Pathan Mohsin Khan Abstract

“Intelligent” consensus predictions for daphnia toxicity of agrochemicals

Pathan Mohsin Khan1, Kunal Roy2, Emilio Benfenati3

1Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India
2Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India
3Laboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa, 19, 20156, Milano, Italy
Agrochemicals are diverse classes of chemical products widely used in the agriculture to prevent, destroy, or control the harmful organisms (insects, fungi, microbes and weeds) or diseases, or to protect the crops before and after harvesting to minimize the loss in the yield in production. In a broad sense, the term agrochemicals include the wide range of pesticides, which include herbicides (used to kill or suppress the growth of plants), fungicides (used to kill or inhibit fungi or fungal spores or control of infections caused by a large number of tremendous pathogen fungal species), insecticides (used for control of insects), microbiocides (inhibit or reduce the infectivity of microbes such as viruses and bacteria), rodenticides (for control of vertebrate pests), nematicides (to kill eelworms, etc.), molluscicides (to kill slugs and snails), and acaricides (to kill mites) [1]. Over the past few years, the ecotoxicological hazard potential of agrochemicals has received much attention in the industries and regulatory agencies. There are only limited experimental ecotoxicological data available for such compounds. Quantitative structure-activity relationship (QSAR) modeling is a ligand based statistical approach proved to be useful in data gap filling [2]. In the current work, we have developed QSAR models for daphnia toxicities of different classes of agrochemicals (fungicides, herbicides, insecticides and microbiocides) individually as well as for the combined set with the application of Organization of Economic Co-operation and Development (OECD) recommended guidelines. The models for the individual data sets as well as for the combined set were generated employing only simple and interpretable two-dimensional descriptors, and subsequently strictly validated using test set compounds. The validated individual as well as global models were subjected for the “intelligent” consensus model generation using the tool available at with an objective to improve the prediction quality and reduced prediction errors [3]. The consensus predictions used in this study are not simple average of predictions from multiple models. It has been considered in the present study that a particular QSAR model may not be equally effective for prediction of all query compounds in the list. All the individual models of different classes of agrochemicals as well as the global set of agrochemicals showed encouraging statistical quality and prediction ability. As per the developed models, generally, lipophilicity, presence of sulphur atoms, number of X (halogen) on an aromatic ring, number of substituted benzene C(sp2), number of chlorine atoms, frequency of C – Cl at topological distance 5, number of multiple bonds, number of heavy atoms, number of rotatable bonds, and an increase in carbon chain length increase the toxicity while polarity, presence of ether moiety in aliphatic chain, presence of two oxygen atoms at a topological distance 8, branching in molecules, count of hydrogen bond acceptor atoms and/or polar surface area decrease the toxicity. The generated models of different classes of agrochemicals as well as the combined set should be applicable for data gap filling for new or untested agrochemical compounds falling within the applicability domain of the developed models.

1. Waxman MF, The agrochemical and pesticides safety handbook. CRC Press, 1998.
2. Roy K, Kar S, Das RN, Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment, Academic Press, NY, 2015.
3. Roy K, Ambure P, Kar S, Ojha PK, Is it possible to improve the quality of predictions from an “intelligent” use of multiple QSAR/QSPR/QSTR models? J Chemom 32, 2018, e2992.