Organophosphorus flame retardants are developmental neurotoxicants in a rat primary brainsphere in vitro model.

Due to regulatory bans and voluntary substitutions, halogenated polybrominated diphenyl ether (PBDE) flame retardants (FR) are increasingly substituted by mainly organophosphorus FR (OPFR). Leveraging a 3D rat primary neural organotypic in vitro model (rat brainsphere), we compare developmental neurotoxic effects of BDE-47-the most abundant PBDE congener-with four OPFR (isopropylated phenyl phosphate-IPP, triphenyl phosphate-TPHP, isodecyl diphenyl phosphate-IDDP, and tricresyl phosphate (also known as trimethyl phenyl phosphate)-TMPP). Employing mass spectroscopy-based metabolomics and transcriptomics, we observe at similar human-relevant non-cytotoxic concentrations (0.1-5 µM) stronger developmental neurotoxic effects by OPFR. This includes toxicity to neurons in the low µM range; all FR decrease the neurotransmitters glutamate and GABA (except BDE-47 and TPHP). Furthermore, n-acetyl aspartate (NAA), considered a neurologic diagnostic molecule, was decreased by all OPFR. At similar concentrations, the FR currently in use decreased plasma membrane dopamine active transporter expression, while BDE-47 did not. Several findings suggest astrogliosis induced by the OPFR, but not BDE-47. At the 5 µM concentrations, the OPFR more than BDE-47 interfered with myelination. An increase of cytokine gene and receptor expressions suggests that exposure to OPFR may induce an inflammatory response. Pathway/category overrepresentation shows disruption in 1) transmission of action potentials, cell-cell signaling, synaptic transmission, receptor signaling, (2) immune response, inflammation, defense response, (3) cell cycle and (4) lipids metabolism and transportation. Taken together, this appears to be a case of regretful substitution with substances not less developmentally neurotoxic in a primary rat 3D model.

Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC-MS based untargeted metabolomics practitioners.

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics. OBJECTIVES: In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC-MS) in untargeted metabolomics. METHODS: All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach. RESULTS: For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%). CONCLUSIONS: Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

Toxicity, recovery, and resilience in a 3D dopaminergic neuronal in vitro model exposed to rotenone.

To date, most in vitro toxicity testing has focused on acute effects of compounds at high concentrations. This testing strategy does not reflect real-life exposures, which might contribute to long-term disease outcome. We used a 3D-human dopaminergic in vitro LUHMES cell line model to determine whether effects of short-term rotenone exposure (100 nM, 24 h) are permanent or reversible. A decrease in complex I activity, ATP, mitochondrial diameter, and neurite outgrowth were observed acutely. After compound removal, complex I activity was still inhibited; however, ATP levels were increased, cells were electrically active and aggregates restored neurite outgrowth integrity and mitochondrial morphology. We identified significant transcriptomic changes after 24 h which were not present 7 days after wash-out. Our results suggest that testing short-term exposures in vitro may capture many acute effects which cells can overcome, missing adaptive processes, and long-term mechanisms. In addition, to study cellular resilience, cells were re-exposed to rotenone after wash-out and recovery period. Pre-exposed cells maintained higher metabolic activity than controls and presented a different expression pattern in genes previously shown to be altered by rotenone. NEF2L2, ATF4, and EAAC1 were downregulated upon single hit on day 14, but unchanged in pre-exposed aggregates. DAT and CASP3 were only altered after re-exposure to rotenone, while TYMS and MLF1IP were downregulated in both single-exposed and pre-exposed aggregates. In summary, our study shows that a human cell-based 3D model can be used to assess cellular adaptation, resilience, and long-term mechanisms relevant to neurodegenerative research.

Joint Bounding of Peaks Across Samples Improves Differential Analysis in Mass Spectrometry-Based Metabolomics.

As mass spectrometry-based metabolomics becomes more widely used in biomedical research, it is important to revisit existing data analysis paradigms. Existing data preprocessing efforts have largely focused on methods which start by extracting features separately from each sample, followed by a subsequent attempt to group features across samples to facilitate comparisons. We show that this preprocessing approach leads to unnecessary variability in peak quantifications that adversely impacts downstream analysis. We present a new method, bakedpi, for the preprocessing of both centroid and profile mode metabolomics data that relies on an intensity-weighted bivariate kernel density estimation on a pooling of all samples to detect peaks. This new method reduces this unnecessary quantification variability and increases power in downstream differential analysis.

Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis.

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

MPTP's pathway of toxicity indicates central role of transcription factor SP1.

Deriving a Pathway of Toxicity from transcriptomic data remains a challenging task. We explore the use of weighted gene correlation network analysis (WGCNA) to extract an initial network from a small microarray study of MPTP toxicity in mice. Five modules were statistically significant; each module was analyzed for gene signatures in the Chemical and Genetic Perturbation subset of the Molecular Signatures Database as well as for over-represented transcription factor binding sites and WGCNA clustered probes by function and captured pathways relevant to neurodegenerative disorders. The resulting network was analyzed for transcription factor candidates, which were narrowed down via text-mining for relevance to the disease model, and then combined with the large-scale interaction FANTOM4 database to generate a genetic regulatory network. Modules were enriched for transcription factors relevant to Parkinson's disease. Transcription factors significantly improved the number of genes that could be connected in a given component. For each module, the transcription factor that had, by far, the highest number of interactions was SP1, and it also had substantial experimental evidence of interactions. This analysis both captures much of the known biology of MPTP toxicity and suggests several candidates for further study. Furthermore, the analysis strongly suggests that SP1 plays a central role in coordinating the cellular response to MPTP toxicity.

Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as weight of evidence, fail due to problems in information redundancy and high dimensionality. The problem is further amplified when potency information (dose/response) of hazards would be estimated. Skin sensitization currently serves as the foster child for AOP and ITS development, as legislative pressures combined with a very good mechanistic understanding of contact dermatitis have led to test development and relatively large high-quality data sets. We curated such a data set and combined a recursive variable selection algorithm to evaluate the information available through in silico, in chemico and in vitro assays. Chemical similarity alone could not cluster chemicals' potency, and in vitro models consistently ranked high in recursive feature elimination. This allows reducing the number of tests included in an ITS. Next, we analyzed with a hidden Markov model that takes advantage of an intrinsic inter-relationship among the local lymph node assay classes, i.e. the monotonous connection between local lymph node assay and dose. The dose-informed random forest/hidden Markov model was superior to the dose-naive random forest model on all data sets. Although balanced accuracy improvement may seem small, this obscures the actual improvement in misclassifications as the dose-informed hidden Markov model strongly reduced " false-negatives" (i.e. extreme sensitizers as non-sensitizer) on all data sets.

OECD validation study to assess intra- and inter-laboratory reproducibility of the zebrafish embryo toxicity test for acute aquatic toxicity testing.

The OECD validation study of the zebrafish embryo acute toxicity test (ZFET) for acute aquatic toxicity testing evaluated the ZFET reproducibility by testing 20 chemicals at 5 different concentrations in 3 independent runs in at least 3 laboratories. Stock solutions and test concentrations were analytically confirmed for 11 chemicals. Newly fertilised zebrafish eggs (20/concentration and control) were exposed for 96h to chemicals. Four apical endpoints were recorded daily as indicators of acute lethality: coagulation of the embryo, lack of somite formation, non-detachment of the tail bud from the yolk sac and lack of heartbeat. Results (LC50 values for 48/96h exposure) show that the ZFET is a robust method with a good intra- and inter-laboratory reproducibility (CV<30%) for most chemicals and laboratories. The reproducibility was lower (CV>30%) for some very toxic or volatile chemicals, and chemicals tested close to their limit of solubility. The ZFET is now available as OECD Test Guideline 236. Considering the high predictive capacity of the ZFET demonstrated by Belanger et al. (2013) in their retrospective analysis of acute fish toxicity and fish embryo acute toxicity data, the ZFET is ready to be considered for acute fish toxicity for regulatory purposes.

Review: toxicometabolomics.

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

Two new approaches to improve the analysis of BALB/c 3T3 cell transformation assay data.

