The nested enzyme-within-enterocyte (NEWE) turnover model for predicting dynamic drug and disease effects on the gut wall.

Physiologically-based pharmacokinetic (PBPK) models provide a framework for in vitro-in vivo extrapolation of metabolic drug clearance. Many of the concepts in PBPK can have consequential impact on more mechanistic systems pharmacology models. In the gut wall, turnover of enzymes and enterocytes are typically lumped into one rate constant that describes the time dependent enzyme activity. This assumption may influence predictability of any sustained and dynamic effects such as mechanism-based inhibition (MBI), particularly when considering translation from healthy to gut disease. A novel multi-level systems PBPK model was developed. This model comprised a 'nested enzyme-within enterocyte' (NEWE) turnover model to describe levels of drug-metabolising enzymes. The ability of the model to predict gut metabolism following MBI and gut disease was investigated and compared to the conventional modelling approach. For MBI, the default NEWE model performed comparably to the conventional model. However, when drug-specific spatial crypt-villous absorption was considered, up to approximately 50% lower impact of MBI was simulated for substrates highly metabolised by cytochrome P450 (CYP) 3A4, interacting with potent inhibitors. Further, the model showed potential in predicting the disease effect of gastrointestinal mucositis and untreated coeliac disease when compared to indirect clinical pharmacokinetic parameters. Considering the added complexity of the NEWE model, it does not provide an attractive solution for improving upon MBI predictions in healthy individuals. However, nesting turnover may enable extrapolation to gut disease-drug interactions. The principle detailed herein may be useful for modelling drug interactions with cellular targets where turnover is significant enough to affect this process.

Abundance of Hepatic Transporters in Caucasians: A Meta-Analysis.

This study aimed to derive quantitative abundance values for key hepatic transporters suitable for in vitro-in vivo extrapolation within a physiologically based pharmacokinetic modeling framework. A meta-analysis was performed whereby data on abundance measurements, sample preparation methods, and donor demography were collated from the literature. To define values for a healthy Caucasian population, a subdatabase was created whereby exclusion criteria were applied to remove samples from non-Caucasian individuals, those with underlying disease, or those with subcellular fractions other than crude membrane. Where a clinically relevant active genotype was known, only samples from individuals with an extensive transporter phenotype were included. Authors were contacted directly when additional information was required. After removing duplicated samples, the weighted mean, geometric mean, standard deviation, coefficient of variation, and between-study homogeneity of transporter abundances were determined. From the complete database containing 24 transporters, suitable abundance data were available for 11 hepatic transporters from nine studies after exclusion criteria were applied. Organic anion transporting polypeptides OATP1B1 and OATP1B3 showed the highest population abundance in healthy adult Caucasians. For several transporters, the variability in abundance was reduced significantly once the exclusion criteria were applied. The highest variability was observed for OATP1B3 > OATP1B1 > multidrug resistance protein 2 > multidrug resistance gene 1. No relationship was found between transporter expression and donor age. To our knowledge, this study provides the first in-depth analysis of current quantitative abundance data for a wide range of hepatic transporters, with the aim of using these data for in vitro-in vivo extrapolation, and highlights the significance of investigating the background of tissue(s) used in quantitative transporter proteomic studies. Similar studies are now warranted for other ethnicities.

Progress curve mechanistic modeling approach for assessing time-dependent inhibition of CYP3A4.

A progress curve method for assessing time-dependent inhibition of CYP3A4 is based on simultaneous quantification of probe substrate metabolite and inhibitor concentrations during the experiment. Therefore, it may overcome some of the issues associated with the traditional two-step method and estimation of inactivation rate (k(inact)) and irreversible inhibition (K(I)) constants. In the current study, seven time-dependent inhibitors were investigated using a progress curve method and recombinant CYP3A4. A novel mechanistic modeling approach was applied to determine inhibition parameters using both inhibitor and probe metabolite data. Progress curves generated for clarithromycin, erythromycin, diltiazem, and N-desmethyldiltiazem were described well by the mechanistic mechanism-based inhibition (MBI) model. In contrast, mibefradil, ritonavir, and verapamil required extension of the model and inclusion of competitive inhibition term for the metabolite. In addition, this analysis indicated that verapamil itself causes minimal MBI, and the formation of inhibitory metabolites was responsible for the irreversible loss of CYP3A4 activity. The k(inact) and K(I) estimates determined in the current study were compared with literature data generated using the conventional two-step method. In the current study, the inactivation efficiency (k(inact)/K(I)) for clarithromycin, ritonavir, and erythromycin were up to 7-fold higher, whereas k(inact)/K(I) for mibefradil, N-desmethyldiltiazem, and diltiazem were, on average, 2- to 4.8-fold lower than previously reported estimates. Use of human liver microsomes instead of recombinant CYP3A4 resulted in 5-fold lower k(inact)/K(I) for erythromycin. In conclusion, the progress curve method has shown a greater mechanistic insight when determining kinetic parameters for MBI in addition to providing a more comprehensive experimental protocol.

IC50-based approaches as an alternative method for assessment of time-dependent inhibition of CYP3A4.

The predictive utility of two in vitro methods (empirical IC(50)-based and mechanistic k(inact)/K(I)) for the assessment of time-dependent cytochrome P450 3A4 (CYP3A4) inhibition has been compared. IC(50) values were determined at multiple pre-incubation time points over 30 min for five CYP3A4 time-dependent inhibitors (verapamil, diltiazem, erythromycin, clarithromycin, and azithromycin). The ability of IC(50) data obtained following pre-incubation to predict k(inact)/K(I) parameters was investigated and its utility was assessed relative to the conventional k(inact)/K(I) model using 50 reported clinical drug-drug interactions (DDIs). Models with either hepatic or hepatic with intestinal components were explored. For low/medium potency time-dependent inhibitors, 81% of the predicted k(inact)/K(I(unbound)) from IC(50) data were within an order of magnitude of the actual values, in contrast to 50% of potent inhibitors. An underprediction trend and > 50% of false-negatives were observed when IC(50) data were used in the DDI hepatic prediction model; incorporation of the intestine improved the prediction accuracy. On the contrary, 86% of the DDI studies were predicted within twofold using k(inact)/K(I) mechanistic approach and the combined hepatic and intestinal model. Use of the empirical IC(50) approach as an alternative to the mechanistic k(inact)/K(I) model for in vivo DDI prediction is limited and is best restricted to preliminary investigations.