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The recent advances in combinatorial chemistry and high throughput screening technologies have led to an explosion in the numbers of possible therapeutic candidates being produced at the early stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a greater need for alternative methods for the prediction of toxicity. Most QSAR models for mutagenicity have been constructed for congeneric series. The prediction requirements of the pharmaceutical industry, however, cover quite diverse chemical structures. This paper reports a study of mutagenicity data for a diverse set of 90 compounds. Good discriminant models have been built for this data set using properties calculated by the techniques of computational chemistry. Jack-knifed (leave one out) predictions for these models are of the order of 85%.

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.

Aim To evaluate and compare the microbial contamination arising from 1 and 2 weeks' use of eye drops by hospital inpatients and hence determine the validity of apportioning a 2 week in use expiry date for these preparations. Methods Eye drop residues were collected from inpatients of Worthing, Southlands, and Brighton General hospitals after 7 days' use (341 samples) and also after 14 days' use (295 samples). The contents of the containers were examined for the presence of contaminating bacteria and fungi. Results The incidence of microbial contamination was shown to be not significantly different (p >0.1 χ² test) between the 7 and 14 day samples. In addition, the contaminating micro-organisms were of a broadly similar pattern between the two sample groups and were mostly those normally associated with the skin. Less frequent contaminants were organisms of environmental origin. None of the micro-organisms isolated were considered to be of clinical significance and the mean number of cells found per sample was very low. Conclusions The evidence therefore suggests that increasing the period of use for eye drops in hospitals from 7 to 14 days would not present a clinically significant threat to patients' health and yet may lead to annual savings to the NHS of P0.5 million.