Recommended approaches in the application of toxicogenomics to derive points of departure for chemical risk assessment

Bioinformatic workflows for deriving transcriptomic points of departure: current status, data gaps, and research priorities

There is a pressing need to increase the efficiency and reliability of toxicological safety assessment for protecting human health and the environment. Although conventional toxicology tests rely on measuring apical changes in vertebrate models, there is increasing interest in the use of molecular information from animal and in vitro studies to inform safety assessment. One promising and pragmatic application of molecular information involves the derivation of transcriptomic points of departure (tPODs). Transcriptomic analyses provide a snapshot of global molecular changes that reflect cellular responses to stressors and progression toward disease. A tPOD identifies the dose level below which a concerted change in gene expression is not expected in a biological system in response to a chemical. A common approach to derive such a tPOD consists of modeling the dose-response behavior for each gene independently and then aggregating the gene-level data into a single tPOD. Although different implementations of this approach are possible, as discussed in this manuscript, research strongly supports the overall idea that reference doses produced using tPODs are health protective. An advantage of this approach is that tPODs can be generated in shorter term studies (e.g. days) compared with apical endpoints from conventional tests (e.g. 90-d subchronic rodent tests). Moreover, research strongly supports the idea that reference doses produced using tPODs are health protective. Given the potential application of tPODs in regulatory toxicology testing, rigorous and reproducible wet and dry laboratory methodologies for their derivation are required. This review summarizes the current state of the science regarding the study design and bioinformatics workflows for tPOD derivation. We identify standards of practice and sources of variability in tPOD generation, data gaps, and areas of uncertainty. We provide recommendations for research to address barriers and promote adoption in regulatory decision making.

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Molecular insights into developmental toxicity induced by PCB77 exposure on zebrafish via integrating transcriptomics with adverse outcome pathway

Polychlorinated biphenyls (PCBs), a typical type of persistent organic pollutants (POPs), were previously widely employed as insulating and heat exchange fluids in transformers and capacitors. Despite knowledge of its adverse effects, the precise mechanism underlying PCB77 toxicity remains enigmatic. In this study, we utilized zebrafish as a model organism to explore the toxic effects of various concentrations of PCB77 (10, 200, and 1000 μg/L) and its molecular toxicity mechanisms. Upon exposure to dosages of PCB77 throughout embryonic and larval stages, the zebrafish exhibited adverse phenotypic manifestations, including deformities, decreased heart rates, increased distances between the bulbus arteriosus (BA) and sinus venosus (SV) and reduced locomotor ability. Transcriptome analysis revealed the common enriched pathways across all PCB77 concentration groups, such as retinol metabolism, steroid hormone biosynthesis, and metabolism of xenobiotics by cytochrome P450, which are closely related to the activity of cytochrome P450 (cyp1a) enzymes. Furthermore, Adverse Outcome Pathway (AOP) framework which integrates AOPs and dose-dependent transcriptomics to predict PCB77-induced adverse outcomes (AOs) revealed that aryl hydrocarbon receptor (AhR) associated AOPs triggered by PCB77 exposure may increase early-life stage mortality and decrease cardiac development, indicating that the primary toxic pathways of PCB77 in zebrafish may involve AhR-mediated signaling. Besides, molecular docking modeling demonstrated that PCB77 could bind to the groove within the AhR domain, suggesting that PCB77 induces embryotoxicity in zebrafish through its interaction with AhR. Collectively, these findings not only deliver a thorough examination of PCB77-induced developmental toxicity as well as the underlying mechanisms, but also validate the efficacy of the analytical approach leveraging AOP framework in unraveling toxicity mechanisms of environmental contaminants, which holds promise for risk assessment associated with novel environmental pollutants.

TPD-seq: A high throughput RNA-seq method to derive transcriptomic points of departure from cell lines

There is growing scientific and regulatory interest in transcriptomic points of departure (tPOD) values from high-throughput in vitro experiments. To further help democratize tPOD research, here we outline 'TPD-seq' which links microplate-based exposure methods involving cell lines for human (Caco-2, Hep G2) and environmental (rainbow trout RTgill-W1) health, with a commercially available RNA-seq kit, with a cloud-based bioinformatics tool (ExpressAnalyst.ca). We applied the TPD-seq workflow to derive tPODs for solvents (dimethyl sulfoxide, DMSO; methanol) and positive controls (3,4-dichloroaniline, DCA; hydrogen peroxide, H2O2) commonly used in toxicity testing. The majority of reads mapped to protein coding genes (∼9 k for fish cells; ∼6 k for human cells), and about 50 % of differentially expressed genes were curve-fitted from which 90 % yielded gene benchmark doses. The most robust transcriptomic responses were caused by DMSO exposure, and tPOD values were 31-155 mM across the cell lines. OECD test guideline 249 (RTgill-W1 cells) recommends the use of DCA and here we calculated a tPOD of ∼5 to 76 μM. Finally, exposure of the two human cell lines to H2O2 resulted in tPOD values that ranged from 0.7 to 1.1 mM in Caco-2 cells and 5-30 μM in Hep G2 cells. The methods outlined here are designed to be performed in laboratories with basic molecular and cell culture facilities, and the performance and scalability of the TPD-seq workflow can be determined with additional case studies.

High-throughput transcriptomics toxicity assessment of eleven data-poor bisphenol A alternatives

Bisphenol A (BPA), a widely used chemical in the production of plastics and epoxy resins, has garnered significant attention due to its association with adverse health effects, particularly its endocrine-disrupting properties. Regulatory measures aimed at reducing human exposure to BPA have led to a proliferation of alternative chemicals used in various consumer and industrial products. While these alternatives serve to reduce BPA exposure, concerns have arisen regarding their safety and potential toxicity as regrettable substitutes. Previous efforts have demonstrated that in vitro high-throughput transcriptomics (HTTr) studies can be used to assess the endocrine-disrupting potential of BPA alternatives, and this strategy produces transcriptomic points-of-departure (tPODs) that are protective of human health when compared to the PODs from traditional rodent studies. In this study, we used in vitro HTTr to assess the potential for toxicity of eleven data-poor legacy chemicals sharing structural similarities to BPA. Human breast cancer MCF-7 cells were exposed to BPA and 11 alternatives at concentrations ranging from 0.1 to 25 μM to assess toxicity. Analysis of global transcriptomic changes and a previously characterized estrogen receptor alpha (ERα) transcriptomic biomarker signature revealed that 9 of 11 chemicals altered gene expression relative to controls. One of the chemicals (2,4'-Bisphenol A) activated the ERα biomarker at the same concentration as BPA (i.e., 4,4'-BPA) but was deemed to be more potent as it induced global transcriptomic changes at lower concentrations. These results address data gaps in support of ongoing screening assessments to identify BPA alternatives with hazard potential and help to identify potential candidates that may serve as safer alternatives.

Next generation risk assessment of biocides (PHMG-p and CMIT/MIT)-induced pulmonary fibrosis using adverse outcome pathway-based transcriptome analysis

Next-generation risk assessment (NGRA) has emerged as a promising alternative to non-animal studies owing to the increasing demand for the risk assessment of inhaled toxicants. In this study, NGRA was used to assess the inhalation risks of two biocides commonly used as humidifier disinfectants: polyhexamethylene guanidine phosphate (PHMG-p) and chloromethylisothiazolinone/methylisothiazolinone (CMIT/MIT). Human bronchial epithelial cell transcriptomic data were processed based on adverse outcome pathways and used to establish transcriptome-based points of departure (tPODs) for each biocide. tPOD values were 0.00500-0.0510 μg/cm2 and 0.0342-0.0544 μg/cm2 for PHMG-p and CMIT/MIT, respectively. tPODs may provide predictive power comparable to that of traditional animal-based PODs (aPODs). The tPOD-based NGRA determined that both PHMG-p and CMIT/MIT present a high inhalation risk. Moreover, the identified PHMG-p posed a higher risk than CMIT/MIT, and children were identified as more susceptible population compared to adults. This finding is consistent with observations from actual exposure events. Our findings suggest that NGRA with transcriptomics offers a reliable approach for risk assessment of specific humidifier disinfectant biocides, while acknowledging the limitations of current models and in vitro systems, particularly regarding uncertainties in pharmacokinetics (PK) and pharmacodynamics (PD).

