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Online Molecular Initiating Events Prediction Tool Webinar

Authors: Abhijit Dasgupta; Vadim Zhernovkov; Boris Kholodenko; Vladimir Lobaskin; Martin Himly;

Online Molecular Initiating Events Prediction Tool Webinar

Abstract

On Tuesday, 18th May 2021, the NanoCommons team, in a joint initiative with the NanoSafety Cluster, offered an online webinar on the use of their Molecular Initiating Event (MIE) Prediction Tool accessible via the NanoCommons Knowledge Base (NC KB). Introduction and Host: Martin Himly, PLUS and Chair of NanoSafety Cluster WG-A on Education, Training, and Communication Webinar Speaker: Abhijit Dasgupta, UCD Panel for Q+A session: Vadim Zhernovkov and Vladimir Lobaskin, UCD Toxicity testing and regulation of advanced materials at the nanoscale i.e., nano safety, is challenged by the growing number of nanomaterials. The existing animal-reliant toxicity testing tools are onerous in terms of time and resources. There is a need for faster, cheaper, sensitive and effective animal alternatives that are supported by mechanistic evidence. Moreover, there is an urgency for developing alternative testing strategies. The Adverse Outcome Pathway (AOP)-based approaches provide pragmatic insights to promote the development of alternative testing strategies. MIE is the first step in an AOP and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. Predicting actual MIEs without time-resolved data establishing the MIE is challenging. Risk assessment requires information on the exposure conditions (e.g., route, dose, duration and frequency) needed to cause an AO. The NanoCommons MIE gene set database (NanoCommons GS-MIE DB) captures: Gene signatures (GS) of MIEs by integrating knowledge from KEGG, REACTOME, GO, WikiPathways public databases; Custom gene sets from published data; To date, manual collection of 132 gene sets representing three different types of MIE actions: MIE1. Disruption of lung surfactant functionality MIE2. Lysosomal destabilization MIE3. Oxidation of cell membrane Attached here you find the pdf of the entire webinar slide set. The webinar recording is amongst others available online at the NanoCommons YouTube channel, for a direct link to the recording of this event click here. Furthermore, you find additional information on related trainings at the NanoCommons Infrastructure, in the NanoCommons Customer Guidance Handbook, and at the ELIXIR TeSS channel of NanoCommons.

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Keywords

toxicity prediction, FAIR data, nanosafety, adverse outcome pathways, in silico modeling

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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