Quantitative Structure-Activity Relationships (QSAR) and Pesticides Ole Christian Hansen Teknologisk Institut Pesticides Research No. 94 2004 Bekæmpelsesmiddelforskning fra Miljøstyrelsen The Danish Environmental Protection Agency will, when opportunity offers, publish reports and contributions relating to environmental research and development projects financed via the Danish EPA. Please note that publication does not signify that the contents of the reports necessarily reflect the views of the Danish EPA. The reports are, however, published because the Danish EPA finds that the studies represent a valuable contribution to the debate on environmental policy in Denmark. Contents FOREWORD 5 PREFACE 7 SUMMARY 9 DANSK SAMMENDRAG 11 1 INTRODUCTION 13 2QSAR 15 2.1 QSAR METHOD 15 2.2 QSAR MODELLING 17 3 PESTICIDES 21 3.1 MODES OF ACTION 21 3.2 QSAR AND PESTICIDES 22 3.2.1 SMILES notation 23 3.3 PHYSICO-CHEMICAL PROPERTIES 24 3.3.1 Boiling point 24 3.3.2 Melting point 25 3.3.3 Solubility in water 27 3.3.4 Vapour pressure 30 3.3.5 Henry’s Law constant 32 3.3.6 Octanol/water partition coefficient (Kow) 34 3.3.7 Sorption 39 3.4 BIOACCUMULATION 47 3.4.1 Bioaccumulation factor for aquatic organisms 47 3.4.2 Bioaccumulation factor for terrestrial organisms 49 3.5 AQUATIC TOXICITY 50 3.5.1 QSAR models on aquatic ecotoxicity 50 3.5.2 Correlations between experimental and estimated ecotoxicity 53 3.5.3 QSARs developed for specific pesticides 57 3.5.4 QSARs derived from pesticides in the report 60 3.5.5 Discussion on estimated ecotoxicity 86 4 SUMMARY OF CONCLUSIONS 89 REFERENCES 93 APPENDIX A 99 3 4 Foreword The concept of similar structures having similar properties is not new. Already in the 1890’s it was discovered, for example, that the anaesthetic potency of substances to aquatic organisms was related to their oil/water solubility ratios, a relationship which led to the use of LogP octanol/water as an estimate of this effect. Today it is known that all chemicals will exhibit a minimum or “basal” narcotic effect which is related to their absorption to cell membranes, and which is well predicted by their lipophilic profile. The use of logP alone can thus explain about half of the toxicity (R2=0.5) of unrelated industrial chemicals to fish, and with closely related substances (such as linear alcohols or ketones) such simple models are highly predictive. More reactive chemicals (“polar narcotics” such as phenols and amines) can also be modelled successfully in this manner. In all, approximately 70% of industrial chemicals fall into one of these two general categories where aquatic toxicity estimates can be expected to be within an order of magnitude. Other parameters such as molecular indices, quantum mechanical properties, shape, size, charge distributions, etc., can greatly improve estimates, particularly for substances which also act via highly reactive toxic mechanisms (such as allylics, or acrylates). The case is not quite as simple for substances with “specific” activities (pesticides or drugs). While simple narcosis will also be present for such chemicals, this may be of little interest compared with intense activity induced by binding to a critical receptor site. This and other factors has resulted in considerable effort by, among others, the drug industry to develop tools which can better predict effects based on structural information. Today numerous computerised systems exist for predicting a large range of effects stretching from biodegradability to cancer. These include fragment based statistical systems such as TOPKAT and MCASE, as well as three- dimensional modelling of ligand docking (COMFA). Some are well suited for screening of large numbers of chemicals, while others are very labour- intensive and best confined to small closely related data sets. Predictive ability will vary depending on both the method used, and the endpoint in question. In general, estimates of environmental effects have been more readily accepted than estimates of mammalian effects. This may be changing rapidly. In general, predictive ability of sophisticated contemporary QSAR systems can often correctly predict the activity of about 80% of the chemicals examined, provided that sufficient biological data exists to cover the domain of the structures. While this may not be good enough for some regulatory purposes, in others it may be sufficient. Even today a great number of chemicals are never synthesised because the potential producer has already determined that they are likely to have harmful properties according to a QSAR estimate. Jay Russel Niemela, Chemical Division. Danish Environment Protection Agency. 