Validation activities of the BALB/c 3T3 cell transformation assay (CTA) - a test method used for the assessment of the carcinogenic potential of compounds - have revealed the need for statistical analysis tailored to specific features of BALB/c 3T3 CTA data. Whereas a standard statistical approach for the Syrian hamster embryo (SHE) CTA was considered sufficient, an international expert group was gathered by the European Centre for the Validation of Alternative Methods (ECVAM) to review commonly applied statistical approaches for BALB/c 3T3 CTA. As it was concluded that none of the commonly applied approaches is entirely appropriate, two novel statistical approaches were found to be recommended for the evaluation of BALB/c 3T3 CTA data accounting for possible non-monotone concentration-response relationship and variance heterogeneity: a negative binomial generalised linear model with William's-type downturn-protected trend tests and a normalisation of the data by a specific transformation allowing for application of a general linear model that estimates effects assuming a normal distribution with William's-type protected tests. Both approaches are described in this article and their performance and the quality of the results they generate is demonstrated using exemplary data. Our work confirmed that both approaches are suitable for the statistical analysis of BALB/c 3T3 CTA data and that each of them is superior to commonly used methods. Furthermore, a procedure dichotomising data into negatives and positives is proposed which allows re-testing in cases where inconclusive data are encountered. The scripts of the statistical evaluation programs written in R - a freely available statistical software - are appended including exemplary outputs (Appendix A).

Report of the EPAA-ECVAM workshop on the validation of Integrated Testing Strategies (ITS).

The use of Integrated Testing Strategies (ITS) permits the combination of diverse types of chemical and toxicological data for the purposes of hazard identification and characterisation. In November 2008, the European Partnership for Alternative Approaches to Animal Testing (EPAA), together with the European Centre for the Validation of Alternative Methods (ECVAM), held a workshop on Overcoming Barriers to Validation of Non-animal Partial Replacement Methods/Integrated Testing Strategies, in Ispra, Italy, to discuss the extent to which current ECVAM approaches to validation can be used to evaluate partial replacement in vitro test methods (i.e. as potential ITS components) and ITS themselves. The main conclusions of these discussions were that formal validation was only considered necessary for regulatory purposes (e.g. the replacement of a test guideline), and that current ECVAM approaches to validation should be adapted to accommodate such test methods. With these conclusions in mind, a follow-up EPAA-ECVAM workshop was held in October 2009, to discuss the extent to which existing validation principles are applicable to the validation of ITS test methods, and to develop a draft approach for the validation of such test methods and/or overall ITS for regulatory purposes. This report summarises the workshop discussions that started with a review of the current validation methodologies and the presentation of two case studies (skin sensitisation and acute toxicity), before covering the definition of ITS and their components, including their validation and regulatory acceptance. The following main conclusions/recommendations were made: that the validation of a partial replacement test method (for application as part of a testing strategy) should be differentiated from the validation of an in vitro test method for application as a stand-alone replacement, especially with regard to its predictive capacity; that, in the former case, the predictive capacity of the whole testing strategy (rather than of the individual test methods) would be more important, especially if the individual test methods had a high biological relevance; that ITS allowing for flexible and ad hoc approaches cannot be validated, whereas the validation of clearly defined ITS would be feasible, although practically quite difficult; and that test method developers should be encouraged to develop and submit to ECVAM not only full replacement test methods, but also partial replacement methods to be placed as parts of testing strategies. The added value from the formal validation of testing strategies, and the requirements needed in view of regulatory acceptance of the data, require further informed discussion within the EPAA forum on the basis of case studies provided by industry.

Mapping for dyslexia and related cognitive trait loci provides strong evidence for further risk genes on chromosome 6p21.

In a genome-wide linkage scan, we aimed at mapping risk loci for dyslexia in the German population. Our sample comprised 1,030 individuals from 246 dyslexia families which were recruited through a single-proband sib pair study design and a detailed assessment of dyslexia and related cognitive traits. We found evidence for a major dyslexia locus on chromosome 6p21. The cognitive trait rapid naming (objects/colors) produced a genome-wide significant LOD score of 5.87 (P = 1.00 × 10⁻7) and the implicated 6p-risk region spans around 10 Mb. Although our finding maps close to DYX2, where the dyslexia candidate genes DCDC2 and KIAA0319 have already been identified, our data point to the presence of an additional risk gene in this region and are highlighting the impact of 6p21 in dyslexia and related cognitive traits.

Gene-Environment Interactions in Developmental Neurotoxicity: a Case Study of Synergy between Chlorpyrifos and CHD8 Knockout in Human BrainSpheres.

