Current Pharmaceutical Design - Volume 9, Issue 20, 2003

A review of the Electron-Conformational (EC) method of pharmacophore (Pha) identification and quantitative bioactivity prediction in drug design and toxicology is presented, which includes the latest advances and improvements of the method as a whole and details of its realization with illustration of results. In the first part devoted to Pha identification the data of conformational analysis and electronic structure calculation of each of the molecules in the training set are used to construct EC matrices of congruity (ECMC) that include atomic interaction indices as diagonal elements, and bond orders and interatomic distances as off-diagonal elements. Multiple comparisons of the ECMC's of the active compounds between themselves and with those of inactive compounds allows one to separate a relatively small number of matrix elements that within certain tolerances are common to all the ECMC’s of the active compounds, while not present in the same combination in the inactive compounds. This is the EC submatrix of activity that represent the Pha, while the tolerances characterize the Pha flexibilities. Distinguished from QSAR approaches, the Pha is obtained here by computational (non-statistical) methods only. The second part of the problem, quantitative activity prediction, is based on using the Pha flexibilities together with the anti-Pha shielding and other auxiliary groups influence in a parameterization and regression analysis procedure that allows for quantitative prediction. An original approach is suggested that side steps the multi-conformational implications. This post-Pha problem is similar to a QSAR approach with special physically transparent descriptors that allow one to avoid chance correlations in the regression procedure. Illustration of the method is given for several drug design problems in which, where sufficiently accurate experimental data are available, the identification of the Pha as a necessary condition of activity is almost 100% correct, while quantitative activity prediction is near to the accuracy of the experimental data, 80% - 90%.

The cannabinoid CB1 and CB2 receptors belong to the Class A, rhodopsin-like family of G protein-coupled receptors. Antagonists for each receptor sub-type, as well as four structural classes of agonists that bind to both receptors, have been identified. An extensive amount of SAR has been developed for agonists and antagonists that bind at CB1, while the SAR of CB2 ligands is only now emerging in the literature. Cannabinoid agonists have been suggested to have potential therapeutic uses as appetite stimulants, analgesics, anti-emetics, anti-diarrheals, antispasmodics, tumor anti-proliferative agents, anti-glaucoma agents and as agents for the treatment of diseases associated with inappropriate retention of aversive memories such as post-traumatic stress disorders and phobias. Cannabinoid CB1 antagonists have been suggested to have potential therapeutic uses as appetite suppressants and as agents that improve memory. This review focuses first on recent CB1 and CB2 SAR and on the pharmacophores that have been developed for ligand recognition at the CB1 receptor. Emerging ideas about how the cannabinoid receptors are activated by agonists or inactivated by inverse agonists are then presented. Challenges for future SAR and pharmacophore development are also identified.

The problem of incorporating receptor flexibility in routine in silico screening of databases of small chemical compounds for the purposes of structure based drug design is still an unsolved problem. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered to represent receptor flexibility. In this paper we review protein flexibility models that have been developed to limit the number of additional search parameters. These models can be roughly divided into five different categories. These are a) use of soft receptors which relax energetic penalties due to steric clashes, b) selection of a few critical degrees of freedom in the receptor binding site, c) use of multiple receptor structures either individually or by combining them using an averaging scheme, d) use of modified molecular simulation methods, and e) use of collective degrees of freedom as a new basis of representation for protein flexibility. All these flexible receptor models strive to balance an improvement in the accuracy of the binding predictions with an increase in computational cost. In addition, other challenges such as the development of accurate solvation models and scoring functions make the receptor flexibility problem even harder.

Pharmacophore discovery is one of the major elements of molecular modeling in the absence of X-ray structural data. While pharmacophores initially made their debut as a means for lead discovery, more recent refinements have brought them into the domain of lead optimization, e.g. as a means to define the molecular alignment in 3D-QSAR. In this review, the experiences of over a decade of confronting and solving the challenges of pharmacophore discovery applied to actual drug discovery are summarized. Also, practical tips are described for using the author's methodology for pharmacophore discovery, DANTE..