Physiologically Based Kinetic 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.

Parameter grouping and Co-estimation in Physiologically-Based kinetic models using genetic algorithms.

Physiologically-based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterisation and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilising a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more a comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

Physiologically based kinetic (PBK) modeling as a new approach methodology (NAM) for predicting systemic levels of gut microbial metabolites

There is a clear need to develop new approach methodologies (NAMs) that combine in vitro and in silico testing to reduce and replace animal use in chemical risk assessment. Physiologically based kinetic (PBK) models are gaining popularity as NAMs in toxico/pharmacokinetics, but their coverage of complex metabolic pathways occurring in the gut are incomplete. Chemical modification of xenobiotics by the gut microbiome plays a critical role in the host response, for example, by prolonging exposure to harmful metabolites, but there is not a comprehensive approach to quantify this impact on human health. There are examples of PBK models that have implemented gut microbial biotransformation of xenobiotics with the gut as a dedicated metabolic compartment. However, the integration of microbial metabolism and parameterization of PBK models is not standardized and has only been applied to a few chemical transformations. A challenge in this area is the measurement of microbial metabolic kinetics, for which different fermentation approaches are used. Without a standardized method to measure gut microbial metabolism ex vivo/in vitro, the kinetic constants obtained will lead to conflicting conclusions drawn from model predictions. Nevertheless, there are specific cases where PBK models accurately predict systemic concentrations of gut microbial metabolites, offering potential solutions to the challenges outlined above. This review focuses on models that integrate gut microbial bioconversions and use ex vivo/in vitro methods to quantify metabolic constants that accurately represent in vivo conditions.

Characterizing the risk related to the exposure to methylmercury over a lifetime: A global approach using population internal exposure

Seafood products accumulate methylmercury throughout the food chain and are the main source of methylmercury exposure. Methylmercury may trigger a number of adverse health effects, such as neurodevelopmental or nephrotoxic effects, the risk of which cannot be ruled out for the French high consumers of seafood. The characterisation of methylmercury-related risks is generally based on short-term dietary exposure without considering changes in consumption and exposure over the lifetime. Additionally, focusing on short-term dietary exposure, the fate of methylmercury (especially its accumulation) in the organism is not considered. The present study proposes a methodology basing risk characterization on estimates of body burden over a lifetime. First, trajectories of dietary exposures throughout lifetime were constructed based on the actual concentrations of total diet studies for a fictive representative French population, taking into account the social, economic and demographic parameters of individuals. Next, the fate of methylmercury in the body was estimated, based on these trajectories, using a specific physiologically-based kinetic (PBK) model that generated a representative pool of body burden trajectories. Simulated hair mercury concentrations were closed to previously reported French representative human biomonitoring data. Results showed that at certain stages of life, concentrations of methylmercury in hair were higher than the human biomonitoring guidance value set at 2.5 μg/g of hair by JECFA. This study showed the added value, in the case of substances accumulating in the body, of estimating dietary exposure over a lifetime and using exposure biomarkers estimated by a PBK model characterize the risk.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling.A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles.Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP’s impact on human health.

Assessing chronic gestational exposure to environmental chemicals in pregnant women: Advancing the co-PBK model

Vulnerable populations, such as pregnant women and their fetuses, confront potential health risks due to exposure to environmental toxic compounds. Computational methods have been popular in assessing chemical exposure to populations, contrasting with traditional cohort studies for human biomonitoring. This study proposes a screening-level approach based on physiologically based kinetic (PBK) modeling to evaluate the steady-state exposure of pregnant women to environmental chemicals throughout pregnancy. To exemplify the modeling application, naphthalene was chosen. Simulation results indicated that maternal fat exhibited significant bioaccumulation potential, with the log-transformed BTF of naphthalene at 0.51 mg kg−1 per mg d−1 in the steady state. The placenta was primarily exposed to 0.83 mg/d naphthalene for a 75.2 kg pregnant woman, considering all exposure routes. In the fetal structure, single-organ fetal PBK modeling estimated a naphthalene exposure of 123.64 mg/d to the entire fetus, while multiple-organ fetal PBK modeling further revealed the bioaccumulation highest in fat tissue. The liver identified as the vital organ for metabolism, kBioT,LiverM was demonstrated with the highest sensitivity among rate constants in the maternal body. Furthermore, the first-order kinetic rate constants related to the placenta and blood were found to impact the distribution process of naphthalene in the fetus, influencing gestational exposure. In conclusion, urgent attention is needed to develop a computational biomonitoring tool for assessing toxic chemical exposure in vulnerable populations.

