Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.     

3-D-QSAR CoMFA and CoMSIA studies on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on tetrahydrofuroyl-L-phenylalanine derivatives as VLA-4 antagonists. The best CoMFA and CoMSIA models that were generated using atom based alignment from a training set of twenty five tetrahydrofuroyl-L-phenylalanine derivatives, are six-component models with good statistics; CoMFA: r(2)(cv)=0.366, r(2)=0.983, s=0.099, F=172.661 and PRESS=4.435; CoMSIA: r(2)(cv)=0.528, r(2)=0.995, s=0.054, F=577.87 and PRESS=3.563. Both of these 3-D-QSAR models were validated using a test set of eleven compounds, whose predicted pIC(50) values fall within one log unit of the actual pIC(50). The contour diagrams obtained for the various CoMFA and CoMSIA field contributions can be mapped back onto structural features to explain the activity trends of the molecules analysed. Based on the spatial arrangement of the various field contributions, novel molecules with improved activity can be designed.     

3D-QSAR design of novel antiepileptic sulfamides

A three-dimensional quantitative structure-activity relationship method, the comparative molecular field analysis (CoMFA), was applied to design new anticonvulsant symmetric sulfamides. The training set (27 structures) was comprised by traditional and new-generation anticonvulsant (AC) ligands that exhibit a potent activity in MES test. Physicochemical determinants of binding, such as steric and electrostatic properties, were mapped onto the molecular structures of the set, in order to interpret graphically the CoMFA results in terms of field contribution maps. The 3D-QSAR models demonstrate a good ability to predict the activity of the designed compounds (r(2)=0.967, q(2)=0.756).     

Correlation weighting of valence shells in QSAR analysis of toxicity

In the rainbow trout (Oncorhynchus mykiss), we studied the acute toxicity LC(50)-96 h of 274 organic pesticides with a wide variety of molecular structures. Optimization of correlation weights of local and global graph invariants (OCWLGI) gave quantitative structure-activity relationships (QSARs) for predicting toxicity. We used a labeled hydrogen-filled graph (LHFG) to elucidate the molecular structure. We also used the extended connectivity of zero ((0)EC(k)), first ((1)EC(k)), and second ((2)EC(k)) order, numbers of path lengths 2 (P2(k)) and 3 (P3(k)) starting from a given vertex in the LHFG, and valence shells of second order (S2(k)). S2(k) is the sum of the degree of vertices at distance 2 from a given vertex k. The presence of three-, five-, and six-member cycles and hydrogen bond indices suggested they might be used as global LHFG invariants. We applied this method to a broad set of pesticides, to predict toxicity for the trout. The best model used weighted S2(k) and global LHFG invariants. Statistical characteristics of this model are as follows: n=233, r(2)=0.7689, r(2)(pred)=0.7688, s=0.75, F=769 (training set); n=41, r(2)=0.6421, r(2)(pred)=0.4241, s=1.14, F=70 (test set).     

Statistical mechanical treatment of α-helices and extended structures in proteins with inclusion of short- and medium-range interactions

A statistical mechanical model of protein conformation with medium-range interactions between theith and (i+k)^th residues (k<-4) is presented. Two two-state models, an α-helix-coil and an extended-structure-coil model, are formulated using the same form of the partition function, but the two models are applied independently to predict the locations of α-helical, extended, and coil segments; in the relatively few cases (<2%) where the predictions from the two models are in conflict, the prediction is scored as an incorrect one. Two independent sets of statistical weights (one set for each model) are derived to describe the interactions between the 20 amino acid residues for each range of interactionk; they are evaluated by minimizing an objective function so that the probability profiles for the α-helix or extended structure, respectively, in proteins computed from these statistical weights correlate optimally with the experimentally observed native conformations of these proteins. Examination of the resulting statistical weights shows that those for the interactions between hydrophobic residues and between a hydrophobic and a hydrophilic residue have reasonable magnitudes compared to what would be expected from the spatial arrangements of the side chains in the α-helix and the extended structure, and that those for the α-helix-coil model correlate well with experimentally determined values of the Zimm-Bragg parameterss and σ of the helix-coil transition theory. From the point of view of a method to predict the conformational states (i.e., α-helix, extended structure, and coil) of each residue, the statistical weights (as inall empirical prediction schemes) depend very much on the proteins used for the data base, since the presently available set of proteins of known structure is still too small for very high predictability; as a result, the correctness of the prediction is not very good for proteins not included in the data base. However, the correctness of the prediction, at least for the 37 proteins utilized as the data base in this study, is 91% and 87% for the α-helix-coil and the extended-structure-coil models, respectively; further, 79% of all the residues are predicted correctly when both the α-helix-coil and extended-structure-coil models are applied independently.     

Prediction of biogas potentials using quick laboratory analyses: Upgrading previous models for application to heterogeneous organic matrices

This study presents an upgrading of the mathematical models to predict anaerobic biogasification potential (ABP) through quick laboratory analyses that have been presented in an earlier study. The aim is to widen the applicability of the models to heterogeneous organic substrates and to improve their reliability through a deeper statistical approach.Three multiple-step linear regressions were obtained using biomass oxygen demand in 20 h (OD₂₀) plus the volatile solids content (VS) of 23 new samples of heterogeneous organic matrices, of 46 samples presented in the earlier work and of the data set comprising all the 69 samples. The two variables chosen were found to be suitable for very heterogeneous materials. To judge the prediction quality, a validation procedure was performed with 12 new samples using model efficiency indexes. The proposed model had good prediction ability for a large variety of organic substrates, and allows the calculation of the ABP value within only 2-day’s laboratory work instead of the 60–90 days required to obtain ABP by anaerobic test.     