A new approach for risk assessment of pulmonary toxicants: a preliminary study using didecyldimethylammonium chloride

Current human risk assessments often rely on animal toxicity data to establish point of departure (POD) values, followed by the application of uncertainty factors. Consequently, there is growing interest in alternative toxicity testing methods that reduce reliance on animal models. In this study, we propose a novel approach for inhalation toxicity risk assessment that integrates in silico and in vitro methods. Human primary alveolar epithelial cells were exposed to aerosolized didecyldimethylammonium chloride (DDAC) to assess cytotoxicity. This was followed by transcriptome analysis and biological pathway investigation, utilizing adverse outcome pathway (AOP), to calculate the POD. Additionally, human DDAC exposure was simulated using a multiple-path particle dosimetry (MPPD) model to predict exposure levels in the human alveolar region via inhalation. The results from in silico and in vitro studies were then compared, and a comprehensive risk assessment was performed. The POD for AOP 452 key events-oxidative stress, inflammation, epithelial-mesenchymal transition (EMT), apoptosis, and autophagy-was found to range between 19.0 and 23.89 ng/cm2, according to benchmark dose calculation tools. The estimated human exposure to DDAC in the alveolar region under actual exposure conditions was 0.164 ng/cm2/day, resulting in a margin of exposure (MOE) ranging from 121 to 145, suggesting caution regarding DDAC inhalation exposure. This study presents a novel risk assessment method that compares estimated human inhalation exposure values to in vitro results, applying the human equivalent concentration concept. Our findings demonstrate the potential for conducting human risk assessments using both in silico and in vitro methods as alternatives to traditional in vivo studies.

Far from Their Origins: A Transcriptomic Investigation on How 2,4-Di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl) Phenol Affects Rainbow Trout Alevins

Benzotriazole ultraviolet stabilizers (BUVSs) are a group of widely used chemicals added to a variety of consumer (e.g., plastics) and industrial (e.g., metal coating) goods. Although detected globally as an environmentally persistent pollutant, BUVSs have received relatively little toxicological attention and only recently have been acknowledged to affect development and the endocrine system in vivo. In our previous study, altered behavior, indicative of potential neurotoxicity, was observed among rainbow trout alevins (day 14 posthatching) that were microinjected as embryos with a single environmentally relevant dose of 2,4-di-tert-butyl-6-(5-chloro-2H-benzotriazol-2-yl) phenol (UV-327). In the present follow-up study, we performed whole-transcriptome profiling (RNA sequencing) of newly hatched alevins from the same batch. The primary aim was to identify biomarkers related to behavior and neurology. Dose-specifically, 1 to 176 differentially expressed genes (DEGs) were identified. In the group presenting altered behavior (273.4 ng g-1), 176 DEGs were identified, yet only a fraction was related to neurological functions, including water, calcium, and potassium homeostasis; acetylcholine transmission and signaling; as well insulin and energy metabolism. The second objective was to estimate the transcriptomic point of departure (tPOD) and assess if point estimate(s) are protective of altered behavior. A tPOD was established at 35 to 94 ng UV-327 g-1 egg, making this tPOD protective of behavioral alterations. Holistically, these transcriptomic alterations provide a foundation for future research on how BUVSs can influence rainbow trout alevin development, while providing support to the hypothesis that UV-327 can influence neurogenesis and subsequent behavioral endpoints. The exact structural and functional changes caused by embryonic exposure to UV-327 remain enigmatic and will require extensive investigation before being deciphered and understood toxicologically. Environ Toxicol Chem 2024;43:2026-2038. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Modeling HepaRG metabolome responses to pyrrolizidine alkaloid exposure for insight into points of departure and modes of action

The new challenges in toxicology demand novel and innovative in vitro approaches for deriving points of departure (PODs) and determining the mode of action (MOA) of chemicals. Therefore, the aim of this original study was to couple in vitro studies with untargeted metabolomics to model the concentration-response of extra- and intracellular metabolome data on human HepaRG cells treated for 48 h with three pyrrolizidine alkaloids (PAs): heliotrine, retrorsine and lasiocarpine. Modeling revealed that the three PAs induced various monotonic and, importantly, biphasic curves of metabolite content. Based on unannotated metabolites, the endometabolome was more sensitive than the exometabolome in terms of metabolomic effects, and benchmark concentrations (BMCs) confirmed that lasiocarpine was the most hepatotoxic PA. Regarding its MOA, impairment of lipid metabolism was highlighted at a very low BMC (first quartile, 0.003 µM). Moreover, results confirmed that lasiocarpine targets bile acids, as well as amino acid and steroid metabolisms. Analysis of the endometabolome, based on coupling concentration-response and PODs, gave encouraging results for ranking toxins according to their hepatotoxic effects. Therefore, this novel approach is a promising tool for next-generation risk assessment, readily applicable to a broad range of compounds and toxic endpoints.

Insights into the synergistic toxicity mechanisms caused by nano- and microplastics with triclosan using a dose-dependent functional genomics approach in Saccharomyces cerevisiae

The emergence of polystyrene (PS) nano- and microplastics (NMPs) and triclosan (TCS) as environmental contaminants has raised concerns about their combined toxicities to organisms, but the complex toxicity arising from their interactions and the underlying molecular mechanisms remain obscure to us. In this study, we comprehensively detected the combined toxicity of PS-NMPs and TCS via the dose-dependent yeast functional genomics profiling. Firstly, our findings demonstrated that the combined exposure to PS-NMPs and TCS elicited a synergistic toxic effect in which the toxicity depended on the size of the PS-NMPs. Secondly, we found that TCS exposure, either alone or in combination with PS-NMPs, influenced lipid biosynthetic processes and ATP export pathways, while the unique responsive genes triggered by combined exposure to TCS and PS-NMPs are significantly enriched in mitochondrial translation, ribosomal small subunit assembly, and tRNA wobble uridine modification. Thirdly, our results demonstrated that point of departure (POD) at the pathway level was positively correlated with IC50, and POD was a more sensitive predictor of toxicity than the apical toxicity endpoints. More importantly, our findings suggested that the combined exposure of PS-NMPs in a size-dependent manner not only alleviated the harmful effects of TCS on glycerophospholipid metabolism, but also exacerbated its negative impact on oxidative phosphorylation. Collectively, our study not only provides new insights into the intricate molecular mechanisms that control the combined toxicity of PS-NMPs and TCS, but also confirms the effectiveness of the dose-dependent functional genomics approach in elucidating the molecular mechanisms of the combined toxicity of pollutants.

The dose disrupts the pathway: application of Paracelsus principle to mechanistic toxicology

Arguably the most famous principle of toxicology is "The dose makes the poison" formulated by Paracelsus in the 16th century. Application of the Paracelsus's principle to mechanistic toxicology may be challenging as one compound may affect many molecular pathways at different doses with different and often nonlinear dose-response relationships. As a result, many mechanistic studies of environmental and occupational compounds use high doses of xenobiotics motivated by the need to see a clear signal indicating disruption of a particular molecular pathway. This approach ignores the possibility that the same xenobiotic may affect different molecular mechanism(s) at much lower doses relevant to human exposures. To amend mechanistic toxicology with a simple and concise guiding principle, I suggest recontextualization of Paracelsus's following its letter and spirit: "The dose disrupts the pathway". Justification of this statement includes observations that many environmental and occupational xenobiotics affect a broad range of molecular cascades, that most molecular pathways are sensitive to chemical exposures, and that different molecular pathways are sensitive to different doses of a chemical compound. I suggest that this statement may become a useful guidance and educational tool in a range of toxicological applications, including experimental design, comparative analysis of mechanistic hypotheses, evaluation of the quality of toxicological studies, and risk assessment.

