INCORPORATION OF IN-VITRO DRUG-METABOLISM DATA INTO PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELS

The liver poses particular problems in constructing physiologically-ba sed pharmacokinetic models since this organ is not only a distribution site for drugs/chemicals but frequently the major site of metabolism. The impact of hepatic drug metabolism in modelling is substantial and it is crucial to the success of the model that in vitro data on biotr ansformation be incorporated in a judicious manner. The value of in vi tro/in vivo extrapolation is clearly demonstrated by considering kinet ic data from incubations with freshly isolated hepatocytes. The determ ination of easily measurable in vitro parameters, such as V-max and K- m, from initial rate studies and scaling to the in vivo situation by a ccounting for hepatocellularity provides intrinsic clearance estimates . A scaling factor of 1200 x 10(6) cells per 250 g rat has proved to b e a robust parameter independent of laboratory technique and insensiti ve to animal pretreatment. Similar procedures can also be adopted for other in vitro systems such as hepatic microsomes and liver slices. An appropriate scaling factor for microsomal studies is the microsomal r ecovery index which allows for the incomplete recovery of cytochrome P -450 with standard differential centrifugation of liver homogenates. T he hepatocellularity of a liver slice has been unsatisfactory in scali ng kinetic parameters from liver slices. The level of success varies f rom drug to drug and substrate diffusion is a competing process to met abolism within the slice incubation system; hence, low clearance drugs are better predicted than high clearance drugs. The use of three live r models (venous-equilibration, undistributed sinusoidal acid dispersi on models) have been compared to predict hepatic clearance from in vit ro intrinsic clearance values. As no consistent advantage of one model over the others could be demonstrated, the simplest, the veno us-equi libration model, is adequate for the currently available data in hepat ocytes. While these successes are encouraging as they establish the fi delity of in vitro systems for in vivo prediction, the level of succes s varies from drug to drug. It is important to address the reasons for failure of prediction by each in vitro system and it is noteworthy th at the current approach simplifies several key issues. (C) 1997 Publis hed by Elsevier Science Ltd.