Physiologically Based Kinetic Modelling Research Articles

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

Using read-across to build physiologically-based kinetic models: Part 1. Development of a KNIME workflow to assist analogue selection for PBK modelling

Read-across refers to the process by which information from one (source) chemical is used to infer information about another similar (target) chemical. This method can be used to fill data gaps and so inform safety assessment where data are lacking for chemicals of interest. As one chemical cannot be considered as absolutely similar to another, only similar with respect to a given property, it is essential to justify the selection of similar chemicals (analogues) for the purposes of read-across. A previously created dataset of available physiologically-based kinetic (PBK) models (referred to as the PBK modelling dataset or PMD) was used in the development of a KNIME workflow. KNIME is a freely-available, open-source analytics platform that allows users to create workflows to analyse and visualise data. The KNIME workflow described here was designed to identify chemical analogues with a corresponding model in the PMD. The PMD combined with the KWAAS enables PBK model information from source chemical(s) to be used in a read-across approach to help develop new PBK models for target chemicals. This KNIME workflow was applied to six chemicals, representing different types of chemical classes (drugs, cosmetics, botanicals, industrial chemicals, pesticides, and food additives) to assess its applicability across various industries. Information acquired from these PBK models can be used to support safety assessment of chemicals and reduce reliance on animal testing.

Predicting acute paraquat toxicity using physiologically based kinetic modelling incorporating in vitro active renal excretion via the OCT2 transporter

Including active renal excretion in physiologically based kinetic (PBK) models can improve their use in quantitative in vitro- in vivo extrapolation (QIVIVE) as a new approach methodology (NAM) for predicting the acute toxicity of organic cation transporter 2 (OCT2) substrates like paraquat (PQ). To realise this NAM, kinetic parameters Vmax and Km for in vitro OCT2 transport of PQ were obtained from the literature. Appropriate scaling factors were applied to translate the in vitro Vmax to an in vivo Vmax. in vitro cytotoxicity data were defined in the rat RLE-6TN and L2 cell lines and the human A549 cell line. The developed PQ PBK model was used to apply reverse dosimetry for QIVIVE translating the in vitro cytotoxicity concentration-response curves to predicted in vivo toxicity dose-response curves after which the lower and upper bound benchmark dose (BMD) for 50% lethality (BMDL50 and BMDU50) were derived by applying BMD analysis. Comparing the predictions to the in vivo reported LD50 values resulted in a conservative prediction for rat and a comparable prediction for human showing proof of principle on the inclusion of active renal excretion and prediction of PQ acute toxicity for the developed NAM.

ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application.

Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats.

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

Applicability of generic PBK modelling in chemical hazard assessment: A case study with IndusChemFate

Toxicology is moving away from animal testing towards in vitro tools to assess chemical safety. This new testing framework requires a quantitative method, i.e. kinetic modelling, which extrapolates effective concentrations in vitro to a bioequivalent human dose in vivo and which can be applied on “high throughput screening” of a wide variety of chemicals. Generic physiologically based kinetic (PBK) models help account for the role of toxicokinetics in setting human toxic exposure levels. Furthermore these models may be parameterized only on in silico QSARs and in vitro metabolism assays, thereby circumventing the use of in vivo toxicokinetics for this purpose. Though several such models exist their applicability domains have yet to be comprehensively assessed. This study extends previous evaluations of the PBK model IndusChemFate and compares it with its more complex biological complement (“TNO Model”). Both models were evaluated with a broad span of chemicals, varying regarding physicochemical properties. The results reveal that the “simpler” performed best, illustrating that IndusChemFate can be a useful first-tier for simulating toxicokinetics based on QSARs and in vitro parameters. Finally, proper quantitative in vitro to in vivo extrapolation conditions were illustrated starting with acetaminophen induced in vitro cytotoxicity in human HepaRG cells.

In vitro–in silico-based prediction of inter-individual and inter-ethnic variations in the dose-dependent cardiotoxicity of R- and S-methadone in humans

New approach methodologies predicting human cardiotoxicity are of interest to support or even replace in vivo-based drug safety testing. The present study presents an in vitro–in silico approach to predict the effect of inter-individual and inter-ethnic kinetic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either individual human liver microsomes or recombinant cytochrome P450 enzymes (rCYPs), were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations to predict inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. Chemical specific adjustment factors were defined and used to derive dose–response curves for the sensitive individuals. Our simulations indicated that Chinese are more sensitive towards methadone-induced cardiotoxicity with Margin of Safety values being generally two-fold lower than those for Caucasians for both methadone enantiomers. Individual PBK models using microsomes and PBK models using rCYPs combined with Monte Carlo simulations predicted similar inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. The present study illustrates how inter-individual and inter-ethnic variations in cardiotoxicity can be predicted by combining in vitro toxicity and metabolic data, PBK modelling and Monte Carlo simulations. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.

PBK modelling of topical application and characterisation of the uncertainty of Cmax estimate: A case study approach

Combined with in vitro bioactivity data, physiologically based kinetic (PBK) models has increasing applications in next generation risk assessment for animal-free safety decision making. A tiered framework of building PBK models for such application has been developed with increasing complexity and refinements, as model parameters determined in silico, in vitro, and with human pharmacokinetic data become progressively available. PBK modelling has been widely applied for oral/intravenous administration, but less so on topically applied chemicals. Therefore, building PBK models for topical applications and characterizing their uncertainties in the tiered approach is critical to safety decision making. The purpose of this study was to assess the confidence of PBK modelling of topically applied chemicals following the tiered framework, using non-animal methods derived parameters. Prediction of maximum plasma concentration (Cmax) and area under the curve were compared to observed kinetics from published dermal clinical studies for five chemicals (diclofenac, salicylic acid, coumarin, nicotine, caffeine). A bespoke Bayesian statistical model was developed to describe the distributions of Cmax errors between the predicted and observed data. We showed a general trend that confidence in model predictions increases when more quality in vitro data, particularly those on hepatic clearance and dermal absorption, are available as model input. The overall fold error distributions are useful for characterizing model uncertainty. We concluded that by identifying and quantifying the uncertainties in the tiered approach, we can increase the confidence in using PBK modelling to help make safety decisions on topically applied chemicals in the absence of human pharmacokinetic data.

