title: Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver.
abstract: Reported predictions of human in vivo hepatic clearance from in vitro data have used a variety of values for the scaling factors human microsomal protein (MPPGL) and hepatocellularity (HPGL) per gram of liver, generally with no consideration of the extent of their inter-individual variability. We have collated and analysed data from a number of sources, to provide weighted meangeo values of human MPPGL and HPGL of 32 mg g-1 (95% Confidence Interval (CI); 29-34 mg.g-1) and 99x10(6) cells.g-1 (95% CI; 74-131 mg.g-1), respectively. Although inter-individual variability in values of MPPGL and HPGL was statistically significant, gender, smoking or alcohol consumption could not be detected as significant covariates by multiple linear regression. However, there was a weak but statistically significant inverse relationship between age and both MPPGL and HPGL. These findings indicate the importance of considering differences between study populations when forecasting in vivo pharmacokinetic behaviour. Typical clinical pharmacology studies, particularly in early drug development, use young, fit, healthy male subjects of around 30 years of age. In contrast, the average age of patients for many diseases is about 60 years of age. The relationship between age and MPPGL observed in this study estimates values of 40 mg.g-1 for a 30 year old individual and 31 mg.g-1 for a 60 year old individual. Investigators may wish to consider the reported covariates in the selection of scaling factors appropriate for the population in which estimates of clearance are being predicted. Further studies are required to clarify the influence of age (especially in paediatric subjects), donor source and ethnicity on values of MPPGL and HPGL. In the meantime, we recommend that the estimates (and their variances) from the current meta-analysis be used when predicting in vivo kinetic parameters from in vitro data.
authors.affiliation: Academic Unit of Clinical Pharmacology, University of Sheffield, Floor M, The Royal Hallamshire Hospital, Sheffield S10 2JF, UK.
authors.forename: Zoe E
authors.initials: ZE
authors.lastname: Barter
authors.forename: Martin K
authors.initials: MK
authors.lastname: Bayliss
authors.forename: Philip H
authors.initials: PH
authors.lastname: Beaune
authors.forename: Alan R
authors.initials: AR
authors.lastname: Boobis
authors.forename: David J
authors.initials: DJ
authors.lastname: Carlile
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Edwards
authors.forename: J Brian
authors.initials: JB
authors.lastname: Houston
authors.forename: Brian G
authors.initials: BG
authors.lastname: Lake
authors.forename: John C
authors.initials: JC
authors.lastname: Lipscomb
authors.forename: Olavi R
authors.initials: OR
authors.lastname: Pelkonen
authors.forename: Geoffrey T
authors.initials: GT
authors.lastname: Tucker
authors.forename: Amin
authors.initials: A
authors.lastname: Rostami-Hodjegan
mesh_terms: D000818:Animals; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D022781:Hepatocytes / Q000378:metabolism*; D006801:Humans; D008099:Liver / Q000378:metabolism*; D008862:Microsomes, Liver / Q000378:metabolism*; D004364:Pharmaceutical Preparations / Q000378:metabolism; D011506:Proteins / Q000378:metabolism
chemical_list: D004364:Pharmaceutical Preparations; D011506:Proteins