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We propose a Bayesian population modeling and virtual bioequivalence assessment approach to establishing dissolution specifications for oral dosage forms. A generalizable semi-physiologically based pharmacokinetic absorption model with six gut segments and liver, connected to a two-compartment model of systemic disposition for bupropion hydrochloride oral dosage forms was developed. Prior information on model parameters for gut physiology, bupropion physicochemical properties, and drug product properties were obtained from the literature. The release of bupropion hydrochloride from immediate-, sustained- and extended-release oral dosage forms was described by a Weibull function. In vitro dissolution data were used to assign priors to the in vivo release properties of the three bupropion formulations. We applied global sensitivity analysis to identify the influential parameters for plasma bupropion concentrations and calibrated them. To quantify inter- and intra-individual variability, plasma concentration profiles in healthy volunteers that received the three dosage forms, each at two doses, were used. The calibrated model was in good agreement with both in vitro dissolution and in vivo exposure data. Markov Chain Monte Carlo samples from the joint posterior parameter distribution were used to simulate virtual crossover clinical trials for each formulation with distinct drug dissolution profiles. For each trial, an allowable range of dissolution parameters ("safe space") in which bioequivalence can be anticipated was established. These findings can be used to assure consistent product performance throughout the drug product life-cycle and to support manufacturing changes. Our framework provides a comprehensive approach to support decision-making in drug product development.

As model‐informed drug development becomes an integral part of modern approaches to the discovery of new therapeutic entities and showing their safety and effectiveness, modalities of incorporating the paradigm into widespread practice require a revisit. Traditionally, modeling and simulation (M&S) have been performed by specialized teams who create bespoke models for each case and have reservations about letting modeling be done by the greater mass of scientists engaged in various stages of drug development. An analogy can be drawn between M&S and automobiles: typical drivers of ordinary cars use them for daily tasks, such as going from point A to B whereas specialized Formula 1 drivers using bespoke individually made cars to test the latest technologies. The reliability and robustness of ordinary cars for the first group requires elements related to quality and endurance that are very different from those applicable to any Formula 1 car supported by a large team of engineers. In this commentary, we frame and analyze the problems concerning the structure and setup of various M&S tools, and their pros and cons. We demonstrate that many misconceptions have precluded having an open discussion on what each modality of M&S tools strives to achieve, and we provide data and evidence that support the move of M&S to main stream use by many, as opposed to specialized usage by few. Parallels are drawn in many other areas involving laboratory instrumentation, statistical analyses, and so on.

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

A full Bayesian statistical treatment of complex pharmacokinetic or pharmacodynamic models, in particular in a population context, gives access to powerful inference, including on model structure. Markov Chain Monte Carlo (MCMC) samplers are typically used to estimate the joint posterior parameter distribution of interest. Among MCMC samplers, the simulated tempering algorithm (TMCMC) has a number of advantages : it can sample from sharp multi-modal posteriors; it provides insight into identifiability issues useful for model simplification; it can be used to compute accurate Bayes factors for model choice; the simulated Markov chains mix quickly and have assured convergence in certain conditions. The main challenge when implementing this approach is to find an adequate scale of auxiliary inverse temperatures (perks) and associated scaling constants. We solved that problem by adaptive stochastic optimization and describe our implementation of TMCMC sampling in the GNU MCSim software. Once a grid of perks is obtained, it is easy to perform posterior-tempered MCMC sampling or likelihood-tempered MCMC (thermodynamic integration, which bridges the joint prior and the posterior parameter distributions, with assured convergence of a single sampling chain). We compare TMCMC to other samplers and demonstrate its efficient sampling of multi-modal posteriors and calculation of Bayes factors in two stylized case-studies and two realistic population pharmacokinetic inference problems, one of them involving a large PBPK model.

Background & AimsOne of the early key events of drug-induced liver injury (DILI) is the activation of adaptive stress responses, a cellular mechanism to overcome stress. Given the diversity of DILI outcomes and lack in understanding of population variability, we mapped the inter-individual variability in stress response activation to improve DILI prediction.Approach & ResultsHigh-throughput transcriptome analysis of over 8,000 samples was performed in primary human hepatocytes of 50 individuals upon 8 to 24 h exposure to broad concentration ranges of stress inducers: tunicamycin to induce the unfolded protein response (UPR), diethyl maleate for the oxidative stress response, cisplatin for the DNA damage response and TNFα for NF-κB signalling. This allowed investigation of the inter-individual variability in concentration-dependent stress response activation, where the average of benchmark concentrations (BMCs) had a maximum difference of 864, 13, 13 and 259-fold between different hepatocytes for UPR, oxidative stress, DNA damage and NF-κB signalling-related genes, respectively. Hepatocytes from patients with liver disease resulted in less stress response activation. Using a population mixed-effect framework, the distribution of BMCs and maximum fold change were modelled, allowing simulation of smaller or larger PHH panel sizes. Small panel sizes systematically under-estimated the variance and resulted in low probabilities in estimating the correct variance for the human population. Moreover, estimated toxicodynamic variability factors were up to 2-fold higher than the standard uncertainty factor of 101/2 to account for population variability during risk assessment, exemplifying the need of data-driven variability factors.ConclusionsOverall, by combining high-throughput transcriptome analysis and population modelling, improved understanding of variability in stress response activation across the human population could be established, thereby contributing towards improved prediction of DILI.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE “lipid accumulation”. KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.