Probabilistic risk assessment - the keystone for the future of toxicology.

Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncertainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, systematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), individual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm – probably!

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.

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.

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.

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.

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.

Generic physiologically based kinetic modelling for farm animals: Part I. Data collection of physiological parameters in swine, cattle and sheep

Physiologically based kinetic (PBK) models for farm animals are of growing interest in food and feed safety with key applications for regulated compounds including quantification of tissue concentrations, kinetic parameters and the setting of safe exposure levels on an internal dose basis. The development and application of these models requires data for physiological, anatomical and chemical specific parameters. Here, we present the results of a structured data collection of anatomical and physiological parameters in three key farm animal species (swine, cattle and sheep). We performed an extensive literature search and meta-analyses to quantify intra-species variability and associated uncertainty of the parameters. Parameters were collected for organ weights and blood flows in all available breeds from 110 scientific publications, of which 29, 48 and 33 for cattle, sheep, and swine, respectively. Organ weights were available in literature for all three species. Blood flow parameter values were available for all organs in sheep but were scarcer in swine and cattle. Furthermore, the parameter values showed a large intra-species variation. Overall, the parameter values and associated variability provide reference values which can be used as input for generic PBK models in these species.

Generic physiologically based kinetic modelling for farm animals: Part II. Predicting tissue concentrations of chemicals in swine, cattle, and sheep

The development of three generic multi-compartment physiologically based kinetic (PBK) models is described for farm animal species, i.e. cattle, sheep, and swine. The PBK models allow one to quantitatively link external dose and internal dose for risk assessment of chemicals relevant to food and feed safety. Model performance is illustrated by predicting tissue concentrations of melamine and oxytetracycline and validated through comparison with measured data. Overall, model predictions were reliable with 71% of predictions within a 3-fold of the measured data for all three species and only 6% of predictions were outside a 10-fold of the measured data. Predictions within a 3-fold change were best for cattle, followed by sheep, and swine (82%, 76%, and 63%). Global sensitivity analysis was performed to identify the most sensitive parameters in the PBK model. The sensitivity analysis showed that body weight and cardiac output were the most sensitive parameters. Since interspecies differences in metabolism impact on the fate of a wide range of chemicals, a key step forward is the introduction of species-specific information on transporters and metabolism including expression and activities.

Integrating physiologically based kinetic (PBK) and Monte Carlo modelling to predict inter-individual and inter-ethnic variation in bioactivation and liver toxicity of lasiocarpine

The aim of the present study was to predict the effect of inter-individual and inter-ethnic human kinetic variation on the sensitivity towards acute liver toxicity of lasiocarpine in the Chinese and the Caucasian population, and to derive chemical specific adjustment factors (CSAFs) by integrating variation in the in vitro kinetic constants Vmax and Km, physiologically based kinetic (PBK) modelling and Monte Carlo simulation. CSAFs were derived covering the 90th and 99th percentile of the population distribution of pyrrole glutathione adduct (7-GS-DHP) formation, reflecting bioactivation. The results revealed that in the Chinese population, as compared to the Caucasian population, the predicted 7-GS-DHP formation at the geometric mean, the 90th and the 99th percentile were 2.1-, 3.3- and 4.3-fold lower respectively. The CSAFs obtained using the 99th percentile values were 8.3, 17.0 and 19.5 in the Chinese, the Caucasian population and the two populations combined, respectively, while the CSAFs were generally 3.0-fold lower at the 90th percentile. These results indicate that when considering the formation of 7-GS-DHP the Caucasian population may be more sensitive towards acute liver toxicity of lasiocarpine, and further point out that the default safety factor of 3.16 for inter-individual human kinetic differences may not be sufficiently protective. Altogether, the results obtained demonstrate that integrating PBK modelling with Monte Carlo simulations using human in vitro data is a powerful strategy to quantify inter-individual variations in kinetics, and can be used to refine the human risk assessment of pyrrolizidine alkaloids.

In silico resources to assist in the development and evaluation of physiologically-based kinetic models

Since their inception in pharmaceutical applications, physiologically-based kinetic (PBK) models are increasingly being used across a range of sectors, such as safety assessment of cosmetics, food additives, consumer goods, pesticides and other chemicals. Such models can be used to construct organ-level concentration-time profiles of xenobiotics. These models are essential in determining the overall internal exposure to a chemical and hence its ability to elicit a biological response. There are a multitude of in silico resources available to assist in the construction and evaluation of PBK models. An overview of these resources is presented herein, encompassing all attributes required for PBK modelling. These include predictive tools and databases for physico-chemical properties and absorption, distribution, metabolism and elimination (ADME) related properties. Data sources for existing PBK models, bespoke PBK software and generic software that can assist in model development are also identified. On-going efforts to harmonise approaches to PBK model construction, evaluation and reporting that would help increase the uptake and acceptance of these models are also discussed.