Physiologically Based Kinetic Research Articles

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

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 (1) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, (2) the method of Rodgers and Rowland for calculating partition coefficients, and (3) 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 22 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 5 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 chemical transplacental passage into a generic PBK model: A QIVIVE approach

With the increasing application of cell culture models as primary tools for predicting chemical safety, the quantitative extrapolation of the effective dose from in vitro to in vivo (QIVIVE) is of increasing importance. For developmental toxicity this requires scaling the in vitro observed dose-response characteristics to in vivo fetal exposure, while integrating maternal in vivo kinetics during pregnancy, in particular transplacental transfer. Here the transfer of substances across the placental barrier, has been studied using the in vitro BeWo cell assay and six embryotoxic compounds of different kinetic complexity. The BeWo assay results were incorporated in an existing generic Physiologically Based Kinetic (PBK) model which for this purpose was extended with rat pregnancy. Finally, as a “proof of principle”, the BeWo PBK model was used to perform a QIVIVE based on developmental toxicity as observed in various different in vitro toxicity assays. The BeWo results illustrated different transport profiles of the chemicals across the BeWo monolayer, allocating the substances into two distinct groups: the ‘quickly-transported’ and the ‘slowly-transported’. BeWo PBK exposure simulations during gestation were compared to experimentally measured maternal blood and fetal concentrations and a reverse dosimetry approach was applied to translate in vitro observed embryotoxicity into equivalent in vivo dose-response curves. This approach allowed for a direct comparison of the in vitro dose-response characteristics as observed in the Whole Embryo Culture (WEC), and the Embryonic Stem Cell test (cardiac:ESTc and neural:ESTn) with in vivo rat developmental toxicity data. Overall, the in vitro to in vivo comparisons suggest a promising future for the application of such QIVIVE methodologies for screening and prioritization purposes of developmental toxicants. Nevertheless, the clear need for further improvements is acknowledged for a wider application of the approach in chemical safety assessment.

The Role of Kinetics as Key Determinant in Toxicity of Pyrrolizidine Alkaloids and Their N-Oxides.

Pyrrolizidine alkaloids (PAs) are a large group of plant constituents of which especially the 1,2- unsaturated PAs raise a concern because of their liver toxicity and potential genotoxic carcinogenicity. This toxicity of PAs depends on their kinetics. Differences in absorption, distribution, metabolism, and excretion (ADME) characteristics of PAs may substantially alter the relative toxicity of PAs. As a result, kinetics will also affect relative potency (REP) values. The present review summarizes the current state-of-the art on PA kinetics and resulting consequences for toxicity and illustrates how physiologically-based kinetic (PBK) modelling can be applied to take kinetics into account when defining the relative differences in toxicity between PAs in the in vivo situation. We conclude that toxicokinetics play an important role in the overall toxicity of pyrrolizidine alkaloids. and that kinetics should therefore be considered when defining REP values for combined risk assessment. New approach methodologies (NAMs) can be of use to quantify these kinetic differences between PAs and their N-oxides, thus contributing to the 3Rs (Replacement, Reduction and Refinement) in animal studies.

PBK Model-Based Prediction of Intestinal Microbial and Host Metabolism of Zearalenone and Consequences for its Estrogenicity.

ScopeThe aim of the present study is to develop physiologically‐based kinetic (PBK) models for rat and human that include intestinal microbial and hepatic metabolism of zearalenone (ZEN) in order to predict systemic concentrations of ZEN and to obtain insight in the contribution of metabolism by the intestinal microbiota to the overall metabolism of ZEN.Methods and ResultsIn vitro derived kinetic parameters, apparent maximum velocities (V max) and Michaelis–Menten constants (K m) for liver and intestinal microbial metabolism of ZEN are included in the PBK models. The models include a sub‐model for the metabolite, α‐zearalenol (α‐ZEL), a metabolite known to be 60‐times more potent as an estrogen than ZEN. Integrating intestinal microbial ZEN metabolism into the PBK models revealed that hepatic metabolism drives the formation of α‐ZEL. Furthermore, the models predicted that at the tolerable daily intake (TDI) of 0.25 µg kg−1 bw the internal concentration of ZEN and α‐ZEL are three‐orders of magnitude below concentrations reported to induce estrogenicity in vitro.ConclusionIt is concluded that combining kinetic data on liver and intestinal microbial metabolism in a PBK model facilitates a holistic view on the role of the intestinal microbiota in the overall metabolism of the foodborne xenobiotic ZEN and its bioactivation to α‐ZEL.

