Intracellular Unbound Atorvastatin Concentrations in the Presence of Metabolism and Transport

Abstract

Accurate prediction of drug target activity and rational dosing regimen design require knowledge of drug concentrations at the target. It is important to understand the impact of processes such as membrane permeability, partitioning, and active transport on intracellular drug concentrations. The present study aimed to predict intracellular unbound atorvastatin concentrations and characterize the effect of enzyme-transporter interplay on these concentrations. Single-pass liver perfusion studies were conducted in rats using atorvastatin (ATV, 1 mM) alone at 4°C and at 37°C in presence of rifampin (RIF, 20 mM) and 1-aminobenzotriazole (ABT, 1 mM), separately and in combination. The unbound intracellular ATV concentration was predicted with a five-compartment explicit membrane model using the parameterized diffusional influx clearance, active basolateral uptake clearance, and metabolic clearance.Chemical inhibition of uptake and metabolism at 37°C proved to be better controls relative to studies at 4°C. The predicted unbound intracellular concentration at the end of the 50-minute perfusion in the 1ABT , 1ABT1RIF, and the ATV-only groups was 6.5 mM, 0.58 mM, and 5.14 mM, respectively. The predicted total liver concentrations and amount recovered in bile were within 0.94-1.3 fold of the observed value in all groups. The fold difference in total liver concentration did not always extrapolate to the fold difference in predicted unbound concentration across groups. Together, these results support the use of compartmental modeling to predict intracellular concentrations in dynamic organ-based systems. These predictions can provide insight into the role of uptake transporters and metabolizing enzymes in determining drug tissue concentrations.

A mechanistic modelling approach for the determination of the mechanisms of inhibition by cyclosporine on the uptake and metabolism of atorvastatin in rat hepatocytes using a high throughput uptake method

“…To compare the passive rate constants to the values found in the literature for atorvastatin, the passive rate constants (k fA and k bA ) were scaled from Model 2 to clearances (P dif and P def ) respectively by multiplying by the relevant volume. P dif was 11-fold greater than P def (0.55 [0.1-11.7] and 0.051 [0.03-0.2] ml/min/1 Â 10 6 cells respectively), possibly due to intracellular binding of atorvastatin (Kulkarni et al, 2016;Paine et al, 2008;Yabe et al, 2011). P dif for atorvastatin was minor (<1%) compared to the transporter mediated uptake clearance (CL up ¼ 3375 [2722-10750] ml/min/1 Â 10 6 cells, Equation (11)), therefore even though the range of values on k fA and thus P dif estimation were large it is of little consequence in the uptake of atorvastatin.…”

“…Atorvastatin is an HMG-CoA reductase inhibitor used to treat hypercholesterolaemia, and is the third most prescribed drug in the USA (Fuentes et al, 2018). Atorvastatin is taken up into hepatocytes with; 96%-98% of the uptake as carrier mediated in rat hepatocytes, high intracellular binding (fraction unbound ¼ 0.011-0.015; Kulkarni et al, 2016;Paine et al, 2008;Yabe et al, 2011), and metabolism through rCyp3a into ortho or para substituted hydroxylated metabolites (Lau et al, 2006;Watanabe et al, 2010).…”

Atorvastatin induces mitochondrial dysfunction and cell apoptosis in HepG2 cells via inhibition of the Nrf2 pathway

“…Owing to the strong influence of active uptake on the kinetics of ATO, the extracellular plasma concentration is actually lower than the hepatic intracellular concentration for ATO. It was reported that the predicted unbound intracellular ATO concentration was 5.14 μM obtained from single-pass liver perfusion experiments, which would be five-to 18fold higher than the extracellular plasma concentration (Kulkarni, Korzekwa, & Nagar, 2016;Paine, Parker, Gardiner, Webborn, & Riley, 2008). Meanwhile, C max at the maximum tolerated dose is a pharmacodynamic concentration rather than a toxic or toxicological concentration.…”

Improved Predictions of Drug–Drug Interactions Mediated by Time-Dependent Inhibition of CYP3A

“…We have successfully modeled membrane partitioning and diffusion into and out of cells using an explicit membrane-modeling approach. 75,76 This suggests that in addition to membrane partitioning, permeability data could be utilized to model parent and metabolite distribution. Efforts to incorporate the TDI models presented here into a physiological framework are currently underway in our laboratory.…”