Testing for in vitro genetic toxicity in high dimensional nanomaterial dose-response experiments
Source abstract
Abstract
The application of nanomaterials in various fields has both benefits and risks. While the unique physico-chemical properties of nanomaterials are valuable for diverse applications, they may also alter how nanomaterials are taken up in the body, thus affecting their potential toxicity to humans. To ensure their safety, the potential risk of nanomaterials needs to be assessed. The risk of a given nanomaterial is typically evaluated through in vitro and in vivo studies. The H2020 NanoInformaTIX project, that is conducted under the European Union's Horizon 2020 research and innovation programme is focusing on the study of nanomaterials. One of its objectives includes the identification of nanomaterial toxicity, of which the first step consists of the detection of a nanomaterial dose-response relationship. In this paper, we focus on this step, which can be done for a given nanomaterial toxicity endpoint of interest, using statistical tests for a monotone dose-response trend, i.e., monotone relationship between the increasing concentration of the nanomaterial and the toxicity endpoint. The significance of the monotone trend is tested using a likelihood ratio test and t-tests. An R package and a Shiny App were developed to perform the analysis. As an illustration, data extracted from the H2020 NanoInformaTIX platform is presented as a case study. This data consists of genetic toxicity studies with DNA strand breaks as the toxicity endpoint of interest. Significant trends were found for 12 out of 26 nanomaterials after adjusting for multiplicity by applying the False discovery rate (FDR) method.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced biosensing and bioanalysis techniques · Gold and Silver Nanoparticles Synthesis and Applications · Nanoparticles: synthesis and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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