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The serotonin 5HT7 receptor has been implicated in numerous physiological and pathological processes from circadian rhythms to depression and schizophrenia. Clonal cell lines heterologously expressing recombinant receptors offer good models for understanding drug-receptor interactions and development of quantitative structure-activity relationships (QSAR). Comparative Molecular Field Analysis (CoMFA) is an important modern QSAR procedure that relates the steric and electrostatic fields of a set of aligned compounds to affinity. Here, we utilized CoMFA to predict affinity for a number of high-affinity ligands at the recombinant guinea pig 5HT7 receptor. Using R-lisuride as the template, a final CoMFA model was derived using procedures similar to those of our recent papers. The final cross-validated model accounted for >85% of the variance in the compound affinity data, while the final non-cross validated model accounted for >99% of the variance. Model evaluation was done using cross-validation methods with groups of 5 ligands. Twenty cross-validation runs yielded an average predictive r2(q2) of 0.779 +/- 0.015 (range: 0.669-0.867). Furthermore, 3D-chemical database search queries derived from the model yielded hit lists of promising agents with high structural similarity to the template. Together, these results suggest a possible basis for high-affinity drug action at 5HT7 receptors.

Agonist affinity changes dramatically as a result of serine to alanine mutations (S193A, S194A, and S197A) within the fifth transmembrane region of D2 dopamine receptors and other receptors for monoamine neurotransmitters. However, agonist 2D-structure does not predict which drugs will be sensitive to which point mutations. Modeling drug-receptor interactions at the 3D level offers considerably more promise in this regard. In particular, a comparison of the same test set of agonists across receptors differing minimally (point mutations) offers promise to enhance the understanding of the structural bases for drug-receptor interactions. We have previously shown that comparative molecular field analysis (CoMFA) can be applied to comparisons of affinity at recombinant D1 and D2 dopamine receptors for the same set of agonists, a differential QSAR. Here, we predicted agonist K(L) for the same set of agonists at wild type D2 vs S193A, S194A, and S197A receptors using CoMFA. Each model used bromocriptine as the template. ln(1/K(L)) values for the low-affinity agonist binding conformation at recombinant wild type and mutant D2 dopamine receptors stably expressed in C6 glioma cells were used as the target property for the CoMFA of the 16 aligned agonist structures. The resulting CoMFA models yielded cross-validated R(2) (q(2)) values ranging from 0.835 to 0.864 and simple R(2) values ranging from 0.999 to 1.000. Predictions of test compound affinities at WT and each mutant receptor were close to measured affinity values. This finding confirmed the predictive ability of the models and their differences from one another. The results strongly support the idea that CoMFA models of the same training set of compounds applied to WT vs mutant receptors can accurately predict differences in drug affinity at each. Furthermore, in a "proof of principle", two different templates were used to derive the CoMFA model for the WT and S193A mutant receptors. Pergolide was chosen as an alternate template because it showed a significant increase in affinity as a result of the S193A mutation. In this instance both the bromocriptine- and pergolide-based CoMFA models were similar to one another but different from those for the WT receptor using bromocriptine- or pergolide- as templates. The pergolide-based S193A model was more strikingly different from that of the WT receptor than was the bromocriptine-based S193A model. This suggests that a "dual-template" approach to differential CoMFA may have special value in elucidating key differences across related receptor types and in determining important elements of the drug-receptor interaction.

A novel method of pharmacophore identification and activity prediction in structure-activity (structure-property) relationships is worked out as an essential extension and improvement of previous publications. In this method each conformation of the molecular systems in the training set of the SAR problem is presented by both electronic structure and geometry parameters arranged in a matrix form. Multiple comparisons of these matrices for the active and inactive compounds allows one to separate a smaller number of matrix elements that are common for all the active compounds and are not present in the same arrangement in the inactive ones. This submatrix of activity represents the pharmacophore (Pha). By introducing the Anti-Pharmacophore Shielding (APS) defined as molecular groups and competing charges outside the Pha that hinder the proper docking of the Pha with the bioreceptor, the procedure of Pha identification is essentially reduced to the treatment of a smaller number of simplest in structure most active and inactive compounds. A simple empirical scheme is suggested to estimate the APS numerically, while the contributions of different conformations of the same compound are taken into account by means of Boltzmann distribution. This enables us to make approximate quantitative predictions of activities. In application to rice blast activity we reached an approximately 100% (within experimental error) prediction probability of the activity qualitatively (yes, no), and with r (2) = 70% quantitatively.

