Our study of drug metabolism regulation and induced hepatotoxicity in cultured primary hepatocytes focuses on acetaminophen (APAP) treatment because (a) APAP-induced hepatotoxicity is the leading cause of acute liver failure in the US, and (b) APAP is used to validate clinical toxicity models. Even though the major metabolic pathways of APAP metabolism (conjugation with activated sulfate and glucuronic acid and oxidation by cytochrome P450s) are known, further study is needed to elucidate the interactions among APAP metabolism, hepatic function and induced signaling, and how these factors coordinately contribute to hepatotoxicity. Many current approaches focus on general toxicological parameters and microarray analysis to identify differentially expressed genes associated with hepatotoxicity as potential toxicity markers. However, these studies do not fully assess hepatic metabolic function or transcriptional regulation.
We have used a combination of chemical kinetic modeling and metabolic flux and pathway analyses to develop insights into the coupling of APAP and central metabolism. A kinetic model was constructed, integrating diverse sets of available data, to understand the dynamics of key species in Phase I and II detoxification and how they are affected by dose and dietary constraints. Dynamic modeling of APAP metabolism illustrated the formation of toxic metabolites in a dose-dependent fashion, the requirement for conjugation species, and increased hepatotoxicity due to ethanol pre-treatment. A Monte Carlo simulation was used to determine maximal and minimal reaction rates for the major modeling within a flux balance analysis framework.
The biochemical network was constructed to reveal interactions among APAP metabolism, central hepatic and ethanol pathways. The main pathways of APAP metabolism as well as the formation of conjugative species from amino acids, salts, and carbohydrates are included. The cytochrome P450-catalyzed oxidation cycle of substrate is based on the mechanism described by [1]. Reaction networks originally developed for isolated perfused whole liver [2] and later modified by [3] and [4] were used to model central hepatic function. Oxidative ethanol degradation pathways included in the system were obtained from [5]. The combined metabolic system (APAP-ethanol-central hepatic) consists of 98 reactions including: APAP metabolism, gluconeogenesis, tricarboxylic acid cycle, urea cycle, serum protein synthesis, intracellular uptake, fatty acid metabolism, amino acid biotransformation, conjugative species synthesis, and ethanol metabolism. Multi-objective programming was applied to the system to determine the cells' capability to metabolize the drug and investigate cellular demands during co-current drug metabolism with ethanol. In this work we utilized the approach of goal programming, which maximizes a primary objective function while constraining the secondary objective function. This approach is useful when dealing with multiple, possibly conflicting goals. This optimization generated a pareto plot illustrating the cells maximum ability to co-metabolize APAP and ethanol and the resulting flux distributions along this line. In addition, pathway analysis was applied over a number of cellular conditions for maximal drug metabolism identified in the multi-objective optimization. Pathway analysis revealed the important metabolic routes at each condition and identified how the metabolic network transitioned from a region of high drug co-current metabolism to low co-current drug metabolism [6].
To validate the results of the metabolic engineering analysis regarding the detoxification activity and interactions with the central metabolism, 40 uptake or release flux rates of external metabolites were quantified experimentally to investigate primary hepatic response to APAP treatment, and in combination with ethanol. These results allow the flux balance system to be overdetermined with a single redundant equation. This redundancy was used to calculate better estimates for the nonmeasured fluxes as well as better estimates for the measured fluxes[7].
In an effort to improve the predictability of the metabolic model we have constructed a gene regulatory network to assess transcriptional control and identify metabolic and regulatory markers important for xenobiotic detoxification and hepatotoxicity. The network was construced using advanced methods of literature mining (Ingenuity Systems), sequence alignment and other computational tools. Preliminary results have identified 22 nuclear factors meeting our search criteria including factors important to xenobiotic metabolism (AHR, ARNT, NFkB, and PPAR) as well as hepatic specific transcription factors HNF1, and HNF4 among others.
This approach may serve to classify the participation of functional markers, identify drug-overdose treatment pathways, clarify the drug-drug interactions, and discover how other properties of the network reveal underlying complexities that cannot be explained by the consideration of individual pathways or the model system in isolation.
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2. Arai, K., et al., Intrahepatic amino acid and glucose metabolism in a D-galactosamine-induced rat liver failure model. Hepatology, 2001. 34(2): p. 360-71.
3. Chan, C., et al., Metabolic flux analysis of cultured hepatocytes exposed to plasma. Biotechnol Bioeng, 2003. 81(1): p. 33-49.
4. Sharma, N.S., M.G. Ierapetritou, and M. Yarmush, Novel quantitative tools for engineering analysis of hepatocyte cultures in bioartificial liver systems. Biotechnol Bioeng, 2005. 92(3): p. 321-35.
5. Yang, F. and D.A. Beard, Thermodynamically based profiling of drug metabolism and drug-drug metabolic interactions: a case study of acetaminophen and ethanol toxic interaction. Biophys Chem. , 2006. 120(2): p. 121-34.
6. S. Klamt et al., Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria. Biotech. Bioeng. 2002; 77: 734-751.
7. Stephanopoulos, G.N., A.A. Aristidou, and J. Nielsen, Metabolic Engineering: Princeples and Methodologies. 1998, San Deigo: Academic Press.