460-x

DO - 10.1007/s11095-014-1460-x

M3 - Article

VL - 32

SP - 260

EP - 274

JO - Pharmaceutical Research

JF - Pharmaceutical Research

SN - 0724-8741

IS - 1

ER -

ID: 31722375