For the determination of acute toxicity of chemicals in zebrafish (Danio rerio) embryos, the OECD test guideline 236, relative to the Fish Embryo Toxicity Test (FET), stipulates a dose-response analysis of four lethal core endpoints and a quantitative characterization of abnormalities including their time-dependency. Routinely, the data are analysed at the different observation times separately. However, observations at a given time strongly depend on the previous effects and should be analysed jointly with them. To solve this problem, we developed multistate models for occurrence of developmental malformations and live events in zebrafish embryos exposed to eight concentrations of valproic acid (VPA) the first five days of life. Observations were recorded daily per embryo. We statistically infer on model structure and parameters using a numerical Bayesian framework. Hatching probability rate changed with time and we compared five forms of its time-dependence; a constant rate, a piecewise constant rate with a fixed hatching time at 48 hours post fertilization, a piecewise constant rate with a variable hatching time, as well as a Hill and Gaussian form. A piecewise constant function of time adequately described the hatching data. The other transition rates were conditioned on the embryo body concentration of VPA, obtained using a physiologically-based pharmaco­kinetic model. VPA impacted mostly the malformation probability rate in hatched and non-hatched embryos. Malformation reversion probability rates were lowered by VPA. Direct mortality was low at the concentrations tested, but increased linearly with internal concentration. The model makes full use of data and gives a finer grain analysis of the teratogenic effects of VPA in zebrafish than the OECD-prescribed approach. We discuss the use of the model for obtaining toxicological reference values suitable for inter-species extrapolation. A general result is that complex multistate models can be efficiently evaluated numerically.

Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (Cmax) time at which Cmax is reached (Tmax), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (Vss) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (Vliver) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.

International audience; Physiologically-based toxicokinetic (PBTK) models are important tools for in vitro to in vivo or inter-species extrapolations in health risk assessment of foodborne and non-foodborne chemicals. Here we present a generic PBTK model implemented in the EuroMix toolbox, MCRA 9 and predict internal kinetics of nine chemicals (three endocrine disrupters, three liver steatosis inducers, and three developmental toxicants), in data-rich and data-poor conditions, when increasingly complex levels of parametrization are applied. At the first stage, only QSAR models were used to determine substance-specific parameters, then some parameter values were refined by estimates from substance-specific or high-throughput in vitro experiments. At the last stage, elimination or absorption parameters were calibrated based on available in vivo kinetic data. The results illustrate that parametrization plays a capital role in the output of the PBTK model, as it can change how chemicals are prioritized based on internal concentration factors. In data-poor situations, estimates can be far from observed values. In many cases of chronic exposure, the PBTK model can be summarized by an external to internal dose factor, and interspecies concentration factors can be used to perform interspecies extrapolation. We finally discuss the implementation and use of the model in the MCRA risk assessment platform.

Adverse outcome pathways (AOPs) are a recent toxicological construct that connects, in a formalized, transparent and quality-controlled way, mechanistic information to apical endpoints for regulatory purposes. AOP links a molecular initiating event (MIE) to the adverse outcome (AO) via key events (KE), in a way specified by key event relationships (KER). Although this approach to formalize mechanistic toxicological information only started in 2010, over 200 AOPs have already been established. At this stage, new requirements arise, such as the need for harmonization and re-assessment, for continuous updating, as well as for alerting about pitfalls, misuses and limits of applicability. In this review, the history of the AOP concept and its most prominent strengths are discussed, including the advantages of a formalized approach, the systematic collection of weight of evidence, the linkage of mechanisms to apical end points, the examination of the plausibility of epidemiological data, the identification of critical knowledge gaps and the design of mechanistic test methods. To prepare the ground for a broadened and appropriate use of AOPs, some widespread misconceptions are explained. Moreover, potential weaknesses and shortcomings of the current AOP rule set are addressed (1) to facilitate the discussion on its further evolution and (2) to better define appropriate vs. less suitable application areas. Exemplary toxicological studies are presented to discuss the linearity assumptions of AOP, the management of event modifiers and compensatory mechanisms, and whether a separation of toxicodynamics from toxicokinetics including metabolism is possible in the framework of pathway plasticity. Suggestions on how to compromise between different needs of AOP stakeholders have been added. A clear definition of open questions and limitations is provided to encourage further progress in the field. © 2017, Springer-Verlag GmbH Germany.

In the original article, we neglected to mention that this work was partly supported by the EU-ToxRisk project (An Integrated European "Flagship" Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002). A correction has therefore been made to the Acknowledgments: "The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking (IMIJU) under grant agreement number 115439, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies' in kind contribution. This work was supported by the 2015 CEFIC-LRI award (AL) and partly supported by the EU-ToxRisk project (An Integrated European "Flagship" Program Driving Mechanism-Based Toxicity Testing and Risk Assessment for the 21st Century) funded by the European Commission under the Horizon 2020 programme (Grant Agreement No. 681002). This publication reflects only the author's views and neither the IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein." The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

While hazard assessment of chemicals can make direct use of descriptive adverse outcome pathways (AOPs), risk assessment requires quantitative relationships from exposure to effect timing and magnitude. To seamlessly integrate the data generated by alternative methods or in vivo testing, quantitative AOPs (qAOPs) providing dose-time-response predictions are more valuable than qualitative AOPs. Here, we compare three approaches to qAOP building: empirical dose-response modeling, Bayesian network (BN) calibration, and systems biology (SB) modeling. These methods were applied to the quantification of a simplified oxidative stress induced chronic kidney disease AOP, on the basis of in vitro data obtained on RPTEC/TERT1 cells exposed to potassium bromate. Effectopedia was used to store the experimental data and the developed models in a unified representation so they can be compared and further analyzed. We argue that despite the fact that dose-response models give adequate fits to the data they should be accompanied by mechanistic SB modeling to gain a proper perspective on the quantification. BNs can be both more precise than dose-response models and simpler than SB models, but more experience with their usage is needed.

