Use of Physiologically Based Kinetic Modeling-Based Reverse

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Use of physiologically based kinetic modeling-based reverse dosimetry to predict in vivo toxicity from in vitro data Jochem Louisse, Karsten Beekmann, and Ivonne Magdalena Catharina Maria Rietjens Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.6b00302 • Publication Date (Web): 21 Oct 2016 Downloaded from http://pubs.acs.org on October 23, 2016

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Chemical Research in Toxicology

Use of physiologically based kinetic modeling-based reverse dosimetry to predict in vivo toxicity from in vitro data Jochem Louisse, Karsten Beekmann, Ivonne M.C.M. Rietjens

Division of Toxicology, Wageningen University, Stippeneng 4, 6708 WE Wageningen, The Netherlands

Corresponding author:

Jochem Louisse Division of Toxicology Wageningen University Stippeneng 4 6708 WE Wageningen The Netherlands Tel: +31 317 483971 Fax: + 31 317 484931 Email: [email protected]

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Abstract

The development of reliable non-animal based testing strategies, such as in vitro bioassays, is the holy grail in current human safety testing of chemicals. However, the use of in vitro toxicity data in risk assessment is not straightforward. One of the main issues is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves. These dose-response curves are needed in toxicological risk assessment to obtain a point of departure to determine safe exposure levels for humans. Recent scientific developments enable this translation of in vitro concentrationresponse curves to in vivo dose-response curves using physiologically based kinetic (PBK) modeling-based reverse dosimetry. The present review provides an overview of the examples available in the literature on the prediction of in vivo toxicity using PBK modeling-based reverse dosimetry of in vitro toxicity data, showing that proofs-ofprinciple are available for toxicity endpoints ranging from developmental toxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity to DNA adduct formation. This review also discusses the promises and pitfalls, and the future perspectives of the approach. Since proofs-of-principle available so far have been provided for the prediction of toxicity in experimental animals, future research should focus on the use of in vitro toxicity data obtained in human models to predict the human situation using human PBK models. This would facilitate human- instead of experimental animal-based approaches in risk assessment.


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Introduction The development of reliable non-animal based testing strategies is the holy grail in current human safety testing of chemicals. Many efforts in this area have focused on the development and use of especially in vitro testing strategies in which cells in culture are exposed to the chemicals providing concentration-response curves. However, no matter how sophisticated the cell models, concentration-response curves from in vitro models are inadequate for human risk and safety assessment because risk assessment requires in vivo dose-response curves from which points of departure (PODs) can be derived for defining safe exposure levels. Such PODs used in risk and safety assessment are for example No Observed Adverse Effect levels (NOAELs), or, preferably, benchmark dose (BMD) values defining the dose levels inducing a limited but measurable response above background level, and the BMDL values, the lower confidence limits of these BMDs. The concentration-response curves for effects on cells in culture do not define these in vivo PODs and can at best be used for identification of possible hazards.1 As a result the use of current non-animal based testing strategies requires a method to translate the in vitro concentration-response curves to in vivo dose-response curves that can replace the data from animal bioassays2. Given that it is widely acknowledged that experimental animals may not be good models to predict effects in humans3, 4 it would be of high value if the in vivo dose-response curves defined could actually relate to humans rather than to rodents. To bridge this gap between in vitro alternative testing strategies and the requirements of modern risk assessment the use of so-called physiologically based kinetic (PBK) modeling-based reverse dosimetry proves to be a way forward.5 Figure 1 illustrates how



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the use of PBK modeling-based reverse dosimetry can modify the current concept for in vitro based testing into a concept that enables the definition of in vivo dose-response curves that are required for risk assessment. The principle is based on the use of PBK models to translate in vitro concentration-response curves to in vivo dose-response curves for selected species including human. This approach can provide dose-response curves for toxicity in human provided the PBK models used, and preferably also the cell models used, relate to the human situation. The present paper provides a description of the approach, including examples, and it describes the promises and pitfalls and future perspectives of this PBK modeling-based reverse dosimetry in predicting in vivo toxicity from in vitro data.

What is a PBK model? A PBK model is a set of mathematical equations that together describe the absorption, distribution, metabolism and excretion (ADME) characteristics of a compound within an organism (Figure 2).6-9 To define the equations in the PBK model, three types of parameters are needed including i) physiological and anatomical parameters (e.g. cardiac output, tissue volumes, and tissue blood flows), ii) physico-chemical parameters (e.g. blood/tissue partition coefficients), and iii) kinetic parameters (e.g. kinetic constants for metabolic reactions).6-9 Values for physiological and anatomical parameters are usually obtained from literature, whereas physico-chemical parameters are often available, can be easily experimentally determined, or –as a last resort- can be obtained by predicting them using quantitative structure activity relationship (QSAR) approaches.9

