Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data

Abstract

The goal of the present study was to assess the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model based on in vitro and in silico input data to predict peak plasma concentrations (Cmax) upon single oral dosing. To this purpose, a dataset was generated of 3960 Cmax predictions for 44 compounds, applying different combinations of in vitro and in silico approaches for chemical parameterization, and comparison of the predictions to reported in vivo data. Best performance was obtained when i) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, ii) the method of Rodgers and Rowland for calculating partition coefficients, and iii) in silico calculated fraction unbound plasma and Papp values (the latter especially for very lipophilic compounds). Based on these input data, the median Cmax of 32 compounds could be predicted within 10-fold of the observed Cmax, with 21 out of these 32 compounds being predicted within 5-fold, and 8 compounds within 2-fold. Overestimations of more than 10-fold were observed for 12 compounds, whereas no underestimations of more than 10-fold occurred. Median Cmax predictions were frequently found to be within 10-fold of the observed Cmax when the scaled unbound hepatic intrinsic clearance (Clint,u) was either higher than 20 L/h or lower than 1 L/h. Similar findings were obtained with a test set of five in-house BASF compounds. Overall, this study provides relevant insights in the predictive performance of a minimal PBK model based on in vitro and in silico input data.

Integrating In Vitro Data and Physiologically Based Kinetic Modeling to Predict and Compare Acute Neurotoxic Doses of Saxitoxin in Rats, Mice, and Humans

“…Briefly, a QSAR-predicted in vitro apparent permeability constant (LogP app ) for Caco-2 cells (−0.241 × 10 –6 cm/s) was scaled to ka and Fa based on the following equations (eqs –): where eq describes the in vitro to in vivo scaling of the Caco-2 apparent permeability (LogP app ) to a human effective permeability (LogP eff ) for passively diffusive compounds . The rat P eff was defined using eq with the human P eff and an interspecies scaling factor . With eqs and , along with an R (the radius of the small intestine) of 0.18 cm for rats or 1 cm for humans, , and a T si (the small intestinal transit time) of 1.47 h for rats or 3.32 h for humans, , the P eff value was converted to a ka and an Fa, which were 0.07 h –1 and 0.1 for rats and 0.14 h –1 and 0.36 for humans, respectively.…”

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

“…PBK models will need to be evaluated against pre-existing in vivo data and the observed performance extrapolated to new chemicals (Cohen Hubal et al 2019;Wambaugh et al 2015). Although PBK-based predictions of in vivo blood concentration based on in vitro data have frequently been shown to be sufficiently adequate for a wide range of chemicals, poor model performance is often due to applications outside the original model domain (Punt et al 2022(Punt et al , 2021aWambaugh et al 2015;Wang 2010;Wetmore et al 2012). Therefore, it would be important to be able to define a priori if a particular chemical-based on its physicochemical properties and chemical-biological properties-is expected to be within or outside of the PBK model domain (Bell et al 2018;Wambaugh et al 2015).…”

“…The selection of which aspects of physiology to include in these PBK models was often a function of both the extrapolation needed as well as expert determination of the processes key to understanding the specific chemicals kinetics, for example transporters for chemicals known to be substrates or lungs for chemicals known to be volatile (Andersen 1995;Campbell et al 2012;Clewell et al 2000;Jones et al 2012a). Within the last decade, however, an important shift has taken place towards so-called bottomup approaches that make use of generic model structures (that is, describing the same aspects of physiology for all chemicals) and in vitro and/or in silico input data to parameterise these models (Bessems et al 2014;Breen et al 2021;Cohen Hubal et al 2019;Honda et al 2019;Jamei et al 2009;Pearce et al 2017b;Punt et al 2021a;Rodgers and Rowland 2006;Wambaugh et al 2018). There were several important drivers for this shift towards bottom-up approaches.…”

Effects of Drugs Formerly Proposed for COVID-19 Treatment on Connexin43 Hemichannels

“…For each drug, 6sixor seven different concentrations were selected, based on peak total plasma concentrations (C max ) retrieved from the literature. Relevant in vitro concentrations are generally 10-fold higher than C max values, therefore concentrations of this range were recalculated from the C max values with a maximum of a 10-fold difference [32][33][34]. Data obtained by the MTT assay were used to generate concentration-response curves.…”