Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes

Abstract

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

Biological and environmental surface interactions of nanomaterials: characterization, modeling, and prediction

“…For example, QSAR models were developed to describe the dermal and percutaneous absorption of either toxic chemicals or therapeutics. Toxicity of environmental pollutants like pesticides and their soil sorption, as well as therapeutic efficacy of drug molecules can also be evaluated using QSAR approach. Wide applicability of QSAR is exemplified by the fact that it can be applied in the studies of pure physicochemical processes, such as surface adsorption of chemical onto an organic membrane .…”

Autonomous Discovery in the Chemical Sciences Part I: Progress

“…Substructure filters are commonly employed to process screening hits [314][315][316] and flag reactive or toxic functional groups. [317][318][319][320][321][322] This is ap roblem of interpretability that has received significant attention in the machine learning community. [323] When the form of the desired interpretation is restricted to molecular substructures,standard approaches of feature selection can be applied to representations based on the presence/absence of certain substructures.…”

Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt

“…Mit einem bestehenden QSAR/QSPR-Modell kann man dessen Wahrnehmung von Strukturattributen untersuchen, um diejenigen zu identifizieren, die füre ine Vorhersageaufgabe am informativsten sind. Häufig werden zur Verarbeitung von Screening-Treffern [314][315][316] und zur Markierung von reaktiven oder toxischen funktionellen Gruppen [317][318][319][320][321][322] Substrukturfilter eingesetzt. Dies ist ein Problem der Interpretierbarkeit, das auf dem Gebiet des maschinellen Lernens große Aufmerksamkeit erfahren hat.…”