Physiologically based kinetic modelling predicts the in vivo relative potency of riddelliine N-oxide compared to riddelliine in rat to be dose dependent

Pyrrolizidine alkaloids (PAs) are toxic plant constituents occurring often in their N-oxide form. This raises the question on the relative potency (REP) values of PA-N-oxides compared to the corresponding parent PAs. The present study aims to quantify the in vivo REP value of riddelliine N-oxide compared to riddelliine using physiologically based kinetic (PBK) modelling, taking into account that the toxicity of riddelliine N-oxide depends on its conversion to riddelliine by intestinal microbiota and in the liver. The models predicted a lower Cmax and higher Tmax for the blood concentration of riddelliine upon oral administration of riddelliine N-oxide compared to the Cmax and Tmax predicted for an equimolar oral dose of riddelliine. Comparison of the area under the riddelliine concentration–time curve (AUCRID) obtained upon dosing either the N-oxide or riddelliine itself revealed a ratio of 0.67, which reflects the in vivo REP for riddelliine N-oxide compared to riddelliine, and appeared to closely match the REP value derived from available in vivo data. The models also predicted that the REP value will decrease with increasing dose level, because of saturation of riddelliine N-oxide reduction by the intestinal microbiota and of riddelliine clearance by the liver. It is concluded that PBK modeling provides a way to define in vivo REP values of PA-N-oxides as compared to their parent PAs, without a need for animal experiments.

Strategies to evaluate in vitro in silico physiologically based kinetic (PBK) models as essential tool in next generation (animal-free) risk evaluations

Strategies to evaluate in vitro in silico physiologically based kinetic (PBK) models as essential tool in next generation (animal-free) risk evaluations

A Systematic Review of Published Physiologically-based Kinetic Models and an Assessment of their Chemical Space Coverage.

Across multiple sectors, including food, cosmetics and pharmaceutical industries, there is a need to predict the potential effects of xenobiotics. These effects are determined by the intrinsic ability of the substance, or its derivatives, to interact with the biological system, and its concentration-time profile at the target site. Physiologically-based kinetic (PBK) models can predict organ-level concentration-time profiles, however, the models are time and resource intensive to generate de novo. Read-across is an approach used to reduce or replace animal testing, wherein information from a data-rich chemical is used to make predictions for a data-poor chemical. The recent increase in published PBK models presents the opportunity to use a read-across approach for PBK modelling, that is, to use PBK model information from one chemical to inform the development or evaluation of a PBK model for a similar chemical. Essential to this process, is identifying the chemicals for which a PBK model already exists. Herein, the results of a systematic review of existing PBK models, compliant with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, are presented. Model information, including species, sex, life-stage, route of administration, software platform used and the availability of model equations, was captured for 7541 PBK models. Chemical information (identifiers and physico-chemical properties) has also been recorded for 1150 unique chemicals associated with these models. This PBK model data set has been made readily accessible, as a Microsoft Excel® spreadsheet, providing a valuable resource for those developing, using or evaluating PBK models in industry, academia and the regulatory sectors.

Predicting the in vivo developmental toxicity of benzo[a]pyrene (BaP) in rats by an in vitro\u2013in silico approach

Developmental toxicity testing is an animal-intensive endpoints in toxicity testing and calls for animal-free alternatives. Previous studies showed the applicability of an in vitro–in silico approach for predicting developmental toxicity of a range of compounds, based on data from the mouse embryonic stem cell test (EST) combined with physiologically based kinetic (PBK) modelling facilitated reverse dosimetry. In the current study, the use of this approach for predicting developmental toxicity of polycyclic aromatic hydrocarbons (PAHs) was evaluated, using benzo[a]pyrene (BaP) as a model compound. A rat PBK model of BaP was developed to simulate the kinetics of its main metabolite 3-hydroxybenzo[a]pyrene (3-OHBaP), shown previously to be responsible for the developmental toxicity of BaP. Comparison to in vivo kinetic data showed that the model adequately predicted BaP and 3-OHBaP blood concentrations in the rat. Using this PBK model and reverse dosimetry, a concentration–response curve for 3-OHBaP obtained in the EST was translated into an in vivo dose–response curve for developmental toxicity of BaP in rats upon single or repeated dose exposure. The predicted half maximal effect doses (ED50) amounted to 67 and 45 mg/kg bw being comparable to the ED50 derived from the in vivo dose–response data reported for BaP in the literature, of 29 mg/kg bw. The present study provides a proof of principle of applying this in vitro–in silico approach for evaluating developmental toxicity of BaP and may provide a promising strategy for predicting the developmental toxicity of related PAHs, without the need for extensive animal testing.

