Illuminating the Druggable Genome through Patent Bioactivity Data

The patent literature is a potentially valuable source of bioactivity data. The SureChEMBL database (https://www.surechembl.org/) is a publicly available large-scale resource that contains compounds extracted on a daily basis from the full text, images and attachments of patent documents, through an automated text and image-mining pipeline. In this paper we describe a process to prioritise 3.7 million life science relevant patents obtained from SureChEMBL, according to how likely they were to contain bioactivity data for potent small molecules on less-studied targets, according to 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. We describe the approach taken, the results obtained, and provide some illustrative examples.


Introduction
One of the most useful and compelling pieces of evidence for the druggability of a new biological target is the existence of molecules that bind with sufficient affinity to modulate the biological activity of the target. However, only about 11% of the proteome has either an approved drug or a compound known to modulate it 1 . Chemical probes represent a special type of small molecule for use in target validation studies, not only having good activity against the target but also selectivity, cellular activity and potentially other relevant criteria 2,3 and are subjected to a peer review process to ensure the quality of any conclusions when used by the wider community.
An additional and potentially valuable source of information and data on bioactive molecules is the patent literature. In drug discovery, patents are routinely filed to protect novel inventions, by both industrial organisations and academic institutions. The relationship between the patent and the "traditional" (academic journal, peer-reviewed) literature has been examined in a number of published studies, mostly focussed on key questions relating to overlap/duplication and publication date. For example, in a 2009 paper the authors found that just 6% of compounds in patents also appeared in the scientific literature in one of the commercial sources included in their study (GVKBIO) 7 . A later study reported that only 10% of a set of compound-target pairs could be found in literature publications within one year of being reported in patents, and that on average data appears 3 to 4 years earlier in patents than in the scientific literature 8 . For compounds that progress into clinical trials and are ultimately approved, the gap between publication of the patent and publication of the compound in the medicinal chemistry literature can be even longer 6 . Another study concluded that the first molecules for a novel target tend to be published first in the literature, whereas novel small molecules (regardless of targets) more frequently appear first in patents than in literature 9 .
These and similar studies suggest that the patent corpus potentially represents a wealth of information that is not available elsewhere and/or may appear in the scientific literature only after a significant time delay. Previous studies that have attempted to search or annotate pharmaceutical patents with target-compound information include Akhondi et al. who produced a gold standard corpus of 200 patents annotated with chemicals, diseases, targets and modes of action, with the goal of benchmarking patent text mining performance 10 ; Suriyawongkul et al. who focused on identifying targets in patent titles that contained bioactive compound information 11 ; Tyrchan et al. who compared different methods to extract key compounds using structural information, and then applied one of the methods to the design of AXL kinase inhibitors 12 ; Fechete et al. who searched full-text patents using keywords related to diabetic nephropathy and further narrowed the search by rules related to frequency and/or patent section of the keywords found, and subsequently extracted the genes mentioned in the claims section of these patents 13 ; and Gigani et al. who performed a search in the SureChEMBL database 14 using keyword and/or chemical structure searches, with the goal of identifying patents with compounds that could activate the BKCa channel 15 . These, and similar studies, also confirm that extracting information from patents poses many challenges, related to the length of the documents, writing style, structure, and content, among others 16,17 .
Patent data are currently available from a number of resources, including Google Patents (https://patents.google.com/), Lens (https://www.lens.org/), Espacenet (https://www.epo.org/searching-for-patents/technical/espacenet.html) and FreePatentsOnline.com. All of these systems allow searching for patents using various criteria. Pubchem provides links to WIPO PATENTSCOPE, a patent database, for every compound contributed by WIPO to PubChem (16 million compounds). This allows users to search for patents associated with each chemical structure. BindingDB includes a curated set of US granted patents, from which protein-compound activity data is extracted.
In the work reported here, we use SureChEMBL 10 (https://www.surechembl.org/), which is a fully automated, chemical-structure-enabled database providing the research community with open and free access to the patent literature. Currently, SureChEMBL sources data from both patent applications and granted patents via full text patents from the US, European and World patent offices and titles and abstracts from Japan. SureChEMBL currently contains ~140 million patents with ~50,000 added monthly. Of these, ~50 million patents are chemically annotated. Approximately 80,000 new compounds are extracted and added each month to the SureChEMBL chemistry database which currently contains more than 20 million unique structures. The data in SureChEMBL can currently be accessed via a web interface that enables users to perform text and chemical structure queries, filter the output and then display the results. The complete set of chemical structures and patent associations are also available for download.
The US National Institutes of Health established the Illuminating the Druggable Genome (henceforth IDG) project in 2014 (https://commonfund.nih.gov/idg), with the goal of improving our understanding of understudied proteins that belong to well-studied protein families, such as ion channels, GPCRs and protein kinases. One of the key deliverables from the IDG project is an informatics platform, Pharos (https://pharos.nih.gov/) that provides researchers with free access to relevant data on targets. An important aspect of the IDG project (and the data in Pharos) is the classification of human proteins into four Target Development Level (TDL) families, based on how well studied these proteins are 18 . In the Tclin category are targets of at least one approved drug; Tchem targets do not have approved drugs but they are modulated by at least one small molecule with a potency above the cut-off specified for the target protein family (≤30 nM for kinases, ≤100 nM for GPCRs and nuclear receptors, ≤10 μM for ion channels and ≤1 μM for other target families); Tbio do not have chemistry qualifying for the Tclin/Tchem categories but have a reasonable amount of information (e.g., a confirmed Mendelian disease phenotype in the Online Mendelian Inheritance in Man (OMIM) database 19 , publications, GO annotation, antibody reagents); while Tdark are understudied proteins with little annotation 16 . The full description of these categories can be found at http://juniper.health.unm.edu/tcrd/. Of particular relevance to the work here is the availability of small molecule modulators for new targets, consistent with other work suggesting that the lack of high-quality chemical probes for understudied targets is an important cause for lack of interest 16,20 . The default IDG process uses ChEMBL 4 , Guide to Pharmacology 21 and DrugCentral 22 to identify bioactive compounds for a target and so to determine whether it can be included in the Tchem category. At the time of writing, Tclin proteins represent ~3% of the human proteome; Tchem proteins represent ~8%; Tbio proteins represent ~58%; and Tdark proteins represent ~31% 3 .
In this paper, we describe methods to systematically mine the SureChEMBL 10 patent corpus to identify new bioactivity data for Tdark/Tbio targets, with the aim of 1) including the bioactivity data in the ChEMBL database and 2) promoting some of these targets to IDG Tchem status. We quantify the added value of such methods in terms of the numbers of targets that are so "promoted" and then provide some specific illustrative examples.

