Current Pharmaceutical Design - Volume 8, Issue 17, 2002

The recent success of the first FDA-approved small-molecule tyrosine kinase inhibitor Gleevec (STI-571, imatinib mesylate) in the treatment of chronic myelogenous leukemia (CML) has focused attention on the potential therapeutic usefulness of inhibitors of other kinase targets. This review shall highlight recent applications of computational chemistry methods, comprising both ligand-based and structure-based approaches, in the discovery and design of kinase inhibitors. In particular, we will focus on ATP-competitive inhibitors of selected kinase targets of therapeutic importance.

Since ancient times the operation of the brain has elicited more than usual interest. Data mining of the human genome is revealing that many CNS abnormalities have a genetic component. As yet this information can not be used directly to cure or ameliorate specific CNS disorders although this is regarded as having great potential for future therapies.Current CNS drug design and 3D QSAR is based on knowing either the structures of key proteins and how smaller molecules interact with them to obtain a pharmacological response, or on hypothesising about key structural features and interactions by a variety of molecular modelling and computational techniques. Methods used include conformational analyses, pharmacophore development and QSAR which are now being actively applied to increase our understanding of how molecules interact with specific sites within the CNS as a basis for the design of new pharmacologically active compounds.In this review we give an overview of the latest strategies used in 3D-QSAR based drug design and survey the most recent applications of these strategies to the CNS. By way of example, accounts are given of computer-based research aimed at drugs targeting GABA, glutamate, dopamine and opioid receptors.

Receptor-based drug discovery can increase the novelty of a hit list over ligandbased models that are dependent on known inhibitors. It is important to explore new conformational and chemical space, but it is difficult to predict the plasticity of the binding site. Receptor-based methods are usually based on crystal structures of ligand-protein complexes, and hit lists can be restricted to the size and shape of the receptor model. Many improvements that accommodate protein flexibility in computer-aided drug design are being developed. These methods are reviewed with the focus being techniques that move beyond the rotation of side chains.The use of multiple protein structures is emerging as the best choice for including more realistic changes in protein conformation, but the optimal way to using these structures is still unclear.

The review deals with the problem of the study of ligand-receptor interactions and the use of Molecular Dynamics (MD) simulation to approach such a problem. After a short review of the fundamentals of MD we describe the medium in which all biology takes place, water. Emphasis is put on the water models appropriate for simulation of macromolecular systems explicitly including the water molecules. We consider the quality of the water model both in terms of simplicity and performance to describe the liquid water properties. Heavy water, although not a biologically viable medium, is considered since many experiments make use of it as a solvent. Sweetness of carbohydrates is considered as an example of the procedure suitable to characterize active sites on the ligands. Consideration is given to the computation of the binding constants through molecular dynamics. The computation of the Free Energy is described and illustrated. The potentiality of MD for studies of ligand-receptor interactions is limited by the computer resources, for even with large computing facilities the need of relatively long simulation times severely restricts the study of large systems. A method is described in which several shells are treated at different levels of approximation, form mechanical response and mean electrical field to quantum mechanics, through stochastic dynamics and atomic classical MD. The review closes with a brief account of the perspectives of the method.

The basic principles of 3-D quantitative structure-activity relationships (QSARs) analysis are discussed in the light of the fuzzy logic concept. According to that concept, the traditionally “one chemical - one structure - one parameter value” relationship in QSAR is suggested to be modified into “one chemical - finite set of structures - range of parameter values” principle. In this respect, two recently developed techniques accounting for conformational flexibility in 3-D QSARs are reviewed. A basic assumption underlying both methods is that chemical behavior in complex biological systems is context-dependent. A molecule can exist and interact in a variety of conformations depending on the specificity of the endpoint under investigation and reaction media. It was demonstrated that selection of “active” conformer(s) in QSAR studies is a task as important as the selection of relevant molecular parameters. Specifically selected “active”conformers, rather than the lowest-energy states of the chemicals are suggested to be used in the correlative QSARs. The method for recognition the common reactivity pattern (COREPA) of structurally heterogeneous compounds that elicit similar biological behaviour is based on all energetically reasonable conformers of chemicals. The principle assumption of the method is that biologically similar chemicals should possess a commonality in their stereoelectronic (reactivity) pattern. Originally developed algorithms for conformer generation are presented in association with the QSAR methods accounting for conformational flexibility of chemicals. Applicability of the QSAR technique for selection active conformers is illustrated by presenting QSAR models derived for Ah binding affinity of PCBs and antimicrobial activity of rifamicin derivatives. Models for predicting estrogenic activity of structurally diverse chemicals and ACE inhibition exemplified the applicability of the COREPA method. The model performance is analyzed by the 3D screening exercise of large chemical inventories with subsequent experimental validation within the EDAEP project. Besides the impact of conformational flexibility of chemicals in 3D QSAR the role of different molecular descriptors is discussed with respect to their ability to describe molecular interactions with different specificity.