Pesticides Research, 94 – Quantitative Structure-Activity Relationships (QSAR) and Pesticides

Quantitative Structure-Activity Relationships (QSAR) and Pesticides

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.