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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

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)

  • Validation of predictions

  • Applicability domain/reliability of predictions

  • Accuracy improvements:

    • additional data

    • feature selection

    • similarity calculation

    • predictions (local regression models)

Future development (I)

  • Usability improvements:

    • additional data (extension of applicability domain, additional endpoints and chemistries)

    • inclusion of ontologies

    • inclusion of protein corona characterisation?

    • particle characterisation without experimental data

      • descriptor calculation from core and coating chemistries

      • ontological descriptors

nano-lazar

: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?