Simple and α,β-unsaturated aldehydes: Correct prediction of genotoxic activity through structure-activity relationship models

Abstract

Aldehydes are widespread environmental and industrial compounds, able to stimulate a range of adverse health effects (e.g., general toxicity, allergenic reactions, mutagenicity, and carcinogenicity). We have previously presented quantitative structure-activity relationships (QSARs) for the genotoxicity of simple and alpha,beta-unsaturated aliphatic aldehydes. In this study, we show that the QSAR models are able to correctly predict--based only on the knowledge of the chemical structure--the genotoxicity of other aldehydes, not considered in the development phase of the models. This adds confidence to the reliability of our QSAR models as tools for the theoretical assessment of the genotoxic hazard posed by aldehydes. The analysis of SOS Chromotest induction ability and the ease of formation of DNA adducts by the aldehydes provided further mechanistic insights.

Guidance on Grouping of Chemicals, Second Edition

“…Experimental "in in chemico" systems could also be used to quantify the electrophile-nucleophile reactivity and to confirm the predicted reaction mechanism to support a read-across for e.g. skin sensitization (Aptula et al, 2006) or mutagenicity (Benigni et al, 2005).…”

Genotoxic Impurities in Pharmaceuticals

“…In a retrospective analysis of starting materials and intermediates involved in API syntheses, the most common structurally alerting groups were found to be aromatic amines, aromatic nitros, alkylating agents and Michael acceptors (Snodin, 2010). One of the strengths of QSAR models is that they contribute to a mechanistic understanding of the activity, and, at the same time, they constitute practical tools to predict the activity of further, untested chemicals solely based on chemical structure (Benigni et al, 2005). Another strength of QSAR models is that they are strictly data-driven, and are not based on a prior hypotheses.…”

“…As error (e.g. incorrect molecular structure or erroneous data from toxicology studies of a chemical) is introduced into the model, amplification of that error is generated and represented in the prediction (Benigni et al, 2005;Valerio, 2009). Cunningham et al (1998) investigated a SAR analysis of the mouse subset of the carcinogenic potency database (CPDB) which also included chemicals tested by the US national toxicology program (NTP).…”

“…Using the generated data as well as the data of another five related chemicals that were investigated previously, a QSAR analysis was performed and the results indicated that LB C_C1 (longest carbon-chlorine bond length), MR (molar refractivity), and E LUM0 (energy of the lowest unoccupied molecular orbital, indicating electrophilicity) were the most significant factors to be considered for discriminating between genotoxins and nongenotoxins. Benigni et al (2005) showed that the QSAR models could correctly predict--based only on the knowledge of the chemical structure--the genotoxicity of simple and unsaturated aldehydes. The active and inactive compounds were separated based on the hydrophobicity (log P) and bulkiness (MR) properties.…”

“…In fact, it cannot be well approached until some mechanistic understanding of nongenotoxic carcinogenesis is achieved. At this time, this approach is unable to grasp the structural features of non-genotoxic carcinogens (Ashby, 1990;Cunningham et al, 1998;Benigni et al, 2005). The other limitation to currently available QSARs is the lack of models for organometallics, complex mixtures (e.g.…”