Dataset Information

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

ABSTRACT: Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.

SUBMITTER: Martin TM

PROVIDER: S-EPMC5796665 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

Martin T M TM Lilavois C R CR Barron M G MG

SAR and QSAR in environmental research 20170601 6

Previous modelling of the median lethal dose (oral rat LD<sub>50</sub>) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict thes ...[more]

PMID: 28703021

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Project description:Prediction of compound toxicity is essential because covering the vast chemical space requiring safety assessment using traditional experimentally-based, resource-intensive techniques is impossible. However, such prediction is nontrivial due to the complex causal relationship between compound structure and in vivo harm. Protein target annotations and in vitro experimental outcomes encode relevant bioactivity information complementary to chemicals' structures. This work tests the hypothesis that utilizing three complementary types of data will afford predictive models that outperform traditional models built using fewer data types. A tripartite, heterogeneous descriptor set for 367 compounds was comprised of (a) chemical descriptors, (b) protein target descriptors generated using an algorithm trained on 190?000 ligand-protein interactions from ChEMBL, and (c) descriptors derived from in vitro cell cytotoxicity dose-response data from a panel of human cell lines. 100 random forests classification models for predicting rat LD50 were built using every combination of descriptors. Successive integration of data types improved predictive performance; models built using the full dataset had an average external correct classification rate of 0.82, compared to 0.73-0.80 for models built using two data types and 0.67-0.78 for models built using one. Pairwise comparisons of models trained on the same data showed that including a third data domain on top of chemistry improved average correct classification rate by 1.4-2.4 points, with p-values <0.01. Additionally, the approach enhanced the models' applicability domains and proved useful for generating novel mechanism hypotheses. The use of tripartite heterogeneous bioactivity datasets is a useful technique for improving toxicity prediction. Both protein target descriptors - which have the practical value of being derived in silico - and cytotoxicity descriptors derived from experiment are suitable contributors to such datasets.

Project description:BackgroundNanomaterials in plant protection promise many benefits over conventional pesticide products. Nano-enabled pesticides may alter the functionality or risk profile of active ingredients. Cationic nanochitin whiskers (NC) possess strong biological activity against wheat aphids. However, toxicity and synergistic effects of NC with chemical pesticides against pest insects has not been systemically reported. This study investigated the insecticidal enhancement by NC with Omethoate (40% EC), Imidacloprid (10% WP), and Acetamiprid (40% WG) for pest control using wheat aphid as piercing-sucking mouthparts insect. Fluorescein isothiocyanate labelled NC was used to monitor the uptake and transportation pathway of NC inside the target insects. Toxicity of NC was tested with Sprague-Dawley (SD) rat. Our findings provide a theoretical basis for future application of NC in plant protection against pest insects.ResultsNCs synthesized by acidic hydrolysis were rod-like nanoparticles in a range of 50-150 nm in length and 30-50 nm in width, which examined by electron microscopy and dynamic light scattering methods. The charge density and zeta potential were about 63 mmol/kg and + 36.4 mV, respectively. By absorption and/or contact action of 30-50 mg/L of NC suspension, the corrected mortality of wheat aphids reached up to 80% or above after 12 h treatment, NC could be distributed through digestive system and relocated from mouth to other tissues inside the insect body. When associated with dilutions of conventional pesticides, the corrected mortality were significantly increased up to 95% or above. The dosage of the chemical pesticide and nanochitin in the mixtures (1:1 by volume) were all reduced to half. The acute oral toxicity Lethal Dose 50% (LD50) to SD rat is greater than 5000 mg/kg BW (body weight) in male and female, acute dermal toxicity LD50 is greater than 2000 mg/kg BW of NC.ConclusionsNC has a strong promotive effect on insecticidal effectiveness of chemical insecticides. It was easily absorbed by plant, transported and distributed from mouth to other tissues of the insects while sucking plant fluid. Low acute oral and dermal toxicity to SD rat indicated that it is safe to apply in agriculture and food industry. NCs has a great potential for water-based nanopesticide formulation to reduce chemical pesticide use for future agro-environmental sustainability.

Project description:Despite the increasing knowledge in both the chemical and biological domains the assimilation and exploration of heterogeneous datasets, encoding information about the chemical, bioactivity and phenotypic properties of compounds, remains a challenge due to requirement for overlap between chemicals assayed across the spaces. Here, we have constructed a novel dataset, larger than we have used in prior work, comprising 579 acute oral toxic compounds and 1427 non-toxic compounds derived from regulatory GHS information, along with their corresponding molecular and protein target descriptors and qHTS in vitro assay readouts from the Tox21 project. We found no clear association between the results of a FAFDrugs4 toxicophore screen and the acute oral toxicity classifications for our compound set; and a screen using a subset of the ToxAlerts toxicophores was also of limited utility, with only slight enrichment toward the toxic set (odds ratio of 1.48). We then investigated to what degree toxic and non-toxic compounds could be separated in each of the spaces, to compare their potential contribution to further analyses. Using an LDA projection, we found the largest degree of separation using chemical descriptors (Cohen's d of 1.95) and the lowest degree of separation between toxicity classes using qHTS descriptors (Cohen's d of 0.67). To compare the predictivity of the feature spaces for the toxicity endpoint, we next trained Random Forest (RF) acute oral toxicity classifiers on either molecular, protein target and qHTS descriptors. RFs trained on molecular and protein target descriptors were most predictive, with ROC AUC values of 0.80-0.92 and 0.70-0.85, respectively, across three test sets. RFs trained on both chemical and protein target descriptors combined exhibited similar predictive performance to the single-domain models (ROC AUC of 0.80-0.91). Model interpretability was improved by the inclusion of protein target descriptors, which allow the identification of specific targets (e.g. Retinal dehydrogenase) with literature links to toxic modes of action (e.g. oxidative stress). The dataset compiled in this study has been made available for future application.

Dataset Information

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

Publications

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification.

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