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

Abstract

1. Determine the inhibition mechanism through which cyclosporine inhibits the uptake and metabolism of atorvastatin in fresh rat hepatocytes using mechanistic models applied to data generated using a high throughput oil spin method. 2. Atorvastatin was incubated in fresh rat hepatocytes (0.05-150 nmol/ml) with or without 20 min pre-incubation with 10 nmol/ml cyclosporine and sampled over 0.25-60 min using a high throughput oil spin method. Micro-rate constant and macro-rate constant mechanistic models were ranked based on goodness of fit values. 3. The best fitting model to the data was a micro-rate constant mechanistic model including non-competitive inhibition of uptake and competitive inhibition of metabolism by cyclosporine (Model 2). The association rate constant for atorvastatin was 150-fold greater than the dissociation rate constant and 10-fold greater than the translocation into the cell. The association and dissociation rate constants for cyclosporine were 7-fold smaller and 10-fold greater, respectively, than atorvastatin. The simulated atorvastatin-transporter-cyclosporine complex derived using the micro-rate constant parameter estimates increased in line with the incubation concentration of atorvastatin. 4. The increased amount of data generated with the high throughput oil spin method, combined with a micro-rate constant mechanistic model helps to explain the inhibition of uptake by cyclosporine following pre-incubation.

Prediction of Clinical Transporter‐Mediated Drug–Drug Interactions via Comeasurement of Pitavastatin and Eltrombopag in Human Hepatocyte Models

“…An approach was presented with structural identifiable micro‐rate constant mechanistic models that can aid in promoting systems pharmacology models of transporters and allows the Michaelis‐Menten assumptions for transporters to be formally tested. The improvement in the structural identifiability analysis results and in the model fit seen here using micro‐rate constants compared with macro‐rate constants is in line with previous studies, 11,12 showing the utility of robust micro‐rate constant mechanistic models in TrDDI analysis in drug development. The developed semimechanistic PBPK model, based on the inhibition of uptake into the liver only, predicted a likely clinical TrDDI between pitavastatin and eltrombopag when compared with use of the static R value.…”

“…Further work on developing a robust high throughput of the uptake method (see refs. 12,38) across more structurally diverse substrates and inhibitors will help increase confidence on the approach presented here using micro‐rate constants.…”

“…This can lead to the structural unidentifiability of the model, affecting the robustness of estimated parameters, upon which critical decisions may be based 7 . However, unlike the use of Michaelis‐Menten kinetics, the structural identifiability of micro‐rate constant mechanistic models are unaffected by this assumption 11,12 …”

Dynamical models for metabolomics data integration

“…Dynamical metabolic models have been applied in various contexts. Recurrently pursued objectives are 1) understanding dynamical processes [15,16], for example, through comparison of competing models [17]; 2) inferring control mechanisms and rate-limiting steps [13,18]; and 3) leveraging those to push some system of interest in specific directions, for example, for strain optimization or drug target identification) [14,19,20].…”

“…A subset of parameters was estimated from measurements of intracellular and extracellular metabolite levels under 25 different experimental conditions. Carter et al [17] performed model selection among four candidate models, calibrated on time-resolved measurements of metabolite levels, to infer the most likely inhibitory mechanism in a drug-drug interaction. Feldman-Salit et al [18] trained a dynamical model of sulfur assimilation in Arabidopsis thaliana on steady-state metabolite measurements, which allowed them to infer dynamical control patterns.…”