Application of in vitro data in physiologically-based kinetic models for quantitative in vitro-in vivo extrapolation: A case-study for baclofen

Physiologically-based kinetic (PBK) models can simulate concentrations of chemicals in tissues over time without animal experiments. Nevertheless, in vivo data are often used to parameterise PBK models. This study aims to illustrate that a combination of kinetic and dynamic readouts from in vitro assays can be used to parameterise PBK models simulating neurologically-active concentrations of xenobiotics. Baclofen, an intrathecally administered drug to treat spasticity, was used as a proof-of-principle xenobiotic.An in vitro blood-brain barrier (BBB) model was used to determine the BBB permeability of baclofen needed to simulate plasma and cerebrospinal concentrations. Simulated baclofen concentrations in individuals and populations of adults and children generally fall within 2-fold of measured clinical study concentrations. Further, in vitro micro-electrode array recordings were used to determine the effect of baclofen on neuronal activity (cell signalling). Using quantitative in vitro-in vivo extrapolations (QIVIVE) corresponding doses of baclofen were estimated. QIVIVE showed that up to 4600 times lower intrathecal doses than oral and intravenous doses induce comparable neurological effects. Most simulated doses were in the range of administered doses. This show that PBK models predict concentrations in the central nervous system for various routes of administration accurately without the need for additional in vivo data.

An integrative approach to define chemical exposure threshold limits for endangered sea turtles

Environmental contaminants pose serious health threats to marine megafauna species, yet methods defining exposure threshold limits are lacking. Here, a three-pillar chemical risk assessment framework is presented based on (1) species- and chemical-specific lifetime bioaccumulation modelling, (2) non-destructive in vitro and in vivo toxicity threshold assessment, and (3) chemical risk quantification. We used the effects of cadmium (Cd) in green sea turtles (Chelonia mydas) as a proof of concept to evaluate the quantitative mechanistic modelling approach. A physiologically-based kinetic (PBK) model simulated Cd tissue concentrations (liver, kidney, muscle, fat, brain, scute, and ‘rest of the body’) in C.mydas. The validated PBK model then translated species-specific in vitro results to in vivo effects. The results showed that the resilience of C.mydas towards Cd kidney toxicity is age-dependent and differs with changing physiology and feeding ecology. Using the model in reverse mode, a steady-state exposure threshold of 0.1 µg/g dry weight Cd in forage was derived and compared to real-world exposure scenarios. Three out of the four globally distinct C.mydas populations assessed are exposed to Cd levels above this threshold limit. This approach can be adapted to other marine species and chemicals to prioritize measures for managing potentially harmful chemical exposures.

A mechanistic model to study the kinetics and toxicity of salicylic acid in the kidney of four virtual individuals

In comparison to liver toxicology, little is known about mechanisms of adverse effects in the kidney and only limited computational models exist to investigate nephrotoxicity. However, the kidney is a major target for toxicity by pharmaceuticals and environmental pollutants. Accumulation is known to play an important role in certain nephrotoxicity pathways. Therefore, physiologically-based kinetic (PBK) and mechanistic models are considered to offer valuable insights into mechanisms of nephrotoxicity. This study addresses the growing attention given to exposure-based and toxicokinetics-driven toxicity which has resulted in increasing recent application of PBK modelling. This research presents the development of a novel mechanistic kidney model embedded in a PBK model parameterised for aspirin and salicylic acid. It is set up to study a combination of young/healthy individuals as well as the elderly, with three exposure scenarios simulating real renal exposure. Key challenges in this endeavour revolve around limited data available in the public literature and uncertainties related to scaling in vitro data to an in vivo setting. Results show that at a low, chronic therapeutic dose the predicted proximal tubular cell concentrations in all considered population groups are below the hypothesised toxicity threshold of 0.09 mM. Sensitivity analyses indicate that at both higher dose scenarios, active transporter activity has the most impact on predicted proximal tubular cell concentrations and therefore individual transporter expression may be key indicators of cellular toxicity. In addition, the fraction unbound of salicylic acid in plasma, the glomerular filtration rate and a decreased urinary pH have a high impact on predicted proximal tubular cell concentrations which largely exceed the toxicity threshold for all individuals at both higher dose scenarios.