5 6 Preface This study considers the current available knowledge on the use of Quantitative Structure-Activity Relationship (QSAR) models relating to the use on pesticides. The use is based on evaluation of the correlations between experimental values and QSAR estimates from the selected QSAR models. The experimental values are obtained from Pesticide Manual (Tomlin 1994, 1997), Linders et al. (1994), and from letters of approval for plant protection preparations containing data from studies submitted by manufacturers to the Danish Environmental Protection Agency (Danish EPA) and evaluated in internal reports (Clausen 1998). QSARs are quantitative models seeking to predict activity such as environmental toxicity derived from the molecular structure. Most often this is accomplished by first correlating properties such as physico-chemical parameters with molecular structure and then correlating toxicity with these parameters. The central paradigm underlying such QSAR modelling is the structure-property similarity principle. This paradigm states that analogous structures have generally similar properties. Since chemicals with similar properties tend to have similar biological activities, toxicity may be predicted from structure alone. More than six million chemical substances are known and humans are exposed to 50,000 to 100,000 of them. As it is impossible to test each substance in a time and cost effective manner, this “guilt by association” approach provides a powerful alternative to direct testing for predicting toxicity for untested substances. While QSARs in environmental toxicology were reported as early as 1869, modern efforts have their roots in the classic turn of the century work of Meyer and Overton on narcotics. Narcosis is the reversible state of arrested protoplasmic activity. It is a physical phenomenon, which is mostly independent of specific molecular structure. It is considered the most common mode of toxic action, at least in short term exposure of aquatic organisms, and the mode of action for about 70% of the industrial organic chemicals. Narcosis is subdivided into several non-specific and specific mechanisms (e.g. non-polar narcosis, polar narcosis etc.). However, pesticides are developed specifically based on their specific mode of action mechanisms and this may affect the predictability of QSARs. Based on the work already performed, the present report uses the most promising descriptors. As most of the preliminary work has been done on simpler molecules an evaluation at this stage may result in a less promising result. However, it has been found reasonable to perform such an analysis to assess the current stage of the use of QSARs on pesticides. 7 8 Summary In environmental risk assessment, information of environmental fate, behaviour and the toxicity of a chemical substance are of basic need. Quantitative Structure-Activity Relationships (QSAR) is a method to derive certain effects or properties of chemical substances in the absence of experimental data. For pesticides, the data requirements demanded for their authorisation normally means that sufficient data for a risk assessment exist. This is rarely the case for additives, impurities, degradation or transformation products. Most QSAR models are developed from simple industrial chemicals and usually only a few pesticides are included. Especially, new pesticides consist of complicated molecular structures acting with specific modes of action and their physico-chemical properties and ecotoxicological effect concentrations may not be estimated sufficiently close to the correct effect value by QSAR. The current document presents the general framework in which QSARs can be used within the risk assessment procedure. Furthermore, it presents recommended QSARs for physico-chemical parameters and ecotoxicological effect concentrations and performs an evaluation of their correlation with approximately 400 selected pesticides including salts and esters. Of physico-chemical properties, the recommended QSARs to estimate the boiling point could not be evaluated due to lack of experimental values. Estimations of the melting point were inaccurate and mainly overestimated by the presented QSAR. The solubility in water was reasonably well correlated with the recommended QSAR and a QSAR based on 322 pesticides was developed. The vapour pressure QSAR based on suggested values from a computer model showed low correlation with the pesticides used. The Henry’s Law constant was evaluated by comparing calculated values with QSAR predicted values. A low correlation was observed. The octanol/water partition coefficient Kow derived by structure fragment analysis demonstrated acceptable agreement with the measured values. As the model was a computerised version, a QSAR based on water solubility was suggested. Several QSARs have been developed to derive the adsorption coefficient Koc. Four recommended