Physiologically Based Kinetic Models Research Articles

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling.

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

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.

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.

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.

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.

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.

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.

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

Current climate trends are likely to expand the geographic distribution of the toxigenic microalgae and concomitant phycotoxins, making intoxications by such toxins a global phenomenon. Among various phycotoxins, saxitoxin (STX) acts as a neurotoxin that might cause severe neurological symptoms in mammals following consumptions of contaminated seafood. To derive a point of departure (POD) for human health risk assessment upon acute neurotoxicity induced by oral STX exposure, a physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed. The PBK models for rats, mice, and humans were built using parameters from the literature, in vitro experiments, and in silico predictions. Available in vitro toxicity data for STX were converted to in vivo dose-response curves via the PBK models established for these three species, and POD values were derived from the predicted curves and compared to reported in vivo toxicity data. Interspecies differences in acute STX toxicity between rodents and humans were found, and they appeared to be mainly due to differences in toxicokinetics. The described approach resulted in adequate predictions for acute oral STX exposure, indicating that new approach methodologies, when appropriately integrated, can be used in a 3R-based chemical risk assessment paradigm.

Physiologically based kinetic modeling of senecionine N-oxide in rats as a new approach methodology to define the effects of dose and endpoint used on relative potency values of pyrrolizidine alkaloid N-oxides.

Over 1,000 pyrrolizidine alkaloids (PAs) and their N-oxides (PA-N-oxides) occur in 3% of all flowering plants. PA-N-oxides are toxic when reduced to their parent PAs, which are bioactivated into pyrrole intermediates that generate protein- and DNA-adducts resulting in liver toxicity and carcinogenicity. Literature data for senecionine N-oxide in rats indicate that the relative potency (REP) value of this PA-N-oxide compared to its parent PA senecionine varies with the endpoint used. The first endpoint was the ratio between the area under the concentration-time curve (AUC) for senecionine upon dosing senecionine N-oxide or an equimolar dose of senecionine, while the second endpoint was the ratio between the amount for pyrrole-protein adducts formed under these conditions. This study aimed to investigate the mode of action underlying this endpoint dependent REP value for senecionine N-oxide with physiologically based kinetic (PBK) modeling. Results obtained reveal that limitation of 7-GS-DHP adduct formation due to GSH depletion, resulting in increased pyrrole-protein adduct formation, occurs more likely upon high dose oral PA administration than upon an equimolar dose of PA-N-oxide. At high dose levels, this results in a lower REP value when based on pyrrole-protein adduct levels than when based on PA concentrations. At low dose levels, the difference no longer exists. Altogether, the results of the study show how the REP value for senecionine N-oxide depends on dose and endpoint used, and that PBK modeling provides a way to characterize REP values for PA-N-oxides at realistic low dietary exposure levels, thus reducing the need for animal experiments.

Physiologically-Based Kinetic Modeling Predicts Similar In Vivo Relative Potency of Senecionine N-Oxide for Rat and Human at Realistic Low Exposure Levels.

This study aims to determine if previously developed physiologically-based kinetic (PBK) model in rat can be modified for senecionine (SEN) and its N-oxide (SENO), and be used to investigate potential species differences between rat and human in relative potency (REP) of the N-oxide relative to the parent pyrrolizidine alkaloid (PA). In vitro derived kinetic parameters including the apparent maximum velocities (Vmax ) and Michaelis-Menten constants (Km ) for SENO reduction and SEN clearance are used to define the PBK models. The rat model is validated with published animal data, and the toxicokinetic profiles of SEN from either orally-administered SENO or SEN are simulated. REP values of SENO relative to SEN amount to 0.84 and 0.89 in rat and human, respectively. The REP value can be dose- and species-dependent, with the values for rat and human being comparable at low realistic exposure scenarios. In summary, PBK modeling serves as a valuable New Approach Methodology (NAM) tool for predicting REP values of PA-N-oxides and may actually result in more accurate REP values for human risk assessment than what would be defined using in vivo animal experiments.

Considering developmental neurotoxicity invitro data for human health risk assessment using physiologically-based kinetic modeling: deltamethrin case study.

Developmental neurotoxicity (DNT) is a potential hazard of chemicals. Recently, an in vitro testing battery (DNT IVB) was established to complement existing rodent in vivo approaches. Deltamethrin (DLT), a pyrethroid with a well-characterized neurotoxic mode of action, has been selected as a reference chemical to evaluate the performance of the DNT IVB. The present study provides context for evaluating the relevance of these DNT IVB results for the human health risk assessment of DLT by estimating potential human fetal brain concentrations after maternal exposure to DLT. We developed a physiologically based kinetic (PBK) model for rats which was then translated to humans considering realistic in vivo exposure conditions (acceptable daily intake [ADI] for DLT). To address existing uncertainties, we designed case studies considering the most relevant drivers of DLT uptake and distribution. Calculated human fetal brain concentrations were then compared with the lowest benchmark concentration achieved in the DNT IVB. The developed rat PBK model was validated on in vivo rat toxicokinetic data of DLT over a broad range of doses. The uncertainty based case study evaluation confirmed that repeated exposure to DLT at an ADI level would likely result in human fetal brain concentrations far below the in vitro benchmark. The presented results indicate that DLT concentrations in the human fetal brain are highly unlikely to reach concentrations associated with in vitro findings under realistic exposure conditions. Therefore, the new in vitro DNT results are considered to have no impact on the current risk assessment approach.

