Jon Winter

Design and Synthesis of Novel Lactate Dehydrogenase A Inhibitors by Fragment-Based Lead Generation

Lactate dehydrogenase A (LDHA) catalyzes the conversion of pyruvate to lactate, utilizing NADH as a cofactor. It has been identified as a potential therapeutic target in the area of cancer metabolism. In this manuscript we report our progress using fragment-based lead generation (FBLG), assisted by X-ray crystallography to develop small molecule LDHA inhibitors. Fragment hits were identified through NMR and SPR screening and optimized into lead compounds with nanomolar binding affinities via fragment linking. Also reported is their modification into cellular active compounds suitable for target validation work.

Structure-Based Design of Potent and Selective Inhibitors of the Metabolic Kinase PFKFB3

A weak screening hit with suboptimal physicochemical properties was optimized against PFKFB3 kinase using critical structure-guided insights. The resulting compounds demonstrated high selectivity over related PFKFB isoforms and modulation of the target in a cellular context. A selected example demonstrated exposure in animals following oral dosing. Examples from this series may serve as useful probes to understand the emerging biology of this metabolic target.

A system for encoding and searching Markush structures.

The encoding and searching of generic chemical structures, so-called Markush structures, have received little attention in the literature of late. The ability to encode and search these complex entities is of use in various branches of chemoinformatics. We describe a general language for encoding Markush structures and algorithms for searching them and give three examples of the utility of such a system: development of general Free-Wilson analyses of chemical series, detection of controlled substances within a large database of molecular structures, and searching of large databases of virtual compounds.

Better compounds faster: the development and exploitation of a desktop predictive chemistry toolkit

Todays drug designer has access to vast quantities of data and an impressive array of sophisticated computational methods. At the same time, there is increasing pressure on the pharmaceutical industry to improve its productivity and reduce candidate drug attrition. We set out to develop a highly integrated suite of design and data analysis tools underpinned by the best predictive chemistry methods and models, with the aim of enabling multi-disciplinary compound design teams to make better informed design decisions. In this article we address the challenges of developing a powerful, flexible and user-friendly toolkit, and of maximising its exploitation by the design community. We describe the impact the toolkit has had on drug discovery projects and give our perspective on the future direction of this activity.

The design and synthesis of novel, potent and orally bioavailable N-aryl piperazine-1-carboxamide CCR2 antagonists with very high hERG selectivity

A novel N-aryl piperazine-1-carboxamide series of human CCR2 chemokine receptor antagonists was discovered. Early analogues were potent at CCR2 but also inhibited the hERG cardiac ion channel. Structural modifications which decreased lipophilicity and basicity resulted in the identification of a sub-series with an improved margin over hERG. The pharmacological and pharmacokinetic properties of the lead compound from this series, N-(3,4-dichlorophenyl)-4-[(2R)-4-isopropylpiperazine-2-carbonyl]piperazine-1-carboxamide, are described.

Compound Passport Service: supporting corporate collection owners in open innovation

A growing number of early discovery collaborative agreements are being put in place between large pharma companies and partners in which the rights for assets can reside with a partner, exclusively or jointly. Our corporate screening collection, like many others, was built on the premise that compounds generated in-house and not the subject of paper or patent disclosure were proprietary to the company. Collaborative screening arrangements and medicinal chemistry now make the origin, ownership rights and usage of compounds difficult to determine and manage. The Compound Passport Service is a dynamic database, managed and accessed through a set of reusable services that borrows from social media concepts to allow sample owners to take control of their samples in a much more active way.

Improved chemical text mining of patents using infinite dictionaries, translation and automatic spelling correction

The text mining of patents and patent applications for chemical structures of interest to medicinal chemists poses a number of unique challenges not encountered in other fields of text analytics. Traditional text mining relies on the co-occurrence of common terms between documents to provide similarity measures that can be used to cluster and rank related documents. The more words shared between two documents, the more similar they are, and the greater the probability that they discuss the same topic. By contrast, in pharmaceutical “composition of matter” patents the novel and unique chemical entities are far more significant than those that can be found elsewhere. Although the text of a pharmaceutical patent may explicitly name thousands of individual compounds, and via generic Markush structures claim an infinite number, the role of these patents is to protect the intellectual property of only one or perhaps two drug candidates. In this work, we present an analysis of the “quality not quantity” of structures extracted by automatic Chemical Named Entity Recognition (CNER) methods both on a small hand-curated benchmark set [1] and a large-scale analysis of a comprehensive database of 12 million patents [2,3]. Our results show the limited value of traditional lexicon/dictionary based approaches in extracting “key” compounds and that the major impediment is not the performance of the name-to-structure software used, but the high rate of OCR errors, typos and lexicographic problems found in patent-office data feeds. To address this problem, novel algorithms for automatic chemical spelling correction have been developed, that take advantage of the grammar used in IUPAC-like nomenclature. This forms a preprocessing pass, independent of the name-to-structure software used, and is shown to greatly improve results in our study.

Abstract LB-C20: Lead generation and optimisation of a series of novel glutaminase (GLS) inhibitors

Glutamine is an essential nutrient for cancer cells and is used to support cancer cell growth. Glutamine is converted to glutamate by the enzyme glutaminase, which is then converted into alpha-ketoglutarate, a metabolic intermediate within the TCA cycle which serves as a precursor for the biosynthesis of ATP, NADPH, fatty acids, glutathione, nucleic acids and amino acids. As such, we regarded glutaminolysis as a key pathway for therapeutic intervention. At the outset of the project there were several reported inhibitors of glutaminase known in the literature; however, they possess undesirable drug like properties. We describe here our efforts to discover novel inhibitors of glutaminase with a promising development profile. From a high throughput screen of approximately 900,000 compounds in the AstraZeneca collection, we were able to identify three series of glutaminase inhibitors. One of these series has been optimised to deliver cell potent, selective and orally bioavailable inhibitors of glutaminase 1. Citation Format: Mark D. Charles, Ceri Cairnduff, Joanna Brookfield, Verity Sabin, Tennyson Ekwuru, Neil Jones, Attilla Ting, James Smith, Willem Nissink, Ray Finlay, Steve Powell, Susan Critchlow, Richard Ward, Matthew Wood, Linette Ruston, Jon Winter, Ian Hollingsworth. Lead generation and optimisation of a series of novel glutaminase (GLS) inhibitors. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr LB-C20.