Multiscale mechanistic models in Systems Pharmacology: development of a model describing Atorvastatin pharmacokinetics through integration of metabolic network in Physiologically Based Pharmacokinetic models.
Nicola Melillo, Lorenzo Pasotti and Paolo Magni.
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 5, Pavia, I-27100, Italy.
Objectives: This work aims to develop a Whole Body Physiologically Based Pharmacokinetic (WB-PBPK) model of Atorvastatin (AS), an HMG-CoA reductase inhibitor, and its metabolite AS-lactone (ASL) to predict drug plasmatic concentration in human through integration of in vitro experiments and prior physiological knowledge. Drug hepatic metabolism was described using a rescaled in vitro derived metabolic network coupled with the PBPK model.
Methods: An adaptation of the Compartmental and Transit (CAT) model was built to describe dissolution, transit and absorption of AS in the intestine following an oral dose. Metabolism due to CYP3A activity in enterocytes was added using an intrinsic clearance derived from in vitro experiments [1] as in [2]. This model was then coupled with a PBPK model describing AS distribution in organs. AS hepatic metabolism was described by using a metabolic network modelled through a set of differential equations representing the dynamic of the enzymatic reactions involved in the drug metabolism process. A metabolic network parametrized through in vitro experiments with hepatocytes was taken from the literature [3] and was integrated in the PBPK model upon appropriate parameters rescaling. The network includes reactions catalysed by the enzymes CYP3A4, UGT1A3 and membrane transporters. Finally, was also developed a PBPK model for ASL and was coupled with the previous one supposing that the formation of ASL can be attributed only to the activity of UGT1A3 in liver.
Results: Predicted Cmax, AUC and tmax of AS venous plasma concentration for 40mg oral administration are in the range of one standard deviation from the mean of clinical data collected by [4]. For the dose of 20mg predicted AS Cmax remains in the range of one standard deviation from the mean of the data [4] but tmax and AUC are underpredicted. Concerning ASL the model under -predicts all the metrics.
Conclusions: This model is a good instrument for the prediction of human in vivo concentration of AS, however, does not well explain the pharmacokinetics of ASL. This happens maybe because the conversion from AS to ASL occurs in other sites than liver where UGT enzymes are expressed, for example gut wall [5] and kidney [6]. In conclusion, these types of models provide a way to describe drug absorption, distribution and metabolic processes in an integrated manner. These models could be useful to understand how the current knowledge supports data explanation.
References:
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