Publisher's version (útgefin grein), The current/traditional human health risk assessment paradigm is challenged by recent scientific and technical advances, and ethical demands. The current approach is considered too resource intensive, is not always reliable, can raise issues of reproducibility, is mostly animal based and does not necessarily provide an understanding of the underlying mechanisms of toxicity. From an ethical and scientific viewpoint, a paradigm shift is required to deliver testing strategies that enable reliable, animal-free hazard and risk assessments, which are based on a mechanistic understanding of chemical toxicity and make use of exposure science and epidemiological data. This shift will require a new philosophy, new data, multidisciplinary expertise and more flexible regulations. Re-engineering of available data is also deemed necessary as data should be accessible, readable, interpretable and usable. Dedicated training to build the capacity in terms of expertise is necessary, together with practical resources allocated to education. The dialogue between risk assessors, risk managers, academia and stakeholders should be promoted further to understand scientific and societal needs. Genuine interest in taking risk assessment forward should drive the change and should be supported by flexible funding. This publication builds upon presentations made and discussions held during the break-out session ‘Advancing risk assessment science – Human health’ at EFSA's third Scientific Conference ‘Science, Food and Society’ (Parma, Italy, 18–21 September 2018)., The European Food Safety Authority (EFSA) and authors wish to thank the participants of the break-out session ‘Advancing risk assessment science – Human health’ at EFSA’s third Scientific Conference ‘Science, Food and Society’ (Parma, Italy, 18–21 September 2018) for their active and valuable contributions to the discussion. We also thank Hans Verhagen for carefully proofreading it.

The aim of this study is to benchmark two Bayesian software tools, namely Stan and GNU MCSim, that use different Markov chain Monte Carlo (MCMC) methods for the estimation of physiologically based pharmacokinetic (PBPK) model parameters. The software tools were applied and compared on the problem of updating the parameters of a Diazepam PBPK model, using time-concentration human data. Both tools produced very good fits at the individual and population levels, despite the fact that GNU MCSim is not able to consider multivariate distributions. Stan outperformed GNU MCSim in sampling efficiency, due to its almost uncorrelated sampling. However, GNU MCSim exhibited much faster convergence and performed better in terms of effective samples produced per unit of time.

Exposure of embryos to mixtures of environmental chemicals can result in congenital malformations. Mixture experiments can provide an indication of the joint effects of substances, but it is practically infeasible to test all possible combinations. The development of mechanistic approaches and integrated models able to predict the effects of mixtures from the concentrations of their individual components, are crucial to assess mixtures associated risks. Azole fungicides can induce craniofacial defects, both after in utero and in vitro exposure. Results obtained in vitro have shown a significant enhancement of teratogenic effects after co-exposure to azoles in comparison to the single exposures. In this project, we evaluated the hypothesis that those molecules concur to imbalance the retinoic acid pathway in specific responsive embryonic tissues. We developed a quantitative adverse outcome pathway for craniofacial malformations, able to simulate the formation of the physiological retinoic acid gradient in the rat embryo hindbrain and its perturbation after exposure to cyproconazole, flusilazole, triadimefon and to their binary mixtures. The underlying system biology model was calibrated using in vitro data and is reasonably predictive of mixtures’ effects for those azoles, thereby confirming the plausibility of the hypothesized pathogenic pathway. This quantitative AOP could have mechanistic or predictive applications in pesticides risk assessment.

Prenatal exposures to perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) have been associated with child health outcomes, but many of these associations remain poorly characterized. The aim of this work was to provide new indicators of foetal exposure for the Spanish INMA birth cohort. First, a pregnancy and lactation physiologically based pharmacokinetic (PBPK) model was calibrated in a population framework to provide quantitative estimates for the PFOA and PFOS placental transfers in humans. The estimated distributions indicated that PFOA crosses the placental barrier at a rate three times higher than PFOS and shows a higher variability between mothers. The PBPK model was then used to back-calculate the time-varying daily intakes of the INMA mothers corrected for their individual history from a spot maternal concentration. We showed the importance of accounting for the mothers' history as different dietary intakes can result in similar measured concentrations at one time point. Finally, the foetal exposure was simulated in target organs over pregnancy using the PBPK model and the estimated maternal intakes. We showed that the pattern of PFOA and PFOS exposures varies greatly among the foetuses. About a third has levels of either one compound always higher than the levels of the other compound. The other two thirds showed different ranking of PFOA and PFOS in terms of concentrations in the target organs. Our simulated foetal exposures bring additional information to the measured maternal spot concentrations and can help to better characterize the prenatal exposure in target organs during windows of susceptibility.