Investigation of Peroxisome Proliferator-Activated Receptor Genes as Requirements for Visual Startle Response Hyperactivity in Larval Zebrafish Exposed to Structurally Similar Per- and Polyfluoroalkyl Substances (PFAS)

BACKGROUND

Per- and polyfluoroalkyl Substances (PFAS) are synthetic chemicals widely detected in humans and the environment. Exposure to perfluorooctanesulfonic acid (PFOS) or perfluorohexanesulfonic acid (PFHxS) was previously shown to cause dark-phase hyperactivity in larval zebrafish.

OBJECTIVES

The objective of this study was to elucidate the mechanism by which PFOS or PFHxS exposure caused hyperactivity in larval zebrafish.

METHODS

Swimming behavior was assessed in 5-d postfertilization (dpf) larvae following developmental (1-4 dpf) or acute (5 dpf) exposure to 0.43 - 7.86 μ M PFOS, 7.87 - 120 μ M PFHxS, or 0.4% dimethyl sulfoxide (DMSO). After developmental exposure and chemical washout at 4 dpf, behavior was also assessed at 5-8 dpf. RNA sequencing was used to identify differences in global gene expression to perform transcriptomic benchmark concentration-response (BMC T ) modeling, and predict upstream regulators in PFOS- or PFHxS-exposed larvae. CRISPR/Cas9-based gene editing was used to knockdown peroxisome proliferator-activated receptors (ppars) pparaa/ab, pparda/db, or pparg at day 0. Knockdown crispants were exposed to 7.86 μ M PFOS or 0.4% DMSO from 1-4 dpf and behavior was assessed at 5 dpf. Coexposure with the ppard antagonist GSK3787 and PFOS was also performed.

RESULTS

Transient dark-phase hyperactivity occurred following developmental or acute exposure to PFOS or PFHxS, relative to the DMSO control. In contrast, visual startle response (VSR) hyperactivity only occurred following developmental exposure and was irreversible up to 8 dpf. Similar global transcriptomic profiles, BMC T estimates, and enriched functions were observed in PFOS- and PFHxS-exposed larvae, and ppars were identified as putative upstream regulators. Knockdown of pparda/db, but not pparaa/ab or pparg, blunted PFOS-dependent VSR hyperactivity to control levels. This finding was confirmed via antagonism of ppard in PFOS-exposed larvae.

DISCUSSION

This work identifies a novel adverse outcome pathway for VSR hyperactivity in larval zebrafish. We demonstrate that developmental, but not acute, exposure to PFOS triggered persistent VSR hyperactivity that required ppard function. https://doi.org/10.1289/EHP13667.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

Empirical analysis of lead neurotoxicity mode of action and its application in health risk assessment

The mode of action (MOA) framework is proposed to inform a biological link between chemical exposures and adverse health effects. Despite a significant increase in knowledge and awareness, the application of MOA in human health risk assessment (RA) remains limited. This study aims to discuss the adoption of MOA for health RA within a regulatory context, taking our previously proposed but not yet validated MOA for lead neurotoxicity as an example. We first conducted a quantitative weight of evidence (qWOE) assessment, which revealed that the MOA has a moderate confidence. Then, targeted bioassays were performed within an in vitro blood-brain barrier (BBB) model to quantitatively validate the scientific validity of key events (KEs) in terms of essentiality and concordance of empirical support (dose/temporal concordance), which increases confidence in utilizing the MOA for RA. Building upon the quantitative validation data, we further conducted benchmark dose (BMD) analysis to map dose-response relationships for the critical toxicity pathways, and the lower limit of BMD at a 5% response (BMDL5) was identified as the point of departure (POD) value for adverse health effects. Notably, perturbation of the Aryl Hydrocarbon Receptor (AHR) signaling pathway exhibited the lowest POD value, measured at 0.0062 μM. Considering bioavailability, we further calculated a provisional health-based guidance value (HBGV) for children's lead intake, determining it to be 2.56 μg/day. Finally, the health risk associated with the HBGV was assessed using the hazard quotient (HQ) approach, which indicated that the HBGV established in this study is a relative safe reference value for lead intake. In summary, our study described the procedure for utilizing MOA in health RA and set an example for MOA-based human health risk regulation.

Integrating gene expression and splicing dynamics across dose-response oxidative modulators

Toxicological risk assessment increasingly utilizes transcriptomics to derive point of departure (POD) and modes of action (MOA) for chemicals. One essential biological process that allows a single gene to generate several different RNA isoforms is called alternative splicing. To comprehensively assess the role of splicing dysregulation in toxicological evaluation and elucidate its potential as a complementary endpoint, we performed RNA-seq on A549 cells treated with five oxidative stress modulators across a wide dose range. Differential gene expression (DGE) showed limited pathway enrichment except at high concentrations. However, alternative splicing analysis revealed variable intron retention events affecting diverse pathways for all chemicals in the absence of significant expression changes. For instance, diazinon elicited negligible gene expression changes but progressive increase in the number of intron retention events, suggesting splicing alterations precede expression responses. Benchmark dose modeling of intron retention data highlighted relevant pathways overlooked by expression analysis. Systematic integration of splicing datasets should be a useful addition to the toxicogenomic toolkit. Combining both modalities paint a more complete picture of transcriptomic dose-responses. Overall, evaluating intron retention dynamics afforded by toxicogenomics may provide biomarkers that can enhance chemical risk assessment and regulatory decision making. This work highlights splicing-aware toxicogenomics as a possible additional tool for examining cellular responses.

Transcriptomic point of departure determination: a comparison of distribution-based and gene set-based approaches

A key step in assessing the potential human and environmental health risks of industrial and agricultural chemicals is to determine the toxicity point of departure (POD), which is the highest dose level that causes no adverse effect. Transcriptomic POD (tPOD) values have been suggested to accurately estimate toxicity POD values. One step in the most common approach for tPOD determination involves mapping genes to annotated gene sets, a process that might lead to substantial information loss particularly in species with poor gene annotation. Alternatively, methods that calculate tPOD values directly from the distribution of individual gene POD values omit this mapping step. Using rat transcriptome data for 79 molecules obtained from Open TG-GATEs (Toxicogenomics Project Genomics Assisted Toxicity Evaluation System), the hypothesis was tested that methods based on the distribution of all individual gene POD values will give a similar tPOD value to that obtained via the gene set-based method. Gene set-based tPOD values using four different gene set structures were compared to tPOD values from five different individual gene distribution methods. Results revealed a high tPOD concordance for all methods tested, especially for molecules with at least 300 dose-responsive probesets: for 90% of those molecules, the tPOD values from all methods were within 4-fold of each other. In addition, random gene sets based upon the structure of biological knowledge-derived gene sets produced tPOD values with a median absolute fold change of 1.3-1.4 when compared to the original biological knowledge-derived gene set counterparts, suggesting that little biological information is used in the gene set-based tPOD generation approach. These findings indicate using individual gene distributions to calculate a tPOD is a viable and parsimonious alternative to using gene sets. Importantly, individual gene distribution-based tPOD methods do not require knowledge of biological organization and can be applied to any species including those with poorly annotated gene sets.

