A global examination of allometric scaling for predicting human drug clearance and the prediction of large vertical allometry

Huadong Tang, Michael Mayersohn

Research output: Contribution to journalReview articlepeer-review

76 Scopus citations

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 (CLs, n = 102) is more predictable than apparent oral clearance (CLpo; n = 24), but caution needs to be applied because of potential CLpo prediction error caused by differences in bioavailability across species. (2) CLs of proteins (n = 10) can be more accurately predicted than that of non-protein chemicals (n = 102). (3) CLs is more predictable for compounds eliminated by renal or biliary excretion (n = 33) than by metabolism (n = 57). (4) CLs 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 CLs 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 (fu) 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.

Original languageEnglish (US)
Pages (from-to)1783-1799
Number of pages17
JournalJournal of pharmaceutical sciences
Volume95
Issue number8
DOIs
StatePublished - Aug 2006

Keywords

  • Allometric scaling
  • C logP
  • Clearance
  • Protein binding
  • Vertical allometry

ASJC Scopus subject areas

  • Pharmaceutical Science

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