QSAR modeling of in vitro inhibition of cytochrome P450 3A4

We report the QSAR modeling of cytochrome P450 3A4 (CYP3A4) enzyme inhibition using four large data sets of in vitro data. These data sets consist of marketed drugs and drug-like compounds all tested in four assays measuring the inhibition of the metabolism of four different substrates by the CYP3A4 enzyme. The four probe substrates are benzyloxycoumarin, testosterone, benzyloxyresorufin, and midazolam. We first show that using state-of-the-art QSAR modeling approaches applied to only one of these four data sets does not lead to predictive models that would be useful for in silico filtering of chemical libraries. We then present the development and the testing of a multiple pharmacophore hypothesis (MPH) that is formulated as a conceptual extension of the traditional QSAR approach to modeling the promiscuous binding of a large variety of drugs to CYP3A4. In the simplest form, the MPH approach takes advantage of the multiple substrate data sets and identifies the binding of test compounds as either proximal or distal relative to that of a given substrate. Application of the approach to the in silico filtering of test compounds for potential inhibitors of CYP3A4 is also presented. In addition to an improvement in the QSAR modeling for the inhibition of CYP3A4, the results from this modeling approach provide structural insights into the drug-enzyme interactions. The existence of multiple inhibition data sets in the BioPrint database motivates the original development of the concept of a multiple pharmacophore hypothesis and provides a unique opportunity for formulating alternative strategies of QSAR modeling of the inhibition of the in vitro metabolism of CYP3A4.     

On the correlation between hydrophobicity, liposome binding and cellular uptake of porphyrin sensitizers

A crucial factor in choosing a porphyrin or analogous photosensitizer for photodynamic therapy (PDT) is its ability to incorporate into the cells. For hydrophobic compounds that partition passively into the cytoplasmic membrane, a partition coefficient between an organic solvent and water, P, is one factor that could be used to predict the molecule's ability to diffuse into biomembranes. We synthesized several porphyrins, modified with two, three or four meso-substituents and studied their spectroscopic and photophysical properties. The octanol-water partitioning coefficients, log P, were calculated as a parameter for hydrophobicity. We found these porphyrins to be very hydrophobic, with log P values in the range of 8.9-11.8. These were correlated with the binding constants of these porphyrins into liposomes, K(b), as well as to their uptake by cells. The correlation between the estimated log P and K(b) is nearly linear but negative, indicating, apparently, that there is lesser binding to liposomes with increased hydrophobicity. On the other hand, all of the studied porphyrins are taken up by cells, but there is no clear correlation between cellular uptake and the log P or K(b). Lipinski's pharmacological "rule of 5" predicts poor permeation of drugs into cells when log P is greater than five. This may be relevant for diffusional binding to liposomes, where aqueous aggregation can interfere strongly with cellular uptake. In such extreme conditions, neither liposome binding nor other rules seem to predict porphyrin behavior in vitro.     

Ligand- and protein-based modeling studies of the inhibitors of human cytochrome P450 2D6 and a virtual screening for potential inhibitors from the Chinese herbal medicine, Scutellaria baicalensis (Huangqin,Baikal Skullcap)

We have previously examined the binding patterns of various substrates to human cytochrome P450 2D6 (CYP2D6) using a series of molecular modeling methods. In this study, we further explored the binding modes of various types of inhibitors to CYP2D6 using a combination of ligand- and protein-based modeling approaches. Firstly, we developed and validated a pharmacophore model for CYP2D6 inhibitors, which consisted of two hydrophobic features and one hydrogen bond acceptor feature. Secondly, we constructed and validated a quantitative structure-activity relationship (QSAR) model for CYP2D6 inhibitors which gave a poor to moderate prediction accuracy. Thirdly, a panel of CYP2D6 inhibitors were subject to molecular docking into the active site of wild-type and mutated CYP2D6 enzyme. We demonstrated that 8 residues in the active site (Leu213, Glu216, Ser217, Gln244, Asp301, Ser304, Ala305, and Phe483) played an important role in the binding to the inhibitors via hydrogen bond formation and/or π-π stacking interaction. Apparent changes in the binding modes of the inhibitors have been observed with Phe120Ile, Glu216Asp, Asp301Glu mutations in CYP2D6. Finally, we screened for potential binders/inhibitors from the Chinese herbal medicine Scutellaria baicalensis (Huangqin, Baikal Skullcap) using the established pharmacophore model for CYP2D6 inhibitors and molecular docking approach. Overall, 18 out of 40 compounds from S. baicalensis were mapped to the pharmacophore model of CYP2D6 inhibitors and most herbal compounds from S. baicalensis could be docked into the active site of CYP2D6. Our study has provided insights into the molecular mechanisms of interaction of synthetic and herbal compounds with human CYP2D6 and further benchmarking studies are needed to validate our modeling and virtual screening results.     

Triplex selective 2-(2-naphthyl)quinoline compounds: origins of affinity and new design principles

A novel competition dialysis assay was used to investigate the structural selectivity of a series of substituted 2-(2-naphthyl)quinoline compounds designed to target triplex DNA. The interaction of 14 compounds with 13 different nucleic acid sequences and structures was studied. A striking selectivity for the triplex structure poly dA:[poly dT](2) was found for the majority of compounds studied. Quantitative analysis of the competition dialysis binding data using newly developed metrics revealed that these compounds are among the most selective triplex-binding agents synthesized to date. A quantitative structure-affinity relationship (QSAR) was derived using triplex binding data for all 14 compounds used in these studies. The QSAR revealed that the primary favorable determinant of triplex binding free energy is the solvent accessible surface area. Triplex binding affinity is negatively correlated with compound electron affinity and the number of hydrogen bond donors. The QSAR provides guidelines for the design of improved triplex-binding agents.     