Bioactivity assessment of organophosphate flame retardants via a dose-dependent yeast functional genomics approach

Organophosphate flame retardants (OPFRs) have been widely detected in multiple environment media and have many adverse effects with complex toxicity mechanisms. However, the early molecular responses to OPFRs have not been fully elucidated, thereby making it difficult to assess their risks accurately. In this work, we systematically explored the point of departure (POD) of biological pathways at genome-wide level perturbed by 14 OPFRs with three substituents (alkyl, halogen, and aryl) using a dose-dependent functional genomics approach in Saccharomyces cerevisiae at 24 h exposure. Firstly, our results demonstrated that the overall biological potency at gene level (PODDRG20) ranged from 0.013 to 35.079 μM for 14 OPFRs, especially the tributyl phosphate (TnBP) exhibited the strongest biological potency with the least PODDRG20. Secondly, we found that structural characteristics of carbon number and logKow were significantly negatively correlated with POD, and carbon number and logKow also significantly affected lipid metabolism associated processes. Thirdly, these early biological pathways of OPFRs toxification were found to be involved in lipid metabolism, oxidative stress, DNA damage, MAPK signaling pathway, and amino acid and carbohydrate metabolism, among which the lipid metabolism was the most sensitive molecular response perturbed by most OPFRs. More importantly, we identified one resistant mutant strain with knockout of ERG2 (YMR202W) gene participated in steroid biosynthesis pathway, which can serve as a key yeast strain of OPFRs toxification. Overall, our study demonstrated an effective platform for accurately assessing OPFRs risks and provided a basis for further green OPFRs development.

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.

Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals

To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments

There is increasing interest to employ in vitro transcriptomics experiments in toxicological testing, for example to determine a point-of-departure (PoD) for chemical safety assessment. However current practices to derive PoD tend to utilise a single exposure time despite the importance of exposure time on the manifestation of toxicity caused by a chemical. Therefore it is important to investigate both concentration and exposure time to determine how these factors affect biological responses, and as a consequence, the derivation of PoDs. In this study, metabolically competent HepaRG cells were exposed to five known toxicants over a range of concentrations and time points for subsequent gene expression analysis, using a targeted RNA expression assay (TempO-Seq). A non-parametric factor-modelling approach was used to model the collective response of all significant genes, which exploited the interdependence of differentially expressed gene responses. This in turn allowed the determination of an isobenchmark response (isoBMR) curve for each chemical in a reproducible manner. For 2 of the 5 chemicals tested, the PoD was observed to vary by 0.5-1 log-order within the 48-h timeframe of the experiment. The approach and findings presented here clearly demonstrate the need to take both concentration and exposure time into account when designing in vitro toxicogenomics experiments to determine PoD. Doing so also provides a means to use concentration-time-response modelling as a basis to extrapolate a PoD from shorter to longer exposure durations, and to identify chemicals of concern that can cause cumulative effects over time.

Risk-based prioritization of PFAS using phenotypic and transcriptomic data from human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes

Per- and polyfluoroalkyl substances (PFAS) are chemicals with important applications; they are persistent in the environment and may pose human health hazards. Regulatory agencies are con­sidering restrictions and bans of PFAS; however, little data exists for informed decisions. Several prioritization strategies were proposed for evaluation of potential hazards of PFAS. Structure-based grouping could expedite the selection of PFAS for testing; still, the hypothesis that structure-effect relationships exist for PFAS requires confirmation. We tested 26 structurally diverse PFAS from 8 groups using human induced pluripotent stem cell-derived hepatocytes and cardiomyocytes, and tested concentration-response effects on cell function and gene expression. Few phenotypic effects were observed in hepatocytes, but negative chronotropy was observed in cardiomyocytes for 8 PFAS. Substance- and cell type-dependent transcriptomic changes were more prominent but lacked substantial group-specific effects. In hepatocytes, we found upregulation of stress-related and extracellular matrix organization pathways, and down-regulation of fat metabolism. In car­diomyocytes, contractility-related pathways were most affected. We derived phenotypic and transcriptomic points of departure and compared them to predicted PFAS exposures. Conservative estimates for bioactivity and exposure were used to derive a bioactivity-to-exposure ratio (BER) for each PFAS; 23 of 26 PFAS had BER > 1. Overall, these data suggest that structure-based PFAS grouping may not be sufficient to predict their biological effects. Testing of individual PFAS may be needed for scientifically-supported decision-making. Our proposed strategy of using two human cell types and considering phenotypic and transcriptomic effects, combined with dose-response analysis and calculation of BER, may be used for PFAS prioritization.

Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). The tPOD calculation methods use data at the level of individual genes and gene set signatures, and compare data processed using the ToxCast Pipeline 2 (tcplfit2), BMDExpress and PLIER (Pathway Level Information ExtractoR). Methods were evaluated by comparing to in vitro PODs from a validated set of high-throughput screening (HTS) assays for a set of estrogenic compounds. Key findings include: (1) for a given chemical and set of experimental conditions, tPODs calculated by different methods can vary by several orders of magnitude; (2) tPODs are at least as sensitive to computational methods as to experimental conditions; (3) in comparison to an external reference set of PODs, some methods give generally higher values, principally PLIER and BMDExpress; and (4) the tPODs from HTTr in this one cell type are mostly higher than the overall PODs from a broad battery of targeted in vitro ToxCast assays, reflecting the need to test chemicals in multiple cell types and readout technologies for in vitro hazard screening.

The phosphoproteome is a first responder in tiered cellular adaptation to chemical stress followed by proteomics and transcriptomics alteration

Next-generation risk assessment (NGRA) for environmental chemicals involves a weight of evidence (WoE) framework integrating a suite of new approach methodologies (NAMs) based on points of departure (PoD) obtained from in vitro assays. Among existing NAMs, the omic-based technologies are of particular importance based on the premise that any apical endpoint change indicative of impaired health must be underpinned by some alterations at the omics level, such as transcriptome, proteome, metabolome, epigenome and genome. Transcriptomic assay plays a leading role in providing relatively conservative PoDs compared with apical endpoints. However, it is unclear whether and how parameters measured with other omics techniques predict the cellular response to chemical perturbations, especially at exposure levels below the transcriptomically defined PoD. Multi-omics coverage may provide additional sensitive or confirmative biomarkers to complement and reduce the uncertainty in safety decisions made using targeted and transcriptomics assays. In the present study, we conducted multi-omics studies of transcriptomics, proteomics and phosphoproteomics on two prototype compounds, coumarin and 2,4-dichlorophenoxyacetic acid (2,4-D), with multiple chemical concentrations and time points, to understand the sensitivity of the three omics techniques in response to chemically-induced changes in HepG2. We demonstrated that, phosphoproteomics alterations occur not only earlier in time, but also more sensitive to lower concentrations than proteomics and transcriptomics when the HepG2 cells were exposed to various chemical treatments. The phosphoproteomics changes appear to approach maximum when the transcriptomics alterations begin to initiate. Therefore, it is proximal to the very early effects induced by chemical exposure. We concluded that phosphoproteomics can be utilized to provide a more complete coverage of chemical-induced cellular alteration and supplement transcriptomics-based health safety decision making.

Mining toxicogenomic data for dose-responsive pathways: implications in advancing next-generation risk assessment

Introduction: While targeted investigation of key toxicity pathways has been instrumental for biomarker discovery, unbiased and holistic analysis of transcriptomic data provides a complementary systems-level perspective. However, in a systematic context, this approach has yet to receive comprehensive and methodical implementation. Methods: Here, we took an integrated bioinformatic approach by re-analyzing publicly available MCF7 cell TempO-seq data for 44 ToxCast chemicals using an alternative pipeline to demonstrate the power of this approach. The original study has focused on analyzing the gene signature approach and comparing them to in vitro biological pathway altering concentrations determined from ToxCast HTS assays. Our workflow, in comparison, involves sequential differential expression, gene set enrichment, benchmark dose modeling, and identification of commonly perturbed pathways by network visualization. Results: Using this approach, we identified dose-responsive molecular changes, biological pathways, and points of departure in an untargeted manner. Critically, benchmark dose modeling based on pathways recapitulated points of departure for apical endpoints, while also revealing additional perturbed mechanisms missed by single endpoint analyses. Discussion: This systems-toxicology approach provides multifaceted insights into the complex effects of chemical exposures. Our work highlights the importance of unbiased data-driven techniques, alongside targeted methods, for comprehensively evaluating molecular initiating events, dose-response relationships, and toxicity pathways. Overall, integrating omics assays with robust bioinformatics holds promise for improving chemical risk assessment and advancing new approach methodologies (NAMs).