Predicting repeated-dosing in vivo drug toxicity from in vitro testing and omics data gathering requires significant support in bioinformatics, mathematical modeling and statistics. We present here the major aspects of the work devoted within the framework of the European integrated Predict-IV to pharmacokinetic modeling of in vitro experiments, physiologically based pharmacokinetic (PBPK) modeling, mechanistic models of toxicity for the kidney and brain, large scale dose–response analyses methods and biomarker discovery tools. All of those methods have been applied to various extent to the drug datasets developed by the project’s partners. Our approach is rather generic and could be adapted to other drugs or drug candidates. It marks a successful integration of the work of the different teams toward a common goal of predictive quantitative in vitro to in vivo extrapolation.

There is a growing impetus to develop more accurate, predictive and relevant in vitro models of renal xenobiotic exposure. As part of the EU-FP7, Predict-IV project, a major aim was to develop models that recapitulate not only normal tissue physiology but also aspects of disease conditions that exist as predisposing risk factors for xenobiotic toxicity. Hypoxia, as a common micro-environmental alteration associated with pathophysiology in renal disease, was investigated for its effect on the toxicity profile of a panel of 14 nephrotoxins, using the human proximal tubular epithelial RPTECT/TERT1 cell line. Changes in ATP, glutathione and resazurin reduction, after 14 days of daily repeat exposure, revealed a number of compounds, including adefovir dipivoxil with enhanced toxicity in hypoxia. We observed intracellular accumulation of adefovir in hypoxia and suggest decreases in the efflux transport proteins MRP4, MRP5, NHERF1 and NHERF3 as a possible explanation. MRP5 and NHERF3 were also down-regulated upon treatment with the HIF-1 activator, dimethyloxalylglycine. Interestingly, adefovir dependent gene expression shifted from alterations in cell cycle gene expression to an inflammatory response in hypoxia. The ability to investigate aspects of disease states and their influence on renal toxin handling is a key advantage of in vitro systems developed here. They also allow for detailed investigations into mechanisms of compound toxicity of potential importance for compromised tissue exposure.

International audience; requirements for pre-market authorisation of pesticides (regulation 283-284/2013) for which comparative in vitro metabolism (rat) human and in vivo rat TK data are required. As a consequence, EFSA has been involved in collaborative research to develop TK tools and databases for food safety in the human health, animal health and ecological area. After problem formulation, risk assessors may start at low tier (e.g. basic information and default values) under time and resource constraints, or use all data available for a particular chemical. In this situation, ADME and toxicity information may allow sound dose response modelling and a full quantitative assessment. In a broad context, developing generic tools and databases allow replacing default values with quantitative metrics for physiological, TK and toxicity parameters in any data situation. First, physiological parameters for species of interest are collected and integrated, within a set of differential equations, into generic models. These range from allometric scaling, one compartment model to full physiologically-based TK models (PB-TK). Third, databases reporting physico-chemical and ADME properties, TK parameters and bioaccumulation provide input data to run the models for predicting blood concentrations or elimination patterns. Applications are illustrated including modelling interspecies differences in renal elimination using allometric scaling, integrating variability in human metabolism into generic PB-TK models and development of a modelling platform: TKplate (Wiecek et al., 2018, Darney et al., 2018). Future perspectives include the development of guidance and case studies to use TK tools in routine risk assessment and training programmes.

One of the goals of environmental risk assessment is to protect the whole ecosystem from adverse effects resulting from exposure to chemicals. Many research efforts have aimed to improve the quantification of dose-response relationships through the integration of toxicokinetics. For this purpose, physiologically-based toxicokinetic (PBTK) models have been developed to estimate internal doses from external doses in a time-dependent manner. In this study, a generic PBTK model was developed and adapted for rainbow trout (Onchorhynchus mykiss), zebrafish (Danio rerio), fathead minnow (Pimephales promelas), and three-spined stickleback (Gasterosteus aculeatus). New mechanistic approaches were proposed for including the effects of growth and temperature in the model. Physiological parameters and their inter-individual variability were estimated based on the results of extensive literature searches or specific experimental data. The PBTK model was implemented for nine environmental contaminants (with log k(ow) from - 0.9 to 6.8) to predict whole-body concentrations and concentrations in various fish's organs. Sensitivity analyses were performed for a lipophilic and a hydrophilic compound to identify which parameters have most impact on the model's outputs. Model predictions were compared with experimental data according to dataset-specific exposure scenarios and were accurate: 50% of predictions were within a 3-fold factor for six out of nine chemicals and 75% of predictions were within a 3-fold factor for three of the most lipophilic compounds studied. Our model can be used to assess the influence of physiological and environmental factors on the toxicokinetics of chemicals and provide guidance for assessing the effect of those critical factors in environmental risk assessment.

Microphysiological systems are fundamental for progressing towards a global paradigm shift in drug development through the generation of patient-on-a-chip models. An increasing number of single- and multi-organ systems have been adopted by the pharmaceutical and cosmetic industries for predictive substance testing. These models run on heterogeneous tissues and cell types from different donors. However, a patient is an individual. Therefore, patient-on-a-chip systems need to be built from tissues from one autologous source. Individual on-chip organ differentiation from a single induced pluripotent stem cell source could provide a solution to this challenge.We designed a four-organ chip based on human physiology. It enables the interconnection of miniaturized human intestine, liver, brain and kidney equivalents. All four organ models were predifferentiated from induced pluripotent stem cells from the same healthy donor and integrated into the microphysiological system. The cross talk led to further differentiation over a 14-day cultivation period under pulsatile blood flow conditions in one common medium deprived of growth factors. This model platform will pave the way for disease induction and subsequent drug testing.