Structure-toxicity relationships for aliphatic compounds encompassing a variety of mechanisms of toxic action to Vibrio fischeri

QSARs based upon the logarithm of the octanol-water partition coefficient, log P, and energy of the lowest unoccupied molecular orbital, ELUMO were developed to model the toxicity of aliphatic compounds to the marine bacterium Vibrio fischeri. Statistically robust, hydrophobic-dependent QSARs were found for chloroalcohols and haloacetonitriles. Modelling of the toxicity of the haloesters and the diones required the use of terms to describe both hydrophobicity and electrophilicity. The differences in intercepts, slopes, and fit of these models suggest different electrophilic mechanisms occur between classes, as well as within the diones and haloesters. In order to model globally the toxicity of aliphatic compounds to V. fischeri, all the data determined in this study were combined with those determined previously for alkanones, alkanals, and alkenals. A highly predictive two-parameter QSAR [pT15 = 0.760(log P) - 0.625(ELUMO) - 0.466; n = 63, s = 0.462, r2 = 0.846, F = 171, Pr > F = 0.0001] was developed for the combined data that models across classes and is independent of mechanisms of action. The toxicity of these compounds to V. fischeri compares well to the toxicity (50% population growth inhibition) to the ciliate Tetrahymena pyriformis (r2 = 0.850).     

Twisted amides inferred from QSAR analysis of hydrophobicity and electronic effects on the affinity of fluoroaromatic inhibitors of carbonic anhydrase.

QSAR has been used to elucidate the origin of the hydrophobicity and binding affinity of a small library of fluoroaromatic inhibitors of F131V carbonic anhydrase II. Our analysis predicted the presence of a twisted amide conformation for several bound inhibitors, which we confirmed crystallographically. We also determined that the hydrophobicity of the inhibitors as a whole results from the fragment hydrophobicities of their fluorobenzyl rings, corrected for field effects and the presence of an intramolecular F.H contact in solution. The loss of this interaction on binding to the enzyme makes the affinity sensitive to the same terms, but with the opposite dependence on the F.H contact. In the case of the four inhibitors bound as twisted amides, this F.H contact must be retained to some extent in the bound state in order for their affinities to be consistent with our QSAR analysis of the entire set of 17 molecules.     

3-D QSAR for intrinsic activity of 5-HT1A receptor ligands by the method of comparative molecular field analysis

The affinity of a ligand for a receptor is usually expressed in terms of the dissociation constant (K_i) of the drug-receptor complex, conveniently measured by the inhibition of radioligand binding. However, a ligand can be an antagonist, a partial agonist, or a full agonist, a property largely independent of its receptor affinity. This property can be quantitated as intrinsic activity (1A), which can range from 0 for a full antagonist to 1 for a full agonist. Although quantitative structure–activity relationship (QSAR) methods have been applied to the prediction of receptor affinity with considerable success, the prediction of IA, even qualitatively, has rarely been attempted. Because most traditional QSAR methods are limited to congeneric series, and there are often major structural differences between agonists and antagonists, this lack of success in predicting IA is understandable. To overcome this limitation, we used the method of comparative molecular field analysis (CoMFA), which, unlike traditional Hansch analysis, permits the inclusion of structurally dissimilar compounds in a single QSAR model. A structurally diverse set of 5-hydroxytryptamine_1A (5-HT_1A) receptor ligands, with literature IA data (determined by the inhibition of 5-HT sensitive forskolin-stimulated adenylate cyclase), was used to develop a 3-D QSAR model correlating intrinsic activity with molecular structure properties of 5HT_1A receptor ligands. This CoMFA model had a crossvalidated r² of 0.481, five components and final conventional r² of 0.943. The receptor model suggests that agonist and antagonist ligands can share parts of a common binding site on the receptor, with a primary agonist binding region that is also occupied by antagonists and a secondary binding site accommodating the excess bulk present in the sidechains of many antagonists and partial agonists. The CoMFA steric field graph clearly shows that agonists tend to be “flatter” (more coplanar) than antagonists, consistent with the difference between the 5-HT_1A agonist and antagonist pharmacophores proposed by Hibert and coworkers. The CoMFA electrostatic field graph suggests that, in the region surrounding the essential protonated aliphatic amino group, the positive molecular electrostatic potential may be weaker in antagonists as compared to agonists. Together, the steric and electrostatic maps suggest that in the secondary binding site region increased hydrophobic binding may enhance antagonist activity. These results demonstrate that CoMFA is capable of generating a statistically crossvalidated 3-D QSAR model that can successfully distinguish between agonist and antagonist 5-HT_1A ligands. To the best of our knowledge, this is the first time this or any other QSAR method has been successfully applied to the correlation of structure with IA rather than potency or affinity. The analysis has suggested various structural features associated with agonist and antagonist behaviors of 5-HT_1A ligands and thus should assist in the future design of drugs that act via 5-HT_1A receptors. © 1993 John Wiley & Sons, Inc.     

A quantitative structure-activity relationship for antitumor activity of long-chain phenols from Ginkgo biloba L

With the aim of obtaining compounds with strong antitumor activity, a quantitative structure-activity relationship (QSAR) of antitumor phenolic compounds (long-chain phenols) was derived using the Hansch-Fujita equation. The ED50 values against Chinese hamster V-79 cells were analyzed in terms of log P as the hydrophobic parameter and the energy of the lowest unoccupied molecular orbital (ELUMO) calculated by using the modified neglect of differential overlap (MNDO) method as the electronic parameter, by means of multiple regression analysis. It was found that the activities mainly depended on log P (an optimum log P of 8.3) and a low-lying ELUMO value. 4-Undecylcatechol, selected on the basis of the above results, exhibited strong antitumor activity against Sarcoma 180 ascites and P-388 lymphocytic leukemia.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.