Generic Solving of Physiologically-based Kinetic Models in Support of Next Generation Risk Assessment Due to Chemicals

Increasing confidence in using in vitro and in silico model-based data to aid the chemical risk assessment process is one of the most significant challenges faced by regulatory authorities. A crucial concern is taking full advantage of scientifically valid physiologically-based kinetic (PBK) models. Nevertheless, risk assessors remain unwilling to employ PBK models in their daily work. Indeed, PBK models are not often included in current official guidance documents. In addition, most users have limited experience with modeling in general. So, the complexity of PBK models and a lack of evaluation methods for their performance certainly contribute to their limited use in practical risk assessment. This paper proposes an innovative and unified modeling framework for writing PBK equations as matrix ordinary differential equations (ODE) and their solutions, expressed with matrix products. This generic PBK solution considers as many state variables as needed to quantify chemical absorption, distribution, metabolism, and excretion processes within living organisms when exposed to chemical substances. This generic PBK model makes possible any compartmentalization to be considered, as well as all appropriate interconnections between compartments and with the external medium. We first introduce our PBK modeling framework, with all the intermediate steps from the matrix ODE to the exact solution. Then we apply this framework to bioaccumulation testing before illustrating its concrete use through complementary case studies regarding species, compounds, and model complexity.

A generic avian physiologically-based kinetic (PBK) model and its application in three bird species

Physiologically-based kinetic (PBK) models are effective tools for designing toxicological studies and conducting extrapolations to inform hazard characterization in risk assessment by filling data gaps and defining safe levels of chemicals. In the present work, a generic avian PBK model for male and female birds was developed using PK-Sim and MoBi from the Open Systems Pharmacology Suite (OSPS). The PBK model includes an ovulation model (egg development) to predict concentrations of chemicals in eggs from dietary exposure. The model was parametrized for chicken (Gallus gallus), bobwhite quail (Colinus virginianus) and mallard duck (Anas platyrhynchos) and was tested with nine chemicals for which in vivo studies were available. Time-concentration profiles of chemicals reaching tissues and egg compartment were simulated and compared to in vivo data. The overall accuracy of the PBK model predictions across the analyzed chemicals was good. Model simulations were found to be in the range of 22–79% within a 3-fold and 41–89% were within 10- fold deviation of the in vivo observed data. However, for some compounds scarcity of in-vivo data and inconsistencies between published studies allowed only a limited goodness of fit evaluation. The generic avian PBK model was developed following a “best practice” workflow describing how to build a PBK model for novel species. The credibility and reproducibility of the avian PBK models were scored by evaluation according to the available guidance documents from WHO (2010), and OECD (2021), to increase applicability, confidence and acceptance of these in silico models in chemical risk assessment.

P17-04 An automated tool for selection of chemical analogues to facilitate development of new physiologically-based kinetic models using a read-across approach

P17-04 An automated tool for selection of chemical analogues to facilitate development of new physiologically-based kinetic models using a read-across approach

P21-31 Criteria for selecting physiologically-based kinetic models for use in developing new models for data-poor analogues via a read-across approach

P21-31 Criteria for selecting physiologically-based kinetic models for use in developing new models for data-poor analogues via a read-across approach

In Vitro-In Vivo Extrapolation by Physiologically Based Kinetic Modeling: Experience With Three Case Studies and Lessons Learned.

Physiologically based kinetic (PBK) modeling has been increasingly used since the beginning of the 21st century to support dose selection to be used in preclinical and clinical safety studies in the pharmaceutical sector. For chemical safety assessment, the use of PBK has also found interest, however, to a smaller extent, although an internationally agreed document was published already in 2010 (IPCS/WHO), but at that time, PBK modeling was based mostly on in vivo data as the example in the IPCS/WHO document indicates. Recently, the OECD has published a guidance document which set standards on how to characterize, validate, and report PBK models for regulatory purposes. In the past few years, we gained experience on using in vitro data for performing quantitative in vitro–in vivo extrapolation (QIVIVE), in which biokinetic data play a crucial role to obtain a realistic estimation of human exposure. In addition, pharmaco-/toxicodynamic aspects have been introduced into the approach. Here, three examples with different drugs/chemicals are described, in which different approaches have been applied. The lessons we learned from the exercise are as follows: 1) in vitro conditions should be considered and compared to the in vivo situation, particularly for protein binding; 2) in vitro inhibition of metabolizing enzymes by the formed metabolites should be taken into consideration; and 3) it is important to extrapolate from the in vitro measured intracellular concentration and not from the nominal concentration to the tissue/organ concentration to come up with an appropriate QIVIVE for the relevant adverse effects.