To obtain values for kinetic


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parameters in PBK models, such as values for parameters in the mathematical equations that describe metabolism in the liver (Vmax and Km in the example as presented in Figure 2), different approaches are used. In general, kinetic parameter values are either derived using in vivo kinetic data, by estimating them using in vitro methods, such as incubations with liver enzymes, or by predicting them with help of in silico models.10 Obviously, in light of the 3Rs of animal use, the use of in vitro metabolic incubations with relevant tissue fractions is preferred over the use of in vivo animal experiments to define the values. With a PBK model, physiologically relevant concentrations of a compound or, when relevant, its active metabolite(s) in any target organ of interest can be modeled for a certain dose, time point and route of administration, allowing analysis of internal concentrations at both high dose levels, as often applied in toxicity studies in animals, but also at more realistic low dose levels to which humans are exposed. Furthermore, such PBK models can be developed for different species based on in vitro and in silico methods, which can facilitate interspecies extrapolation as well as predictions for the human situation without the need for human experiments. In addition, by incorporating variability in anatomical, physiological and biochemical parameters, such as kinetic constants for metabolic conversions by individual human samples and/or specific isoenzymes, modeling of interindividual variations and genetic polymorphisms becomes feasible.11-19 The development of a PBK model starts by definition of the conceptual model, and translation of the conceptual model into a mathematical model, which requires definition of the various parameter values.9 Once this has been done the equations can be solved



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after which the model performance needs to be evaluated based on comparison of predictions made to available experimental data on in vivo kinetics, for example from the literature.9 Upon validation of the model against available in vivo data on kinetics, the PBK model can be used to make predictions and this may include its use to convert in vitro concentration-response curves to relevant in vivo exposure levels by so-called reverse dosimetry.

PBK modeling-based reverse dosimetry A PBK model can predict the concentration of a compound and its relevant metabolites in any tissue at any point in time and for any dose level, within its applicability domain. In PBK modeling-based reverse dosimetry the PBK model is used in the reverse order; that is to calculate what dose level would be needed to obtain the concentration of a compound or of its metabolite in the blood or in a tissue of interest. In this reverse dosimetry approach, in vitro concentrations are set equal to blood or tissue levels of the respective compound in the PBK model, following which the PBK model can calculate the corresponding in vivo dose level for any given route of administration, if adequately described in the model. PBK modeling-based reverse dosimetry can be applied for two purposes, being 1) the estimation of human exposure levels based on biomonitoring data (data on chemical levels in human tissues and fluids), and 2) the prediction of toxic dose levels based on in vitro toxicity data. For the first purpose, PBK modeling based reverse dosimetry has already been applied for decades. The present review provides a few examples on how PBK modeling-facilitated reverse dosimetry has been used to estimate


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human exposure based on biomonitoring data, but focuses on the use of PBK modelingfacilitated reverse dosimetry to predict toxic dose levels based on in vitro toxicity data.

PBK modeling-based reverse dosimetry to predict human exposure levels based on human biomonitoring data Biomonitoring data have proven to be an important source for identifying the presence of chemicals in human populationsPBK modeling can be used to support the interpretation of human biomonitoring data from the perspective of exposure reconstruction and risk characterization, by relating a measured concentration of a chemical in a human tissue or fluid to an exposure level.20 PBK modeling-based reverse dosimetry has been applied to estimate human exposures based on biomonitoring data for various chemicals. PBK modeling-based reverse dosimetry was used for example to examine the correlation between biomonitoring data for di-n-butyl and di-(2-ethylhexyl) phthalate (DEP and DEHP) metabolites in urine and environmental exposure.21 To this end human PBK models for DBP and DEHP and their primary metabolites – mono-n-butyl and mono-2ethylhexyl phthalate (MBP and MEHP) – during pregnancy were developed from published rodent PBK models. To estimate daily intake of DBP and DEHP from data on population-based measures of urinary metabolite levels, Monte Carlo analyses were performed using the PBK model and reverse dosimetry to generate an inverted distribution between urine concentration and steady state exposure. In this way a predicted distribution of daily intake for DEP and DEHP was generated and biomonitoring data could be translated into an exposure distribution for the population as a whole.21



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In another example, human biomonitoring data on chloroform were converted to a population estimate of environmental chloroform exposure.22 The biomonitoring data consisted of chloroform blood concentrations and by using a PBK modeling-based reverse dosimetry approach, considering several routes and sources of exposure including ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering, posterior distributions for chloroform concentration in tap water and ambient household air could be defined.22 This illustrates how PBK modelingbased reverse dosimetry can aid in the interpretation of human biomonitoring data in the context of the exposure assessment in risk evaluation. PBK modeling-based reverse dosimetry has also been used to estimate exposures to trichloroethylene that correspond to levels measured in fluids and/or tissues in human biomonitoring studies.23 A PBK model for trichloroethylene including multiple routes of exposure, such as oral exposure via water ingestion and inhalation exposure during shower events was used in a reverse dosimetry approach to derive estimates of trichloroethylene concentrations in drinking water based on given measurements of trichloroethylene in blood. The data obtained were compared to established maximum contaminant levels in drinking water, illustrating how a reverse dosimetry approach can be used to facilitate interpretation of human biomonitoring data in a health risk context. More examples of estimation of human exposure based on PBK modeling-facilitated reverse dosimetry of biomonitoring data can be found for other compounds including acrylamide,24

acrylonitrile,25

cadmium,24

carbaryl,26

chloroform,27

2,4-

dichlorophenoxyacetic acid,24 polychlorinated biphenyls,28,29 phthalates,30 toluene,24 trihalomethanes,31 and trichloroethylene.22, 23, 27, 32, 33


