Aldosterone is a hormone that exerts manifold deleterious effects on the kidneys, blood vessels, and heart which can lead to pathophysiological consequences. Inhibition of the mineralocorticoid receptor (MR) is a proven therapeutic concept for the management of associated diseases. Use of the currently marketed MR antagonists spironolactone and eplerenone is restricted, however, due to a lack of selectivity in spironolactone and the lower potency and efficacy of eplerenone. Several pharmaceutical companies have implemented programs to identify drugs that overcome the known liabilities of steroidal MR antagonists. Herein we disclose an extended SAR exploration starting from cyano-1,4-dihydropyridines that were identified by high-throughput screening. Our efforts led to the identification of a dihydronaphthyridine, BAY 94-8862, which is a potent, selective, and orally available nonsteroidal MR antagonist currently under investigation in a clinical phase II trial.
Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.
Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.
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