BACKGROUND: Autism spectrum disorder (ASD) is a major public health concern caused by complex genetic and environmental components. Mechanisms of gene-environment (G×E) interactions and reliable biomarkers associated with ASD are mostly unknown or controversial. Induced pluripotent stem cells (iPSCs) from patients or with clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9)-introduced mutations in candidate ASD genes provide an opportunity to study (G×E) interactions. OBJECTIVES: In this study, we aimed to identify a potential synergy between mutation in the high-risk autism gene encoding chromodomain helicase DNA binding protein 8 (CHD8) and environmental exposure to an organophosphate pesticide (chlorpyrifos; CPF) in an iPSC-derived human three-dimensional (3D) brain model. METHODS: This study employed human iPSC-derived 3D brain organoids (BrainSpheres) carrying a heterozygote CRISPR/Cas9-introduced inactivating mutation in CHD8 and exposed to CPF or its oxon-metabolite (CPO). Neural differentiation, viability, oxidative stress, and neurite outgrowth were assessed, and levels of main neurotransmitters and selected metabolites were validated against human data on ASD metabolic derangements. RESULTS: Expression of CHD8 protein was significantly lower in CHD8 heterozygous knockout (CHD8+/-) BrainSpheres compared with CHD8+/+ ones. Exposure to CPF/CPO treatment further reduced CHD8 protein levels, showing the potential (G×E) interaction synergy. A novel approach for validation of the model was chosen: from the literature, we identified a panel of metabolic biomarkers in patients and assessed them by targeted metabolomics in vitro. A synergistic effect was observed on the cholinergic system, S-adenosylmethionine, S-adenosylhomocysteine, lactic acid, tryptophan, kynurenic acid, and α-hydroxyglutaric acid levels. Neurite outgrowth was perturbed by CPF/CPO exposure. Heterozygous knockout of CHD8 in BrainSpheres led to an imbalance of excitatory/inhibitory neurotransmitters and lower levels of dopamine. DISCUSSION: This study pioneered (G×E) interaction in iPSC-derived organoids. The experimental strategy enables biomonitoring and environmental risk assessment for ASD. Our findings reflected some metabolic perturbations and disruption of neurotransmitter systems involved in ASD. The increased susceptibility of CHD8+/- BrainSpheres to chemical insult establishes a possibly broader role of (G×E) interaction in ASD. https://doi.org/10.1289/EHP8580.

More powerful haplotype sharing by accounting for the mode of inheritance.

The concept of haplotype sharing (HS) has received considerable attention recently, and several haplotype association methods have been proposed. Here, we extend the work of Beckmann and colleagues [2005 Hum. Hered. 59:67-78] who derived an HS statistic (BHS) as special case of Mantel's space-time clustering approach. The Mantel-type HS statistic correlates genetic similarity with phenotypic similarity across pairs of individuals. While phenotypic similarity is measured as the mean-corrected cross product of phenotypes, we propose to incorporate information of the underlying genetic model in the measurement of the genetic similarity. Specifically, for the recessive and dominant modes of inheritance we suggest the use of the minimum and maximum of shared length of haplotypes around a marker locus for pairs of individuals. If the underlying genetic model is unknown, we propose a model-free HS Mantel statistic using the max-test approach. We compare our novel HS statistics to BHS using simulated case-control data and illustrate its use by re-analyzing data from a candidate region of chromosome 18q from the Rheumatoid Arthritis (RA) Consortium. We demonstrate that our approach is point-wise valid and superior to BHS. In the re-analysis of the RA data, we identified three regions with point-wise P-values<0.005 containing six known genes (PMIP1, MC4R, PIGN, KIAA1468, TNFRSF11A and ZCCHC2) which might be worth follow-up.

Genome-wide linkage analysis of malaria infection intensity and mild disease.

Although balancing selection with the sickle-cell trait and other red blood cell disorders has emphasized the interaction between malaria and human genetics, no systematic approach has so far been undertaken towards a comprehensive search for human genome variants influencing malaria. By screening 2,551 families in rural Ghana, West Africa, 108 nuclear families were identified who were exposed to hyperendemic malaria transmission and were homozygous wild-type for the established malaria resistance factors of hemoglobin (Hb)S, HbC, alpha(+) thalassemia, and glucose-6-phosphate-dehydrogenase deficiency. Of these families, 392 siblings aged 0.5-11 y were characterized for malaria susceptibility by closely monitoring parasite counts, malaria fever episodes, and anemia over 8 mo. An autosome-wide linkage analysis based on 10,000 single-nucleotide polymorphisms was conducted in 68 selected families including 241 siblings forming 330 sib pairs. Several regions were identified which showed evidence for linkage to the parasitological and clinical phenotypes studied, among them a prominent signal on Chromosome 10p15 obtained with malaria fever episodes (asymptotic z score = 4.37, empirical p-value = 4.0 x 10(-5), locus-specific heritability of 37.7%; 95% confidence interval, 15.7%-59.7%). The identification of genetic variants underlying the linkage signals may reveal as yet unrecognized pathways influencing human resistance to malaria.

