What patents tell us about drug repurposing for cancer: A landscape analysis

Searching for Novel Noncovalent Nuclear Export Inhibitors through a Drug Repurposing Approach

Chromosomal region maintenance protein 1 (CRM1) is a validated anticancer drug target, and its covalent inhibitor KPT-330 has been approved for marketing. However, the development of CRM1 inhibitors, especially the noncovalent ones, is still very limited. Drug repurposing is an effective strategy to develop drug leads for new targets. In this work, we virtually screened a library of marketed drugs and identified zafirlukast as a new CRM1 inhibitor. Biochemical and structural analysis revealed that zafirlukast was a noncovalent CRM1 inhibitor that bound to four subpockets in the nuclear-export-signal (NES) groove. Methylation of the sulfonamide group rendered zafirlukast completely inactive against CRM1. Zafirlukast inhibited the growth of a variety of cancer cells and worked synergistically with the drug doxorubicin. Taken together, these works laid a solid foundation for reshaping zafirlukast as a valuable lead compound for further design of noncovalent, specific, and potent CRM1 inhibitors toward the treatment of various cancers.

PEMT: a patent enrichment tool for drug discovery

MOTIVATION

Drug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision-making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources.

RESULTS

Here, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes.

AVAILABILITY AND IMPLEMENTATION

PEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pypi.org/project/PEMT/, respectively.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Illuminating the druggable genome through patent bioactivity data

The patent literature is a potentially valuable source of bioactivity data. In this article we describe a process to prioritise 3.7 million life science relevant patents obtained from the SureChEMBL database (https://www.surechembl.org/), according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, based on the classification developed by the Illuminating the Druggable Genome (IDG) project. The overall goal was to select a smaller number of patents that could be manually curated and incorporated into the ChEMBL database. Using relatively simple annotation and filtering pipelines, we have been able to identify a substantial number of patents containing quantitative bioactivity data for understudied targets that had not previously been reported in the peer-reviewed medicinal chemistry literature. We quantify the added value of such methods in terms of the numbers of targets that are so identified, and provide some specific illustrative examples. Our work underlines the potential value in searching the patent corpus in addition to the more traditional peer-reviewed literature. The small molecules found in these patents, together with their measured activity against the targets, are now accessible via the ChEMBL database.