title: Enabling clearance predictions to emerge from in silico actions of quasi-autonomous hepatocyte components.
abstract: We demonstrate the feasibility of using in silico hepatocyte cultures (ISHCs) to provide predictions of the intrinsic clearance (CL) of compounds in hepatocyte cultures. We compare results with predictions obtained using a multiple linear regression method. Our expectation is that the method can be extended to predict in vivo clearance of new compounds in humans. Within ISHCs, mobile "compounds" carry information describing referent compound properties. We used an iterative refinement protocol for ISHC refinement and development of parameterization methods. Quasi-autonomous "hepatocytes" and their components (including "transporters" and "enzymes") use a small, event-specific subset of compound properties to decide how to interact with encountered compounds each simulation cycle. The probability of occurrence for each event is specified by a rule that uses a subset of compound properties known to influence that event in vitro. ISHC experiments mimic in vitro counterparts. In silico clearance is measured the same as in vitro clearance and is used to predict a corresponding CL value. For 39 of 73 compounds having calculated CL S.D.s, 79% of ISHC predictions and 23% of regression predictions were within CL ± 2 S.D. For all 73 compounds, 38% of ISHC predictions and 32% of regression predictions were within a factor of 2 of the referent CL values. ISHC details during simulations stand as a mechanistic hypothesis of how clearance phenomena emerge during in vitro experiments.
authors.affiliation: Joint Graduate Group in Bioengineering, University of California, Berkeley, California, USA.
authors.forename: Shahab
authors.initials: S
authors.lastname: Sheikh-Bahaei
authors.forename: C Anthony
authors.initials: CA
authors.lastname: Hunt
mesh_terms: D002478:Cells, Cultured; D022781:Hepatocytes / Q000166:cytology / Q000378:metabolism*; D006801:Humans; D016014:Linear Models; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D010599:Pharmacokinetics; D012680:Sensitivity and Specificity
chemical_list: D004364:Pharmaceutical Preparations