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PBK modeling-based reverse dosimetry to predict toxic dose levels based on in vitro toxicity data A second important field of application of PBK modeling-based reverse dosimetry is the translation of in vitro concentration-response data to in vivo dose-response data. Reverse dosimetry is needed to convert in vitro results, generated at cell or sub-cellular level, into dose-response or potency information for the entire target organism, such as laboratory animals or humans, in order to use the data for a risk assessment.5 An important issue to be considered in this reverse dosimetry approach to predict in vivo toxicity is what parameter to use to relate the exposure scenario of a compound of interest to its toxic effect. In general, the exposure can be expressed as the Cmax (maximal concentration) or as the AUC (the area under the concentration-time curve). The parameter to use (Cmax or AUC) to relate exposure to toxicity may depend on the mode of action of the chemical of interest. In most reverse dosimetry approaches reported in the literature, predictions have been based on relating the toxic effect to the dose metric Cmax. However, also examples of chemicals are known for which the toxic effect is best related to the AUC, such as alltrans-retinoic acid (ATRA).34 Indeed, our study showed that when applying reverse dosimetry with in vitro developmental toxicity of ATRA obtained in the ES-D3 cell differentiation assay of the embryonic stem cell test (EST), the use of the AUC as dose metric to relate exposure to toxicity appeared more predictive of dose-response curves for in vivo developmental toxicity of ATRA than the use of the Cmax (Figure 3).35 Another issue to consider is the role of protein binding. When the adverse effect is related to the concentration of the free (unbound) form of the chemical, which is often assumed to be the case, correction for protein binding is essential. For this correction, data are



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required on the free fraction of the compound of interest in the blood and on the free fraction of the compound of interest in the culture medium applied in the in vitro assay.36 Prior to extrapolation of the in vitro concentration or AUC to in vivo Cmax or AUC values, a correction for these differences in free fraction can be included by multiplying the in vitro concentration or AUC with a correction factor based on the differences in fraction unbound in blood versus culture medium. To determine the unbound fraction, different methods can be used, such as equilibrium dialysis, ultrafiltration, ultracentrifugation, solid-phase microextraction (SPME), and others.37 Once the in vitro concentration-response curve has been translated into an in vivo doseresponse curve using PBK modeling-based reverse dosimetry, subsequent BMD modeling can be applied on the predicted in vivo dose-response data, enabling the definition of a POD for risk assessment, such as a BMD(L)10. The next section presents some examples providing proofs-of-principle of the approach.

Examples of PBK modeling-based reverse dosimetry to predict in vivo doseresponse curves based on in vitro toxicity data First proofs-of-principle that in vivo dose-response curves and PODs for human risk assessment can be defined based on PBK modeling-based reverse dosimetry of in vitro toxicity data have become available over the last decade. Below, examples for toxicity endpoints ranging from neurotoxicity, developmental toxicity, hepatotoxicity and for other endpoints, including DNA adduct formation and nuclear receptor activation or inhibition are described. Also, high-throughput reverse dosimetry approaches have been developed, mainly with the aim to prioritize chemicals for further risk assessment.


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Reverse dosimetry approaches to predict in vivo neurotoxicity This first example of the prediction of in vivo toxic dose levels based on PBK modelingbased reverse dosimetry of in vitro toxicity data comes from DeJongh and coworkers.38, 39 They predicted the toxicity of the neurotoxic compounds benzene, toluene, lindane, acrylamide, paraoxon, caffeine, diazepam and phenytoin based on PBK modeling-based reverse dosimetry of EC20 values obtained in in vitro toxicity studies with human neuroblastoma (SH-SY5Y) cells.39,

40

The predicted toxic dose levels were compared

with lowest observed adverse effect level (LOAEL) values reported in the literature. The discrepancies between predicted PODs and LOAEL values from in vivo studies ranged from 2- to10-fold. In a later study by Forsby and Blaauboer, neurotoxicity of the same set of chemicals was predicted based on PBK modeling-based reverse dosimetry of EC20 values obtained in in vitro toxicity studies with SH-SY5Y cells, but now using a wide range of in vitro readout parameters for neurotoxicity.40 Also this study showed a good correlation between predicted toxicity dose levels and in vivo effect levels as reported in the literature, but showed that for diazepam, predictions were only reasonable when performing reverse dosimetry using data from in vitro studies on GABAA receptor function. The differences in sensitivity of the in vitro models and as a consequence the differences in in vivo predictions, depend on the specific readout parameters in the in vitro models, which relate to different mode of actions underlying the toxicity. Therefore, a battery of in vitro models are expected to be required to cover a range of mode of actions that may underlie the toxicity.



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Reverse dosimetry approaches to predict in vivo developmental toxicity Pioneering work in the prediction of in vivo developmental toxicity using PBK modelingbased reverse dosimetry is from Verwei and coworkers.41 In their study, EC50 values from the ES-D3 cell differentiation assay of the EST were translated to in vivo dose levels, which were compared with in vivo effect levels for developmental toxicity as reported in the literature. The study showed that in vivo effect levels for ethylene glycol monomethyl ether (EGME), ethylene glycol monoethyl ether (EGEE), methotrexate and retinoic acid were correctly predicted, but that the in vivo effect level for 5-fluorouracil was overestimated. The authors concluded that a combination of in vitro and in silico techniques appears to be a promising alternative test method for risk assessment of embryotoxic compounds.41 A first proof-of-principle of the prediction of a full in vivo dose-response curves for developmental toxicity was performed for a group of glycol ethers, i.e. EGME, EGEE, ethylene glycol monobutyl ether (EGBE), and ethylene glycol monophenyl ether (EGPE). Predictions were based on the translation of in vitro concentration-response curves of the toxic metabolites of these glycol ethers obtained in the differentiation assay of the EST by a PBK model that describes the kinetics of these glycol ethers and their toxic metabolites in rat.42 The concentration-response curves obtained in the EST were converted to in vivo dose-response curves using PBK modeling-based reverse dosimetry and appeared to match the available in vivo data reported in literature (see examples for EGME and EGEE in Figure 4).42,

