Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach

doi:10.1016/j.ecoenv.2012.03.018

Ecotoxicology and Environmental Safety

Volume 80, 1 June 2012, Pages 308-313

https://doi.org/10.1016/j.ecoenv.2012.03.018 Get rights and content

Abstract

Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.

Graphical Abstract

Highlights

► Creation of ms-QSTR model for prediction of ecotoxicological profiles in fungicides. ► The validation of the model was carried out by means of external prediction series. ► Contributions of the fragments to different ecotoxicological profiles are calculated. ► Substructural alerts responsible for the toxicities are extracted and analyzed.

Introduction

Pesticides are substances or mixture of substances intended for preventing, destroying, repelling or mitigating any pest (Plimmer et al., 2003). These pests include insects, fungal, bacterial and viral species, weeds, mollusks, birds, mammals, fish, nematodes (roundworms), and microbes that destroy property, spread disease or are a vector for disease or cause a nuisance. Agriculture will always have to cope with crop losses caused by these biotic stresses. In this sense, during the last fifty years the use of the pesticides has contributed to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health (Waxman, 1998). One of the most important groups of pesticides is fungicides which have played a decisive role for the control of infections caused by a considerable number of extreme pathogen fungal species (Plimmer et al., 2003). New families of compounds with potential fungicidal activity have been tested against several fungal species (Khambay et al., 2003, Li et al., 2009, Li et al., 2005). Also, some computational methodologies for the rational design of agrochemical fungicides have been studied (Speck-Planche et al., 2011a).

However, as in all pesticides, agrochemical fungicides have benefits, but there are also drawbacks, such as potential environmental toxicity which include humans. From one side, several works have been realized for the better understanding of the mechanisms of toxicity of agrochemical fungicides (Faust et al., 2003, Gustafsson et al., 2010, Hassold and Backhaus, 2009, Jager, 2004, Ochoa-Acuna et al., 2009, Porsbring et al., 2009, Santana et al., 2009, Wang et al., 2009, Zhu and Shan, 2009). On the other hand, Chemoinformatics has been essential for the understanding and/or solution of different chemical and biological phenomena in diverse fields of science, including pesticides (Senior et al., 2011). Anyway, no efforts have been made in order to apply this important discipline to study the toxicity of agrochemical fungicides in an appropriate manner. Unfortunately, all the studies based or not based on Chemoinformatics, have been carried out using structurally related compounds and only against few species which are considered as ecotoxicological indicators (Noss, 1990). Each species has a specific sensitivity for each chemical. For this reason, the main goal should be a computational methodology able to assess the ecotoxicological profiles of several agrochemical fungicides against several species of organisms. From one side, this fact would provide major knowledge about the toxicity of agrochemical fungicides and their negative incidence in different ecosystems. On the other hand, substructural patterns related to the safety of fungicides could be determined. In order to overcome this problem, we develop the first unified multi-species (ms) approach based on Chemoinformatics. Here, an ms-QSTR discriminant model based on substructural descriptors and heterogeneous database of compounds is constructed. The purpose of this model is the simultaneous assessment of multiple ecotoxicological profiles of agrochemical fungicides.

Section snippets

Substructural descriptors

The constant and impressive development of Chemoinformatics has made it possible that, nowadays, there are more than 4000 of molecular descriptors which can be used to solve different problems in Chemistry, Biology and related sciences (Todeschini and Consonni, 2009). In the specific case of this work, we opted for the use of descriptors known as functional group counts (FGC). They can be considered as substructural or fragment-based descriptors which are defined as the number of specific

ms-QSTR discriminant model

Taking into consideration the previous ideas about the strategy of variable selection and the principle of parsimony, the best model obtained by us, contains 6 descriptors. This model has the two types of descriptors calculated by us, FGCs and descriptors like μ_k: $EToxP = 2.842 (R C O N R_{2}) - 0.507 (R = C s) + 0.899 (C R X_{3}) + 1.353 {\bar{μ}}_{1}^{(A b - R 2)} - 1.028 \cdot 1 0^{- 2} Δ {μ_{3}}^{(P o l)} + 0.889 Δ μ_{1}^{(A b - s u m B 20)} + 0.167$ $N = 219 λ = 0.478 D^{2} = 6.727 p < 0.00 1$

The meanings of the different descriptors appear summarized in Table 2. It is necessary to point out

Conclusions

In this work, an ms-QSTR discriminant model based on substructural descriptors was obtained, using a heterogeneous database of compounds, for classification and prediction of ecotoxicological profiles in agrochemical fungicides against several indicator species. Our chemoinformatic approach allowed us to predict ecotoxicological profiles in more general situation than other computational methodologies which have as principal limitation, the assessment of toxicity against only one indicator

Acknowledgments

The authors acknowledge the Portuguese Fundação para a Ciência e a Tecnologia (FCT) for financial support (project PTDC/AGR-AAM/105044/2008 and grant no PEst-C/EQB/LA0006/2011). F. L. acknowledges also FCT for the grant SFRH/BPD/63666/2009 co-financed by the European Social Found.

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Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach

Abstract

Graphical Abstract

Highlights

Introduction

Section snippets

Substructural descriptors

ms-QSTR discriminant model

Conclusions

Acknowledgments

Bioorg. Med. Chem.

Aquat. Toxicol.

Bioorg. Med. Chem.

Ecotoxicol. Environ. Saf.

Aquat. Toxicol.

Bioorg. Med. Chem

Eur. J. Med. Chem.

Bioorg. Med. Chem.

Ecotoxicol. Environ. Saf.

Chemosphere

Chemometr. Intell. Lab. Syst.

ChemDraw Ultra, v8.0

Detailed estimation of bioinformatics prediction reliability through the Fragmented Prediction Performance Plots

BMC Bioinformatics

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins

J. Proteome Res.

OPP Pesticide Ecotoxicity Database

Spectral moments of the edge adjacency matrix in molecular graphs. 1. Definition and applications for the prediction of physical properties of alkanes

J. Chem. Inf. Comput. Sci

Spectral moments of the edge adjacency matrix in molecular graphs. 2. Molecules containing heteroatoms and QSAR applications

J. Chem. Inf. Comput. Sci.

Spectral moments of the edge adjacency matrix in molecular graphs. 3. Molecules containing cycles

J. Chem. Inf. Comput. Sci.

What are the limits of applicability for graph theoretic descriptors in QSPR/QSAR? Modeling dipole moments of aromatic compounds with TOPS-MODE descriptors

J. Chem. Inf. Comput. Sci

Creating molecular diversity from antioxidants in Brazilian propolis. Combination of TOPS-MODE QSAR and virtual structure generation

Mol. Divers.