Multi-linear regression analysis, preliminary biotic ligand modeling, and cross species comparison of the effects of water chemistry on chronic lead toxicity in invertebrates

The current study examined the chronic toxicity of lead (Pb) to three invertebrate species: the cladoceran Ceriodaphnia dubia, the snail Lymnaea stagnalis and the rotifer Philodina rapida. The test media consisted of natural waters from across North America, varying in pertinent water chemistry parameters including dissolved organic carbon (DOC), calcium, pH and total CO(2). Chronic toxicity was assessed using reproductive endpoints for C. dubia and P. rapida while growth was assessed for L. stagnalis, with chronic toxicity varying markedly according to water chemistry. A multi-linear regression (MLR) approach was used to identify the relative importance of individual water chemistry components in predicting chronic Pb toxicity for each species. DOC was an integral component of MLR models for C. dubia and L. stagnalis, but surprisingly had no predictive impact on chronic Pb toxicity for P. rapida. Furthermore, sodium and total CO(2) were also identified as important factors affecting C. dubia toxicity; no other factors were predictive for L. stagnalis. The Pb toxicity of P. rapida was predicted by calcium and pH. The predictive power of the C. dubia and L. stagnalis MLR models was generally similar to that of the current C. dubia BLM, with R(2) values of 0.55 and 0.82 for the respective MLR models, compared to 0.45 and 0.79 for the respective BLMs. In contrast the BLM poorly predicted P. rapida toxicity (R(2)=0.19), as compared to the MLR (R(2)=0.92). The cross species variability in the effects of water chemistry, especially with respect to rotifers, suggests that cross species modeling of invertebrate chronic Pb toxicity using a C. dubia model may not always be appropriate.     

DAT/SERT selectivity of flexible GBR 12909 analogs modeled using 3D-QSAR methods

The dopamine reuptake inhibitor GBR 12909 (1-{2-[bis(4-fluorophenyl)methoxy]ethyl}-4-(3-phenylpropyl)piperazine, 1) and its analogs have been developed as tools to test the hypothesis that selective dopamine transporter (DAT) inhibitors will be useful therapeutics for cocaine addiction. This 3D-QSAR study focuses on the effect of substitutions in the phenylpropyl region of 1. CoMFA and CoMSIA techniques were used to determine a predictive and stable model for the DAT/serotonin transporter (SERT) selectivity (represented by pK(i) (DAT/SERT)) of a set of flexible analogs of 1, most of which have eight rotatable bonds. In the absence of a rigid analog to use as a 3D-QSAR template, six conformational families of analogs were constructed from six pairs of piperazine and piperidine template conformers identified by hierarchical clustering as representative molecular conformations. Three models stable to y-value scrambling were identified after a comprehensive CoMFA and CoMSIA survey with Region Focusing. Test set correlation validation led to an acceptable model, with q(2)=0.508, standard error of prediction=0.601, two components, r(2)=0.685, standard error of estimate=0.481, F value=39, percent steric contribution=65, and percent electrostatic contribution=35. A CoMFA contour map identified areas of the molecule that affect pK(i) (DAT/SERT). This work outlines a protocol for deriving a stable and predictive model of the biological activity of a set of very flexible molecules.     

Prediction of the aqueous solvation free energy of organic compounds by using autocorrelation of molecular electrostatic potential surface properties combined with response surface analysis

Several quantitative structure-property relationship (QSPR) approaches have been explored for the prediction of aqueous solubility or aqueous solvation free energies, DeltaG(sol), as crucial parameter affecting the pharmacokinetic profile and toxicity of chemical compounds. It is mostly accepted that aqueous solvation free energies can be expressed quantitatively in terms of properties of the molecular surface electrostatic potentials of the solutes. In the present study we have introduced autocorrelation molecular electrostatic potential (autoMEP) vectors in combination with nonlinear response surface analysis (RSA) as alternative 3D-QSPR strategy to evaluate the aqueous solvation free energy of organic compounds. A robust QSPR model (r(cv)=0.93) has been obtained by using a collection of 248 organic chemicals. An external test set based on 23 molecules confirmed the good predictivity of the autoMEP/RSA model suggesting its further applicability in the in silico prediction of water solubility of large organic compound libraries.     

Comparative quantitative structure toxicity relationships for flavonoids evaluated in isolated rat hepatocytes and HeLa tumor cells

Quantitative structure activity relationship (QSAR) equations were obtained to describe the cytotoxicity of 22 polyphenols using toxicity (logLD50) representing the concentration for 50% cell survival in 2 h for isolated rat hepatocytes, log P representing octanol/water partitioning, and/or E(p/2) representing redox potential. One- and two-parameter equations were derived for the quantitative structure toxicity relationships (QSTR) for polyphenol induced hepatocyte cytotoxicity: e.g. log C(hepatocyte) (microM)=-0.65(-0.08)log P+4.12(-0.15) (n=19, r(2)=0.80, s=0.33, P<1 x 10(-6)). One- and two-parameter QSAR equations were also derived to describe the inhibitory effects of 13 polyphenols on tumor cell growth when incubated with HeLa cells for 3 days: e.g. log C(tumor) (microM)=-0.34(+/-0.04)log P+2.40(+/-0.07) (n=11, r(2)=0.90, s=0.13, P<1 x 10(-5)). These findings point to lipophilicity as a major characteristic determining polyphenol cytotoxicity. The E(p/2) also played a significant role in polyphenol cytotoxicity towards both cell types: e.g. log C(hepatocyte) (microM)=-0.60(+/-0.06)log P+2.01(+/-0.43)E(p/2) (V)+3.86(+/-0.12) (n=9, r(2)=0.96, s=0.15, P<0.005). The involvement of log P and E(p/2) could be explained if polyphenol cytotoxicity involved the formation of radicals, which interacted with the mitochondrial inner membrane resulting in a disruption of the membrane potential.