43

The predicted dose-response data were used to

determine a BMDL10 value, which was compared with BMDL10 values derived from the in vivo data in rats, and this analysis showed that the differences were at maximum 6-


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fold, indicating the great potential of the reverse dosimetry approach to predict in vivo toxicity. A second example comes from a study in which the developmental toxicity of phenol was predicted.44 Applying PBK modeling-based reverse dosimetry, the concentrationresponse data obtained in the differentiation assay of the EST were translated to an in vivo dose-response curve from which a BMDL05 as the POD for risk assessment was derived.44 The predicted POD appeared to fall within the range of NOAEL values obtained from in vivo developmental toxicity data from the literature, which appeared to vary among each other to an even larger extent (Figure 5).44 Also the in vivo developmental toxicity of ATRA was predicted using in vitro toxicity

data from the differentiation assay of the EST and BPK modeling-based reverse dosimetry.35 Figure 3 already presented the results obtained as an illustration that in this example predictions based on AUC were better than those based on Cmax. The predicted in vivo dose-response data were used for BMD modeling, and the obtained BMDL10 value for rat were in line with BMDL10 values derived from in vivo developmental toxicity data in rats reported in the literature, showing a maximum difference of 6-fold.35

Reverse dosimetry approaches to predict systemic toxicity, kidney toxicity, and liver toxicity. Besides for neurotoxicity and developmental toxicity, PBK modeling-based reverse dosimetry has also been applied to predict systemic toxicity upon repeated dosing, kidney toxicity, and liver toxicity. Gubbels-van Hal and coworkers used PBK modeling-based reverse dosimetry of EC10 and EC50 values from cytotoxicity studies of 10 chemicals in



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human lymphocytes and rat hepatocytes and compared the predicted toxic dose levels to in vivo LD50 values from acute toxicity studies and effect levels from 28-day repeated dose toxicity studies.45 In general, the authors overpredicted the in vivo toxicity using their PBK modeling-based reverse dosimetry approach and the authors indicated that this may have to do with the low cell density used in in vitro assays compared to the in vivo situation, as cytotoxicity has been shown to decrease when the cell density in in vitro assays increases.

45, 46

Furthermore, it can be expected that various modes of action

underlie the in vivo toxicity that the authors aimed to predict, which may not be adequately represented in the in vitro cytotoxicity studies, indicating that a careful selection of adequate in vitro models is required for a more reliable prediction of in vivo toxicity based on PBK modeling-based reverse dosimetry. Abdullah and coworkers predicted dose-dependent nephrotoxicity of aristolochic acids using PBK modeling-based reverse dosimetry.47 They demonstrated that in vitro toxicity data obtained with LLC-PK1 porcine kidney cells and MDCK canine cells proved adequate in vitro data to predict in vivo toxicity and a POD for kidney toxicity induced by aristolochic acid I in rats, mice and humans.47 BMDL10 values derived from their predicted dose-response data were compared with effect levels for rat and mice as reported in the literature. The PODs derived from their predicted dose-response data were in general in the same order of magnitude as the PODs reported in the literature.47 Klein and coworkers used PBK modeling-based reverse dosimetry to predict hepatotoxicity of bosentan and valproic acid in humans up to a 28-day exposure.48 Based on their predicted toxicity dose levels, they estimated that a 28-day exposure to the daily recommended dose of bosentan would be safe, but that valproic acid at the daily


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recommended dose would cause liver toxicity to 4% of their virtual population upon a 3week exposure and 47% of their virtual population upon a 4-week exposure.48 The authors could not validate their predictions against in vivo data, but they mentioned that literature shows that valproic acid causes elevation in liver transaminases in 5–10% of patients and that bosentan causes mild hepatotoxicity in approximately 10% of patients,48 indicating that only for valproic acid, prediction of hepatotoxicity in humans using the PBK modeling-based reverse dosimetry approach appeared promising. This indicates that the approach cannot yet be applied without false negatives for the safety assessment of drugs for humans. To move this field forward, it would be of interest to assess why the hepatotoxicity of bosentan was not predicted using the PBK modeling-based reverse dosimetry approach. To this end, one could assess whether the internal concentrations were adequately predicted by the PBK model applied, and/or to what extent the in vitro model applied could capture the mode of action underlying bosentan-induced hepatotoxicity.

Reverse dosimetry approaches to predict in vivo DNA adduct formation and nuclear receptor activation or inhibition. PBK modeling-based reverse dosimetry has also been applied to predict dose levels that affect other biological endpoints, such as the formation of in vivo DNA adduct levels or the activation or inhibition of nuclear receptors. In the study of Paini and coworkers, in vivo DNA adduct levels of the alkenylbenzene estragole, which is present in different herbs and spices, were predicted by translating concentration-response data on estragole-DNA adduct formation in rat liver cells to the in



