A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as "forever chemicals" and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that "fatty acid metabolism" was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an "omics"-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

Body Metrics and the Gut Microbiome in Response to Macronutrient Limitation in the Zebrafish Danio rerio

Background

Healthy and predictable physiologic homeostasis is paramount in animal models for biomedical research. Proper macronutrient intake is an essential and controllable environmental factor for maintaining animal health and promoting experimental reproducibility.

Objective and Methods

Evaluate reductions in dietary macronutrient composition on body weight metrics, composition, and gut microbiome in Danio rerio.

Methods

D. rerio were fed reference diets deficient in either protein or lipid content for 14 weeks.

Results

Diets of reduced-protein or reduced-fat resulted in lower weight gain than the standard reference diet in male and female D. rerio. Females fed the reduced-protein diet had increased total body lipid, suggesting increased adiposity compared with females fed the standard reference diet. In contrast, females fed the reduced-fat diet had decreased total body lipid compared with females fed the standard reference diet. The microbial community in male and female D. rerio fed the standard reference diet displayed high abundances of Aeromonas, Rhodobacteraceae, and Vibrio. In contrast, Vibrio spp. were dominant in male and female D. rerio fed a reduced-protein diet, whereas Pseudomonas displayed heightened abundance when fed the reduced-fat diet. Predicted functional metagenomics of microbial communities (PICRUSt2) revealed a 3- to 4-fold increase in the KEGG (Kyoto Encyclopedia of Genes and Genomes) functional category of steroid hormone biosynthesis in both male and female D. rerio fed a reduced-protein diet. In contrast, an upregulation of secondary bile acid biosynthesis and synthesis and degradation of ketone bodies was concomitant with a downregulation in steroid hormone biosynthesis in females fed a reduced-fat diet.

Conclusions

These study outcomes provide insight into future investigations to understand nutrient requirements to optimize growth, reproductive, and health demographics to microbial populations and metabolism in the D. rerio gut ecosystem. These evaluations are critical in understanding the maintenance of steady-state physiologic and metabolic homeostasis in D. rerio. Curr Dev Nutr 20xx;x:xx.

Predictive Design and Analysis of Drug Transport by Multiscale Computational Models Under Uncertainty

Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.

Development, testing, parameterisation, and calibration of a human PBPK model for the plasticiser, di-(2-ethylhexyl) terephthalate (DEHTP) using in silico, in vitro and human biomonitoring data

A physiologically based pharmacokinetic model for di-(2-ethylhexyl) terephthalate (DEHTP) based on a refined model for di-(2-propylheptyl) phthalate (DPHP) was developed to interpret the metabolism and biokinetics of DEHTP following a single oral dose of 50 mg to three male volunteers. In vitro and in silico methods were used to generate parameters for the model. For example, measured intrinsic hepatic clearance scaled from in vitro to in vivo and plasma unbound fraction and tissue:blood partition coefficients (PCs) were predicted algorithmically. Whereas the development and calibration of the DPHP model was based upon two data streams, blood concentrations of parent chemical and first metabolite and the urinary excretion of metabolites, the model for DEHTP was calibrated against a single data stream, the urinary excretion of metabolites. Despite the model form and structure being identical significant quantitative differences in lymphatic uptake between the models were observed. In contrast to DPHP the fraction of ingested DEHTP entering lymphatic circulation was much greater and of a similar magnitude to that entering the liver with evidence for the dual uptake mechanisms discernible in the urinary excretion data. Further, the absolute amounts absorbed by the study participants, were much higher for DEHTP relative to DPHP. The in silico algorithm for predicting protein binding performed poorly with an error of more than two orders of magnitude. The extent of plasma protein binding has important implications for the persistence of parent chemical in venous blood-inferences on the behaviour of this class of highly lipophilic chemicals, based on calculations of chemical properties, should be made with extreme caution. Attempting read across for this class of highly lipophilic chemicals should be undertaken with caution since basic adjustments to PCs and metabolism parameters would be insufficient, even when the structure of the model itself is appropriate. Therefore, validation of a model parameterized entirely with in vitro and in silico derived parameters would need to be calibrated against several human biomonitoring data streams to constitute a data rich source chemical to afford confidence for future evaluations of other similar chemicals using the read-across approach.

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Pluripotent Stem Cell Assays: Modalities and Applications For Predictive Developmental Toxicity

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A systematic scoping review of the literature evaluated the embryonic stem cell test (EST).

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1533 publications included 18 publications testing 10 or more compounds in human or mouse EST.

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Selected case examples included 5-fluorouracil, thalidomide, and caffeine.

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Applicability, limitations, and recommendations for further work are discussed.

