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Cirrhosis includes chronic, advanced fibrosis (scarring) of liver, a major site of drug metabolism and clearance. It results from the perpetuation of the normal wound healing response and subsequent distortion of hepatic histoarchitecture. The disease complicates drug therapy management because it alters hepatic drug disposition, which, in turn, alters pharmacokinetic (PK) and/or pharmacodynamic characteristics. The nature of that alteration is dependent on the nature and the extent of disease. With some exceptions (Hung et al., 2002), little is known about exactly how the cirrhotic changes affect hepatic drug disposition. Improved, mechanistic insight is needed to enable rational drug therapy based on clinical measures of both type and extent of liver disease.
We have used a new strategy to achieve improved insight. It involves developing and experimenting on multi-scale In Silico Livers (ISLs) (Park, Ropella, Kim, Roberts, & Hunt, 2009). Key ISL and support components were represented as independent, interacting agents. In silico experiments were conducted using an automated, high-performance software environment capable of parallel computation. We started with ISLs that validated for drug and sucrose disposition profiles of normal rat livers in situ (Yan, Ropella, Park, Roberts, & Hunt, 2008). The models were then subjected to cycles of experimentation and iterative refinement to achieve simulated drug disposition profiles that were acceptably similar to those from two different types of diseased livers (Hung et al., 2002). The diseased ISLs were created independently: for each type, an increasing number of normal ISL characteristics were altered systematically until simulated drug disposition profiles were experimentally indistinguishable from the referent profiles.
Having achieved a degree of validation, we posit that the causative, mechanistic details occurring during ISL execution may have hepatic counterparts during disposition, as diagrammed in Figure 1. Differences in micro-mechanistic details between the normal and the two “diseased” ISLs are hypotheses about corresponding differences between the normal and diseased livers, and about differences between diseased livers. Achieving a degree of profile similarity is evidence supporting those hypotheses. The differences in dynamic, multilevel details during execution of the two diseased ISLs provide a plausible, physiologically based explanation of the disease-caused differences in hepatic drug disposition. With additional rounds of refinement and validation, future ISLs are expected to provide increasingly useful scientific predictions and deeper insight into mechanistic details of disease progression and its role in hepatic PK.
Figure 1. Relationships between perfused rat liver models and In Silico Livers (ISLs). (A) Perfused rat livers in their experimental context are the referent systems. During experiments, liver components interact with transiting drug molecules causing changes in the compound’s outflow profile. Systemic behaviors at all levels are reflected in the collected data. (B) ISL components are designed, coded, verified, and assembled based on mechanism and component descriptions that are physiologically based. Concretizable mapping 1 is intended to exist from ISL components and interactions to hepatic physiological and micro-anatomic details. System dynamics during execution (mapping 2) are intended to abstractly represent plausible corresponding dynamics occurring within the liver during disposition. Measures of dynamics provide time series data that are intended to mimic counterpart measures of wet-laboratory perfusion experiments. Achieving measurable similarities makes mapping 3 quantitative. (C) Diseased counterparts to normal ISLs can be achieved by iteratively altering a subset of normal parameter values, mechanisms, and events into diseased counterparts that validate. Conceptual mappings are offered to relate differences in parameter values (normal to diseased) to measures of histopathology.