Toxicological Mechanisms and Potencies of Organophosphate Esters in KGN Human Ovarian Granulosa Cells as Revealed by High-throughput Transcriptomics

Despite the growing number of studies reporting potential risks associated with exposure to organophosphate esters (OPEs), their molecular mechanisms of action remain poorly defined. We used the high-throughput TempO-Seq™ platform to investigate the effects of frequently detected OPEs on the expression of ∼3000 environmentally responsive genes in KGN human ovarian granulosa cells. Cells were exposed for 48 h to one of five OPEs (0.1 to 50 μM): tris(methylphenyl) phosphate (TMPP), isopropylated triphenyl phosphate (IPPP), tert-butylphenyl diphenyl phosphate (BPDP), triphenyl phosphate (TPHP), or tris(2-butoxyethyl) phosphate (TBOEP). The sequencing data indicate that four OPEs induced transcriptional changes, whereas TBOEP had no effect within the concentration range tested. Multiple pathway databases were used to predict alterations in biological processes based on differentially expressed genes. At lower concentrations, inhibition of the cholesterol biosynthetic pathway was the predominant effect of OPEs; this was likely a consequence of intracellular cholesterol accumulation. At higher concentrations, BPDP and TPHP had distinct effects, primarily affecting pathways involved in cell cycle progression and other stress responses. Benchmark concentration (BMC) modelling revealed that BPDP had the lowest transcriptomic point of departure. However, in vitro to in vivo extrapolation modeling indicated that TMPP was bioactive at lower concentrations than the other OPEs. We conclude that these new approach methodologies provide information on the mechanism(s) underlying the effects of data-poor compounds and assist in the derivation of protective points of departure for use in chemical read-across and decision-making.

Characterizing temporal variability and repeatability of dose-dependent functional genomics approach for evaluating triclosan toxification

Dose-dependent functional genomics approach has shown great advantage in identifying the molecular initiating event (MIE) of chemical toxification and yielding point of departure (POD) at genome-wide scale. However, POD variability and repeatability derived from experimental design (settings of dose, replicate number, and exposure time) has not been fully determined. In this work, we evaluated POD profiles perturbed by triclosan (TCS) using dose-dependent functional genomics approach in Saccharomyces cerevisiae at multiple time points (9 h, 24 h and 48 h). The full dataset (total 9 concentrations with 6 replicates per treatment) at 9 h was subsampled 484 times to generate subsets of 4 dose groups (Dose A - Dose D with varied concentration range and spacing) and 5 replicate numbers (2 reps - 6 reps). Firstly, given the accuracy of POD and the experimental cost, the POD profiles from 484 subsampled datasets demonstrated that the Dose C group (space narrow at high concentrations and wide dose range) with three replicates was best choice at both gene and pathway levels. Secondly, the variability of POD was found to be relatively robustness and stability across different experimental designs, but POD was more dependent on the dose range and interval than the number of replicates. Thirdly, MIE of TCS toxification was identified to be the glycerophospholipid metabolism pathway at all-time points, supporting the ability of our approach to accurately recognize MIE of chemical toxification at both short- and long-term exposure. Finally, we identified and validated 13 key mutant strains involved in MIE of TCS toxification, which could serve as biomarkers for TCS exposure. Taken together, our work evaluated the repeatability of dose-dependent functional genomics approach and the variability of POD and MIE of TCS toxification, which will benefit the experimental design for future dose-dependent functional genomics study.

Applying genomics in regulatory toxicology: a report of the ECETOC workshop on omics threshold on non-adversity

In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC's activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or "noise" can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics methods, and a number of case examples were presented in illustration of this point. What is apparent is that high dimension data will always be subject to varying processing pipelines and hence interpretation, depending on the context they are used in. Yet, they can provide valuable input for regulatory toxicology, with the pre-condition being robust methods for the collection and processing of data together with a comprehensive description how the data were interpreted, and conclusions reached.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

Delving into the molecular initiating event of cadmium toxification via the dose-dependent functional genomics approach in Saccharomyces cerevisiae

Determining dose-response relationship is essential for comprehensively revealing chemical-caused effects on organisms. However, uncertainty and complexity of gene/protein interactions cause the inability of traditional toxicogenomic methods (e.g., transcriptomics, proteomics and metabolomics) to effectively establish the direct relationship between chemical exposure and genes. In this work, we built an effective dose-dependent yeast functional genomics approach, which can clearly identify the direct gene-chemical link in the process of cadmium (Cd) toxification from a genome-wide scale with wide range concentrations (0.83, 2.49, 7.48, 22.45, 67.34, 202.03 and 606.1 μM). Firstly, we identified 220 responsive strains, and found that 142, 110, 91, 34, 8, 0 and 0 responsive strains can be respectively modulated by seven different Cd exposure concentrations ranging from high to low. Secondly, our results demonstrated that these genes induced by the high Cd exposure were mainly enriched in the process of cell autophagy, but ones caused by the low Cd exposure were primarily involved in oxidative stress. Thirdly, we found that the top-ranked GO biological processes with the lowest point of departure (POD) were transmembrane transporter complex and mitochondrial respiratory chain complex III, suggesting that mitochondrion might be the toxicity target of Cd. Similarly, nucleotide excision repair was ranked first in KEGG pathway with the least POD, indicating that this dose-dependent functional genomics approach can effectively detect the molecular initiating event (MIE) of cadmium toxification. Fourthly, we identified four key mutant strains (RIP1, QCR8, CYT1 and QCR2) as biomarkers for Cd exposure. Finally, the dose-dependent functional genomics approach also performed well in identifying MIE for additional genotoxicity chemical 4-nitroquinoline-1-oxide (4-NQO) data. Overall, our study developed a dose-dependent functional genomics approach, which is powerful to delve into the MIE of chemical toxification and is beneficial for guiding further chemical risk assessment.

Considerations for application of benchmark dose modeling in radiation research: workshop highlights

BACKGROUND

Exposure to different forms of ionizing radiation occurs in diverse occupational, medical, and environmental settings. Improving the accuracy of the estimated health risks associated with exposure is therefore, essential for protecting the public, particularly as it relates to chronic low dose exposures. A key aspect to understanding health risks is precise and accurate modeling of the dose-response relationship. Toward this vision, benchmark dose (BMD) modeling may be a suitable approach for consideration in the radiation field. BMD modeling is already extensively used for chemical hazard assessments and is considered statistically preferable to identifying low and no observed adverse effects levels. BMD modeling involves fitting mathematical models to dose-response data for a relevant biological endpoint and identifying a point of departure (the BMD, or its lower bound). Recent examples in chemical toxicology show that when applied to molecular endpoints (e.g. genotoxic and transcriptional endpoints), BMDs correlate to points of departure for more apical endpoints such as phenotypic changes (e.g. adverse effects) of interest to regulatory decisions. This use of BMD modeling may be valuable to explore in the radiation field, specifically in combination with adverse outcome pathways, and may facilitate better interpretation of relevant in vivo and in vitro dose-response data. To advance this application, a workshop was organized on June 3rd, 2022, in Ottawa, Ontario that brought together BMD experts in chemical toxicology and the radiation scientific community of researchers, regulators, and policy-makers. The workshop's objective was to introduce radiation scientists to BMD modeling and its practical application using case examples from the chemical toxicity field and demonstrate the BMDExpress software using a radiation dataset. Discussions focused on the BMD approach, the importance of experimental design, regulatory applications, its use in supporting the development of adverse outcome pathways, and specific radiation-relevant examples.

