No abstract

Molecular modeling based on the X-ray crystal structure of the Tang-Ghosh heptapeptide inhibitor 1 (OM99-2) of BACE led to the design and synthesis of a series of constrained P(1)' analogues. A cyclopentane ring was incorporated in 1 spanning the P(1)' Ala methyl group and the adjacent methylene carbon atom of the chain. Progressive truncation at the P(2)'-P(4)' sites led to a potent truncated analogue 5 with good selectivity over Cathepsin D. Using the same backbone replacement concept, a series of cyclopentane, cyclopentanone, tetrahydrofuran, pyrrolidine, and pyrrolidinone analogues were synthesized with considerable variation at the P and P' sites. The cyclopentanone and 2-pyrrolidinone analogues 45 and 57 showed low nM BACE inhibition. X-ray cocrystal structures of two analogues 5 and 45 revealed excellent convergence with the original inhibitor 1 structure while providing new insights into other interactions which could be exploited for future modifications.

The metabolism of xenobioticsand more specifically drugsin the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts' predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts' prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS.

As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.