In extension and improvement of previous results, a novel method is worked out for pharmacophore identification and activity prediction in structure-activity relationships. In this method, as in our previous works, each molecular system (conformation) of the training set is described by a matrix with both electron structural parameters (atomic charges, bond orders, etc.) and interatomic distances as matrix elements. This description includes a rather full geometry of charge and/or reactivity distribution thus providing a much better representation of the molecular properties in their interaction with the target. By multiple comparison of these matrices for the active and inactive compounds of the training set, a relatively small number of matrix elements are revealed that are common for all the active compounds and are not present in the same combination in the inactive ones. In this way a set of electronic and geometry parameters is obtained that characterize the pharmacophore (Pha). A major improvement of this scheme is reached by introducing the anti-pharmacophore shielding (APS) and a proper treatment of the conformational problem. The APS is defined as molecular groups and competing charges outside the basic skeleton (the Pha plus the inert neighbor atoms that do not affect the activity) that hinder the proper docking of the Pha with the bioreceptor thus diminishing (partially or completely) the activity. A simple empirical formula is derived to estimate the relative contribution of APS numerically. Two main issues are most affected by the APS: (1) the procedure of Pha identification is essentially simplified because only a small number of molecular systems with the highest activity and simplest structures (systems without APS) should be tried for this purpose; (2) with the APS known numerically, we can make a quantitative (or semiquantitative) prediction of relative activities. The contributions of different conformations (of the same molecular system) that possess the Pha and different APS is taken into account by means of a Boltzmann distribution at given temperatures. Applied to an example, rice blast activity, this approach proved to be rather robust and efficient. In validation of the method, the screening of 39 new compounds yields approximately 100% (within experimental error) prediction probability of the activity qualitatively (yes, no), and with r2=0.66 quantitatively.

We have previously shown that using agonist affinity at recombinant receptors selectively expressed in clonal cells as the dependent variable in three-dimensional quantitative structure-activity relationship studies (3D-QSAR) presents a unique opportunity for accuracy and precision in measurement. Thus, a comparison of affinity's structural determinants for a set of compounds at two different recombinant dopamine receptors represents an attainable goal for 3D-QSAR. A molecular database of bound conformations of 16 structurally diverse agonists was established by alignment with a high-affinity template compound for the D1 receptor, 3-allyl-6-bromo-7,8-dihydroxy-1-phenyl-2,3,4, 5-tetrahydro-1H-benzazepin. A second molecular database of the bound conformations of the same compounds was established against a second template for the D2 receptor, bromocriptine. These aligned structures suggested three-point pharmacophore maps (one cationic nitrogen and two electronegative centers) for the two dopamine receptors, which differed primarily in the height of the nitrogen above the plane of the catechol ring and in the nature of the hydrogen-bonding region. The ln(1/KL) values for the low-affinity agonist binding conformation at recombinant D1 and D2 dopamine receptors stably expressed in C6 glioma cells were used as the target property for the CoMFA (comparative molecular field analysis) of the 16 aligned structures. The resulting CoMFA models yielded cross-validated R2 (q2) values (standard error of prediction) of 0. 879 (1.471, with five principal components) and 0.834 (1.652, with five principal components) for D1 and D2 affinity, respectively. The simple R2 values (standard error of the estimate) were 0.994 (0.323) and 0.999 (0.116), respectively, for D1 and D2 receptor. F values were 341 and 2465 for D1 and D2 models, respectively, with 5 and 10 df. The predictive utility of the CoMFA model was evaluated at both receptors using the dopamine agonists, apomorphine and 7-OH-DPAT. Predictions of KL were accurate at both receptors. Flexible 3D searches of several chemical databases (NCI, MDDR, CMC, ACD, and Maybridge) were done using basic pharmacophore models at each receptor to determine the similarity of hit lists between the two models. The D1 and D2 models yielded different lists of lead compounds. Several of the lead compounds closely resembled high-affinity training set compounds. Finally, homology modeling of agonist binding to the D2 receptor revealed some consistencies and inconsistencies with the CoMFA-derived D2 model and provided a possible rationale for features of the D2 CoMFA contour map. Together these results suggest that CoMFA-homology based models may provide useful insights concerning differential agonist-receptor interactions at related receptors. The results also suggest that comparisons of CoMFA models for two structurally related receptors may be a fruitful approach for differential QSAR.

Determination of quantitative structure-activity relationship (QSAR) for affinity at particular dopamine (DA) receptors has become an even greater priority with the cloning of five DA receptor subtypes. The use of agonist affinity at recombinant receptors selectively expressed in clonal cells as the dependent variable in QSAR presents a unique opportunity for accuracy and precision in measurement of biological values. Bound conformations of 11 agonists (for which both affinity data at the recombinant D1A DA receptor and stereochemical configurations were available) were determined by alignment with a template compound, SKF38393, which shows high affinity and selectivity for D1A receptors and is fairly rigid in structure. These aligned structures suggested a 3-point pharmacophore map (one cationic nitrogen and two electronegative centers) of the D1A DA receptor. This map shows both similarities and differences when compared with a previously reported D2 DA receptor pharmacophore map based on biological data from rat brain and with a recently published map of the native D1 DA receptor using several semirigid compounds. Log(1/K(d)) values at recombinant D1A DA receptors were used as the target property for a CoMFA (comparative molecular field analysis) of the 11 aligned structures. The resulting CoMFA model yielded a cross-validated r(2)(q(2)) value of 0.829 and a simple r(2) = 0.96. In contrast, when a CoMFA model was developed for 10 of these compounds using striatal D1 K(d) values, the q(2) value was reduced to 0.178. These results are consistent with the idea that drug affinity data obtained from clonal cells expressing recombinant receptors may be superior to that obtained using heterogeneous mixtures of native receptors prepared from brain membranes. The predictive utility of the CoMFA model was evaluated using several high-affinity dopamine agonists and m- and p-tyramine, two compounds with a single hydroxyl group on the aromatic ring. Predictions were fairly accurate for all compounds but the two tyramines.