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.

Use cases, best practice and reporting standards for metabolomics in regulatory toxicology.

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier to its translation is lack of performance and reporting standards. The MEtabolomics standaRds Initiative in Toxicology (MERIT) project brings together international experts from multiple sectors to address this need. Here, we identify the most relevant applications for metabolomics in regulatory toxicology and develop best practice guidelines, performance and reporting standards for acquiring and analysing untargeted metabolomics and targeted metabolite data. We recommend that these guidelines are evaluated and implemented for several regulatory use cases.

Animal-Free Chemical Safety Assessment.

The exponential growth of the Internet of Things and the global popularity and remarkable decline in cost of the mobile phone is driving the digital transformation of medical practice. The rapidly maturing digital, non-medical world of mobile (wireless) devices, cloud computing and social networking is coalescing with the emerging digital medical world of omics data, biosensors and advanced imaging which offers the increasingly realistic prospect of personalized medicine. Described as a potential "seismic" shift from the current "healthcare" model to a "wellness" paradigm that is predictive, preventative, personalized and participatory, this change is based on the development of increasingly sophisticated biosensors which can track and measure key biochemical variables in people. Additional key drivers in this shift are metabolomic and proteomic signatures, which are increasingly being reported as pre-symptomatic, diagnostic and prognostic of toxicity and disease. These advancements also have profound implications for toxicological evaluation and safety assessment of pharmaceuticals and environmental chemicals. An approach based primarily on human in vivo and high-throughput in vitro human cell-line data is a distinct possibility. This would transform current chemical safety assessment practice which operates in a human "data poor" to a human "data rich" environment. This could also lead to a seismic shift from the current animal-based to an animal-free chemical safety assessment paradigm.

A probabilistic model of human variability in physiology for future application to dose reconstruction and QIVIVE.

The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico, and in chemico systems can be integrated and utilized for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus, a realistic description of the variation in the physiology of the human population being modeled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.

The application of global sensitivity analysis in the development of a physiologically based pharmacokinetic model for m-xylene and ethanol co-exposure in humans.

Global sensitivity analysis (SA) was used during the development phase of a binary chemical physiologically based pharmacokinetic (PBPK) model used for the analysis of m-xylene and ethanol co-exposure in humans. SA was used to identify those parameters which had the most significant impact on variability of venous blood and exhaled m-xylene and urinary excretion of the major metabolite of m-xylene metabolism, 3-methyl hippuric acid. This analysis informed the selection of parameters for estimation/calibration by fitting to measured biological monitoring (BM) data in a Bayesian framework using Markov chain Monte Carlo (MCMC) simulation. Data generated in controlled human studies were shown to be useful for investigating the structure and quantitative outputs of PBPK models as well as the biological plausibility and variability of parameters for which measured values were not available. This approach ensured that a priori knowledge in the form of prior distributions was ascribed only to those parameters that were identified as having the greatest impact on variability. This is an efficient approach which helps reduce computational cost.

Quantitative global sensitivity analysis of a biologically based dose-response pregnancy model for the thyroid endocrine system.

A deterministic biologically based dose-response model for the thyroidal system in a near-term pregnant woman and the fetus was recently developed to evaluate quantitatively thyroid hormone perturbations. The current work focuses on conducting a quantitative global sensitivity analysis on this complex model to identify and characterize the sources and contributions of uncertainties in the predicted model output. The workflow and methodologies suitable for computationally expensive models, such as the Morris screening method and Gaussian Emulation processes, were used for the implementation of the global sensitivity analysis. Sensitivity indices, such as main, total and interaction effects, were computed for a screened set of the total thyroidal system descriptive model input parameters. Furthermore, a narrower sub-set of the most influential parameters affecting the model output of maternal thyroid hormone levels were identified in addition to the characterization of their overall and pair-wise parameter interaction quotients. The characteristic trends of influence in model output for each of these individual model input parameters over their plausible ranges were elucidated using Gaussian Emulation processes. Through global sensitivity analysis we have gained a better understanding of the model behavior and performance beyond the domains of observation by the simultaneous variation in model inputs over their range of plausible uncertainties. The sensitivity analysis helped identify parameters that determine the driving mechanisms of the maternal and fetal iodide kinetics, thyroid function and their interactions, and contributed to an improved understanding of the system modeled. We have thus demonstrated the use and application of global sensitivity analysis for a biologically based dose-response model for sensitive life-stages such as pregnancy that provides richer information on the model and the thyroidal system modeled compared to local sensitivity analysis.