New European Union statistics on laboratory animal use - what really counts!

Seven years after the last release, the European Commission has again collated and released data on laboratory animal use. The new report is the first to correspond to the requirements of the new Directive 2010/63/EU. Beside minor problems in reporting, the new reporting format is a major step forward, with additional new categories like severity allowing insight into animal use related questions that goes far beyond the previous reports. An in-depth analysis confirms a slight decrease in animal use from 2015 to 2017, but also compared to the 2005, 2008 and 2011 reports, though the new reporting scheme makes this comparison difficult. Notable success is evident for replacing rabbit pyrogen testing but, in general, the implementation of accepted alternative methods lags behind expec-tations. Beside the roughly 10 million animals per year covered in the report, about 8 million animals were identified that fall under the Directive but are not included in this number. Their omission downplays the impact of REACH on animal use. The report, second to none in its detail internationally, represents an important instrument for benchmarking and strategi-cally focusing activities in the 3Rs.

Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

Cancer is a comparatively well-studied disease, yet despite decades of intense focus, we demonstrate here using data from The Cancer Genome Atlas that a substantial number of genes implicated in cancer are relatively poorly studied. Those genes will likely be missed by any data analysis pipeline, such as enrichment analysis, that depends exclusively on annotations for understanding biological function. There is no indication that the amount of research - indicated by number of publications - is correlated with any objective metric of gene significance. Moreover, these genes are not missing at random but reflect that our information about genes is gathered in a biased manner: poorly studied genes are more likely to be primate-specific and less likely to have a Mendelian inheritance pattern, and they tend to cluster in some biological processes and not others. While this likely reflects both technological limitations as well as the fact that well-known genes tend to gather more interest from the research community, in the absence of a concerted effort to study genes in an unbiased way, many genes (and biological processes) will remain opaque.

Author Correction: Functionally Enigmatic Genes in Cancer: Using TCGA Data to Map the Limitations of Annotations.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The exposome - a new approach for risk assessment.

Complementing the human genome with an exposome reflects the increasingly obvious impact of environmental exposure, which far exceeds the role of genetics, on human health. Considering the complexity of exposures and, in addition, the reactions of the body to exposures - i.e., the exposome - reverses classical exposure science where the precise measurement of single or few exposures is associated with specific health or environmental effects. The complete description of an individual's exposome is impossible; even less so is that of a population. We can, however, cast a wider net by foregoing some rigor in assessment and compensating with the statistical power of rich datasets. The advent of omics technologies enables a relatively cheap, high-content description of the biological effects of substances, especially in tissues and biofluids. They can be combined with many other rich data-streams, creating big data of exposure and effect. Computational methods increasingly allow data integration, discerning the signal from the noise and formulating hypotheses of exposure-effect relationships. These can be followed up in a targeted way. With a better exposure element in the risk equation, exposomics - new kid on the block of risk assessment - promises to identify novel exposure (interactions) and health/environment effect associations. This may also create opportunities to prioritize the more relevant chemicals for risk assessment, thereby lowering the burden on hazard assessment in an expo-sure-driven approach. Technological developments and synergies between approaches, quality assurance (ultimately as Good Exposome Practices), and the integration of mechanistic thinking will advance this approach.

Exploring new technologies in biomedical research.

The Health Law, Policy & Ethics Project at Emory University School of Law and the Human Toxicology Project Consortium of the Humane Society of the United States co-sponsored a symposium on October 23, 2017, to showcase innovations using human-based in silico and in vitro models for drug and device discovery. The goal of the symposium was to introduce researchers and students to exciting new tools and possible future careers that will increase understanding of disease and improve the search for effective therapeutics, while reducing reliance on animal testing. The symposium concluded with a discussion between scientists and lawyers about the legal regulation of new biomedical research technologies.