Derivation of a Human In Vivo Benchmark Dose for Perfluorooctanoic Acid From ToxCast In Vitro Concentration-Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation.

A computational workflow which integrates physiologically based kinetic (PBK) modeling, global sensitivity analysis (GSA), approximate Bayesian computation (ABC), and Markov Chain Monte Carlo (MCMC) simulation was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow accounts for parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for perfluorooctanoic acid (PFOA) and high throughput screening (HTS) in vitro concentration–response data, determined in a human liver cell line, from the ToxCast/Tox21 database. In vivo benchmark doses (BMDs) for PFOA intake (ng/kg BW/day) and drinking water exposure concentrations (µg/L) were calculated from the in vivo dose responses and compared to intake values derived by the European Food Safety Authority (EFSA). The intake benchmark dose lower confidence limit (BMDL5) of 0.82 was similar to 0.86 ng/kg BW/day for altered serum cholesterol levels derived by EFSA, whereas the intake BMDL5 of 6.88 was six-fold higher than the value of 1.14 ng/kg BW/day for altered antibody titer also derived by the EFSA. Application of a chemical-specific adjustment factor (CSAF) of 1.4, allowing for inter-individual variability in kinetics, based on biological half-life, gave an intake BMDL5 of 0.59 for serum cholesterol and 4.91 (ng/kg BW/day), for decreased antibody titer, which were 0.69 and 4.31 the EFSA-derived values, respectively. The corresponding BMDL5 for drinking water concentrations, for estrogen receptor binding activation associated with breast cancer, pregnane X receptor binding associated with altered serum cholesterol levels, thyroid hormone receptor α binding leading to thyroid disease, and decreased antibody titer (pro-inflammation from cytokines) were 0.883, 0.139, 0.086, and 0.295 ng/ml, respectively, with application of no uncertainty factors. These concentrations are 5.7-, 36-, 58.5-, and 16.9-fold lower than the median measured drinking water level for the general US population which is approximately, 5 ng/ml.

New framework for a non-animal approach adequately assures the safety of cosmetic ingredients – A case study on caffeine

This case study on the model substance caffeine demonstrates the viability of a 10-step read-across (RAX) framework in practice. New approach methodologies (NAM), including RAX and physiologically-based kinetic (PBK) modelling were used to assess the consumer safety of caffeine. Appropriate animal systemic toxicity data were used from the most relevant RAX analogue while assuming that no suitable animal toxicity data were available for caffeine. Based on structural similarities, three primary metabolites of the target chemical caffeine (theophylline, theobromine and paraxanthine) were selected as its most relevant analogues, to estimate a point of departure in order to support a next generation risk assessment (NGRA). On the basis of the pivotal mode of action (MOA) of caffeine and other methylxanthines, theophylline appeared to be the most potent and suitable analogue. A worst-case aggregate exposure assessment determined consumer exposure to caffeine from different sources, such as cosmetics and food/drinks. Using a PBK model to estimate human blood concentrations following exposure to caffeine, an acceptable Margin of Internal Exposure (MOIE) of 27-fold was derived on the basis of a RAX using theophylline animal data, which suggests that the NGRA approach for caffeine is sufficiently conservative to protect human health.

Next generation risk assessment (NGRA): Bridging in vitro points-of-departure to human safety assessment using physiologically-based kinetic (PBK) modelling – A case study of doxorubicin with dose metrics considerations

Using the chemical doxorubicin (DOX), the objective of the present study was to evaluate the impact of dose metrics selection in the new approach method of integrating physiologically-based kinetic (PBK) modelling and relevant human cell-based assays to inform a priori the point of departure for human health risk. We reviewed the literature on the clinical consequences of DOX treatment to identify dosing scenarios with no or mild cardiotoxicity observed. Key concentrations of DOX that induced cardiomyocyte toxicity in vitro were derived from studies of our own and others. A human population-based PBK model of DOX was developed and verified against pharmacokinetic data. The model was then used to predict plasma and extracellular and intracellular heart concentrations of DOX under selected clinical settings and compared with in vitro outcomes, based on several dose metrics: Cmax (maximum concentration) or AUC (area under concentration-time curve) in free or total form of DOX. We found when using in vitro assays to predict cardiotoxicity for DOX, AUC is a better indicator. Our study illustrates that when appropriate dose metrics are used, it is possible to combine PBK modelling with in vitro-derived toxicity information to define margins of safety and predict low-risk human exposure levels.

