Classification models for identifying substances exhibiting acute contact toxicity in honeybees (Apis mellifera)$. Issue 9 (2nd September 2018)

• Record Type: Journal Article
• Authors: Venko, K.; Drgan, V.; Novič, M.
• Is Part Of: SAR and QSAR in environmental research. Volume 29:Issue 9(2018)
• Page Start: 743
• Page End: 754
• Publication Date: 2018-09-02
• Subjects: honeybee -- acute contact toxicity -- pesticides -- in silico models -- computational toxicology; Structure-activity relationships (Biochemistry) -- Periodicals; QSAR (Biochemistry) -- Periodicals; 572.4
• DOI: 10.1080/1062936X.2018.1513953
• Languages: English
• ISSNs: 1062-936X

Abstract

ABSTRACT: Nowadays, environmental and biological endpoints can be predicted with in silico approaches if sufficient experimental data of good quality are available. Since the experimental evaluation of acute contact toxicity towards honeybees ( Apis mellifera) is a complex and expensive assay, the computational models that follow OECD principles for this endpoint prediction represent important alternatives for safety prioritisation of chemicals, especially pesticides. We developed and validated counter-propagation artificial neural network (CPANN) models for in silico evaluation of toxicity of pesticides towards honeybees by using new in-house software. The data set included 254 pesticides with their toxicological experimental values (acute contact toxicity after 48 h of exposure – LD50 [μg/bee]). The 2D structures of compounds were mathematically represented with 56 Dragon molecular descriptors (MDs). The two-category models were developed to separate compounds as toxic or non-toxic for two different thresholds: (i) toxic when LD50 < 1 μg/bee and (ii) toxic when LD50 < 100 μg/bee. The models give reliable predictions in an external validation set and cover a large structural space. They were applied to a structurally diverse data set of 395 experimentally untested pesticides; 19% of them were predicted as highly toxic towards bees.