Methods
The starting point for our work was the set of patents extracted from SureChEMBL covering the years 2012 to 2018, flagged as life-science relevant according to the IPC codes 23 or CPC codes 24 present in the patents, for a total of 3.7 million patents (the specific codes used were A01, A23, A24, A61, A62B, C05, C06, C07, C08, C09, C10, C11, C12, C13, C14, G01N). These codes classify the patents into different areas of technology. The goal was to find patents with bioactivity data of small molecules against understudied targets (Tdark or Tbio categories according to the IDG classification).
Separately, we identified patents that might contain information about IDG Tbio and Tdark targets. A list of Tdark and Tbio IDG target names and gene symbols was obtained from the Target Central Resource Database (36,044 target names/symbols). We searched for these target names and their gene symbols in the patent titles, abstracts, descriptions and claims sections, in the context of specific phrases that could indicate bioactivity data of small molecules against them, for example: "inhibitor/s of x", "activity of x", among others. The combination of these 2 procedures allowed us to classify the patents into 6 groups: patents with tables and bioactivity keywords, and targets mentioned in titles or abstracts; patents with tables and bioactivity keywords, and targets mentioned in descriptions or claims sections; patents without tables with bioactivity keywords, and targets mentioned in titles or abstracts; patents without tables with bioactivity keywords, and targets mentioned in descriptions and claims; patents with tables and bioactivity keywords but no targets; and patents without tables with bioactivity keywords and without targets (figure 1). Following this automated annotation/filtering process, patents from each group were manually examined to confirm the presence of the correct Tbio/Tdark target, the presence of quantitative bioactivity measurements, and that the Tbio/Tdark target was the molecular target to which these bioactivity measurements applied. This final check is required because some of the patents were found to have data only on targets that did not belong to the IDG list of understudied targets; other patents did contain data exclusively on the targets of particular interest to us. Some patents fell into both categories.
For patents with confirmed bioactivity data, details of compounds synthesised, biological assays performed, and bioactivity measurements were manually extracted, according to the standard ChEMBL curation procedure described previously 25 and loaded into the ChEMBL database. All bioactivities against all the targets present in these patents (irrespective of their inclusion or not in the IDG Tbio/Tdark categories) were extracted by the curators.