Physiologically based kinetic modelling based prediction of in vivo rat and human acetylcholinesterase (AChE) inhibition upon exposure to diazinon

The present study predicts in vivo human and rat red blood cell (RBC) acetylcholinesterase (AChE) inhibition upon diazinon (DZN) exposure using physiological based kinetic (PBK) modelling-facilitated reverse dosimetry. Due to the fact that both DZN and its oxon metabolite diazoxon (DZO) can inhibit AChE, a toxic equivalency factor (TEF) was included in the PBK model to combine the effect of DZN and DZO when predicting in vivo AChE inhibition. The PBK models were defined based on kinetic constants derived from in vitro incubations with liver fractions or plasma of rat and human, and were used to translate in vitro concentration–response curves for AChE inhibition obtained in the current study to predicted in vivo dose–response curves. The predicted dose–response curves for rat matched available in vivo data on AChE inhibition, and the benchmark dose lower confidence limits for 10% inhibition (BMDL10 values) were in line with the reported BMDL10 values. Humans were predicted to be 6-fold more sensitive than rats in terms of AChE inhibition, mainly because of inter-species differences in toxicokinetics. It is concluded that the TEF-coded DZN PBK model combined with quantitative in vitro to in vivo extrapolation (QIVIVE) provides an adequate approach to predict RBC AChE inhibition upon acute oral DZN exposure, and can provide an alternative testing strategy for derivation of a point of departure (POD) in risk assessment.

Gaining acceptance in next generation PBK modelling approaches for regulatory assessments – An OECD international effort

Physiologically Based Kinetic (PBK) models are valuable tools to help define safe external levels of chemicals based on internal doses at target organs in experimental animals, humans and organisms used in environmental risk assessment. As the toxicity testing paradigm shifts to alternative testing approaches, PBK model development has started to rely (mostly or entirely) on model parameters quantified using in vitro or in silico methods. Recently, the Organisation for Economic Cooperation and Development (OECD) published a guidance document (GD) describing a scientific workflow for characterising and validating PBK models developed using in vitro and in silico data. The GD provides an assessment framework for evaluating these models, with emphasis on the major uncertainties underlying model inputs and outputs. To help end-users submit or evaluate a PBK model for regulatory purposes, the GD also includes a template for documenting the model characteristics, and a checklist for evaluating the quality of a model. This commentary highlights the principles, criteria and tools laid out in the OECD PBK model GD, with the aim of facilitating the dialogue between model developers and risk assessors.

Prediction of dose-dependent in vivo acetylcholinesterase inhibition by profenofos in rats and humans using physiologically based kinetic (PBK) modeling-facilitated reverse dosimetry

Organophosphate pesticides (OPs) are known to inhibit acetylcholine esterase (AChE), a critical effect used to establish health-based guidance values. This study developed a combined in vitro–in silico approach to predict AChE inhibition by the OP profenofos in rats and humans. A physiologically based kinetic (PBK) model was developed for both species. Parameter values for profenofos conversion to 4-bromo-2-chlorophenol (BCP) were derived from in vitro incubations with liver microsomes, liver cytosol, and plasma from rats (catalytic efficiencies of 1.1, 2.8, and 0.19 ml/min/mg protein, respectively) and humans (catalytic efficiencies of 0.17, 0.79, and 0.063 ml/min/mg protein, respectively), whereas other chemical-related parameter values were derived using in silico calculations. The rat PBK model was evaluated against literature data on urinary excretion of conjugated BCP. Concentration-dependent inhibition of rat and human AChE was determined in vitro and these data were translated with the PBK models to predicted dose-dependent AChE inhibition in rats and humans in vivo. Comparing predicted dose-dependent AChE inhibition in rats to literature data on profenofos-induced AChE inhibition revealed an accurate prediction of in vivo effect levels. Comparison of rat predictions (BMDL10 of predicted dose–response data of 0.45 mg/kg bw) and human predictions (BMDL10 of predicted dose–response data of 0.01 mg/kg bw) suggests that humans are more sensitive than rats, being mainly due to differences in kinetics. Altogether, the results demonstrate that in vivo AChE inhibition upon acute exposure to profenofos was closely predicted in rats, indicating the potential of this novel approach method in chemical hazard assessment.