Traditionally, the solution to reduce parameter dimensionality in a physiologically-based pharmacokinetic (PBPK) model is through expert judgment. However, this approach may lead to bias in parameter estimates and model predictions if important parameters are fixed at uncertain or inappropriate values. The purpose of this study was to explore the application of global sensitivity analysis (GSA) to ascertain which parameters in the PBPK model are non-influential, and therefore can be assigned fixed values in Bayesian parameter estimation with minimal bias. We compared the elementary effect-based Morris method and three variance-based Sobol indices in their ability to distinguish “influential” parameters to be estimated and “non-influential” parameters to be fixed. We illustrated this approach using a published human PBPK model for acetaminophen (APAP) and its two primary metabolites APAP-glucuronide and APAP-sulfate. We first applied GSA to the original published model, comparing Bayesian model calibration results using all the 21 originally calibrated model parameters (OMP, determined by “expert judgment”-based approach) vs. the subset of original influential parameters (OIP, determined by GSA from the OMP). We then applied GSA to all the PBPK parameters, including those fixed in the published model, comparing the model calibration results using this full set of 58 model parameters (FMP) vs. the full set influential parameters (FIP, determined by GSA from FMP). We also examined the impact of different cut-off points to distinguish the influential and non-influential parameters. We found that Sobol indices calculated by eFAST provided the best combination of reliability (consistency with other variance-based methods) and efficiency (lowest computational cost to achieve convergence) in identifying influential parameters. We identified several originally calibrated parameters that were not influential, and could be fixed to improve computational efficiency without discernable changes in prediction accuracy or precision. We further found six previously fixed parameters that were actually influential to the model predictions. Adding these additional influential parameters improved the model performance beyond that of the original publication while maintaining similar computational efficiency. We conclude that GSA provides an objective, transparent, and reproducible approach to improve the performance and computational efficiency of PBPK models.

OpenRiskNet (https://openrisknet.org/) is an EU funded infrastructure project with the main objective to develop an open e-infrastructure providing resources and services to a variety of industries requiring risk assessment, including chemicals, cosmetic ingredients, drugs and nanomaterials. The OpenRiskNet approach is to work on different case studies to test and evaluate requirements to overcome the fragmentation of data and tools and to provide solutions improving the harmonization of data, usability and interoperability of application programming interfaces (APIs) and the deployment into virtual infrastructure. The cases present real-world settings such as systems biology approaches for grouping compounds, read-across applications using chemical and biological similarity, and identifying areas of concern based only on alternative methods approaches. We discuss our progress on the OpenRiskNet goal of harmonizing data and metadata within APIs that can be added to already existing analysis and modeling services and data warehouses. We also demonstrate how these APIs can easily be used towards the generation of full risk assessment workflows either using scripting languages or workflow managers. Finally, we show the first approaches to make these APIs semantically rich by annotating data with human- and computer-readable data schemata. OpenRiskNet has initiated the Associated Partners Programme strengthening the working ties between the OpenRiskNet members and other organizations within the scientific community.

Toxicological responses to chemical insult are largely regulated by transcriptionally activated pathways that may be independent, correlated and partially or fully overlapping. Investigating the dynamics of the interactions between stress responsive transcription factors from toxicogenomic data and defining the signature of each of them is an additional step toward a system level understanding of perturbation driven mechanisms. To this end, we investigated the segregation of the genes belonging to the three following transcriptionally regulated pathways: the AhR pathway, the Nrf2 pathway and the ATF4 pathway. Toxicogenomic datasets from three projects (carcinoGENOMICS, Predict-IV and TG-GATEs) obtained in various experimental conditions (in human and rat in vitro liver and kidney models and rat in vivo, with bolus administration and with repeated doses) were combined and consolidated where overlaps between datasets existed. A bioinformatic analysis was performed to refine pathways' signatures and to create chemical activation capacity scores to classify chemicals by their potency and selectivity of activation of each pathway. With some refinement such an approach may improve chemical safety classification and allow biological read across on a pathway level.Corrigendum: Investigation of Nrf2, AhR and ATF4 Activation in Toxicogenomic Databases10.3389/fgene.2019.00517

Valproic acid (VPA) is used in the management of seizures, bipolar disorder, and migraines however, its use is associated with a number of adverse effects including hepatic steatosis. Whole-body physiologically based toxicokinetic (PBTK) models were developed to simulate the tissue concentrations of VPA in the rat and human. The models were parameterised using physicochemical properties and reverse translation approaches from published data. In order to recover the extended systemic exposure observed in rodent pre-clinical studies, it was necessary to incorporate the enterohepatic recirculation resultant from deconjugation of biliary cleared glucoronidated metabolites in the gut, and the subsequent reabsorption of parent compound. In vitro reporter assays showed activation of the pregnane X receptor (PXR) and peroxisome proliferator activated receptor alpha (PPARa) in response to VPA exposure; both previously identified as molecular initiating events (MIEs) in the hepatic steatosis adverse outcome pathway. Using a steady-state biokinetic model of in vitro distribution, nominal VPA treatment concentrations identified as activating PXR and PPARa in vitro were used to predict the corresponding intracellular concentrations. Using the verified rat and human PBTK models, a reverse-dosimetry approach was employed to determine the oral dose resulting in maximal hepatic concentrations corresponding to the intracellular concentration associated with activating these MIEs in vitro. Doses of 3.7 mg/kg and 1.7 mg/kg in rat and human, respectively, were determined to result in hepatic concentrations associated with activation of MIEs in the steatosis AOP. As might be expected, the MIE activating dose determined in rat, was more than two orders of magnitude lower than the oral LOAEL identified in rat repeat dose toxicity studies (500mg/kg) linked with histopathological scoring of hepatic steatosis. However, the MIE activating dose in humans, equivalent to ∼125mg, is well within the therapeutic dosing range. Repeat dosing at therapeutic levels could result in sustained activation of MIEs associated with adverse hepatic outcomes. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 681002.

