Systematic Omics Analysis Review (SOAR) tool to support risk assessment.

Environmental health risk assessors are challenged to understand and incorporate new data streams as the field of toxicology continues to adopt new molecular and systems biology technologies. Systematic screening reviews can help risk assessors and assessment teams determine which studies to consider for inclusion in a human health assessment. A tool for systematic reviews should be standardized and transparent in order to consistently determine which studies meet minimum quality criteria prior to performing in-depth analyses of the data. The Systematic Omics Analysis Review (SOAR) tool is focused on assisting risk assessment support teams in performing systematic reviews of transcriptomic studies. SOAR is a spreadsheet tool of 35 objective questions developed by domain experts, focused on transcriptomic microarray studies, and including four main topics: test system, test substance, experimental design, and microarray data. The tool will be used as a guide to identify studies that meet basic published quality criteria, such as those defined by the Minimum Information About a Microarray Experiment standard and the Toxicological Data Reliability Assessment Tool. Seven scientists were recruited to test the tool by using it to independently rate 15 published manuscripts that study chemical exposures with microarrays. Using their feedback, questions were weighted based on importance of the information and a suitability cutoff was set for each of the four topic sections. The final validation resulted in 100% agreement between the users on four separate manuscripts, showing that the SOAR tool may be used to facilitate the standardized and transparent screening of microarray literature for environmental human health risk assessment.

What do the data show? Knowledge map development for comprehensive environmental assessment.

Environmental and human health risk assessments benefit from using data that cross multiple scientific domains. Although individual data points may often be readily understood, the total picture can be difficult to envision. This is especially true with gaps in the data (e.g., with emerging substances such as engineered nanomaterials [ENM]), such that simply presenting only known information can result in a skewed picture. This study describes a method for building knowledge maps (KM) to visually summarize factors relevant to risk assessment in a relatively easy to interpret format. The KMs were created in the context of the comprehensive environmental assessment (CEA) approach for research planning and risk management of environmental contaminants. Recent applications of CEA to emerging substances such as engineered nanomaterials that have numerous data gaps have suggested that a more visually based depiction of information would improve the approach. We developed KM templates as a pilot project, to represent pertinent aspects of conceptual domains, and to highlight gaps in available information for one particular portion of a specific CEA application: the comparison of environmental transport, transformation, and fate of multiwalled carbon nanotubes (MWCNTs) and decabromodiphenyl ether as flame retardants. The results are 3 KM templates representing Physical Properties, Transport, and Transformation. The 3 templates were applied to both substances, resulting in a total of 6 KMs. In addition to presenting the KMs, this paper details the process used to generate them, to aid KM development for other sections of CEA applied to MWCNTs, or to apply the process to new CEA applications.

Evaluation of microelectrode array data using Bayesian modeling as an approach to screening and prioritization for neurotoxicity testing.

The need to assess large numbers of chemicals for their potential toxicities has resulted in increased emphasis on medium- and high-throughput in vitro screening approaches. For such approaches to be useful, efficient and reliable data analysis and hit detection methods are also required. Assessment of chemical effects on neuronal network activity using microelectrode arrays (MEAs) has been proposed as a screening tool for neurotoxicity. The current study examined a Bayesian data analysis approach for assessing effects of a 30 chemical training set on activity of primary cortical neurons grown in multi-well MEA plates. Each well of the MEA plate contained 64 microelectrodes and the data set contains the number of electrical spikes registered by each electrode over the course of each experiment. A Bayesian data analysis approach was developed and then applied to several different parsings of the data set to produce probability determinations for hit selection and ranking. This methodology results in an approach that is approximately 74% sensitive in detecting chemicals in the training set known to alter neuronal function (23 expected positives) while being 100% specific in detecting chemicals expected to have no effect (7 expected negatives). Additionally, this manuscript demonstrates that the Bayesian approach may be combined with a previously published weighted mean firing rate approach in order to produce a more robust hit detection method. In particular, when combined with the weighted mean firing rate approach, the joint analysis produces a sensitivity of approximately 96% and a specificity of 100%. These results demonstrate the utility of a novel approach to analysis of MEA data and support the use of neuronal networks grown on MEAs as a for neurotoxicity screening approach.

Evaluation of multi-well microelectrode arrays for neurotoxicity screening using a chemical training set.

Microelectrode array (MEA) approaches have been proposed as a tool for detecting functional changes in electrically excitable cells, including neurons, exposed to drugs, chemicals or particles. However, conventional single well-MEA systems lack the throughput necessary for screening large numbers of uncharacterized compounds. Recently, multi-well MEA (mwMEA) formats have become available to address the need for increased throughput. The current experiments examined the effects of a training set of 30 chemicals on spontaneous activity in networks of cortical neurons grown on mwMEA plates. Each plate contained 12 wells with 64 microelectrodes/well, for a total of 768 channels. Of the 30 chemicals evaluated, 23 were known to alter neuronal function in vivo ("positives"), including 6 GABAergic and 3 glutamatergic antagonists/agonists, 4 pyrethroids, 3 metals, 2 cholinesterase inhibitors, 2 nicotinic acetylcholine receptor agonists, valproic acid, verapamil, and fluoxetine. Seven compounds expected to have no effect on neuronal function were tested as "negatives" (glyphosate, acetaminophen, salicylic acid, paraquat, saccharin, d-sorbitol and amoxicillin). Following collection of 33 min of baseline activity, chemical effects (50 µM or highest soluble concentration) were recorded for 33 min. Twenty of the positives altered the mean network spike rate by more than the 14% threshold (two standard deviations from the mean for DMSO control). The three positives without effect were bifenthrin, nicotine and imidacloprid. None of the negative compounds caused a change in activity beyond the threshold. Based on these results, the mwMEA assay has both high sensitivity (87% identification of positive compounds) and specificity (100% identification of negative compounds). These experiments demonstrate the capacity of mwMEAs to screen compounds for neurotoxic effects mediated by a broad variety of mechanisms.