Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogues.

This report describes a new set of amino acid side chain descriptors, the isotropic surface area (ISA), and the electronic charge index (ECI) relevant to peptide quantitative structure--activity relationship (QSAR) studies. These features are derived from optimized three-dimensional structures of the natural and unnatural amino acids. Since the descriptors are derived considering side chain three-dimensional structure, 3D-QSARs result. Using the method of partial least squares, 3D-QSARs of peptide sets were developed. A comparison of the results to those obtained with the principal properties or z-scales shows that the ISA and ECI are comparable for parameterizing the structural variability of the peptide series and represent an interesting alternative.

A comparative molecular field analysis study of N-benzylpiperidines as acetylcholinesterase inhibitors.

A series of 1-benzyl-4-[2-(N-benzoylamino)ethyl]piperidine derivatives and of N-benzylpiperidine benzisoxazoles has been investigated using the comparative molecular field analysis (CoMFA) approach. These compounds have been found to inhibit the metabolic breakdown of the neurotransmitter acetylcholine (ACh) by the enzyme acetylcholinesterase (AChE) and hence alleviate memory deficits in patients with Alzheimer's Disease by potentiating cholinergic transmission. Development of the CoMFA model considered two separate alignments: (i) alignment I which emphasized the electrostatic fitting of the subject compounds and (ii) alignment II which emphasized their steric fitting. In addition, the inhibitor compounds were considered both as neutral species and as N-piperidine-protonated species. The resulting 3D-QSAR indicates a strong correlation between the inhibitory activity of these N-benzylpiperidines and the steric and electronic factors which modulate their biochemical activity. A CoMFA model with considerable predictive ability was obtained.

Derivation of a pharmacophore model for anandamide using constrained conformational searching and comparative molecular field analysis.

Constrained molecular dynamics simulations on anandamide, together with a systematic distance comparison search, have revealed a specific low-energy conformer whose spatial disposition of the pharmacophoric elements closely matches that of HHC. This conformer enables near superposition of the following: (1) the oxygen of the carboxyamide and the phenolic hydroxyl group of HHC, (2) the hydroxyl group of the ethanol and the cyclohexyl hydroxyl group of HHC, (3) the alkyl tail and the lipophilic side chain of HHC, and (4) the polyolefin loop and the tricyclic ring structure of HHC. The close matching of common pharmacophoric elements of anandamide with HHC offers persuasive evidence of the biological relevance of this conformer. The proposed pharmacophore model was capable of discriminating between structurally related compounds exhibiting different pharmacological potency for the CB1 cannabinoid receptor, i.e., anandamide and N-(2-hydroxyethyl)prostaglandinamide. Furthermore, a 3D-QSAR model was derived using CoMFA for a training set of 29 classical and nonclassical analogues which rationalized the binding affinity in terms of steric and electrostatic properties and, more importantly, which predicted the potency of anandamide in excellent agreement with experimental data. The ABC tricyclic HU-210/HU-211 and ACD tricyclic CP55,243/CP55,244 enantiomeric pairs were employed as test compounds to validate the present CoMFA model. For each enantiomeric pair, the CoMFA-predicted log Ki values correctly identified that enantiomer exhibiting the higher affinity for the receptor.

Three-dimensional quantitative structure-activity relationship study of the cannabimimetic (aminoalkyl)indoles using comparative molecular field analysis.

The present study describes the implementation of comparative molecular field analysis (CoMFA) to develop two 3D-QSAR (quantitative structure-activity relationship) models (CoMFA models 1 and 2) of the cannabimimetic (aminoalkyl)indoles (AAIs) for CB1 cannabinoid receptor binding affinity, based on pKi values measured using radioligand binding assays that displace two different agonist ligands, [3H]CP-55940 and [3H]WIN-55212-2. Both models exhibited a strong correlation between the calculated steric-electrostatic fields and the observed biological activity for the respective training set compounds. In light of the basicity of the morpholine nitrogen in the AAIs, separate CoMFA models were built for the AAIs as unprotonated and protonated species. Comparison of the statistical parameters resulting from these CoMFA models failed to provide unequivocal evidence as to whether the AAIs are protonated or neutral as receptor-bound species. Although the training sets of CoMFA model 1 and CoMFA model 2 differed with respect to composition and to the choice of displacement radioligand in each biological assay, their CoMFA StDevCoeff contour plots reveal similarities in terms of identifying those regions around the AAIs that are important for CB1 cannabinoid receptor binding such as the sterically favored region around the C3 aroyl group and the sterically forbidden region around the indole ring. When the experimental pKi values for the training set compounds to displace the AAI radioligand [3H]WIN-55212-2 were plotted against the pKi values as predicted for the same compounds to displace the cannabinoid radioligand [3H]CP-55940, the correlation was moderately strong (r = 0.73). However, the degree of correlation may have been lowered by the structural differences in the compounds comprising the training sets for CoMFA model 1 and CoMFA model 2. Taken together, the results of this study suggest that the binding site region within the CB1 cannabinoid receptor can accommodate a wide range of structurally diverse cannabimimetic analogues including the AAIs.

Quantitative structure-activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species.

