No abstract

Over the last few years many articles have been published in an attempt to provide performance benchmarks for virtual screening tools. While this research has imparted useful insights, the myriad variables controlling said studies place significant limits on results interpretability. Here we investigate the effects of these variables, including analysis of calculation setup variation, the effect of target choice, active/decoy set selection (with particular emphasis on the effect of analogue bias) and enrichment data interpretation. In addition the optimization of the publicly available DUD benchmark sets through analogue bias removal is discussed, as is their augmentation through the addition of large diverse data sets collated using WOMBAT.

Polycomb repressive complex 2 (PRC2) has been shown to play a major role in transcriptional silencing in part by installing methylation marks on lysine 27 of histone 3. Dysregulation of PRC2 function correlates with certain malignancies and poor prognosis. EZH2 is the catalytic engine of the PRC2 complex and thus represents a key candidate oncology target for pharmacological intervention. Here we report the optimization of our indole-based EZH2 inhibitor series that led to the identification of CPI-1205, a highly potent (biochemical IC50 = 0.002 μM, cellular EC50 = 0.032 μM) and selective inhibitor of EZH2. This compound demonstrates robust antitumor effects in a Karpas-422 xenograft model when dosed at 160 mg/kg BID and is currently in Phase I clinical trials. Additionally, we disclose the co-crystal structure of our inhibitor series bound to the human PRC2 complex.

In this chapter we review the use of 3-D pharmacophores in drug discovery. Recent advances are highlighted, including the application of pharmacophore descriptors generated both from ligands and protein binding sites. The application of 3-D pharmacophore fingerprints as molecular descriptors for similarity and diversity applications such as virtual screening, library design and QSAR is discussed. In addition, we highlight the quantification of structure-based diversity using site-derived fingerprints, and review virtual screening methods using both single refined hypotheses and the fingerprints of multiple potential hypotheses. Further, we discuss methods that take protein flexibility and molecular shape-into account. Each of the above techniques are reviewed with particular reference to the recent advances, advantages and challenges of each methodology.

An alternative method for determining structure-activity correlations is presented. Ligand molecules are described using data matrices derived from the results of N by N (each molecule compared to every other) molecular similarity calculations. The matrices were analyzed using a neural network pattern recognition technique and partial least squares statistics, with the results obtained compared to those achieved using comparative molecular field analysis (CoMFA). The molecular series used in the study comprised 31 steroids. The resultant pattern recognition analysis showed clustering of compounds with high, intermediate, and low affinity into separate regions of the neuron output plots. The cross-validated correlation coefficients obtained from statistical analyses of the matrices against steroid binding data compared well with those achieved using CoMFA. These results show that data matrices derived from molecular similarity calculations can provide the basis for rapid elucidation of both qualitative and quantitative structure-activity relationships.

The biological role played by non-BET bromodomains remains poorly understood, and it is therefore imperative to identify potent and highly selective inhibitors to effectively explore the biology of individual bromodomain proteins. A ligand-efficient nonselective bromodomain inhibitor was identified from a 6-methyl pyrrolopyridone fragment. Small hydrophobic substituents replacing the N-methyl group were designed directing toward the conserved bromodomain water pocket, and two distinct binding conformations were then observed. The substituents either directly displaced and rearranged the conserved solvent network, as in BRD4(1) and TAF1(2), or induced a narrow hydrophobic channel adjacent to the lipophilic shelf, as in BRD9 and CECR2. The preference of distinct substituents for individual bromodomains provided selectivity handles useful for future lead optimization efforts for selective BRD9, CECR2, and TAF1(2) inhibitors.

Fueled by advances in molecular structure determination, tools for structure-based drug design are proliferating rapidly. Lead discovery through searching of ligand databases with molecular docking techniques represents an attractive alternative to high-throughout random screening. The size of commercial databases imposes severe computational constraints on molecular docking, compromising the level of calculational detail permitted for each putative ligand. We describe alternative philosophies for docking which effectively address this challenge. With respect to the dynamic aspects of molecular recognition, these strategies lie along a spectrum of models bounded by the Lock-and-Key and Induced-Fit theories for ligand binding. We explore the potential of a rigid model in exploiting species specificity and of a tolerant model in predicting absolute ligand binding affinity. Current molecular docking methods are limited primarily by their ability to rank docked complexes; we therefore place particular emphasis on this aspect of the problem throughout our validation of docking strategies.

The discovery of asunaprevir (BMS-650032, 24) is described. This tripeptidic acylsulfonamide inhibitor of the NS3/4A enzyme is currently in phase III clinical trials for the treatment of hepatitis C virus infection. The discovery of 24 was enabled by employing an isolated rabbit heart model to screen for the cardiovascular (CV) liabilities (changes to HR and SNRT) that were responsible for the discontinuation of an earlier lead from this chemical series, BMS-605339 (1), from clinical trials. The structure-activity relationships (SARs) developed with respect to CV effects established that small structural changes to the P2* subsite of the molecule had a significant impact on the CV profile of a given compound. The antiviral activity, preclincial PK profile, and toxicology studies in rat and dog supported clinical development of BMS-650032 (24).