We have integrated in vitro and in silico data to describe the toxicity of chloroacetaldehyde (CAA) on renal cells via its production from the metabolism of ifosfamide (IFO) by hepatic cells. A pharmacokinetic (PK) model described the production of CAA by the hepatocytes and its transport to the renal cells. A system biology model was coupled to the PK model to describe the production of reactive oxygen species (ROS) induced by CAA in the renal cells. In response to the ROS production, the metabolism of glutathione (GSH) and its depletion were modeled by the action of an NFE2L2 gene-dependent pathway. The model parameters were estimated in a Bayesian context via Markov Chain Monte Carlo (MCMC) simulations based on microfluidic experiments and literature in vitro data. Hepatic IFO and CAA in vitro intrinsic clearances were estimated to be 1.85 x 10(-9) μL s(-1) cell(-1) and 0.185 x 10(-9) μL s(-1) cell(-1) ,respectively (corresponding to an in vivo intrinsic IFO clearance estimate of 1.23 l h(-1) , to be compared to IFO published values ranging from 3 to 10 l h(-1) ). After model calibration, simulations made at therapeutic doses of IFO showed CAA renal intracellular concentrations ranging from 11 to 131 μM. Intracellular CAA concentrations above 70 μM induced intense ROS production and GSH depletion. Those responses were time and dose dependent, showing transient and non-linear kinetics. Those results are in agreement with literature data reporting that intracellular CAA toxic concentrations range from 35 to 320 μM, after therapeutic ifosfamide dosing. The results were also consistent with in vitro CAA renal cytotoxicity data.

A new in vitro microfluidic platform (integrated insert dynamic microfluidic platform, IIDMP) allowing the co-culture of intestinal Caco-2 TC7 cells and of human primary hepatocytes was used to test the absorption and first-pass metabolism of two drugs: phenacetin and omeprazole. The metabolism of these drugs by CYP1A2, CYP2C19 and CYP3A4 was evaluated by the calculation of bioavailabilities and of intrinsic clearances using a pharmacokinetic (PK) model. To demonstrate the usefulness of the device and of the PK model, predictions were compared with in vitro and in vivo results from the literature. Based on the IIDMP experiments, hepatic in vivo clearances of phenacetin and omeprazole in the IIDMP were predicted to be 3.10 ± 0.36 and 1.46 ± 0.25 ml/min/kg body weight, respectively. This appeared lower than the in vivo observed data with values ranging between 11.9-19.6 and 5.8-7.5 ml/min/kg body weight, respectively. Then the calculated hepatic and intestinal clearances led to predicting an oral bioavailability of 0.85 and 0.77 for phenacetin and omeprazole versus 0.92 and 0.78 using separate data from the simple monoculture of Caco-2 TC7 cells and hepatocytes in Petri dishes. When compared with the in vivo data, the results of oral bioavailability were overestimated (0.37 and 0.71, respectively). The feasibility of co-culture in a device allowing the integration of intestinal absorption, intestinal metabolism and hepatic metabolism in a single model was demonstrated. Nevertheless, further experiments with other drugs are needed to extend knowledge of the device to predict oral bioavailability and intestinal first-pass metabolism.

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

We present a systems biology analysis of rat primary hepatocytes response after exposure to 10 μM and 100 μM flutamide in liver microfluidic biochips. We coupled an in vitro pharmacokinetic (PK) model of flutamide to a system biology model of its reactive oxygen species (ROS) production and scavenging by the Nrf2 regulated glutathione production. The PK model was calibrated using data on flutamide kinetics, hydroxyflutamide and glutathione conjugates formation in microfluidic conditions. The parameters of Nrf2-related gene activities and the subsequent glutathione depletion were calibrated using microarray data from our microfluidic experiments and literature information. Following a 10 μM flutamide exposure, the model predicted a recovery time to baseline levels of glutathione (GSH) and ROS in agreement with our experimental observations. At 100 μM, the model predicted that metabolism saturation led to an important accumulation of flutamide in cells, a high ROS production and complete GSH depletion. The high levels of ROS predicted were consistent with the necrotic switch observed by transcriptomics, and the high cell mortality we had experimentally observed. The model predicted a transition between recoverable GSH depletion and deep GSH depletion at about 12.5 μM of flutamide (single perfusion exposure). Our work shows that in vitro biochip experiments can provide supporting information for complex in silico modeling including data from extra cellular and intra cellular levels. We believe that this approach can be an efficient strategy for a global integrated methodology in predictive toxicology.