In vitro-in silico study on the influence of dose, fraction bioactivated and endpoint used on the relative potency value of pyrrolizidine alkaloid N-oxides compared to parent pyrrolizidine alkaloids

Pyrrolizidine alkaloids (PAs) and their N-oxides (PA-N-oxides) are phytotoxins found in food, feed and the environment. Yet, limited data exist from which the relative potency of a PA-N-oxide relative to its corresponding PA (REPPANO to PA) can be defined. This study aims to investigate the influence of dose, fraction bioactivated and endpoint on the REPPANO to PA of a series of pyrrolizidine N-oxides using in vitro-in silico data and physiologically based kinetic (PBK) modeling. The first endpoint used to calculate the REPPANO to PA was the ratio of the area under the concentration–time curve of PA resulting from an oral dose of PA-N-oxide divided by that from an equimolar dose of PA (Method 1). The second endpoint was the ratio of the amount of pyrrole-protein adducts formed under these conditions (Method 2). REPPANO to PA values appeared to decrease with increasing dose, with the decrease for Method 2 already starting at lower dose level than for Method 1. At dose levels as low as estimated daily human intakes, REPPANO to PA values amounted to 0.92, 0.81, 0.78, and 0.68 for retrorsine N-oxide, seneciphylline N-oxide, riddelliine N-oxide and senecivernine N-oxide, respectively, and became independent of the dose or fraction bioactivated, because no GSH depletion, saturation of PA clearance or PA-N-oxide reduction occurs. Overall, the results demonstrate the strength of using PBK modeling in defining REPPANO to PA values, thereby substantiating the use of the same approach for other PA-N-oxides for which in vivo data are lacking.

Using Read-Across to build Physiologically-Based Kinetic models: Part 2. Case studies for atenolol and flumioxazin

Read-across, wherein information from a data-rich chemical is used to make a prediction for a similar chemical that lacks the relevant data, is increasingly being accepted as an alternative to animal testing. Identifying chemicals that can be considered as similar (analogues) is crucial to the process. Two resources have been developed previously to address the issue of analogue selection and facilitate physiologically-based kinetic (PBK) model development, using read-across. Chemical-specific PBK models, available in the literature, were collated to form a PBK model dataset (PMD) of over 7,500 models. A KNIME workflow was created to accompany this PMD that can aid the selection of appropriate chemical analogues from chemicals within this dataset (i.e. chemicals that are similar to a target of interest and are known to have an existing PBK model). Information from the PBK model for the source chemical can then be used in a read-across approach to inform the development of a new PBK model for the target. The application of these resources is tested here using two case studies (i) for the drug atenolol and (ii) for the plant protection product, flumioxazin. New PBK models were constructed for these two target chemicals using data obtained from source chemicals, identified by the workflow as being similar (analogues). In each case, the published PBK model for the source chemical was initially reproduced, as accurately as possible, before being adapted and used as a template for the target chemical. The performance of the new PBK models was assessed by comparing simulation outputs to existing data on key kinetic properties for the targets. The results demonstrate that a read-across approach can be successfully applied to develop new PBK models for data-poor chemicals, thus enabling their deployment during early-stage risk assessment. This assists prediction of internal exposure whilst reducing reliance on animal testing.

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.

Studying mixture effects on uptake and tissue distribution of PFAS in zebrafish (Danio rerio) using physiologically based kinetic (PBK) modelling

Per- and polyfluoroalkyl substances (PFAS) are ubiquitously distributed in the aquatic environment. They include persistent, mobile, bioaccumulative, and toxic chemicals and it is therefore critical to increase our understanding on their adsorption, distribution, metabolism, excretion (ADME). The current study focused on uptake of seven emerging PFAS in zebrafish (Danio rerio) and their potential maternal transfer. In addition, we aimed at increasing our understanding on mixture effects on ADME by developing a physiologically based kinetic (PBK) model capable of handling co-exposure scenarios of any number of chemicals. All studied chemicals were taken up in the fish to varying degrees, whereas only perfluorononanoate (PFNA) and perfluorooctanoate (PFOA) were quantified in all analysed tissues. Perfluorooctane sulfonamide (FOSA) was measured at concerningly high concentrations in the brain (Cmax over 15 μg/g) but also in the liver and ovaries. All studied PFAS were maternally transferred to the eggs, with FOSA and 6:2 perfluorooctane sulfonate (6,2 FTSA) showing significant (p < 0.02) signs of elimination from the embryos during the first 6 days of development, while perfluorobutane sulfonate (PFBS), PFNA, and perfluorohexane sulfonate (PFHxS) were not eliminated in embryos during this time-frame. The mixture PBK model resulted in >85 % of predictions within a 10-fold error and 60 % of predictions within a 3-fold error. At studied levels of PFAS exposure, competitive binding was not a critical factor for PFAS kinetics. Gill surface pH influenced uptake for some carboxylates but not the sulfonates. The developed PBK model provides an important tool in understanding kinetics under complex mixture scenarios and this use of New Approach Methodologies (NAMs) is critical in future risk assessment of chemicals and early warning systems.

