title: A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry.
abstract: Allometrically scaled data sets (138 compounds) used for predicting human clearance were obtained from the literature. Our analyses of these data have led to four observations. (1) The current data do not provide strong evidence that systemic clearance (CL(s); n = 102) is more predictable than apparent oral clearance (CL(po); n = 24), but caution needs to be applied because of potential CL(po) prediction error caused by differences in bioavailability across species. (2) CL(s) of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CL(s) is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CL(s) predictability for hepatically eliminated compounds followed the order: high CL (n = 11) > intermediate CL (n = 17) > low CL (n = 29). All examples of large vertical allometry (% error of prediction greater than 1000%) occurred only when predicting human CL(s) of drugs having very low CL(s). A qualitative analysis revealed the application of two potential rules for predicting the occurrence of large vertical allometry: (1) ratio of unbound fraction of drug in plasma (f(u)) between rats and humans greater than 5; (2) C logP greater than 2. Metabolic elimination could also serve as an additional indicator for expecting large vertical allometry.
authors.affiliation: Department of Pharmaceutical Sciences, College of Pharmacy, The University of Arizona, Tucson, 85721, USA.
authors.forename: Huadong
authors.initials: H
authors.lastname: Tang
authors.forename: Michael
authors.initials: M
authors.lastname: Mayersohn
mesh_terms: D001682:Biological Availability; D016208:Databases, Factual; D006801:Humans; D008657:Metabolic Clearance Rate / Q000187:drug effects / Q000502:physiology; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000378:metabolism*; D011237:Predictive Value of Tests; D013678:Technology, Pharmaceutical / Q000379:methods*
chemical_list: D004364:Pharmaceutical Preparations