Predicting Human Hepatic

Many mechanisms of clearance exist to remove a drug from the body. Total clearance is the sum of these dilferent clearance processes [eqn (13.4)], which can be described through in vivo measurements 

Thus total clearance can be estimated by independently predicting each of the major clearance processes indicated. The liver is by far the main route of clearance for most drugs, therefore it has been the focus for much of the development of pharmacokinetic modeling methods. 

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Riley RJ, McGinnity DF, Austin RP. 2005. A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes. Drug Metab Dispos 33 1304—1311. 

By using in vitro preparations of human enzymes it is possible to predict those antibiotics that will adversely affect the metabolism of other drugs [110]. Such studies have shown that rifaximin, at concentrations ranging from 2 to 200 ng/ml, did not inhibit human hepatic cytochrome P450 isoenzymes 1A2, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1 and 3A4 [34], In an in vitro hepatocyte induction model, rifaximin was shown to induce cytochrome P450 3A4 (CYP3A4) [34], an isoenzyme which rifampicin is known to induce [109],... 

S. Suzuki, A. Kagayama, A. et ah Prediction of human hepatic clearance from in vivo animal experiments and in vitro metabolic studies with liver microsomes from animals and humans. Drug Metab Dispos 2001, 29, 1316-1324. 

The incorporation of in vitro metaboHsm data into allometric seating of compounds cleared by hepatic metabolism has been extensively evaluated [18] and shown to accurately predict human clearance. In this review it is suggested that the utility of such methods are most appropriately applied in drug candidate selection, to confirm early estimates and to support early clinical studies. 

Neural networks are a relatively new tool in data modelling in the field of pharmacokinetics [54—56]. Using this approach, non-linear relationships to predicted properties are better taken into account than by multiple linear regression [45]. Human hepatic drug clearance was best predicted from human hepatocyte data, followed by rat hepatocyte data, while in the studied data set animal in vivo data did not significantly contribute to the predictions [56]. 

In a first step the scaling of intrinsic clearances determined in rat hepatocytes was compared to in vivo clearance. When taking account of non-linearity, the estimated hepatic metabolic clearance values were in reasonable agreement with observed total clearances, which ranged from 7 to 35 mL/min/kg, and it was considered reasonable to estimate the expected clearances in human by a similar scaling of human hepatocyte data. The error around the mean predicted human clearance was based on the variability seen in different batches of human hepatocytes. 

Birkett DJ, Mackenzie PI, Veronese ME, Miners JO (1993) In vitro approaches can predict human drug metabolism. Trends Pharmacol Sci 14 292-294 Bloomer JC, Boyd HF, Hickey DMB et al. (2001) 1-(Arylpiperazinylamidoalkyl)-pyrimidones Orally Active Inhibitors of Lipoprotein-Associated Phospholipase A2. Bioorg Med Chem Lett 11 1925-1929 Brandon EFA, Raap CD, Meijerman I et al. (2003) An update on in vitro test methods in human hepatic drug biotransformation research pros and cons. Toxicol Applied Pharmacol 189 233-246... 

Hakooz N, Ito K, Rawden H, Gill H, Lemmers L, Boobis AR, Edwards RJ, Carlile DJ, Lake BG, Houston JB. 2006. Determination of a human hepatic microsomal scaling factor for predicting in vivo drug clearance. Pharm Res 23 533-539. 

We have tested the ability of covalent binding measurements in predicting idiosyncratic hepatotoxicity by examining the binding of eighteen drugs (nine hepatotoxins and nine nonhepatotoxins) to human hepatic tissue,... 

Schneider G, Coassolo P, Lave T. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J Med Chem 1999 42 5072-6. 

Naritomi, Y. Terashita, S. Kimura, S. Suzuki, A. Kagayama, A. Sugiyama, Y. Prediction of Human Hepatic Clearance from In Vivo Animal Experiments and In Vitro Metabolic Studies with Liver Microsomes from Animals and Humans, Drug Metab. Dispos. 29(10), 1316-1324 (2001). 

B. A. Hoener, Predicting the hepatic-clearance of xenobiotics in humans from in-vitro data. Biopharm Drug Dispos 15 295-304 (1994). 

The following section describes a human cytochrome P450 platform expressed in Escherichia coli which has proved to be robust, reproducible and predictive of human hepatic first pass drug metabolism and compares it to similar commercially available systems. 

An unanswered question does remain regarding the extension of our findings to predict the adsorption characteristics of human hepatitis type A. This virus is the etiologic agent of infectious hepatitis, which is considered to be the most serious problem in the transmission of waterborne virus disease (i). It is similar to other enteroviruses in terms of size (27 nm in diameter), density in CsCl gradients (1.34 g cm ), stability in the presence of chemical and physical agents, and probable nucleic acid type (69). However, electrokinetic properties of this virus have yet to be characterized. This information is required before accurate predictions of electrostatic components of adsorption can be made for this virus. 

For more on the merits and demerits of microsomes, cytosol, S9, and other metabolizing systems in predicting drug metabolism, see Brandon, E.F.A., et al. An update on in vitro test methods in human hepatic drug biotransformation research Pros and cons. Toxicol. Appl. Pharmacol. 2003, 189, 231-246. 

The studies described in this section demonstrate both success and challenges, and reflect the current understanding of prediction of hepatic clearance using in vitro human metabolic data. In contrast to the well-defined concepts, readily applicable biochemical methods, and sensitive analytical techniques available to studies of metabolic stability and enzyme kinetics, efforts to accurately predict in vivo, using in vitro data, require further refinement. 

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