Results
As a result of this work, bioactivity data from 225 patents were loaded into ChEMBL, corresponding to 657 targets (including single proteins, protein families, protein complexes, organisms, cell lines and protein-protein interactions) and 18,319 compounds. For 145 of these targets, this represents the only source of information of bioactivity data in ChEMBL.
We examined the distribution of patent families among the different groups shown in Figure  1, to identify which group or groups were more or less likely to contain useful information, as this might facilitate the task of identifying the most useful patents in future analyses. The group that had the highest percentage of positive patents was group 1: 48 positive patent families in the 291 families examined (16.4%), followed by group 3: 88 positive families in the 1,912 examined (4.6%). There was no overlap between these 2 groups. Group 2 had 93 positive patent families, but 45 of them were already present in group 1. Group 4 had 95 positive patent families, but 86 of them were already present in group 3. There were very few patents with data in group 5 or group 6 (0.2 % and 0.5 % patent families of the examined ones, respectively) ( As BindingDB also extracts data from patents, we were interested to examine the overlap between the two data sets. For all the targets found, we performed a search by target in BindingDB with the goal of comparing the results from the two different databases. Because BindingDB extracts only US granted patents, we used the patent family in order to do the comparison. We found 33 targets in both BindingDB and our method. Of the 70 patent families found by our method for these targets, 20 were also found in BindingDB. 50 families were found exclusively with our method, and 34 families were found exclusively by BindingDB.

Examples of understudied targets with bioactivities found in patents
In the next section we briefly describe three specific examples of targets where we were able to identify and curate bioactivity data from the patent workflow described above.

LATS1
LATS kinases are critical for organism fitness, genome integrity and cancer prevention. The core kinase module is evolutionarily conserved from yeast through flies to humans, although effectors and biological impact have expanded over the course of evolution 26 . The family includes 2 members, LATS1 and LATS2, which play important roles in the control of tumor development and cell cycles through multiple mechanisms and signaling pathways, including those of p53, Hippo, and Wnt 27 . LATS1 phosphorylates Yes-associated protein (YAP) 1 and inhibits its translocation into the nucleus to regulate genes important for cell proliferation, cell death, and cell migration. Several recent studies have clearly established a role for the Hippo pathway in regulating cell contact inhibition, organ size control, and cancer development in mammals.
As a result of the current work 289 new molecules associated with this target were identified, 184 of which satisfy the IDG cut-off criteria. The source of these compounds is the patent US-20180344738-A1 28 , which describes molecules designed to promote cell proliferation, with applications such as chronic wound healing, promoting liver regrowth, or treating limbal stem cell deficiency. Of the molecules currently in ChEMBL associated with this target (extracted from 44 different papers), the most potent compounds are overwhelmingly from this one patent, albeit they do all contain the same core template.