CONCLUSIONS

Although further deliberations are needed to advance the use of BMD modeling in the radiation field, these initial discussions and partnerships highlight some key steps to guide future undertakings related to new experimental work.

Quantitative systems pharmacology of the eye: Tools and data for ocular QSP

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery.

In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals

Since initial regulatory action in 2010 in Canada, bisphenol A (BPA) has been progressively replaced by structurally related alternative chemicals. Unfortunately, many of these chemicals are data-poor, limiting toxicological risk assessment. We used high-throughput transcriptomics to evaluate potential hazards and compare potencies of BPA and 15 BPA alternative chemicals in cultured breast cancer cells. MCF-7 cells were exposed to BPA and 15 alternative chemicals (0.0005-100 µM) for 48 h. TempO-Seq (BioSpyder Inc) was used to examine global transcriptomic changes and estrogen receptor alpha (ERα)-associated transcriptional changes. Benchmark concentration (BMC) analysis was conducted to identify 2 global transcriptomic points of departure: (1) the lowest pathway median gene BMC and (2) the 25th lowest rank-ordered gene BMC. ERα activation was evaluated using a published transcriptomic biomarker and an ERα-specific transcriptomic point of departure was derived. Genes fitting BMC models were subjected to upstream regulator and canonical pathway analysis in Ingenuity Pathway Analysis. Biomarker analysis identified BPA and 8 alternative chemicals as ERα active. Global and ERα transcriptomic points of departure produced highly similar potency rankings with bisphenol AF as the most potent chemical tested, followed by BPA and bisphenol C. Further, BPA and transcriptionally active alternative chemicals enriched similar gene sets associated with increased cell division and cancer-related processes. These data provide support for future read-across applications of transcriptomic profiling for risk assessment of data-poor chemicals and suggest that several BPA alternative chemicals may cause hazards at similar concentrations to BPA.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

The contribution of larval zebrafish transcriptomics to chemical risk assessment

In Canada, the Canadian Environmental Protection Act (1999) requires human health and environmental risk assessments be conducted for new substances prior to their manufacture or import. While this toxicity data is historically obtained using rodents, in response to the international effort to eliminate animal testing, Health Canada is collaborating with the National Research Council (NRC) of Canada to develop a New Approach Method by refining existing NRC zebrafish models. The embryo/larval zebrafish model evaluates systemic (whole body) general toxicity which is currently unachievable with cell-based testing. The model is strengthened using behavioral, toxicokinetic and transcriptomic responses to assess non-visible indicators of toxicity following chemical exposure at sub-phenotypic concentrations. In this paper, the predictive power of zebrafish transcriptomics is demonstrated using two chemicals; Raloxifene and Resorcinol. Raloxifene exposure produced darkening of the liver and malformation of the nose/mandible, while Resorcinol exposure produced increased locomotor activity. Transcriptomic analysis correlated differentially expressed genes with the phenotypic effects and benchmark dose calculations determined that the transcriptomic Point of Departure (POD) occurred at subphenotypic concentrations. Correlating gene expression with apical (phenotypic) effects strengthens confidence in evaluation of chemical toxicity, thereby demonstrating the significant advancement that the larval zebrafish transcriptomics model represents in chemical risk assessment.

Application of a new approach methodology (NAM)-based strategy for genotoxicity assessment of data-poor compounds

The conventional battery for genotoxicity testing is not well suited to assessing the large number of chemicals needing evaluation. Traditional in vitro tests lack throughput, provide little mechanistic information, and have poor specificity in predicting in vivo genotoxicity. New Approach Methodologies (NAMs) aim to accelerate the pace of hazard assessment and reduce reliance on in vivo tests that are time-consuming and resource-intensive. As such, high-throughput transcriptomic and flow cytometry-based assays have been developed for modernized in vitro genotoxicity assessment. This includes: the TGx-DDI transcriptomic biomarker (i.e., 64-gene expression signature to identify DNA damage-inducing (DDI) substances), the MicroFlow^® assay (i.e., a flow cytometry-based micronucleus (MN) test), and the MultiFlow^® assay (i.e., a multiplexed flow cytometry-based reporter assay that yields mode of action (MoA) information). The objective of this study was to investigate the utility of the TGx-DDI transcriptomic biomarker, multiplexed with the MicroFlow^® and MultiFlow^® assays, as an integrated NAM-based testing strategy for screening data-poor compounds prioritized by Health Canada's New Substances Assessment and Control Bureau. Human lymphoblastoid TK6 cells were exposed to 3 control and 10 data-poor substances, using a 6-point concentration range. Gene expression profiling was conducted using the targeted TempO-Seq™ assay, and the TGx-DDI classifier was applied to the dataset. Classifications were compared with those based on the MicroFlow^® and MultiFlow^® assays. Benchmark Concentration (BMC) modeling was used for potency ranking. The results of the integrated hazard calls indicate that five of the data-poor compounds were genotoxic in vitro, causing DNA damage via a clastogenic MoA, and one via a pan-genotoxic MoA. Two compounds were likely irrelevant positives in the MN test; two are considered possibly genotoxic causing DNA damage via an ambiguous MoA. BMC modeling revealed nearly identical potency rankings for each assay. This ranking was maintained when all endpoint BMCs were converted into a single score using the Toxicological Prioritization (ToxPi) approach. Overall, this study contributes to the establishment of a modernized approach for effective genotoxicity assessment and chemical prioritization for further regulatory scrutiny. We conclude that the integration of TGx-DDI, MicroFlow^®, and MultiFlow^® endpoints is an effective NAM-based strategy for genotoxicity assessment of data-poor compounds.

Derivation of metabolic point of departure using high-throughput in vitro metabolomics: investigating the importance of sampling time points on benchmark concentration values in the HepaRG cell line

Amongst omics technologies, metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still a relatively unexplored area. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with one of four chemicals (aflatoxin B₁, benzo[a]pyrene, cyclosporin A, or rotenone), each at seven concentrations (aflatoxin B₁, benzo[a]pyrene, cyclosporin A: from 0.2048 μM to 50 μM; rotenone: from 0.04096 to 10 μM) and five sampling time points (2, 6, 12, 24 and 48 h). The study explored three approaches to derive PODs using benchmark concentration modelling applied to single features in the metabolomics datasets or annotated metabolites or lipids: (1) the 1st rank-ordered unannotated feature, (2) the 1st rank-ordered putatively annotated feature (using a recently developed HepaRG-specific library of polar metabolites and lipids), and (3) 25th rank-ordered feature, demonstrating that for three out of four chemical datasets all of these approaches led to relatively consistent BMC values, varying less than tenfold across the methods. In addition, using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical specific. Furthermore, a possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway (AOP) was demonstrated. The study highlights that advances in technologies enable application of in vitro metabolomics at scale; however, greater confidence in metabolite identification is required to ensure PODs are mechanistically anchored.