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vivo situation with help of PBK modeling-based reverse dosimetry.49 In a later study, the authors validated their approach by comparing their predicted DNA adduct levels with DNA adduct levels as measured in rats and their results indicated that predicted DNA adduct levels differed less than 2–fold compared with the DNA adduct levels actually measured in vivo.50 It should be noted, that DNA adduct formation is not a toxicity endpoint, but rather a biomarker of exposure. So far, no in vitro methods are available that can recapitulate the process of chemical-induced carcinogenesis. A better understanding of how specific DNA adducts relate to carcinogenic potentials of chemicals will be essential to enable the application of DNA adduct data in the prediction of cancer incidences in the future. PBK modeling-based reverse dosimetry has also been applied to translate effect concentrations from estrogen receptor (ER)α or androgen receptor (AR) activity in chemical-activated luciferase expression (CALUX) assays, focusing on the dermal exposure route.51 Using this approach, the authors estimated what dermal dose of bisphenol A, estradiol, procymidone and vinclozulin are expected to lead to alterations in biological pathways in which the ERα and AR play a role, defined by Judson and coworkers as the biological pathway altering dose (BPAD)52. The authors concluded that their estimated BPADs differed up to a factor 55, due to the impact of applied dose and dermal exposure scenario on skin permeation kinetics. Although the predicted BPAD values could not be validated against in vivo data due to the absence of in vivo data on the BPAD, the authors suggested that the approach may be a valuable Tier 1 for chemicals for which no toxicity data upon dermal exposure exist.51 However, since the authors did not evaluate how well their model predict internal concentrations upon


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application of the chemicals using the diverse exposure scenarios, and since the relation between the BPAD and a dose that cause adverse effects has not been defined, the approach should be further refined before it may be used to predict in vivo toxicity.

High throughput reverse dosimetry approaches to prioritize chemicals for further risk assessment The U.S. Tox21 and ToxCast research programs have provided a large dataset on the bioactivity of hundreds of chemicals, as measured in a battery of in vitro assays, which can be useful to prioritize chemicals for a further risk assessment.53 Chemicals with high priority for further risk assessment are those that show activity in the in vitro bioassays at concentrations that can be encountered in relevant in vivo exposure scenarios. PBK modeling-based reverse dosimetry approaches, using simple PBK models, have been applied to translate effect concentrations obtained in the U.S. Tox21 and ToxCast research programs to predicted in vivo dose levels in humans, called oral equivalent doses.52-58 By comparing these oral equivalent doses with estimated human exposure levels, chemicals can be prioritized for risk assessment, being those for which oral equivalent doses exceed or are close to human exposure levels. So although these high throughput reverse dosimetry approaches, using simple PBK models, may not provide quantitative PODs for risk assessment, they appear to be promising tools to prioritize chemicals for further risk assessment.52-58 Based on this high throughput reverse dosimetry approach, Thomas and coworkers proposed a tiered approach for chemical risk assessment consisting of three tiers.59 In the first tier, data from high-throughput in vitro assays are translated to in vivo doses (PODs) using PBK modeling-based reverse dosimetry using generic PBK models, which are



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compared to human exposure estimates to yield a margin of exposure (MOE). For chemicals that enter the second tier, the authors suggest to perform short-term in vivo studies, to extend kinetic evaluations and to refine human exposure estimates, providing more accurate estimates of the POD and the MOE. In the third tier proposed by the authors, the chemicals will be tested in traditional animal studies that are currently used to assess chemical safety.59

Promises and pitfalls of PBK modeling-based reverse dosimetry of in vitro toxicity data to predict in vivo toxicity One important strength of the PBK modeling-based reverse dosimetry approach is that by replacing the PBK parameters in a rat model by those for human, dose-response curves for human toxicity can be predicted. So far, we have shown that in this way we can indeed predict dose-response curves for toxicity towards humans.35, 42, 44 However, for the predicted human dose-response curves obtained so far no human data to actually validate these predictions were available. Therefore, an important challenge for the future is to provide proofs-of-principle for predicting and validating dose-response curves for human toxicity. This is important given that the relevance of rodent data for human risk assessment is seriously questioned.3, 4 Validating the use of PBK modeling-based reverse dosimetry to predict dose-response curves for toxicity in human is an important challenge but, if successful, will prove to be a unique way to predict human toxicity and facilitate safety testing using human data. Another aspect that needs further optimization is the prediction of effects upon repeated dose exposure. For ATRA, for example, it has been shown that blood levels decrease


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upon repeated dosing (up to ninefold in rats) due to increased expression levels of biotransformation enzymes.60, 61 In order to make predictions for repeated dosing in such cases, the increase in expression levels of biotransformation enzymes upon exposure has to be incorporated in the PBK model, to account for the corresponding changes in clearance. This phenomenon needs further consideration in future reverse dosimetry work. Also, the development and validation of in vitro methods for the determination of parameter values for kinetic processes is required. At present, several in vitro models are available that can be used to determine parameter values for hepatic clearance, such as incubations with hepatocytes, subcellular fractions and/or recombinant enzymes, including methods to scale the in vitro data to values that can be used in PBK models.10 Although the available in vitro models are widely applied for PBK model development, a recent analysis indicated that there appears to be a systematic bias in the estimation of intrinsic clearance from in vitro versus in vivo data, with in vitro-based estimates underestimating in vivo clearance for low values of intrinsic clearance, and overestimating in vivo clearance for high values of intrinsic clearance.62 For other kinetic processes, such as intestinal absorption and renal clearance, well-established models are lacking,10 indicating the need for the development of such models, including methods to scale the in vitro data to values that can be used in PBK models. In case of the prediction of specific toxicity endpoints, such as developmental toxicity and neurotoxicity, parameter values for kinetic processes relevant for the target tissues may be needed, such as for placental transfer and transfer across the blood-brain barrier, respectively. For example, when using PBK modeling-based reverse dosimetry for



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developmental toxicity, the placental transfer of chemicals may need to be considered. If the embryo Cmax or AUC of a compound is similar to the maternal plasma Cmax or AUC, it can be decided to not incorporate a separate compartment for the embryo in the PBK model. However, the assumption of equal maternal and embryonal concentrations cannot be made for all chemicals, indicating that in vitro placental transport models should be used in order to predict the placental transfer of these chemicals to the fetus. The in vitro BeWo b30 transwell system may be an adequate model to derive parameter values for placental transfer in PBK models.63, 64

Future perspectives Additional aspects that remain to be considered before the initial proofs-of-principle can be accepted as a novel approach for human safety testing include the topics discussed in the next sections.

