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Read across toxicity predictions with nano-lazar
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center
Christoph Helma, Denis Gebele, Micha Rautenberg in silico toxicology gmbh .. image:: http://www.enanomapper.net/sites/all/themes/theme807/logo.png :align: center
Requirements for nanoparticle read-across
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incremental
- Nanoparticle characterisation - Toxicity measurements
eNanoMapper particle characterisation
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incremental
- Nanoparticles imported: 464 - Nanoparticles with particle characterisation: 394 - Nanoparticles with toxicity data: 167 - Nanoparticles with toxicity data and particle characterisation: 160
eNanoMapper toxicity endpoints
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incremental
- Toxicity endpoints: 41 - Toxicity endpoints with more than one measurement value: 22 - Toxicity endpoints with more than 10 measurements: 2
Selected data
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Protein corona dataset Au particles (105 particles) Toxicity endpoint: Net cell association (A549 cell line)
Read across procedure
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incremental
- Identify relevant properties (statistically significant correlation with toxicity: 14 from 30 properties) - Calculate similarities (weighted cosine similarity with correlation coefficients as weights) - Identify neighbors (particles with similarity > 0.95) - Calculate prediction (weighted average from neighbors with similarities as weights) Algorithms for feature selection, similarity calculation and predictions may change in the future.
Future development (I)
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Validation of predictions
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Applicability domain/reliability of predictions
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Accuracy improvements:
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additional data
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feature selection
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similarity calculation
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predictions (local regression models)
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Future development (I)
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Usability improvements:
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additional data (extension of applicability domain, additional endpoints and chemistries)
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inclusion of ontologies
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inclusion of protein corona characterisation?
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particle characterisation without experimental data
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descriptor calculation from core and coating chemistries
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ontological descriptors
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nano-lazar
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:webinterface: nano-lazar.in-silico.ch/predict :presentation: nano-lazar.in-silico.ch/enm-workshop.html :Source code: github.com/enanomapper/nano-lazar :issues: github.com/enanomapper/nano-lazar/issues
Your comments, ideas, recommendations?