title: Predicting human hepatic clearance from in vitro drug metabolism and transport data: a scientific and pharmaceutical perspective for assessing drug-drug interactions.
abstract: OBJECTIVE
  Membrane transporters and metabolism are major determinants of the hepatobiliary elimination of drugs. This work investigates several key questions for drug development. Such questions include which drugs demonstrate transporter-based clearance in the clinic, and which in vitro methods are most suitable for drug classification, i.e. transporter- vs metabolism-dependent compound class categories. Additional questions posed are: what is the expected quantitative change in exposure in the presence of a transporter- and/or metabolism-inhibiting drug, and which criteria should trigger follow-up clinical drug-drug interaction studies.
  
  
  METHODS
  A well-established method for (human) liver clearance prediction that considers all four physiological processes driving hepatic drug elimination (namely sinusoidal uptake and efflux, metabolism and biliary secretion) was applied. Suspended hepatocytes, liver microsomes and sandwich-cultured hepatocytes were used as in vitro models to determine the individual intrinsic clearance for 13 selected compounds with various physicochemical and pharmacokinetic properties.
  
  
  RESULTS
  Using this in vitro-in vivo extrapolation method a good linear correlation was observed between predicted and reported human hepatic clearances. Linear regression analysis revealed much improved correlations compared with other prediction methods.
  
  
  CONCLUSIONS
  The presented approach serves as a basis for accurate compound categorization within the Biopharmaceutics Drug Disposition Classification System (BDDCS) and was applied to anticipate metabolism- and transporter-based drug-drug interactions using different static prediction methods. A decision tree proposal is provided and helps to guide clinical studies on active processes influencing hepatic elimination. All recommendations in this paper are generally intended to support early pre-clinical and clinical drug development and the filing of a new drug application.
authors.affiliation: Drug-Drug Interaction Section, Drug Metabolism and Pharmacokinetics, Novartis Institutes of Biomedical Research, Basel, Switzerland. gian.camenisch@novartis.com
authors.forename: Gian
authors.initials: G
authors.lastname: Camenisch
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Umehara
mesh_terms: D001692:Biological Transport; D003663:Decision Trees; D015195:Drug Design; D004347:Drug Interactions; D022781:Hepatocytes / Q000378:metabolism*; D006801:Humans; D016014:Linear Models; D008862:Microsomes, Liver / Q000378:metabolism*; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism*
chemical_list: D004364:Pharmaceutical Preparations