This special issue of Toxicology in Vitro is dedicated to disseminating the results of the EU-funded collaborative project "Profiling the toxicity of new drugs: a non animal-based approach integrating toxicodynamics and biokinetics" (Predict-IV; Grant 202222). The project's overall aim was to develop strategies to improve the assessment of drug safety in the early stage of development and late discovery phase, by an intelligent combination of non animal-based test systems, cell biology, mechanistic toxicology and in silico modeling, in a rapid and cost effective manner. This overview introduces the scope and overall achievements of Predict-IV.

BACKGROUND : Combining computational toxicology with ExpoCast exposure estimates and ToxCastTM assay data gives us access to predictions of human health risks stemming from exposures to chemical mixtures. OBJECTIVES : We explored, through mathematical modeling and simulations, the size of potential effects of random mixtures of aromatase inhibitors on the dynamics of women's menstrual cycles. M ETHODS : We simulated random exposures to millions of potential mixtures of 86 aromatase inhibitors. A pharmacokinetic model of intake and disposition of the chemicals predicted their internal concentration as a function of time (up to 2 y). A ToxCastTM aromatase assay provided concentration-inhibition relationships for each chemical. The resulting total aromatase inhibition was input to a mathematical model of the hormonal hypothalamus– pituitary–ovarian control of ovulation in women. RESULTS : Above 10% inhibition of estradiol synthesis by aromatase inhibitors, noticeable (eventually reversible) effects on ovulation were predicted. Exposures to individual chemicals never led to such effects. In our best estimate, ∼ 10% of the combined exposures simulated had mild to catastrophic impacts on ovulation. A lower bound on that figure, obtained using an optimistic exposure scenario, was 0.3%. CONCLUSIONS : These results demonstrate the possibility to predict large-scale mixture effects for endocrine disrupters with a predictive toxicology approach that is suitable for high-throughput ranking and risk assessment. The size of the effects predicted is consistent with an increased risk of infertility in women from everyday exposures to our chemical environment.

Highlights • A workflow for an exposure driven chemical safety assessment to avoid animal testing. • Hypothesis based on existing data, in silico modelling and biokinetic considerations. • A tool to inform targeted and toxicologically relevant in vitro testing., We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an “ab initio” assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.

We developed a microfluidic platform to investigate paracetamol intestinal and liver first pass metabolism. This approach was coupled with a mathematical model to estimate intrinsic in vitro parameters and to predict in vivo processes. The kinetic modeling estimated the paracetamol and paracetamol sulfate permeabilities, the sulfate and glucuronide effluxes in the intestine compartment. Based on a gut model, we estimated intrinsic intestinal clearance of between 26 and 77 L/h for paracetamol in humans, a permeability of 10 L/h, and a gut availability between 0.17 and 0.53 (compared to 0.95-1 in vivo). The role played by the liver in paracetamol metabolism was estimated via in vitro intrinsic clearances of 7.6, 13.6, and 11.5 µL/min/10(6) cells for HepG2/C3a, rat primary hepatocytes, and human primary hepatocytes, respectively. Based on a parallel tube model to describe the liver, the paracetamol hepatic clearance, and the paracetamol hepatic availability in humans were estimated at 6.5 mL/min/kg of bodyweight (BDW) and 0.7, respectively (when compared to 5 mL/min/kg of BDW and 0.77 to 0.88 for in vivo values, respectively). The drug availability was predicted ranging between 0.24 and 0.41 (0.88 in vivo). The overall approach provided a first step in an integrated strategy combining in silico/in vitro methods based on microfluidic for evaluating drug absorption, distribution and metabolism processes.

Explicitly modeling underlying relationships between a survival endpoint and processes that generate longitudinal measured or reported outcomes potentially could improve the efficiency of clinical trials and provide greater insight into the various dimensions of the clinical effect of interventions included in the trials. Various strategies have been proposed for using longitudinal findings to elucidate intervention effects on clinical outcomes such as survival. The application of specifically Bayesian approaches for constructing models that address longitudinal and survival outcomes explicitly has been recently addressed in the literature. We review currently available methods for carrying out joint analyses, including issues of implementation and interpretation, identify software tools that can be used to carry out the necessary calculations, and review applications of the methodology.

Information on toxicokinetics is critical for animal-free human risk assessment. Human external exposure must be translated into human tissue doses and compared with in vitro actual cell exposure associated to effects (in vitro-in vivo comparison). Data on absorption, distribution, metabolism and excretion in humans (ADME) could be generated using in vitro and QSAR tools. Physiologically-based toxicokinetic (PBTK) computer modelling could serve to integrate disparate in vitro and in silico findings. However, there are only few freely-available PBTK platforms currently available. And although some ADME parameters can be reasonably estimated in vitro or in silico, important gaps exist. Examples include unknown or limited applicability domains and lack of (high-throughput) tools to measure penetration of barriers, partitioning between blood and tissues and metabolic clearance. This paper is based on a joint EPAA--EURL ECVAM expert meeting. It provides a state-of-the-art overview of the availability of PBTK platforms as well as the in vitro and in silico methods to parameterise basic (Tier 1) PBTK models. Five high-priority issues are presented that provide the prerequisites for wider use of non-animal based PBTK modelling for animal-free chemical risk assessment.

