Welcome To ToxNano

Toxicity Data Analysis for Nanomaterials

Model Features

Raw Data Visualization

Plate visualization allows initial visual inspection of HTS and HC datasets to evaluate the consistency of sample replicates and the effectiveness of positive/negative controls. Rapid data visualization can then guide the selection of suitable statistical methods for subsequent data analysis.

Visualize Plates

Generate Heatmaps

HTS Processing

Hit-identification can be performed to identify samples of high bioactivity (i.e. ‘hits’) for further confirmatory tests, as well as toxicity response interpretation and modeling. Heat maps can subsequently be generated to depict sample bioactivity (summarized HTS data) in a color map for convenient visual inspection.


Outlier Removal and Plate Normalization

Outlier removal from experimental HTS datasets may be required in order to exclude abnormal values (i.e. statistically inconsistent and thus unlikely to belong to the dataset), thus ensuring robust and reliable inference of ENM toxicity. HTS plate normalization is essential in order to account for plate-to-plate variability, remove systematic errors (e.g. positional effects) and compare/combine data from different plates.

Self Organizing Maps

Hit Identification

SOM Clustering

Clustering can be performed to extract information that is useful for ENM risk assessment and decision making. For example, SOM clustering analysis can group together ENMs of similar HTS bioactivity profiles, indicating that these ENMs might share common toxicity mechanisms.


Data Driven Toxicity Models

ToxNano provides a suit of general purpose data driven models for (1) evaluating the body of evidence, (2) assessing the significance of ENMs attributes correlating with toxicity, (3) integrating information (qualitative and quantitative), (4) predictive toxicology, and (5) tiered approaches for correlating ENMs properties and experimental settings with toxicity.

Data Driven Models

QSAR Development

Structure-Activity Relationships of Nanomaterials (Nano-SAR)

A variety of structure-activity relationships are currently available in ToxNano for a wide range of ENMs including, metal, metal oxides and Quantum Dots (QD), as well as various surface modified ENMs. ToxNano is designed to allow for the addition of new toxicity models as the body of knowledge expands


Evaluation of Body of Evidence

A particular challenge in toxicity evaluation is to assess if advanced literature data mining/knowledge-extraction techniques can be implemented for use where a sufficiently large body of literature exists, instead of using exhaustive parametric experimental studies in an era of limited resources. ToxNano provides the approach to mining the large body of evidence in order to answer the following: 1) Are the available literature data for a specific ENMs sufficient, to unambiguously identify relevant parameters that govern ENM toxicity? and 2) Can literature data mining and knowledge extraction be used to identify gaps and suggest possible approaches to streamline toxicity studies?

Evaluation of published body of evidence

Hierarchical Clustering

Hierarchical Clustering

Hierarchical Clustering can be performed to identify groups of ENMs of similar HTS bioactivity profiles. This helps to visually integrate the information and quantitatively identify common toxicity mechanisms.

About


The High Throughput Screening (HTS) Data Analysis Tools (HDAT) is a suite of computational and visualization tools developed in order to meet the requirements of rapid and reliable analyses of data generated by HTS studies of ENMs toxicity. HDAT provides different plate normalization methods, various HTS summarization statistics, hierarchical and self-organizing map (SOM)-based clustering analysis, and visualization of raw and processed data using both heat map and SOM. HDAT has been successfully used in numerous HTS studies of ENM toxicity, thereby enabling analysis of toxicity mechanisms and development of structure–activity relationships for ENM toxicity. HDAT encourages the standardization of and future advances in HTS as well as facilitating convenient inter-laboratory comparisons of HTS datasets.

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