Acetylcholinesterase Inhibition in Rats and Humans Following Acute Fenitrothion Exposure Predicted by Physiologically Based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation.

Worldwide use of organophosphate pesticides as agricultural chemicals aims to maintain a stable food supply, while their toxicity remains a major public health concern. A common mechanism of acute neurotoxicity following organophosphate pesticide exposure is the inhibition of acetylcholinesterase (AChE). To support Next Generation Risk Assessment for public health upon acute neurotoxicity induced by organophosphate pesticides, physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed in this study, with fenitrothion (FNT) as an exemplary organophosphate pesticide. Rat and human PBK models were parametrized with data derived from in silico predictions and in vitro incubations. Then, PBK model-based QIVIVE was performed to convert species-specific concentration-dependent AChE inhibition obtained from in vitro blood assays to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived. The obtained values for rats and humans were comparable with reported no-observed-adverse-effect levels (NOAELs). Humans were found to be more susceptible than rats toward erythrocyte AChE inhibition induced by acute FNT exposure due to interspecies differences in toxicokinetics and toxicodynamics. The described approach adequately predicts toxicokinetics and acute toxicity of FNT, providing a proof-of-principle for applying this approach in a 3R-based chemical risk assessment paradigm.

Assessing human internal exposure to chemicals at different physical activity levels: A physiologically based kinetic (PBK) model incorporating metabolic equivalent of task (MET)

Physical activity levels have the potential to impact human internal exposure to environmental chemicals. However, the current lack of simple modeling approaches hinders the high-throughput screening of chemical exposure at different physical activity levels. To address this gap, this study proposes a straightforward model for assessing human internal exposure to chemicals. Our approach is based on the physiologically based kinetic (PBK) model and utilizes the metabolic equivalent of task (MET) to characterize internal exposure to chemicals at varying activity levels. To facilitate the application of this model, we have developed an Excel-based operation tool, allowing users to easily modify the MET value and generate simulation results for different physical activity levels. The simulation results demonstrate that as physical activity levels increase, the biotransfer factors (BTFs) of chemicals decrease, suggesting that higher physical activity levels reduce the bioaccumulation potential of chemicals. The intensified physical activity enhances the overall elimination kinetics of chemicals from the human body. However, the simulated concentrations of chemicals in the human body increase with higher physical activity levels, due to the significantly increased external exposure to chemicals, such as through inhalation. Our proposed modeling approach, along with the operational tool, enables high-throughput simulation of human chronic internal exposure to chemicals at different physical activity levels, where the findings can assist in screening chemicals for further health risk assessment. To accomplish this, the model incorporates certain assumptions and utilizes generic model input values. However, due to the intricate nature of the interaction between external and internal exposures at different physical activity levels, validating the simulation through experimental studies becomes challenging and is not performed in this study. For future studies, we recommend incorporating more MET-related physiological input variables, improving energy balance estimates, comprehending external exposure estimates, and conducting cohort studies to enhance and validate the proposed modeling approach.

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.

P14-18: Development of a physiologically based kinetic (PBK) model to assess regional and systemic concentrations of inhaled permethrin in rats

P14-18: Development of a physiologically based kinetic (PBK) model to assess regional and systemic concentrations of inhaled permethrin in rats

P22-40: Predictive performance of physiologically based kinetic (PBK) models based on in-silico/in vitro-in vivo extrapolation (IS/IVIVE)

P22-40: Predictive performance of physiologically based kinetic (PBK) models based on in-silico/in vitro-in vivo extrapolation (IS/IVIVE)

P06-16: Physiologically-based kinetic (PBK) modeling-facilitated reverse dosimetry to predict in vivo developmental toxicity of benzo[a]pyrene (B[a]P) in rats

P06-16: Physiologically-based kinetic (PBK) modeling-facilitated reverse dosimetry to predict in vivo developmental toxicity of benzo[a]pyrene (B[a]P) in rats

S12-03: Toxicokinetics, physiologically-based kinetic (PBK) model and read-across – regulatory perspective

S12-03: Toxicokinetics, physiologically-based kinetic (PBK) model and read-across – regulatory perspective

CEC02-05: Physiologically-based Kinetic (PBK) model assessment framework – PBK model evaluation toolbox

CEC02-05: Physiologically-based Kinetic (PBK) model assessment framework – PBK model evaluation toolbox

S12-01: Physiologically-Based Kinetic (PBK) Models and Applications In Read-Across

S12-01: Physiologically-Based Kinetic (PBK) Models and Applications In Read-Across

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.