Douglas B. Kitchen

Early phase drug discovery: Cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design.

Bromodomains are key transcriptional regulators that are thought to be druggable epigenetic targets for cancer, inflammation, diabetes and cardiovascular therapeutics. Of particular importance is the first of two bromodomains in bromodomain containing 4 protein (BRD4(1)). Protein-ligand docking in BRD4(1) was used to purchase a small, focused screening set of compounds possessing a large variety of core structures. Within this set, a small number of weak hits each contained a dihydroquinoxalinone ring system. We purchased other analogs with this ring system and further validated the new hit series and obtained improvement in binding inhibition. Limited exploration by new analog synthesis showed that the binding inhibition in a FRET assay could be improved to the low μM level making this new core a potential hit-to-lead series. Additionally, the predicted geometries of the initial hit and an improved analog were confirmed by X-ray co-crystallography with BRD4(1).

Design, selection, and evaluation of a general kinase-focused library

The human kinome is a highly conserved target family composed of more than 500 different proteins. Kinases play fundamental roles in many intracellular pathways such as cytokinesis, cell proliferation, differentiation, and apoptosis, and are therefore implicated in various diseases. Currently there are significant efforts in therapeutic areas such as cancer and inflammation to identify novel kinase inhibitors. Random screening of a discovery compound library often results in a hit rate of 0.1 %, whereas focused library screening could improve this rate to 1 %. Consequently, libraries focused toward kinases have become starting points in screening campaigns and are complementary to conventional high-throughput screening (HTS) of discovery libraries. Compound collections catered to target families such as kinases present a unique opportunity to explore discrete chemical, biological, and property spaces. In contrast, HTS libraries are built to represent maximum diversity in the chemical and biological properties of compounds. Focused libraries are also appealing due to decreased synthesis, repository management, and screening costs. The present work describes a rapid computational process to select a collection of compounds targeted as kinase inhibitors, combining the advantages of 2D and 3D virtual screening methods for use in kinase-focused screening campaigns. In designing a general kinase screening library, the challenge lies in defining the chemical and biological space that identifies compounds with utility against any of the many possible kinase targets. Whereas some inhibitors such as staurosporine are known to be active against many kinases, others show a specific inhibitory profile. 7] The limited selectivity of many inhibitors is due to the fact that the catalytic ATP binding domain targeted is highly conserved. In recognition of the difficulty of designing selective kinase inhibitors, dualand multitarget kinase inhibitors were developed. Such “dirty drugs” (i.e. , sorefenib, sunitinib) have gained much attention in recent years and show potential to be advantageous in cancer therapy. These clinical developments have also contributed to the increased interest in general kinase inhibitor libraries. In this work, our goal was to develop a focused library selection procedure based on a 2D similarity search combined with 3D target-based filtering. A recent review argues that “2D fingerprints are surprisingly effective in many search situations in comparison with more complex 3D designs”. Indeed, 2D approaches allow a rapid analogue search from various databases. We find the combination of 2D and 3D methods has distinct advantages; it can decrease the number of false negatives, and 2D methods can represent a pre-filtering tool that enables real-time 3D virtual screening using traditional docking algorithms tailored to the evaluation of large numbers of molecules. Herein we describe our methods and characterize the general kinase-focused library.

The design and synthesis of novel SGLT2 inhibitors: C-glycosides with benzyltriazolopyridinone and phenylhydantoin as the aglycone moieties.

The sodium glucose co-transporter 2 (SGLT2) has received considerable attention in recent years as a target for the treatment of type 2 diabetes mellitus. This report describes the design, synthesis and structure-activity relationship (SAR) of C-glycosides with benzyltriazolopyridinone and phenylhydantoin as the aglycone moieties as novel SGLT2 inhibitors. Compounds 5p and 33b demonstrated high potency in inhibiting SGLT2 and high selectivity against SGLT1. The in vitro ADMET properties of these compounds will also be discussed.

Systems biology-guided identification of synthetic lethal gene pairs and its potential use to discover antibiotic combinations

Mathematical models of metabolism from bacterial systems biology have proven their utility across multiple fields, for example metabolic engineering, growth phenotype simulation, and biological discovery. The usefulness of the models stems from their ability to compute a link between genotype and phenotype, but their ability to accurately simulate gene-gene interactions has not been investigated extensively. Here we assess how accurately a metabolic model for Escherichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the accuracy rate is between 25% and 43%. The most common failure modes were incorrect computation of single gene essentiality and biological information that was missing from the model. Moreover, we performed virtual and biological screening against several synthetic lethal pairs to explore whether two-compound formulations could be found that inhibit the growth of Gram-negative bacteria. One set of molecules was identified that, depending on the concentrations, inhibits E. coli and S. enterica serovar Typhimurium in an additive or antagonistic manner. These findings pinpoint specific ways in which to improve the predictive ability of metabolic models, and highlight one potential application of systems biology to drug discovery and translational medicine.

Lead Optimization toward Proof-of-Concept Tools for Huntington’s Disease within a 4-(1H-Pyrazol-4-yl)pyrimidine Class of Pan-JNK Inhibitors

Through medicinal chemistry lead optimization studies focused on calculated properties and guided by X-ray crystallography and computational modeling, potent pan-JNK inhibitors were identified that showed submicromolar activity in a cellular assay. Using in vitro ADME profiling data, 9t was identified as possessing favorable permeability and a low potential for efflux, but it was rapidly cleared in liver microsomal incubations. In a mouse pharmacokinetics study, compound 9t was brain-penetrant after oral dosing, but exposure was limited by high plasma clearance. Brain exposure at a level expected to support modulation of a pharmacodynamic marker in mouse was achieved when the compound was coadministered with the pan-cytochrome P450 inhibitor 1-aminobenzotriazole.

Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC

Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100–200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.

Design and Characterization of Novel Covalent Bromodomain and Extra-Terminal Domain (BET) Inhibitors Targeting a Methionine

BET proteins are key epigenetic regulators that regulate transcription through binding to acetylated lysine (AcLys) residues of histones and transcription factors through bromodomains (BDs). The disruption of this interaction with small molecule bromodomain inhibitors is a promising approach to treat various diseases including cancer, autoimmune and cardiovascular diseases. Covalent inhibitors can potentially offer a more durable target inhibition leading to improved in vivo pharmacology. Here we describe the design of covalent inhibitors of BRD4(BD1) that target a methionine in the binding pocket by attaching an epoxide warhead to a suitably oriented noncovalent inhibitor. Using thermal denaturation, MALDI-TOF mass spectrometry, and an X-ray crystal structure, we demonstrate that these inhibitors selectively form a covalent bond with Met149 in BRD4(BD1) but not other bromodomains and provide durable transcriptional and antiproliferative activity in cell based assays. Covalent targeting of methionine offers a novel approach to drug discovery for BET proteins and other targets.