Definition of the target organ in PBK modeling-based reverse dosimetry approaches To actually proof that by defining in vivo dose-response curves for various organs also the most sensitive organ can be defined remains to be proven. Can the approach for example discriminate a liver toxin from a kidney toxin and a cardio toxicant from a compound causing liver cancer? This question can be answered by testing the selected compounds in in vitro bioassays that are relevant for the diverse in vivo toxicity endpoints and translate the in vitro concentration-response curves thus obtained to in vivo dose-response curves. If these in vivo dose-response curves adequately match results from in vivo bioassays they should also be able to identify the target organ and critical


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effect, reflected by the dose-response curve that shows effects at the lowest dose levels. Given that toxicity is a matter of dose it is expected that compounds will test positive in various in vitro bioassays and that PBK modeling-based reverse dosimetry is essential to calculate at what dose levels internal concentrations in the various target organs will reach toxic levels. It is expected that for example metabolism including detoxification and bioactivation will be different in different target organs and that this will influence target organ selectivity. By including these metabolic conversions in the PBK models these differences can be taken into account. The PBK models can also accommodate the formation of a toxic metabolite in one organ and subsequent transport of the toxic metabolite to the target organ. Without taking such kinetic considerations into account the conclusions from in vitro data for in vivo risk assessment may not be adequate.

Choice of in vitro model for toxicity testing The current proofs-of-principle on the prediction of in vivo dose-response curves using PBK modeling-facilitated reverse dosimetry have mainly used cells lines of non-human origin. In order to assess risks for humans, future PBK modeling-facilitated reverse dosimetry approaches should focus on providing proofs-of-principle to make predictions for humans. This requires the use of human in vitro models. Application of human models, such as induced pluripotent stem cell-derived tissue models, is regarded as a promising tool for the assessment of human toxicity.65 Such human cell models are (commercially) available to study for example cardiotoxicity, hepatotoxicity or neurotoxicity.



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The in vitro models applied should be able to detect the toxicity related to the relevant mode of action that underlies the toxicity in the in vivo situation. This assumes that the mode of action underlying in vivo adverse outcomes are known. For example, if the toxicity is mediated via a specific receptor, and if this receptor is absent in the in vitro model, it can be expected that the effect concentrations that are obtained in the in vitro model are not adequate for PBK modeling-based reverse dosimetry to predict in vivo toxic dose levels. Often, the mode of action underlying the toxicity is not well understood, so there is uncertainty whether the in vitro model is appropriate to determine relevant concentration-response relationships that can be used for PBK modeling-based reverse dosimetry to predict in vivo toxicity. This indicates that a thorough understanding of modes of action underlying the toxicity of chemicals is required to select relevant in vitro models. At present, such information on modes of action underlying the toxicity of chemicals is gathered in so-called adverse outcome pathways (AOPs). An AOP is a conceptual construct that integrates existing knowledge on the linkage between a molecular initiating event (MIE), a series of intermediate key events (KEs), with an adverse outcome (AO) at a biological level of organization relevant to risk assessment.66, 67

Based on knowledge of the links between MIEs, KEs and AOs, the appropriateness of

in vitro models for toxicity testing can be determined, by assessing whether the model can recapitulate important processes of the AOP.

Definition of a generic PBK model Since the definition of a PBK model for each individual compound can be resource- and time-consuming, future efforts need to be directed at the development of generic PBK


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models for which the parameters can be derived from computer models (like models to predict partition coefficients) or from high throughput in vitro model systems and QSAR approaches. An initial strategy to define such generic PBK models has been described.53 This strategy included only a few kinetic processes and contained only a few experimental parameters including plasma protein binding, metabolic clearance measured at two concentrations using hepatocytes, bi-direction permeability of an intestinal barrier using Caco-2 cells in a transwell model and red blood cell partitioning.53 Using this strategy, chemicals could be ranked for priority testing. However, the approach could not provide quantitative PODs for risk assessment. Nevertheless this generic model could be taken as the starting point for developing an advanced generic PBK model, extending the model with the following characteristics: -

Ability to include full kinetics and not only two concentrations for hepatic clearance so that processes of saturation will be taken into account.

-

Including all target organs relevant for safety testing and not only the liver, including the prediction of target organ concentrations in addition to plasma concentrations, using partition coefficients to model target tissue distribution.

-

Prediction of the formation of reactive metabolites in the target organ of interest.

-

Including the placental barrier for evaluation of effects in developmental toxicity by adding the fetal compartment to the model and using the BeWo transport model to define parameter values for placental transfer.63, 64



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-

Including excretion pathways like renal and biliary clearance as well as possible enterohepatic circulation, which will be a real challenge given that currently no adequate in vitro models to measure these processes are available.

-

Including the evaluation of repeated dosing regimens to model (sub)chronic exposure.

-

Including the blood brain barrier for evaluation of neurotoxicity effects.

