An Investigation into the Prediction of the Plasma Concentration-Time Profile and Its Interindividual Variability for a Range of Flavin-Containing Monooxygenase Substrates Using a Physiologically Based Pharmacokinetic Modeling Approach

Abstract

Our recent paper demonstrated the ability to predict in vivo clearance of flavin-containing monooxygenase (FMO) drug substrates using in vitro human hepatocyte and human liver microsomal intrinsic clearance with standard scaling approaches. In this paper, we apply a physiologically based pharmacokinetic (PBPK) modeling and simulation approach (M&S) to predict the clearance, area under the curve (AUC), and values together with the plasma profile of a range of drugs from the original study. The human physiologic parameters for FMO, such as enzyme abundance in liver, kidney, and gut, were derived from in vitro data and clinical pharmacogenetics studies. The drugs investigated include itopride, benzydamine, tozasertib, tamoxifen, moclobemide, imipramine, clozapine, ranitidine, and olanzapine. The fraction metabolized by FMO for these drugs ranged from 21% to 96%. The developed PBPK models were verified with data from multiple clinical studies. An attempt was made to estimate the scaling factor for recombinant FMO (rFMO) using a parameter estimation approach and automated sensitivity analysis within the PBPK platform. Simulated oral clearance using in vitro hepatocyte data and associated extrahepatic FMO data predicts the observed in vivo plasma concentration profile reasonably well and predicts the AUC for all of the FMO substrates within 2-fold of the observed clinical data; seven of the nine compounds fell within 2-fold when human liver microsomal data were used. rFMO overpredicted the AUC by approximately 2.5-fold for three of the nine compounds. Applying a calculated intersystem extrapolation scalar or tissue-specific scalar for the rFMO data resulted in better prediction of clinical data. The PBPK M&S results from this study demonstrate that human hepatocytes and human liver microsomes can be used along with our standard scaling approaches to predict human in vivo pharmacokinetic parameters for FMO substrates.

In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals

“…Methods for estimating a variety of pharmacokinetic parameters for pharmaceuticals or medicines have been reported, and these parameters are often then used for predicting plasma concentration–time profiles in individuals and to investigate drug interactions. − Recently, PBPK modeling platforms and parameter estimation tools have been applied to potentially enable animal-free risk assessment of general chemicals . Because of the limited number of industrial or food chemicals with sufficient in vivo toxicokinetic data, the application of simple PBPK modeling to indicate potential chemical hazards in humans and other mammals is anticipated. , We report here that the plasma, hepatic, and renal concentrations of 246 diverse industrial chemicals (including general chemicals, food components, pesticides, drugs, and medicines), as illustrated in Figure after virtual oral doses in rats, could be reasonably estimated by solving the differential equations that constitute our PBPK models. , …”

Scaling Factors for Clearance in Adult Liver Cirrhosis

“…Tables Table 1. Comparing MPPGL-based scaling factors *From (Ozaki, et al, 2016), ** Unpublished meta-analyses of literature functional liver volume data from imaging techniques (Li, 2003;Lin, et al, 1998;Reddy, et al, 2018;Shan, et al, 2005;Zhu, 1999) via personal communication with Trevor Johnson, Simcyp, Sheffield, UK.…”

“…Population): using Equation 3 below from Barter et al, (2008) that describes relationship between MPPGL and age (in years) in healthy subjects, and accounting for the change in the functional hepatocyte volume as a reflection of the functional reserve of the liver (1.469 L for CP-A, 1.17 L for CP-B, and 0.94 L for CP-C). These functional hepatocyte volumes are implemented into the simulator according to unpublished meta-analysis of tissue imaging literature data (Li, 2003;Lin, et al, 1998;Reddy, et al, 2018;Shan, et al, 2005;Zhu, 1999).…”

Preparation of solid dispersion systems for enhanced dissolution of poorly water soluble diacerein: In-vitro evaluation, optimization and physiologically based pharmacokinetic modeling

“…In our study, first order and Higuchi diffusion model plots yielded greater R 2 values than plots for zero order model (Table 4). Hence, dissolution profiles of plain DCN and SD (1-5), (8)(9)(10)(11) as well as (15)(16)(17)(18) followed the first order model. The rest the systems followed the Higuchi diffusion model.…”

“…This is because it enables the prediction of plasma concentration time curves from in-vitro data, providing a useful source supporting decisions during the different phases of drug development [17]. It is a simulation top-down approach that involves estimation of model parameters via clinically reported PK data [18] where, successful simulation of in-vivo fate and accurate PK parameters of formulations can be achieved. Recently, PBPK modeling has received great attention for the prediction of systemic drug concentrations in healthy and special populations [19].…”