Unexplored Therapeutic Opportunities in the Human Genome

Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2018 Unexplored therapeutic opportunities in the human genome Oprea, Tudor I ; Bologa, Cristian G ; Brunak, Søren ; et al ; von Mering, Christian Abstract: A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development. DOI: https://doi.org/10.1038/nrd.2018.14 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-165505 Journal Article Published Version Originally published at: Oprea, Tudor I; Bologa, Cristian G; Brunak, Søren; et al; von Mering, Christian (2018). Unexplored therapeutic opportunities in the human genome. Nature Reviews. Drug Discovery, 17(5):317-332. DOI: https://doi.org/10.1038/nrd.2018.14 ANALYSIS Unexplored therapeutic opportunities in the human genome Tudor I. Oprea1,2,3,4*, Cristian G. Bologa1, Søren Brunak4, Allen Campbell5, Gregory N. Gan2, Anna Gaulton6, Shawn M. Gomez7,8, Rajarshi Guha9, Anne Hersey6, Jayme Holmes1, Ajit Jadhav9, Lars Juhl Jensen4, Gary L. Johnson8, Anneli Karlson6,20, Andrew R. Leach6, Avi Ma’ayan10, Anna Malovannaya11, Subramani Mani1, Stephen L. Mathias1, Michael T. McManus12, Terrence F. Meehan6, Christian von Mering13, Daniel Muthas14, Dac-Trung Nguyen9, John P. Overington6,21, George Papadatos6,22, Jun Qin11, Christian Reich15, Bryan L. Roth8, Stephan C. Schürer16, Anton Simeonov9, Larry A. Sklar2,17,18, Noel Southall9, Susumu Tomita19, Ilinca Tudose6,23, Oleg Ursu1, Dušica Vidovic´16, Anna Waller17, David Westergaard4, Jeremy J. Yang1 and Gergely Zahoránszky-Köhalmi1,24 Abstract | A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome. In a strategic effort to map the knowledge gaps around proteins encoded by the human genome and to promote the exploration of currently understudied, but potentially druggable, proteins, the US National Institutes of Health launched the Illuminating the Druggable Genome (IDG) initiative in 2014. In this article, we discuss how the systematic collection and processing of a wide array of genomic, proteomic, chemical and disease-related resource data by the IDG Knowledge Management Center have enabled the development of evidence-based criteria for tracking the target development level (TDL) of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. We then present spotlights on the TDL categories as well as key drug target classes, including G protein-coupled receptors, protein kinases and ion channels, which illustrate the nature of the unexplored opportunities for biomedical research and therapeutic development. Target selection and prioritization are common goals for For the purposes of this article, we define knowledge Drug Externally administered, academic and commercial drug research organizations. as the consensus of information aggregated from dif- possibly endogenous but While motivations differ, in all cases, the target selection ferent sources and information as structured data, with mostly xenobiotic, substances task is fundamentally one of resource allocation in the a contextual layer that supports a broad range of data that are administered to face of incomplete information. Consequently, target analytics. Data have quantity, quality and dimensionality patients in order to influence the outcome of a disease, selection strategies (and metric-based approaches to (for example, genomic knowledge is defined in relation 1 syndrome or condition. assess their success) remain complex and are hindered to associations with distinct entities such as molecular by multiple bottlenecks. Some bottlenecks pertain to the probes and disease concepts). Data, like facts, may also data themselves, such as disjointed, disparate data and have an expiration date (Supplementary Box S1), and metadata standards, data recording errors and accessi- thus knowledge is subject to change. Yet, within a given bility issues; overcoming these issues will require human time frame, knowledge provides context for interpre- and computational efforts and coordination across mul- tation and integration of emergent data, information tiple communities. Another set of bottlenecks pertains to and models. *e-mail: [email protected] the scientists involved. These include a tendency to focus Data-driven drug discovery strategies rely on the on a small subset of well-known genes2 and the tendency integration of proprietary and internal data with doi:10.1038/nrd.2018.14 Published online 23 Feb 2018; to avoid riskier research paths, driven by poor research third-party resources — both public databases, such corrected online 23 Mar 2018 funding climates3. as PubMed, PubChem4, ChEMBL5 and The Cancer NATURE REVIEWS | DRUG DISCOVERY VOLUME 17 | MAY 2018 | 317 © 2018 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. ANALYSIS 6 Drug targets Genome Atlas (TCGA ), and commercial databases, Illuminating the Druggable Genome. “The reluctance 2 Molecular entities present in such as Integrity. This integration requires fusion and to work on the unknown” (REF. ) is inherent to the living systems that, upon reconciliation of heterogeneous and sometimes con- scientific endeavour, partly due to our subconscious interaction with therapeutic flicting data sources and types. Although many of tendency to choose research subjects more likely to agents or their by-products, these resources are already partially interlinked, data confirm what we already know or believe8. In a deliber- result in modified biological responses that lead to heterogeneity, complexity and incompleteness, as well ate, strategic attempt to map the knowledge gaps around therapeutic outcomes. The as contextual information and metadata capture, pose potential drug targets and to prompt exploration of cur- interaction between a drug and substantial barriers to reliable systematic analyses of all rently understudied but potentially druggable proteins, its target leads, directly or data required to address biomedical research questions, the US National Institutes of Health (NIH) launched the indirectly, to observable clinical 1 outcomes. such as target prioritization in drug discovery . Illuminating the Druggable Genome (IDG) initiative in With the increasing scale and variety of data genera- 2014. As part of this broad, multimillion-dollar initiative, Druggable genome tion, collection and curation in the biomedical sciences, the IDG Knowledge Management Center (KMC) aims Originally defined by Hopkins there is an unmet need for in-depth, accurate and truth- to systematize general and specific biomedical knowl- and Groom as the set of genes ful integration of multiple scientific domains across edge by processing a wide array of genomic, proteomic, that encode proteins that could be modulated by an disciplines. Once successful, these data and knowledge chemical and disease-related resources (BOX 1), with the orally administered small integration efforts enable us to ask both global and explicit goal of supporting target hypothesis genera- molecule, as estimated by fundamental questions about genes, proteins and the tion and subsequent knowledge creation, especially for Lipinski’s ‘rule of five’ processes they are involved in. Integrated resources also genes and proteins that are not well studied. guidelines. allow us to address aspects of reproducibility7 via con- In this article, we first define objective, evidence-based cordance of similar data types from unrelated sources criteria for tracking target development levels (TDLs) for and deficits in our knowledge of biological systems human proteins, using multiple sets of current knowl- and their function. More generally, data integration edge. We discuss the data collected by the KMC on facilitates our ability to quantify knowledge using an TDLs, which show the existence of a substantial knowl- evidence-based approach. edge deficit concerning a large portion of the human proteome (one out of three proteins). Reflecting the goal of illuminating the druggable genome, we then present spotlights on the TDL categories, as well as on key target Author addresses classes, including G protein-coupled receptors (GPCRs), protein kinases and ion channels. 1Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA. Knowledge-based protein classification

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