Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models

Natalia Sizochenko , Bakhtiyor Rasulev , Agnieszka Gajewicz , Elena Mokshyna , Victor E. Kuz'min , Jerzy Leszczynski , Tomasz Puzyn

Abstract

Knowledge about the toxicity of nanomaterials and factors responsible for such phenomena are important tasks necessary for efficient human health protection and safety risk estimation associated with nanotechnology. In this study, the causation inference method within structure-activity relationship modeling for nanomaterials was introduced to elucidate the underlying structure of the nanotoxicity data. As case studies, the structure-activity relationships for toxicity of metal oxide nanoparticles (nano-SARs) towards BEAS-2B and RAW 264.7 cell lines were established. To describe the nanoparticles, the simple ionic, fragmental and “liquid drop model” based descriptors that represent the nanoparticles' structure and characteristics were applied. The developed classification nano-SAR models were validated to confirm reliability of predicting toxicity for all studied metal oxide nanoparticles. Developed models suggest different mechanisms of nanotoxicity for the two types of cells.
Author Natalia Sizochenko (FCh / DEChR / LECh)
Natalia Sizochenko,,
- Laboratory of Environmental Chemometrics
, Bakhtiyor Rasulev
Bakhtiyor Rasulev,,
-
, Agnieszka Gajewicz (FCh / DEChR / LECh)
Agnieszka Gajewicz,,
- Laboratory of Environmental Chemometrics
, Elena Mokshyna
Elena Mokshyna,,
-
, Victor E. Kuz'min
Victor E. Kuz'min,,
-
, Jerzy Leszczynski
Jerzy Leszczynski,,
-
, Tomasz Puzyn (FCh / DEChR / LECh)
Tomasz Puzyn,,
- Laboratory of Environmental Chemometrics
Journal seriesRSC Advances, ISSN 2046-2069, (A 30 pkt)
Issue year2015
Vol5
No95
Pages77739-77745
Publication size in sheets0.5
DOIDOI:10.1039/c5ra11399g
URL http://pubs.rsc.org/en/content/articlepdf/2015/ra/c5ra11399g
Languageen angielski
LicenseJournal (articles only); published final; Uznanie Autorstwa - Użycie Niekomercyjne (CC-BY-NC); with publication
Score (nominal)35
ScoreMinisterial score = 35.0, 20-12-2017, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 20-12-2017, ArticleFromJournal
Publication indicators WoS Impact Factor: 2015 = 3.289 (2) - 2015=3.485 (5)
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