Predictive Chemometric Modeling of Toxicological Hazard, and Risk Assessment of Chemicals and Pharmaceuticals

One of the major economic alternatives to experimental toxicity testing is the use of quantitativestructure–activity relationships (QSARs) which are used in formulating regulatory decisions of environ-mental protection agencies. In this background, we havemodeled a large diverse group of 297 chemicalsfor their toxicity to Daphnia magna using mechanistically interpretable descriptors. Three-dimensional(3D) (electronic and spatial) and two-dimensional (2D) (topological and information content indices)descriptors along with physicochemical parameter logKo/w (n-octanol/water partition coefficient) andstructural descriptors were used as predictor variables. The QSAR models were developed by stepwisemultiple linear regression (MLR), partial least squares (PLS), genetic function approximation (GFA), andgenetic PLS (G/PLS). All the models were validated internally and externally. Among several modelsdeveloped using different chemometric tools, the best model based on both internal and external vali-dation characteristics was a PLS equation with 7 descriptors and three latent variables explaining 67.8%leave-one-out predicted variance and 74.1% external predicted variance. The PLS model suggests thathigher lipophilicity and electrophilicity, less negative charge surface area and presence of ether linkage,hydrogen bond donor groups and acetylenic carbons are responsible for greater toxicity of chemicals.The developed model may be used for prediction of toxicity, safety and risk assessment of chemicals toachieve better ecotoxicological management and prevent adverse health consequences.© 2009 Elsevier B.V. All rights reserved.