Title:<i>In Silico</i> Assessment of the Acute Toxicity of Chemicals: Recent Advances and New Model for Multitasking Prediction of Toxic Effect
VOLUME: 15 ISSUE: 8
Author(s):Valeria V. Kleandrova, Feng Luan, Alejandro Speck-Planche and M. Natália D.S. Cordeiro
Affiliation:REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
Keywords:Acute toxicity, artificial neural networks, laboratory animals, mtk-QSTR, route of administration, spectral moments.
Abstract:The assessment of acute toxicity is one of the most important stages to
ensure the safety of chemicals with potential applications in pharmaceutical sciences,
biomedical research, or any other industrial branch. A huge and indiscriminate number
of toxicity assays have been carried out on laboratory animals. In this sense,
computational approaches involving models based on quantitative-structure activity/toxicity relationships (QSAR/QSTR)
can help to rationalize time and financial costs. Here, we discuss the most significant advances in the last 6 years focused
on the use of QSAR/QSTR models to predict acute toxicity of drugs/chemicals in laboratory animals, employing large and
heterogeneous datasets. The advantages and drawbacks of the different QSAR/QSTR models are analyzed. As a
contribution to the field, we introduce the first multitasking (mtk) QSTR model for simultaneous prediction of acute
toxicity of compounds by considering different routes of administration, diverse breeds of laboratory animals, and the
reliability of the experimental conditions. The mtk-QSTR model was based on artificial neural networks (ANN), allowing
the classification of compounds as toxic or non-toxic. This model correctly classified more than 94% of the 1646 cases
present in the whole dataset, and its applicability was demonstrated by performing predictions of different chemicals such
as drugs, dietary supplements, and molecules which could serve as nanocarriers for drug delivery. The predictions given
by the mtk-QSTR model are in very good agreement with the experimental results.