International audience; In food safety, sound risk characterisation requires the integration of external dose (oral exposure), internal dose (toxicokinetics (TK) and toxicodynamic (TD) data. Here, a workflow allowing such integration is presented and illustrated with examples from the human health, animal health and ecological area. Since its creation in 2002, the European Food Safety Authority (EFSA) has performed risk assessments for more than 5000 chemicals. An open source database: OpenFoodTox, designed using OECD harmonised templates, and providing summary toxicological data used by EFSA for setting safe levels (reference points and reference values) of food and feed chemicals in humans, animals and the environment has been recently published. OpenFoodTox is available in a. EFSA's Data Warehouse as a microstrategy tool, b. EFSA's knowledge junction for full download, c. The OECD e-chem portal. Since 2014, EFSA has funded collaborative research to develop generic TK and PB-TK models in the R freeware available on an open source modelling platform: TK Plate, to support integration of exposure, TK and TD parameters under different situations (data poor, data rich). Several models have been developed including TK and PB-TK models in humans, farm and companion animals (pig, chicken, cat, sheep), fish (zebra fish, rainbow trout, fathead minnow) and Dynamic Energy Budget (DEB) models for terrestrial and aquatic organisms. The first prototype of TKPlate has been designed for human TK and PB-PK models using the US-EPA ‘httk’ package and includes variability distributions for human metabolism. Future developments of OpenFoodTox 2.0 and TK Plate include: 1. Data collection for physico-chemical properties, TK, biodegradation and bioaccumulation data, exposure data, 2. Integration of QSAR tools to predict TK and TD properties, 3. Integration of all models in TKplate for all species, 4. Development of a guidance document to illustrate the global use of TK and metabolism data in chemical risk assessment with case studies (e.g. regulated products and contaminants). In conclusion, the importance of international cooperation is emphasised, in particular between national, international scientific advisory bodies and academic institutions as the corner stone for the translation of 21st century toxicology into harmonised methodologies and tools and for the training of the next generation of risk assessors.

International audience; Toxicokinetics (TK) is the endpoint that informs about the penetration into and fate within the body of a toxic substance, including the possible emergence of metabolites. Traditionally, the data needed to understand those phenomena have been obtained in vivo. Currently, with a drive towards non-animal testing approaches, TK has been identified as a key element to integrate the results from in silico, in vitro and already available in vivo studies. TK is needed to estimate the range of target organ doses that can be expected from realistic human external exposure scenarios. This information is crucial for determining the dose/concentration range that should be used for in vitro testing. Vice versa, TK is necessary to convert the in vitro results, generated at tissue/cell or sub-cellular level, into dose response or potency information relating to the entire target organism, i.e. the human body (in vitro-in vivo extrapolation, IVIVE). Physiologically based toxicokinetic modelling (PBTK) is currently regarded as the most adequate approach to simulate human TK and extrapolate between in vitro and in vivo contexts. The fact that PBTK models are mechanism-based which allows them to be 'generic' to a certain extent (various extrapolations possible) has been critical for their success so far. The need for high-quality in vitro and in silica data on absorption, distribution, metabolism as well as excretion (ADME) as input for PBTK models to predict human dose-response curves is currently a bottleneck for integrative risk assessment. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

Environmental risk assessment of chemicals for the protection of ecosystems integrity is a key regulatory and scientific research field which is undergoing constant development in modelling approaches and harmonisation with human risk assessment. This review focuses on state-of-the-art toxicokinetic tools and models that have been applied to terrestrial and aquatic species relevant to environmental risk assessment of chemicals. Both empirical and mechanistic toxicokinetic models are discussed using the results of extensive literature searches together with tools and software for their calibration and an overview of applications in environmental risk assessment. These include simple tools such as one-compartment models, multi-compartment models to physiologically-based toxicokinetic (PBTK) models, mostly available for aquatic species such as fish species and a number of chemical classes including plant protection products, metals, persistent organic pollutants, nanoparticles. Data gaps and further research needs are highlighted.

International audience; The dynamics of genital shedding of HSV-2 DNA was assessed in HIV-1-infected women taking combination antiretroviral therapy (cART). HIV-1 RNA, HIV-1 DNA and HSV DNA loads were measured during 12-18 months using frozen plasma, PBMC and cervicovaginal lavage samples from 22 HIV-1-infected women, including 17 women naive for antiretroviral therapy initiating cART and 5 women with virological failure switching to a new regimen. Nineteen (86%) women were HSV-2-seropositive. Among HSV-2-/HIV-1-co-infected women, HIV-1 RNA loads showed a rapid fall from baseline after one month of cART, in parallel in paired plasma and cervicovaginal secretions. In contrast, HIV-1 DNA loads did not show significant variations from baseline up to 18 months of treatment in both systemic and genital compartments. HSV DNA was detected at least once in 12 (63%) of 19 women during follow up: HSV-2 shedding in the genital compartment was observed in 11% of cervicovaginal samples at baseline and in 16% after initiating or switching cART. Cervicovaginal HIV-1 RNA loads were strongly associated with plasma HIV-1 RNA loads over time, but not with cervicovaginal HSV DNA loads. Reactivation of genital HSV-2 replication frequently occurred despite effective cART in HSV-2-/HIV-1-co-infected women. Genital HSV-2 replication under cART does not influence cervicovaginal HIV-1 RNA or DNA shedding.