Histone-lysine N-methyltransferase SUV39H2
Suppressor of variegation 3-9 homolog 2 (SUV39H2, also known as KMT1B) is a member of the SUV39 subfamily of lysine methyltransferases (KMTs). It is an embryonic-specific protein and restricted to the testis in adult tissues of healthy individuals, localised in the nucleus 29 . SUV39H2 is primarily responsible for di-and tri-methylation of histone 3 lysine 9 (H3K9), which result in localized transcriptional silencing 30 . Deregulated H3K9me2/3 results in widespread abnormalities in various cellular functions such as cell proliferation, hypoxia, inflammation, cellular senescence, autophagy, and apoptosis. H3K9 methyltransferases have been implicated in several human diseases, including cancers, cocaine addiction, and HIV-1 latency. There is evidence that dysregulation of SUV39H2 contributes to carcinogenesis and is involved in invasion and metastasis of malignancy, and SUV39H2 is ubiquitously overexpressed in cancer tissues, such as leukemia, lymphomas, lung cancer, breast cancer, colorectal cancer, gastric cancer, and hepatocellular cancer. Some small molecule inhibitors against H3K9 methyltransferases have been developed, but only against some of them (SUV39H1, G9a, and GLP. 39). No H3K9 methyltransferase inhibitors are currently in clinical trials 31 .
At the start of this work there were 19 molecules in ChEMBL with bioactivity data against SUV39H2, all of them from papers, but none of these molecules were within the IDG cutoff.
Our patent workflow identified 460 molecules from just a single patent (US-20180273529-A1 32 ), all within the corresponding IDG cutoff.

G protein-coupled receptor 6
GPR6 is a constitutively highly active G-protein coupled receptor 33,34 . This receptor is mainly expressed in mammalian brain, specifically striatum and hypothalamus, with very low expression at peripheral level. Some endogenous ligands have been proposed for this receptor but due to inconsistencies among assays, it is categorized as orphan by the International Union of Basic and Clinical Pharmacology (IUPHAR) 35 . The physiological role of GPR6 has not been fully elucidated but it has been suggested that it could be involved in several diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease 36 and schizophrenia.
187 molecules (from patents WO-2018183145-A1 37 and EP-2882722-A1 38 ) against GPR6 were identified from this work. These were complemented by an additional 130 molecules from BindingDB (from patent US-9181249-B2 39 ). Figure 2 shows a timeline with patent and publication numbers by year for GPR6, showing that in this particular case significantly more data were reported via patent disclosures than in the scientific literature. Interestingly, a new clinical candidate (currently in phase 2 clinical trials) for Parkinson's disease, CVN424 (a GPR6 inverse agonist), has been disclosed 40 . This molecule can be found in patents as early as 2015 (US-9181249-B2: "Substituted pyrido [3,4-b]pyrazines as GPR6 modulators") but with reported bioactivities above the IDG criteria.

Discussion
The overall goal of this work was to identify bioactivity data on understudied targets from the patent literature, which could allow us to promote targets to the IDG Tchem category. We focused on small molecules only, but our workflow also identified several patents concerning antibodies or RNA as therapeutic agents. For the purposes of this work, we did not progress these patents further, but clearly they could also be useful in the context of "illuminating" new targets.
It should be noted that the work described here was conducted over a period of time, during which complementary data from other sources was added to the various resources concerned. This reflects the natural evolution of the underlying databases, each with its own update mechanism and release schedule procedures. Thus, for example, targets designated as Tdark or Tbio at the time when the research was initiated may have been promoted to a higher category based on separate evidence whilst the patent bioactivity work described here was underway. In the narrative below, the data reflects the particular snapshot corresponding to the time on which the work was initiated or completed, as appropriate.
Two of the proteins included in the list of targets that we searched for (sclerostin and exportin-1) were originally classified as Tbio at the start of this work, but later promoted to Tclin after the approval of the first in class drugs romosozumab and selinexor respectively.
In addition to these 2 targets that are now Tclin, 74 Tdark/Tbio targets were promoted to the Tchem category on the basis of the bioactivity data identified from our patent analysis. Coincidentally, over the same period of time, bioactivity data for 30 Tbio/Tdark targets was added into ChEMBL from the scientific literature. There was an overlap of 21 targets between the 2 sets. This shows the value of using patents as an additional source of bioactivity data.
The work described here involved manually reviewing many patents from the six groups described in Figure 1. As expected, the group with higher percentage of positive patents was Group 1 followed by Group 3. Groups 2 and 4 had lower percentages but still delivered useful and relevant patents. Clearly, for future work, it would be advantageous to develop methods that can reduce the level of manual review that is required. This is the focus on current ongoing work to develop machine learning models able to predict which patents should be prioritised for human examination and potential curation.

Conclusions
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. This 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 and Pharos, and have contributed to the "illumination" of previously dark targets.