Adverse Outcome Pathway 'Footprinting': A Novel Approach to the Integration of 21st Century Toxicology Information into Chemical Mixtures Risk Assessment

For over a decade, New Approach Methodologies (NAMs) such as structure-activity/read-across, -omics technologies, and Adverse Outcome Pathway (AOP), have been considered within regulatory communities as alternative sources of chemical and biological information potentially relevant to human health risk assessment. Integration of NAMs into applications such as chemical mixtures risk assessment has been limited due to the lack of validation of qualitative and quantitative application to adverse health outcomes in vivo, and acceptance by risk assessors. However, leveraging existent hazard and dose-response information, including NAM-based data, for mixture component chemicals across one or more levels of biological organization using novel approaches such as AOP 'footprinting' proposed herein, may significantly advance mixtures risk assessment. AOP footprinting entails the systematic stepwise profiling and comparison of all known or suspected AOPs involved in a toxicological effect at the level of key event (KE). The goal is to identify key event(s) most proximal to an adverse outcome within each AOP suspected of contributing to a given health outcome at which similarity between mixture chemicals can be confidently determined. These key events are identified as the 'footprint' for a given AOP. This work presents the general concept, and a hypothetical example application, of AOP footprinting as a key methodology for the integration of NAM data into mixtures risk assessment.

Pilot testing and optimization of a larval fathead minnow high throughput transcriptomics assay

Concentrations at which global gene expression profiles in cells or animals exposed to a test substance start to differ significantly from those of controls have been proposed as an alternative point of departure for use in screening level hazard assessment. The present study describes pilot testing of a high throughput compatible transcriptomics assay with larval fathead minnows. One day post hatch fathead minnows were exposed to eleven different concentrations of three metals, three selective serotonin reuptake inhibitors, and four neonicotinoid-like compounds for 24 h and concentration response modeling was applied to whole body gene expression data. Transcriptomics-based points of departure (tPODs) were consistently lower than effect concentrations reported in apical endpoint studies in fish. However, larval fathead minnow-based tPODs were not always lower than concentrations reported to elicit apical toxicity in other aquatic organisms like crustaceans or insects. Random in silico subsampling of data from the pilot assays was used to evaluate various assay design and acceptance considerations such as transcriptome coverage, number of replicate individuals to sequence per treatment, and minimum number of differentially expressed genes to produce a reliable tPOD estimate. Results showed a strong association between the total number of genes for which a concentration response relationship could be derived and the overall variability in the resulting tPOD estimates. We conclude that, for our current assay design and analysis pipeline, tPODs based on fewer than 15 differentially expressed genes are likely to be unreliable for screening and that interindividual variability in gene expression profiles appears to be a more significant driver of tPOD variability than sample size alone. Results represent initial steps toward developing high throughput transcriptomics assays for use in ecological hazard screening.

Characterizing Variability and Uncertainty Associated with Transcriptomic Dose-Response Modeling

Transcriptomics dose-response analysis (TDRA) has emerged as a promising approach for integrating toxicogenomics data into a risk assessment context; however, variability and uncertainty associated with experimental design are not well understood. Here, we evaluated n = 55 RNA-seq profiles derived from Japanese quail liver tissue following exposure to chlorpyrifos (0, 0.04, 0.1, 0.2, 0.4, 1, 2, 4, 10, 20, and 40 μg/g; n = 5 replicates per group) via egg injection. The full dataset was subsampled 637 times to generate smaller datasets with different dose ranges and spacing (designs A-E) and number of replicates (n = 2-5). TDRA of the 637 datasets revealed substantial variability in the gene and pathway benchmark doses, but relative stability in overall transcriptomic point-of-departure (tPOD) values when tPODs were calculated with the "pathway" and "mode" methods. Further, we found that tPOD values were more dependent on the dose range and spacing than on the number of replicates, suggesting that optimal experimental designs should use fewer replicates (n = 2 or 3) and more dose groups to reduce uncertainty in the results. Finally, tPOD values ranged by over ten times for all surveyed experimental designs and tPOD types, suggesting that tPODs should be interpreted as order-of-magnitude estimates.

Are Non-animal Systemic Safety Assessments Protective? A Toolbox and Workflow

An important question in toxicological risk assessment is whether non-animal new approach methodologies (NAMs) can be used to make safety decisions that are protective of human health, without being overly conservative. In this work, we propose a core NAM toolbox and workflow for conducting systemic safety assessments for adult consumers. We also present an approach for evaluating how protective and useful the toolbox and workflow are by benchmarking against historical safety decisions. The toolbox includes physiologically based kinetic (PBK) models to estimate systemic Cmax levels in humans, and 3 bioactivity platforms, comprising high-throughput transcriptomics, a cell stress panel, and in vitro pharmacological profiling, from which points of departure are estimated. A Bayesian model was developed to quantify the uncertainty in the Cmax estimates depending on how the PBK models were parameterized. The feasibility of the evaluation approach was tested using 24 exposure scenarios from 10 chemicals, some of which would be considered high risk from a consumer goods perspective (eg, drugs that are systemically bioactive) and some low risk (eg, existing food or cosmetic ingredients). Using novel protectiveness and utility metrics, it was shown that up to 69% (9/13) of the low risk scenarios could be identified as such using the toolbox, whilst being protective against all (5/5) the high-risk ones. The results demonstrated how robust safety decisions could be made without using animal data. This work will enable a full evaluation to assess how protective and useful the toolbox and workflow are across a broader range of chemical-exposure scenarios.

Evaluating the Influences of Confounding Variables on Benchmark Dose using a Case Study in the Field of Ionizing Radiation

Purpose A vast amount of data regarding the effects of radiation stressors on transcriptional changes has been produced over the past few decades. These data have shown remarkable consistency across platforms and experimental design, enabling increased understanding of early molecular effects of radiation exposure. However, the value of transcriptomic data in the context of risk assessment is not clear and represents a gap that is worthy of further consideration. Recently, benchmark dose (BMD) modeling has shown promise in correlating a transcriptional point of departure (POD) to that derived using phenotypic outcomes relevant to human health risk assessment. Although frequently applied in chemical toxicity evaluation, our group has recently demonstrated application within the field of radiation research. This approach allows the possibility to quantitatively compare radiation-induced gene and pathway alterations across various datasets using BMD values and derive meaningful biological effects. However, before BMD modeling can confidently be used, an understanding of the impact of confounding variables on BMD outputs is needed. Methods: To this end, BMD modeling was applied to a publicly available microarray dataset (Gene Expression Omnibus #GSE23515) that used peripheral blood ex-vivo gamma-irradiated at 0.82 Gy/min, at doses of 0, 0.1, 0.5 or 2 Gy, and assessed 6 hours post-exposure. The dataset comprised six female smokers (F-S), six female non-smokers (F-NS), six male smokers (M-S), and six male non-smokers (M-NS). Results: A combined total of 412 genes were fit to models and the BMD distribution was noted to be bi-modal across the four groups. A total of 74, 41, 62 and 62 genes were unique to the F-NS, M-NS, F-S and M-S groups. Sixty-two BMD modeled genes and nine pathways were common across all four groups. There were no differential sensitivity of responses in the robust common genes and pathways. Conclusion: For radiation-responsive genes and pathways common across the study groups, the BMD distribution of transcriptional activity was unaltered by sex and smoking status. Although further validation of the data is needed, these initial findings suggest BMD values for radiation relevant genes and pathways are robust and could be explored further in future studies.

Time-resolved quantitative phosphoproteomics reveals cellular responses induced by caffeine and coumarin

Protein phosphorylation is a critical way that cells respond to external signals and environmental stresses. However, the patterns of cellular response to chemicals at different times were largely unknown. Here, we used quantitative phosphoproteomics to analyze the cellular response of kinases and signaling pathways, as well as pattern change of phosphorylated substrates in HepG2 cells that were exposed to caffeine and coumarin for 10 min and 24 h. Comparing the 10 min and 24 h groups, 33 kinases were co-responded and 32 signaling pathways were co-enriched in caffeine treated samples, while 48 kinases and 34 signaling pathways were co-identified in coumarin treated samples. Instead, the percentage of co-identified phosphorylated substrates only accounted for 4.31% and 9.57% between 10 min and 24 h in caffeine and coumarin treated samples, respectively. The results showed that specific chemical exposure led to a bunch of the same kinases and signaling pathways changed in HepG2 cells, while the phosphorylated substrates were different. In addition, it was found that insulin signaling pathway was significantly enriched by both the caffeine and coumarin treatment. The pattern changes in phosphorylation of protein substrates, kinases and signaling pathways with varied chemicals and different time course shed light on the potential mechanism of cellular responses to endless chemical stimulation.