This manuscript provides a review focused on embryonic stem cell-based models and their place within the landscape of alternative developmental toxicity assays. Against the background of the principles of developmental toxicology, the wide diversity of alternative methods using pluripotent stem cells developed in this area over the past half century is reviewed. In order to provide an overview of available models, a systematic scoping review was conducted following a published protocol with inclusion criteria, which were applied to select the assays. Critical aspects including biological domain, readout endpoint, availability of standardized protocols, chemical domain, reproducibility and predictive power of each assay are described in detail, in order to review the applicability and limitations of the platform in general and progress moving forward to implementation. The horizon of innovative routes of promoting regulatory implementation of alternative methods is scanned, and recommendations for further work are given.

Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modelling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC), Markov Chain Monte Carlo (MCMC) simulation and the Virtual Cell Based Assay (VCBA) for the estimation of the active, free in vitro concentration of chemical in the reaction medium was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow was designed to estimate parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for bisphenol A (BPA) and high throughput screening (HTS) in vitro concentration-response data, for estrogen and pregnane X receptor activation determined in human liver and kidney cell lines, from the ToxCast/Tox21 database. In vivo benchmark dose 10% lower confidence limits (BMDL₁₀) for oral uptake of BPA (ng/kg BW/day) were calculated from the in vivo dose-responses and compared to the human equivalent dose (HED) BMDL₁₀ for relative kidney weight change in the mouse derived by European Food Safety Authority (EFSA). Three from four in vivo BMDL₁₀ values calculated in this study were similar to the EFSA values whereas the fourth was much smaller. The derivation of an uncertainty factor (UF) to accommodate the uncertainties associated with measurements using human cell lines in vitro, extrapolated to in vivo, could be useful for the derivation of Health Based Guidance Values (HBGV).

Probabilistic risk assessment - the keystone for the future of toxicology

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

The Gut Microbiota of Naturally Occurring and Laboratory Aquaculture Lytechinus variegatus Revealed Differences in the Community Composition, Taxonomic Co-Occurrence, and Predicted Functional Attributes

Sea urchins, in many instances, are collected from the wild, maintained in the laboratory aquaculture environment, and used as model animals for various scientific investigations. It has been increasingly evident that diet-driven dysbiosis of the gut microbiome could affect animal health and physiology, thereby impacting the outcome of the scientific studies. In this study, we compared the gut microbiome between naturally occurring (ENV) and formulated diet-fed laboratory aquaculture (LAB) sea urchin Lytechinus variegatus by amplicon sequencing of the V4 region of the 16S rRNA gene and bioinformatics tools. Overall, the ENV gut digesta had higher taxa richness with an abundance of Propionigenium, Photobacterium, Roseimarinus, and Flavobacteriales. In contrast, the LAB group revealed fewer taxa richness, but noticeable abundances of Arcobacter, Agarivorans, and Shewanella. However, Campylobacteraceae, primarily represented by Arcobacter spp., was commonly associated with the gut tissues of both ENV and LAB groups whereas the gut digesta had taxa from Gammaproteobacteria, particularly Vibrio spp. Similarly, the co-occurrence network displayed taxonomic organizations interconnected by Arcobacter and Vibrio as being the key taxa in gut tissues and gut digesta, respectively. Predicted functional analysis of the gut tissues microbiota of both ENV and LAB groups showed a higher trend in energy-related metabolisms, whereas amino acids, carbohydrate, and lipid metabolisms heightened in the gut digesta. This study provides an outlook of the laboratory-formulated diet-fed aquaculture L. variegatus gut microbiome and predicted metabolic profile as compared to the naturally occurring animals, which should be taken into consideration for consistency, reproducibility, and translatability of scientific studies.

Derivation of a Human In Vivo Benchmark Dose for Perfluorooctanoic Acid From ToxCast In Vitro Concentration-Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modeling, global sensitivity analysis (GSA), approximate Bayesian computation (ABC), and Markov Chain Monte Carlo (MCMC) simulation was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow accounts for parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for perfluorooctanoic acid (PFOA) and high throughput screening (HTS) in vitro concentration–response data, determined in a human liver cell line, from the ToxCast/Tox21 database. In vivo benchmark doses (BMDs) for PFOA intake (ng/kg BW/day) and drinking water exposure concentrations (µg/L) were calculated from the in vivo dose responses and compared to intake values derived by the European Food Safety Authority (EFSA). The intake benchmark dose lower confidence limit (BMDL₅) of 0.82 was similar to 0.86 ng/kg BW/day for altered serum cholesterol levels derived by EFSA, whereas the intake BMDL₅ of 6.88 was six-fold higher than the value of 1.14 ng/kg BW/day for altered antibody titer also derived by the EFSA. Application of a chemical-specific adjustment factor (CSAF) of 1.4, allowing for inter-individual variability in kinetics, based on biological half-life, gave an intake BMDL₅ of 0.59 for serum cholesterol and 4.91 (ng/kg BW/day), for decreased antibody titer, which were 0.69 and 4.31 the EFSA-derived values, respectively. The corresponding BMDL₅ for drinking water concentrations, for estrogen receptor binding activation associated with breast cancer, pregnane X receptor binding associated with altered serum cholesterol levels, thyroid hormone receptor α binding leading to thyroid disease, and decreased antibody titer (pro-inflammation from cytokines) were 0.883, 0.139, 0.086, and 0.295 ng/ml, respectively, with application of no uncertainty factors. These concentrations are 5.7-, 36-, 58.5-, and 16.9-fold lower than the median measured drinking water level for the general US population which is approximately, 5 ng/ml.

