title: Physiology-based IVIVE predictions of tramadol from in vitro metabolism data.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Medical Biochemistry and Clinical Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium.
authors.forename: Huybrecht
authors.initials: H
authors.lastname: T'jollyn
authors.forename: Jan
authors.initials: J
authors.lastname: Snoeys
authors.forename: Pieter
authors.initials: P
authors.lastname: Colin
authors.forename: Jan
authors.initials: J
authors.lastname: Van Bocxlaer
authors.forename: Pieter
authors.initials: P
authors.lastname: Annaert
authors.forename: Filip
authors.initials: F
authors.lastname: Cuyckens
authors.forename: An
authors.initials: A
authors.lastname: Vermeulen
authors.forename: Achiel
authors.initials: A
authors.lastname: Van Peer
authors.forename: Karel
authors.initials: K
authors.lastname: Allegaert
authors.forename: Geert
authors.initials: G
authors.lastname: Mannens
authors.forename: Koen
authors.initials: K
authors.lastname: Boussery
mesh_terms: D000701:Analgesics, Opioid / Q000097:blood / Q000378:metabolism / Q000493:pharmacokinetics*; D003198:Computer Simulation; D003577:Cytochrome P-450 Enzyme System / Q000235:genetics / Q000378:metabolism; D006801:Humans; D066298:In Vitro Techniques; D008657:Metabolic Clearance Rate; D008862:Microsomes, Liver / Q000201:enzymology / Q000378:metabolism*; D008954:Models, Biological; D011237:Predictive Value of Tests; D011994:Recombinant Proteins; D014018:Tissue Distribution; D014147:Tramadol / Q000097:blood / Q000378:metabolism / Q000493:pharmacokinetics*
chemical_list: D000701:Analgesics, Opioid; D011994:Recombinant Proteins; D014147:Tramadol; D003577:Cytochrome P-450 Enzyme System