-

Including the possibility for route-to-route extrapolation.

-

Enabling modeling of interspecies as well as inter-individual differences for example by including Monte Carlo modeling of distributions for physiological parameters, and by including the activity of relevant reactions established by using in vitro incubations with different human tissue samples, thus taking interindividual differences into account.

The need for careful evaluation of generic PBK models is illustrated by a study preformed to further evaluate the reliability of a rat PBK model predicting the kinetics of selected registered pesticides (MW: 326–594 g/mol; log P: 2.96–3.99).68 Besides the distribution of the test substance between the compartments (basic model), in vitro data on hepatic clearance from rat liver S9-fraction as well as on intestinal permeability from Caco-2- or PAMPA-assays were included. For each pesticide, Cmax at different dose levels were compared to existing in vivo data. Modeled Cmax values in the same order of magnitude as the Cmax values determined in vivo were considered to be correctly predicted. The results obtained revealed that with the most simple model that lacks description of hepatic clearance and intestinal permeability, the Cmax of only 36% of the chemicals was correctly predicted (i.e. it was in the same order of magnitude as the in


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vivo Cmax). When hepatic clearance was included, correct predictions increased to 43%. When additionally intestinal permeability data were included, the Cmax for up to 50% of the chemicals were in the same order of magnitude as the in vivo data.68 This illustrates that increasing the number of processes included in a generic model is likely to increase the predictivity while it also illustrates that the accuracy of PBK modeling should be carefully taken into consideration, when these models are intended for risk assessments based on data obtained with PBK modeling-based reverse dosimetry approaches.

Definition of uncertainty factors. Another important issue to be considered is how to account for uncertainty when PBK modeling-based reverse dosimetry predicted PODs would be used in human risk assessment. In current safety assessment practice default uncertainty factors of 10 for interspecies differences and of 10 for inter-individual differences are used to convert PODs from the dose-response curves from animal-based bioassays to safe levels of human exposure. The question is what uncertainty factors would adequately cover the uncertainties connected to the data obtained with PBK modeling-based reverse dosimetry of in vitro toxicity data. From results obtained so far it appeared that the PBK modelingbased reverse dosimetry approach often predicts in vivo dose-response curves and corresponding PODs within one order of magnitude from experimental data. As indicated above, a study performed to evaluate the reliability of a generic rat PBK model predicting the kinetics of selected registered pesticides showed that plasma concentrations calculated based on their generic PBK model differed by more than an order of magnitude from in vivo plasma concentrations for about 50% of the chemicals assessed.68



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This would imply that when such a generic PBK model would be used in safety testing, an uncertainty factor of 10 for uncertainty in the prediction of in vivo kinetics would not always fully cover the uncertainty thus introduced in the risk assessment, suggesting that an additional uncertainty factor may need to be introduced. However, given that PODs derived from different animal bioassays testing the same compound also result in significant differences (see for example the literature data included in Figure 5), one might argue that part of the uncertainty is also implicit in experimental animal-based bioassays. Furthermore, when based on data from human in vitro bioassays and human PBK models, one could use human dose-response curves to define PODs as the basis for deriving safe levels of exposure and this would in theory eliminate the need for the uncertainty factor for interspecies variation for extrapolation from rat to human. This would take the risk assessment to a level where it can be based on human data eliminating the concerns over the inadequacy of animal data to predict human risk. This indicates the need for proofs-of-principle on the prediction of human toxicity based on PBK modeling-based reverse dosimetry approaches.

Concluding remarks The use of in vitro toxicity data in risk assessment for the in vivo situation is not straightforward. One crucial issue is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves because these dose-response curves are needed in toxicological risk assessment, in order to obtain a POD to determine safe exposure levels for humans. This translation of in vitro concentration-response curves to in vivo dose-response curves is at present an important bottleneck and


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challenge for the use of in vitro toxicity assays in risk assessment practice. Recent developments enable this translation of in vitro concentration-response curves to in vivo dose-response curves using PBK modeling-based reverse dosimetry, and proofs-ofprinciple have been provided for adequate prediction of animal toxicity. Future research should focus on the PBK modeling-based reverse dosimetry of in vitro toxicity data obtained in human models, which would provide an approach for a more humanized risk assessment, without the need of animal-based testing.

Funding Sources We have received funding for our reverse dosimetry work by the Netherlands Organisation for Health Research and Development (ZonMw, project numbers 114000088 and 114011002), BASF SE (Grant Number 6153511230), Nestlé Research Center, and the Ministry of Education of Malaysia (Project number- KPT (BS) 860828565598).



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Abbreviations list AO

adverse outcome

AOP

adverse outcome pathway

AR

androgen receptor

ATRA

all-trans-retinoic acid

AUC

area under the concentration-time curve

BMD

benchmark dose

BMDL

lower confidence limit of BMD

BPAD

biological pathway altering dose

CALUX

chemical-activated luciferase expression

Cmax

maximal concentration

DEP

di-n-butyl phthalate

DEHP

di-(2-ethylhexyl) phthalate

EGME

ethylene glycol monomethyl ether

EGEE

ethylene glycol monoethyl ether

EGBE

ethylene glycol monobutyl ether

EGPE

ethylene glycol monophenyl ether

ER

estrogen receptor

EST

embryonic stem cell test

KE

key event

LOAEL

lowest observed adverse effect level

MBP

mono-n-butyl phthalate

MEHP

mono-2-ethylhexyl phthalate

MIE

molecular initiating event

NOAEL

no observed adverse effect level

PBK

physiologically based kinetic

POD

point of departure

QSAR

quantitative structure activity relationship

SPME

solid-phase microextraction


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Author biographies Jochem Louisse Dr. Louisse is assistant professor at the division of Toxicology of Wageningen University, The Netherlands. He holds a PhD from WUR and worked as a postdoctoral researcher at the European Centre for the Validation of Alternative Methods (EURL ECVAM) of the Joint Research Centre of the European Commission. His research focusses on the development of non-animal based testing methods that can be applied in toxicological risk assessment, including the development of human stem cell-based tissue models for toxicity testing and the application of PBK modeling-based reverse dosimetry to translate in vitro concentration-response data to predicted in vivo dose-response data.