Incorporating renal excretion via the OCT2 transporter in physiologically based kinetic modelling to predict in vivo kinetics of mepiquat in rat

The present study aimed at incorporating active renal excretion via the organic cation transporter 2 (OCT2) into a generic rat physiologically based kinetic (PBK) model using an in vitro human renal proximal tubular epithelial cell line (SA7K) and mepiquat chloride (MQ) as the model compound. The Vmax (10.5 pmol/min/mg protein) and Km (20.6 μM) of OCT2 transport of MQ were determined by concentration-dependent uptake in SA7K cells using doxepin as inhibitor. PBK model predictions incorporating these values in the PBK model were 6.7–8.4-fold different from the reported in vivo data on the blood concentration of MQ in rat. Applying an overall scaling factor that also corrects for potential differences in OCT2 activity in the SA7K cells and in vivo kidney cortex and species differences resulted in adequate predictions for in vivo kinetics of MQ in rat (2.3–3.2-fold). The results indicate that using SA7K cells to define PBK parameters for active renal OCT2 mediated excretion with adequate scaling enables incorporation of renal excretion via the OCT2 transporter in PBK modelling to predict in vivo kinetics of mepiquat in rat. This study demonstrates a proof-of-principle on how to include active renal excretion into generic PBK models.

Assessment of the predictive capacity of a physiologically based kinetic model using a read-across approach

With current progress in science, there is growing interest in developing and applying Physiologically Based Kinetic (PBK) models in chemical risk assessment, as knowledge of internal exposure to chemicals is critical to understanding potential effects in vivo. In particular, a new generation of PBK models is being developed in which the model parameters are derived from in silico and in vitro methods. To increase the acceptance and use of these “Next Generation PBK models”, there is a need to demonstrate their validity. However, this is challenging in the case of data-poor chemicals that are lacking in kinetic data and for which predictive capacity cannot, therefore, be assessed. The aim of this work is to lay down the fundamental steps in using a read across framework to inform modellers and risk assessors on how to develop, or evaluate, PBK models for chemicals without in vivo kinetic data. The application of a PBK model that takes into account the absorption, distribution, metabolism and excretion characteristics of the chemical reduces the uncertainties in the biokinetics and biotransformation of the chemical of interest. A strategic flow-charting application, proposed herein, allows users to identify the minimum information to perform a read-across from a data-rich chemical to its data-poor analogue(s). The workflow analysis is illustrated by means of a real case study using the alkenylbenzene class of chemicals, showing the reliability and potential of this approach. It was demonstrated that a consistent quantitative relationship between model simulations could be achieved using models for estragole and safrole (source chemicals) when applied to methyleugenol (target chemical). When the PBK model code for the source chemicals was adapted to utilise input values relevant to the target chemical, simulation was consistent between the models. The resulting PBK model for methyleugenol was further evaluated by comparing the results to an existing, published model for methyleugenol, providing further evidence that the approach was successful. This can be considered as a “read-across” approach, enabling a valid PBK model to be derived to aid the assessment of a data poor chemical.

A new approach methodology (NAM) for the prediction of (nor)ibogaine-induced cardiotoxicity in humans.

The development of non-animal-based new approach methodologies (NAMs) for chemical risk assessment and safety evaluation is urgently needed. The aim of the present study was to investigate the applicability of an in vitro-in silico approach to predict human cardiotoxicity of the herbal alkaloid ibogaine and its metabolite noribogaine, which are promising anti-addiction drugs. Physiologically based kinetic (PBK) models were developed using in silico-derived parameters and biokinetic data obtained from in vitro liver microsomal incubations and Caco-2 transport studies. Human induced pluripotent stem cell-derived cardiomyocytes combined with a multi-electrode array (MEA) assay were used to determine in vitro concentration-dependent cardiotoxicity reflected by prolongation of field potential duration, which was subsequently translated to in vivo dose-dependent prolongation of the QTc (heart rate corrected duration from ventricular depolarization to repolarization) using PBK modeling-based reverse dosimetry. Results showed that the predictions matched well with in vivo kinetic data and QTc data for ibogaine and noribogaine available in the literature, indicating a good performance of the NAM. Benchmark dose analysis of the predicted dose response curves adequately predicted the onset of in vivo cardiotoxicity detected by QTc prolongation upon oral exposure to ibogaine and noribogaine. The present study provides an additional proof-of-principle of using PBK modeling-based reverse dosimetry as a NAM to predict human cardiotoxicity.