Read across toxicity predictions with nano-lazar
Christoph Helma
in silico toxicology gmbh
Requirements
- Nanoparticle characterisation
- Toxicity measurements
eNanoMapper data import
- Nanoparticles imported: 464
- Nanoparticles with particle characterisation: 394
- Nanoparticles with toxicity data: 167
- Nanoparticles with toxicity data and particle characterisation: 160
eNanoMapper toxicity endpoints
- 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 (106 particles) Toxicity endpoint:
Read across procedure
- Identify relevant fragments (significant correlation with toxicity) TODO list of fragments, number
- Calculate similarities (weighted cosine similarity, correlation coefficients = weights)
- Identify neighbors (particles with more than 0.95 similarity)
- Calculate prediction (weighted average from neighbors, similarities = weights)
Future development
- Validation of predictions
- Applicability domain/reliability of predictions
- Accuracy improvements: - additional data - feature selection - similarity calculation - predictions (local regression models)
- Usability improvements: - additional data (extension of applicability domain, additional endpoints and chemistries) - inclusion of ontologies - descriptor calculation directly from core and coating chemistries