A microRNA or messenger RNA point of departure estimates an apical endpoint point of departure in a rat developmental toxicity model

Traditional developmental toxicity testing practice examines fetal apical endpoints to identify a point of departure (POD) for risk assessment. A potential new testing paradigm involves deriving a POD from a comprehensive analysis of molecular-level change. Here, the rat ketoconazole endocrine-mediated developmental toxicity model was used to test the hypothesis that maternal epigenomic (miRNA) and transcriptomic (mRNA) PODs are similar to fetal apical endpoint PODs. Sprague-Dawley rats were exposed from gestation day (GD) 6-21 to 0, 0.063, 0.2, 0.63, 2, 6.3, 20, or 40 mg/kg/day ketoconazole. Dam systemic, liver, and placenta PODs, along with GD 21 fetal resorption, body weight, and skeletal apical PODs were derived using BMDS software. GD 21 dam liver and placenta miRNA and mRNA PODs were obtained using three methods: a novel individual molecule POD accumulation method, a first mode method, and a gene set method. Dam apical POD values ranged from 2.0 to 38.6 mg/kg/day; the lowest value was for placenta histopathology. Fetal apical POD values were 10.9-20.3 mg/kg/day; the lowest value was for fetal resorption. Dam liver miRNA and mRNA POD values were 0.34-0.69 mg/kg/day, and placenta miRNA and mRNA POD values were 2.53-6.83 mg/kg/day. Epigenomic and transcriptomic POD values were similar across liver and placenta. Deriving a molecular POD from dam liver or placenta was protective of a fetal apical POD. These data support the conclusion that a molecular POD can be used to estimate, or be protective of, a developmental toxicity apical POD.

Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data

Estimation of points of departure (PoDs) from high-throughput transcriptomic data (HTTr) represents a key step in the development of next-generation risk assessment (NGRA). Current approaches mainly rely on single key gene targets, which are constrained by the information currently available in the knowledge base and make interpretation challenging as scientists need to interpret PoDs for thousands of genes or hundreds of pathways. In this work, we aimed to address these issues by developing a computational workflow to investigate the pathway concentration-response relationships in a way that is not fully constrained by known biology and also facilitates interpretation. We employed the Pathway-Level Information ExtractoR (PLIER) to identify latent variables (LVs) describing biological activity and then investigated in vitro LVs' concentration-response relationships using the ToxCast pipeline. We applied this methodology to a published transcriptomic concentration-response data set for 44 chemicals in MCF-7 cells and showed that our workflow can capture known biological activity and discriminate between estrogenic and antiestrogenic compounds as well as activity not aligning with the existing knowledge base, which may be relevant in a risk assessment scenario. Moreover, we were able to identify the known estrogen activity in compounds that are not well-established ER agonists/antagonists supporting the use of the workflow in read-across. Next, we transferred its application to chemical compounds tested in HepG2, HepaRG, and MCF-7 cells and showed that PoD estimates are in strong agreement with those estimated using a recently developed Bayesian approach (cor = 0.89) and in weak agreement with those estimated using a well-established approach such as BMDExpress2 (cor = 0.57). These results demonstrate the effectiveness of using PLIER in a concentration-response scenario to investigate pathway activity in a way that is not fully constrained by the knowledge base and to ease the biological interpretation and support the development of an NGRA framework with the ability to improve current risk assessment strategies for chemicals using new approach methodologies.

Predicting nonlinear relationships between external and internal concentrations with physiologically based pharmacokinetic modeling

Although external concentrations are more readily quantified and often used as the metric for regulating and mitigating exposures to environmental chemicals, the toxicological response to an environmental chemical is more directly related to its internal concentrations than the external concentration. The processes of absorption, distribution, metabolism, and excretion (ADME) determine the quantitative relationship between the external and internal concentrations, and these processes are often susceptible to saturation at high concentrations, which can lead to nonlinear changes in internal concentrations that deviate from proportionality. Using generic physiologically-based pharmacokinetic (PBPK) models, we explored how saturable absorption or clearance influence the shape of the internal to external concentration (IEC) relationship. We used the models for hypothetical chemicals to show how differences in kinetic parameters can impact the shape of an IEC relationship; and models for styrene and caffeine to explore how exposure route, frequency, and duration impact the IEC relationships in rat and human exposures. We also analyzed available plasma concentration data for 2,4-dichlorophenoxyacetic acid to demonstrate how a PBPK modeling approach can be an alternative to common statistical methods for analyzing dose proportionality. A PBPK modeling approach can be a valuable tool used in the early stages of a chemical safety assessment program to optimize the design of longer-term animal toxicity studies or to interpret study results.

Evaluation of the human hazard of the liver and lung tumors in mice treated with permethrin based on mode of action

The non-genotoxic synthetic pyrethroid insecticide permethrin produced hepatocellular adenomas and bronchiolo-alveolar adenomas in female CD-1 mice, but not in male CD-1 mice or in female or male Wistar rats. Studies were performed to evaluate possible modes of action (MOAs) for permethrin-induced female CD-1 mouse liver and lung tumor formation. The MOA for liver tumor formation by permethrin involves activation of the peroxisome proliferator-activated receptor alpha (PPARα), increased hepatocellular proliferation, development of altered hepatic foci, and ultimately liver tumors. This MOA is similar to that established for other PPARα activators and is considered to be qualitatively not plausible for humans. The MOA for lung tumor formation by permethrin involves interaction with Club cells, followed by a mitogenic effect resulting in Club cell proliferation, with prolonged administration producing Club cell hyperplasia and subsequently formation of bronchiolo-alveolar adenomas. Although the possibility that permethrin exposure may potentially result in enhancement of Club cell proliferation in humans cannot be completely excluded, there is sufficient information on differences in basic lung anatomy, physiology, metabolism, and biologic behavior of tumors in the general literature to conclude that humans are quantitatively less sensitive to agents that increase Club cell proliferation and lead to tumor formation in mice. The evidence strongly indicates that Club cell mitogens are not likely to lead to increased susceptibility to lung tumor development in humans. Overall, based on MOA evaluation it is concluded that permethrin does not pose a tumorigenic hazard for humans, this conclusion being supported by negative data from permethrin epidemiological studies.

Use of in vitro metabolism and biokinetics assays to refine predicted in vivo and in vitro internal exposure to the cosmetic ingredient, phenoxyethanol, for use in risk assessment

A novel approach was developed to help characterize the biokinetics of the cosmetic ingredient, phenoxyethanol, to help assess the safety of the parent and its major stable metabolite. In the first step of this non-animal tiered approach, primary human hepatocytes were used to confirm or refute in silico predicted metabolites, and elucidate the intrinsic clearance of phenoxyethanol. A key result was the identification of the major metabolite, phenoxyacetic acid (PAA), the exposure to which in the kidney was subsequently predicted to far exceed that of phenoxyethanol in blood or other tissues. Therefore, a novel aspect of this approach was to measure in the subsequent step the formation of PAA in the cells dosed with phenoxyethanol that were used to provide points of departure (PoDs) and express the intracellular exposure as the Cmax and AUC24. This enabled the calculation of the intracellular concentrations of parent and metabolite at the PoD in the cells used to derive this value. These concentrations can be compared with in vivo tissue levels to conclude on the safety margin. The lessons from this case study will help to inform the design of other non-animal safety assessments.