Development of a Web-Based Toolbox to Support Quantitative In-Vitro-to-In-Vivo Extrapolations (QIVIVE) within Nonanimal Testing Strategies

The goal of the present study was to develop an online web-based toolbox that contains generic physiologically based kinetic (PBK) models for rats and humans, including underlying calculation tools to predict plasma protein binding and tissue:plasma distribution, to be used for quantitative in-vitro-to-in-vivo extrapolations (QIVIVE). The PBK models within the toolbox allow first estimations of internal plasma and tissue concentrations of chemicals to be made, based on the logP and pKa of the chemicals and values for intestinal uptake and intrinsic hepatic clearance. As a case study, the toolbox was used to predict oral equivalent doses of in vitro ToxCast bioactivity data for the food additives methylparaben, propyl gallate, octyl gallate, and dodecyl gallate. These oral equivalent doses were subsequently compared with human exposure estimates, as a low tier assessment allowing prioritization for further assessment. The results revealed that daily intake levels of especially propyl gallate can lead to internal plasma concentrations that are close to in vitro biological effect concentrations, particularly with respect to the inhibition of human thyroid peroxidase (TPO). Estrogenic effects were not considered likely to be induced by the food additives, as daily exposure levels of the different compounds remained 2 orders of magnitude below the oral equivalent doses for in vitro estrogen receptor activation. Overall, the results of the study show how the toolbox, which is freely accessible through www.qivivetools.wur.nl, can be used to obtain initial internal dose estimates of chemicals and to prioritize chemicals for further assessment, based on the comparison of oral equivalent doses of in vitro biological activity data with human exposure levels.

An open source physiologically based kinetic model for the chicken (Gallus gallus domesticus): Calibration and validation for the prediction residues in tissues and eggs

Xenobiotics from anthropogenic and natural origin enter animal feed and human food as regulated compounds, environmental contaminants or as part of components of the diet. After dietary exposure, a chemical is absorbed and distributed systematically to a range of organs and tissues, metabolised, and excreted. Physiologically based kinetic (PBK) models have been developed to estimate internal concentrations from external doses. In this study, a generic multi-compartment PBK model was developed for chicken. The PBK model was implemented for seven compounds (with log K_ow range -1.37-6.2) to quantitatively link external dose and internal dose for risk assessment of chemicals. Global sensitivity analysis was performed for a hydrophilic and a lipophilic compound to identify the most sensitive parameters in the PBK model. Model predictions were compared to measured data according to dataset-specific exposure scenarios. Globally, 71% of the model predictions were within a 3-fold change of the measured data for chicken and only 7% of the PBK predictions were outside a 10-fold change. While most model input parameters still rely on in vivo experiments, in vitro data were also used as model input to predict internal concentration of the coccidiostat monensin. Future developments of generic PBK models in chicken and other species of relevance to animal health risk assessment are discussed.

Allometric Scaling of physiologically-relevant organoids

The functional and structural resemblance of organoids to mammalian organs suggests that they might follow the same allometric scaling rules. However, despite their remarkable likeness to downscaled organs, non-luminal organoids are often reported to possess necrotic cores due to oxygen diffusion limits. To assess their potential as physiologically relevant in vitro models, we determined the range of organoid masses in which quarter power scaling as well as a minimum threshold oxygen concentration is maintained. Using data on brain organoids as a reference, computational models were developed to estimate oxygen consumption and diffusion at different stages of growth. The results show that mature brain (or other non-luminal) organoids generated using current protocols must lie within a narrow range of masses to maintain both quarter power scaling and viable cores. However, micro-fluidic oxygen delivery methods could be designed to widen this range, ensuring a minimum viable oxygen threshold throughout the constructs and mass dependent metabolic scaling. The results provide new insights into the significance of the allometric exponent in systems without a resource-supplying network and may be used to guide the design of more predictive and physiologically relevant in vitro models, providing an effective alternative to animals in research.

Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling

Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.