The recognition of adverse effects due to environmental endocrine disruptors in humans and wildlife has focused attention on the need for predictive tools to select the most likely estrogenic chemicals from a very large number of chemicals for subsequent screening and/or testing for potential environmental toxicity. A three-dimensional quantitative structure-activity relationship (QSAR) model using comparative molecular field analysis (CoMFA) was constructed based on relative binding affinity (RBA) data from an estrogen receptor (ER) binding assay using calf uterine cytosol. The model demonstrated significant correlation of the calculated steric and electrostatic fields with RBA and yielded predictions that agreed well with experimental values over the entire range of RBA values. Analysis of the CoMFA three-dimensional contour plots revealed a consistent picture of the structural features that are largely responsible for the observed variations in RBA. Importantly, we established a correlation between the predicted RBA values for calf ER and their actual RBA values for human ER. These findings suggest a means to begin to construct a more comprehensive estrogen knowledge base by combining RBA assay data from multiple species in 3D-QSAR based predictive models, which could then be used to screen untested chemicals for their potential to bind to the ER. Another QSAR model was developed based on classical physicochemical descriptors generated using the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program. The predictive ability of the CoMFA model was superior to the corresponding CODESSA model.

Comparative molecular field analysis as a tool to evaluate mode of action of chemical hybridization agents.

The 3D-QSAR method of comparative molecular field analysis (CoMFA) was applied to three patent families of chemical hybridization agents (CHAs) in the MON21200 class of chemistry. The models for each CHA family gave good correlations between the variations in log percent male sterility and in the steric-electrostatic properties of the patent set. For all CHA families, observed sterility rates are generally higher for the sodium salts than for the corresponding esters. This is consistent with our CoMFA models which show that negative charge is favored in the region of the carboxylate group. The CoMFA models also indicated that for the MON21200 family increased steric bulk at the 4-position on the phenyl ring is associated with enhanced activity. However, for the RH0007 and the HYBRID families, male sterility is generally enhanced with increased steric bulk at the 2- or 3-position on the phenyl ring. Although the models cannot provide unambiguous conclusions about a common mode of action, similarities in the CoMFA contour maps provided some clues for a common agrophore for these three CHA families.

Use of moment of inertia in comparative molecular field analysis to model chromatographic retention of nonpolar solutes.

A quantitative structure-retention relationship (QSRR) was developed from chromatographic data on 31 unsubstituted 3-6 ring polycyclic aromatic hydrocarbons (PAHs) using the 3D-QSAR method known as comparative molecular field analysis (CoMFA). The resulting CoMFA model gave an excellent correlation to high-performance liquid chromatography retention data for these PAHs yielding r2 values of 0.947 (conventional) and 0.865 (cross-validated). The steric and electrostatic contributions to the CoMFA model were 100% and 0%, respectively. A unique feature of this study was the use of moment of inertia, I, as a basis for CoMFA alignment of the PAH molecules. The moment of inertia also provided an alternative method for calculating the solute length-to-breadth ratio (L/B), which has been applied in previous QSRR studies as a molecular descriptor for PAH retention. By virtue of its mathematical simplicity and lack of ambiguity, the present derivation of L/B from I offers several advantages over other geometry-based schemes. Finally, Ix was evaluated for use as a molecular descriptor in QSRR regression analysis to predict the log of the retention index (log I) for these PAHs. The correlation with PAH retention was weak when the moment of inertia was considered alone but improved dramatically (r2 = 0.928) when the moment of inertia and connectivity index chi were used in combination as descriptors.

Preprocessing of HPLC trace impurity patterns by wavelet packets for pharmaceutical fingerprinting using artificial neural networks.

The immediate objective of this research program is to evaluate several computer-based classifiers as potential tools for pharmaceutical fingerprinting based on analysis of HPLC trace organic impurity patterns. In the present study, wavelet packets (WPs) are investigated for use as a preprocessor of the chromatographic data taken from commercial samples of L-tryptophan (LT) to extract input data appropriate for classifying the samples according to manufacturer using artificial neural networks (ANNs) and the standard classifiers KNN and SIMCA. Using the Haar function, WP decompositions for levels L = 0-10 were generated for the trace impurity patterns of 253 chromatograms corresponding to LT samples that had been produced by six commercial manufacturers. Input sets of N = 20, 30, 40, and 50 inputs were constructed, each one consisting of the first N/2 WP coefficents and corresponding positions from the overall best level (L = 2). The number of hidden nodes in the ANNs was also varied to optimize performance. Optimal ANN performance based on percent correct classifications of test set data was achieved by ANN-30-30-6 (97%) and ANN-20-10-6 (94%), where the integers refer to the numbers of input, hidden, and output nodes, respectively. This performance equals or exceeds that obtained previously (Welsh, W.J.; et al.Anal.Chem. 1996, 68, 3473) using 46 inputs from a so-called Window preprocessor (93%). KNN performance with 20 inputs (97%) or 30 inputs (90%) from the WP preprocessor also exceeded that obtained from the Window preprocessor (85%), while SIMCA performance with 20 inputs (86%) or 30 inputs (82%) from the WP preprocessor was slightly inferior to that obtained from the Window preprocessor (87%). These results indicate that, at least for the ANN and KNN classifiers considered here, the WP preprocessor can yield superior performance and with fewer inputs compared to the Window preprocessor.

Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting.

The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceutical fingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameter data set. A localized fingerprint region was identified within the original input parameter space from which a subset of input parameters was extracted leading to enhanced ANN performance. The present results confirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting.