Regression rnodel validation by permutation tests was explored. Especially in cases where the rnodel significance is doubtful, a permutation test adds crucial information which can often can be decisive for the existence of the rnodel. The background and applicability of the test procedure are described. As an example, the use of permutation tests was extended to validation and investigation of four predictor variable selection techniques, namely MUSEUM, GOLPE, VIP and IVS-PLS. "he selection methods are briefly reviewed and cornpared. The permutation tests were applied before, during and after variable selection. Some similarities and differences in the behaviour of the variable selection techniques were found and are cornmented upon. O
Estimating the toxicity of reactive xenobiotics to aquatic organisms requires physicochemical descriptors of passive transport and chemical reactions with nucleophilic biological ligands. Herein, electrophiles whose toxic action is attributed to nucleophilic substitution (SN), Michael-type addition and Schiff-base formation were examined. Training sets for each molecular mechanism were generated through substructure search applied to chemicals in a fathead minnow (Pimephules promelus) database. Based on a delineation of compounds by a presumed molecular mechanism, relationships between modes of toxic action, potency (96-hour LC,, values) and mechanistically-appropriate quantum-chemica1 descriptors were explored. Monohalo-C(sp3) function which may give rise to S, reactivity was encountered in 35 compounds.The inclusion of E L , , , , a nonspecific electrophilicity descriptor, to the generic LC,, -hydrophobicity relation increased the explained variance from r' = 36% to 69%. Eighteen potential Michael-type acceptors, mainly acrylates, were identified by the presence of a localized CC double bond at an E, position to a polar group. Due to different modes of action, the toxic potency of these chemicals varies almost independently of hydrophobicity (? = 0.12). Two additional electronic descriptors that are consistent with the likely molecular mechanism provide a multivariate QSAR with ? = 0.78. Forty-five aldehydes and 3 formamides comprised the training set associated with probable Schiff-base mechanism of toxicity. The results suggest a marginal increase of toxic potency from that expected due to narcosis for more electrophilic carbonyl groups. Overall, it was concluded that regressions based on data sets that combine reactive chemicals with narcotics typically require an electronic descriptor in addition to hydrophobicity, even if the compounds all contain a common electrophilic moiety related to the putative specific reaction mechanism. However, without the generation of additional toxicity data from chemical sets that incorporate a broader range of electronic and steric character, it will likely remain extremely difficult to develop a quantitative ability to predict the potency of electrophilic compounds.
The log-log relationship between the bioconcentration tendency of organic chemicals in fish and the n -octanol/water partition coefficients breaks down for very hydrophobic compounds. The use of parabolic and bilinear models allows this problem to be overcome. The QSAR equation log BCF = 0.910 log P - 1.975 log (6.8 10(-7) P + 1) - 0.786 (n = 154; r = 0.950; s = 0.347; F = 463.51) was found to be a good predictor of bioconcentration in fish.
To advance techniques for screening large data sets of diverse structures for toxicologically active compounds, an algorithm was developed that is not dependent upon a predetermined and specified toxicophore or an alignment of conformers to a lead compound. Instead, the approach provides the means to identify and quantify specific global and local stereoelectronic characteristics associated with active compounds through a comparison of energeticallyreasonable conformer distributions for specific descriptors. To illustrate the algorithm, the stereoelectronic requirements associated with the binding affinity of 28 steroidal and non-steroidal ligands to the androgen receptor were defined. Common ranges of interatomic distances, atomic charges, and atom polarizabilities of oxygen atoms for conformers of the ligands with the highest affinity for the androgen receptor (most active) did not overlap with those identified for conformers with the lowest binding affinity (least active). Using a set of stereoelectronic parameters that provided a maximal measure of pairwise similarity among the conformers of the most active ligands, a model was developed to screen compounds for binding affinity. The model was capable of discriminating inactive ligands, as defined by a specified binding affinity threshold. This modeling technique could be a useful initial component in an integrated approach of employing computational and toxicological techniques in hazard identifications for large databases.
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