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Karsten Beekmann Dr. Karsten Beekmann is assistant professor at the Division of Toxicology at Wageningen University, The Netherlands. His research focuses on the metabolism of foodborne xenobiotics. The emphasis of his current work is on the role of the gut microbiota in toxicology, studying the gut microbial metabolism of foodborne xenobiotics, and the consequences of the metabolism for their biological activities. Dr. Beekmann also works on in vitro models that can be used to generate data to describe gut microbial metabolism in PBK models.

Ivonne MCM Rietjens Ivonne M.C.M. Rietjens is full professor in Toxicology and head of the division of Toxicology at Wageningen University, The Netherlands. She is an elected member of the Royal Netherlands Academy of Arts and Sciences (KNAW) and member of many (inter)national advisory committees. Her research focusses on i) low dose risk assessment of food-borne genotoxic carcinogens, ii) safety and risk assessment of botanicals and botanical preparations, iii) alternatives for animal testing using PBK modeling-based reverse dosimetry, and iv) modes of action of food-borne toxic chemicals.


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Figure legends Figure 1. Schematic presentation of the PBK modeling-based reverse dosimetry approach for definition of in vivo dose-response curves for different species including human based on concentration-response curves from in vitro assays.

Figure 2: Example of a PBK model and equations for the liver compartment. The model can predict the concentration of a toxic compound and its relevant metabolites in any tissue at any point in time and for any dose level.

Figure 3. Predicted and reported in vivo developmental toxicity of ATRA.35 Predicted dose-response curves for developmental toxicity of ATRA were obtained using PBK modeling-based reverse dosimetry of in vitro concentration-response data obtained in the ES-D3 cell differentiation assay of the EST using the Cmax (solid line) or the AUC (dashed line) as dose metric to relate exposure to toxicity. In vivo data on developmental toxicity were taken from literature and include data from a study of Wise and coworkers (incidence of skull malformations; diamonds)69 and Bürgin and Schmitt (incidence of torso and limbs malformations; circles)70.

Figure 4. Predicted and reported in vivo developmental toxicity of EGME and EGEE upon oral exposure (A) and inhalation exposure (B). Predicted dose-response curves for developmental toxicity of EGME (solid line) and EGEE (dashed line) were obtained using PBK modeling-based reverse dosimetry of in vitro concentration-response data of their toxic metabolites (methoxyacetic acid and ethoxyacetic acid, respectively) obtained



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in the ES-D3 cell differentiation assay of the EST. In vivo data were taken from the literature, from Toraason and coworkers (EGME oral, resorptions (closed circle), cardiac malformations (closed square))71, Goad and Cramner (EGEE oral, resorptions (open circle), cardiac malformations (open squares))72, Chester (EGEE oral, fetal deaths (open triangle))73, Stenger (EGEE oral, skeletal malformations (open diamond))74, Nelson and coworkers (EGME inhalation, resorptions (closed circle), visceral malformations (dash), skeletal malformations (closed diamond))75, and Andrew and Hardin (EGEE inhalation, resorptions (open circle) cardiac malformations (open square))76.

Figure 5. Comparison of the POD (BMDL05 (lower confidence limit of the benchmark dose for 5% effect)) predicted for developmental toxicity of phenol using PBK modelingbased reverse dosimetry of in vitro data from the differentiation assay of the EST (solid vertical line) to PODs (No Observed Adverse Effect Levels) derived from in vivo developmental toxicity studies in rat in literature (closed circle)77-80(for details see Strikwold et al. (2013)44).


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PBK modeling-based reverse dosimetry

in vitro concentration-response

predicted in vivo dose-response

lung

arterial blood

slowly perfused tissue

venous blood

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rapidly perfused tissue

fat

liver metabolites stomach

intestine

faeces

oral dose

Figure 1 ACS Paragon Plus Environment


Physiologically based kinetic (PBK) model

Example equation liver:

inhalation lung slowly perfused tissue rapidly perfused tissue fat

arterial blood

Uptake from GI tract

venous blood

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dA/dt = + KA * AGI

Transport from arterial to venous blood

+ QL * (CA - CV) Metabolism

liver metabolites stomach

intestine

- Vmax * CL/ (Km + CL)

faeces

oral dose


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fraction affected

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0 -1

dose ATRA (mg kg bw )



B

fraction affected

A

fraction affected

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0

0 -1

dose (mmol kg bw )


dose (ppm)

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BMDL05 from predicted dose-response data Fetal bodyweight decrease Malformations

10

100

Fetal deaths

79

Fetal deaths

80

78

1000 -1

dose (mg kg bw )


77


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in vitro concentration-response

PBK modeling-based reverse dosimetry


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predicted in vivo dose-response

Use of Physiologically Based Kinetic Modeling-Based Reverse

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