A Workflow for Global Sensitivity Analysis of PBPK Models.

Physiologically based pharmacokinetic (PBPK) models have a potentially significant role in the development of a reliable predictive toxicity testing strategy. The structure of PBPK models are ideal frameworks into which disparate in vitro and in vivo data can be integrated and utilized to translate information generated, using alternative to animal measures of toxicity and human biological monitoring data, into plausible corresponding exposures. However, these models invariably include the description of well known non-linear biological processes such as, enzyme saturation and interactions between parameters such as, organ mass and body mass. Therefore, an appropriate sensitivity analysis (SA) technique is required which can quantify the influences associated with individual parameters, interactions between parameters and any non-linear processes. In this report we have defined the elements of a workflow for SA of PBPK models that is computationally feasible, accounts for interactions between parameters, and can be displayed in the form of a bar chart and cumulative sum line (Lowry plot), which we believe is intuitive and appropriate for toxicologists, risk assessors, and regulators.

A human PBPK model for ethanol describing inhibition of gastric motility.

A physiologically based pharmacokinetic model for investigating inter-individual and inter-racial variability in ethanol pharmacokinetics is presented. The model is a substantial modification of an existing model which described some genetic polymorphisms in the hepatic alcohol dehydrogenase enzymes. The model was modified to incorporate a description of ethanol absorption from the stomach and gastro-intestinal tract and the retardation of gastric emptying due to a concentration-dependent inhibition of gastric peristalsis. In addition, intra-venous and intra-arterial routes of administration were added to investigate whether the biological structure of the model provided a core which may be easily adapted for any route of exposure. The model is proposed as suitable for the investigation of the effects of both acute and chronic ethanol exposure.

Metabolism of ethylbenzene by human liver microsomes and recombinant human cytochrome P450s (CYP).

The enzyme kinetics of the initial hydroxylation of ethylbenzene to form 1-phenylethanol were determined in human liver microsomes. The individual cytochrome P450 (CYP) forms catalysing this reaction were identified using selective inhibitors and recombinant preparations of hepatic CYPs. Production of 1-phenylethanol in hepatic microsomes exhibited biphasic kinetics with a high affinity, low Km, component (mean Km = 8 microM; V(max) = 689 pmol/min/mg protein; n = 6 livers) and a low affinity, high Km, component (Km = 391 microM; V(max) = 3039 pmol/min/mg protein; n = 6). The high-affinity component was inhibited 79%-95% (mean 86%) by diethyldithiocarbamate, and recombinant CYP2E1 was shown to metabolise ethylbenzene with low Km (35 microM), but also low (max) (7 pmol/min/pmol P450), indicating that this isoform catalysed the high-affinity component. Recombinant CYP1A2 and CYP2B6 exhibited high V(max) (88 and 71 pmol/min/pmol P450, respectively) and high Km (502 and 219 microM, respectively), suggesting their involvement in catalysing the low-affinity component. This study has demonstrated that CYP2E1 is the major enzyme responsible for high-affinity side chain hydroxylation of ethylbenzene in human liver microsomes. Activity of this enzyme in the population is highly variable due to induction or inhibition by physiological factors, chemicals in the diet or some pharmaceuticals. This variability can be incorporated into the risk assessment process to improve the setting of occupational exposure limits and guidance values for biological monitoring.

A quantitative structure-activity relationship analysis on a series of alkyl benzenes metabolized by human cytochrome p450 2E1.

The results of quantitative structure-activity relationships for eight alkyl benzenes undergoing oxidative metabolism via human CYP2E1 are reported. Molecular orbital calculations via the AM1 method were employed for the generation of electronic structural descriptors against experimentally generated kinetic data for CYP2E1-mediated metabolism. The findings point to the importance of electronic structural properties of the molecules themselves, particularly the role of frontier orbitals, in determining rates of metabolism. Other factors appear to be responsible for the affinity of these substrates for the CYP2E1 enzyme however, such as its lipophilic character. The results are consistent with the interactive molecular modeling of these compounds within the putative active site of human CYP2E1 constructed from the CYP2C5 template, where it was found that pi-pi stacking interactions between aromatic rings are important for the binding of substrates to the CYP2E1 active site, together with contributions from desolvation entropy changes accompanying substrate binding.