Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

Kinases form the backbone of numerous cell signaling pathways, with their dysfunction similarly implicated in multiple pathologies. Further facilitated by their druggability, kinases are a major focus of therapeutic development efforts in diseases such as cancer, infectious disease and autoimmune disorders. While their importance is clear, the role or biological function of nearly one-third of kinases is largely unknown. Here, we describe a data resource, the Dark Kinase Knowledgebase (DKK; https://darkkinome.org), that is specifically focused on providing data and reagents for these understudied kinases to the broader research community. Supported through NIH’s Illuminating the Druggable Genome (IDG) Program, the DKK is focused on data and knowledge generation for 162 poorly studied or ‘dark’ kinases. Types of data provided through the DKK include parallel reaction monitoring (PRM) peptides for quantitative proteomics, protein interactions, NanoBRET reagents, and kinase-specific compounds. Higher-level data is similarly being generated and consolidated such as tissue gene expression profiles and, longer-term, functional relationships derived through perturbation studies. Associated web tools that help investigators interrogate both internal and external data are also provided through the site. As an evolving resource, the DKK seeks to continually support and enhance knowledge on these potentially high-impact druggable targets.

Highlights d Existing small-molecule collections vary greatly on selectivity and target coverage d A data-driven approach to library design enhances diversity and library performance d The LSP-OptimalKinase library enhances selectivity and coverage for kinome targets d The LSP-MoA library optimally targets 1,852 genes in the liganded genome

ABSTRACTThe functions of protein kinases have been widely studied and many kinase inhibitors have been developed into FDA-approved therapeutics. A substantial fraction of the human kinome is nonetheless understudied. In this perspective, members of the NIH Understudied Kinome Consortium mine publicly available databases to assess the functionality of these understudied kinases as well as their potential to be therapeutic targets for drug discovery campaigns. We start with a re-analysis of the kinome as a whole and describe criteria for creating an inclusive set of 710 kinase domains as well as a curated set of 557 protein kinase like (PKL) domains. We define an understudied (‘dark’) kinome by quantifying the public knowledge on each kinase with a PKL domain using an automatic reading machine. We find a substantial number are essential in the Cancer Dependency Map and differentially expressed or mutated in disease databases such as The Cancer Genome Atlas. Based on this and other data, it seems likely that the dark kinome contains biologically important genes, a subset of which may be viable drug targets.

Patients with melanoma resistant to RAF/MEK inhibitors (RMi) are frequently resistant to other therapies, such as immune checkpoint inhibitors (ICI), and individuals succumb to their disease. New drugs that control tumor growth and favorably modulate the immune environment are therefore needed. We report that the small-molecule CX-6258 has potent activity against both RMisensitive (RMS) and-resistant (RMR) melanoma cell lines. Haspin kinase (HASPIN) was identified as a target of CX-6258. HASPIN inhibition resulted in reduced proliferation, frequent formation of micronuclei, recruitment of cGAS, and activation of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway. In murine models, CX-6258 induced a potent cGAS-dependent type-I IFN response in tumor cells, increased IFNg-producing CD8 þ T cells, and reduced Treg frequency in vivo. HASPIN was more strongly expressed in malignant compared with healthy tissue and its inhibition by CX-6258 had minimal toxicity in ex vivo-expanded human tumor-infiltrating lymphocytes (TIL), proliferating TILs, and in vitro differentiated neurons, suggesting a potential therapeutic index for anticancer therapy. Furthermore, the activity of CX-6258 was validated in several Ewing sarcoma and multiple myeloma cell lines. Thus, HASPIN inhibition may overcome drug resistance in melanoma, modulate the immune environment, and target a vulnerability in different cancer lineages. Significance: HASPIN inhibition by CX-6258 is a novel and potent strategy for RAF/MEK inhibitor-resistant melanoma and potentially other tumor types. HASPIN inhibition has direct antitumor activity and induces a favorable immune microenvironment.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. Repurposing can yield a useful therapeutic and also accelerate proof of concept studies that ultimately lead to a new molecular entity. We present a novel machine learning framework, DRIAD (Drug Repurposing In AD), that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD was validated on gene lists known to be associated with AD from other studies and subsequently applied to evaluate lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs were inspected for common trends among their nominal molecular targets and their "off-targets", revealing a high prevalence of kinases from the Janus (JAK), Unc-51-like (ULK) and NIMA-related (NEK) families. These kinase families are known to modulate pathways related to innate immune signaling, autophagy, and microtubule formation and function, suggesting possible disease-modifying mechanisms of action. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be evaluated in a clinical trial.

Libraries of highly annotated small molecules have many uses in chemical genetics, drug discovery and drug repurposing. Many such libraries have become available, but few data-driven approaches exist to compare these libraries and design new ones. In this paper, we describe such an approach that makes use of data on binding selectivity, target coverage and induced cellular phenotypes as well as chemical structure and stage of clinical development. We implement the approach as R software and a Web-accessible tool (http://www.smallmoleculesuite.org) that uses incomplete and often confounded public data in combination with user preferences to score and create libraries. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them, leading us to design a new LSP-OptimalKinase library that outperforms all previous collections in terms of target coverage and compact size. We also assemble a mechanism of action library that optimally covers 1852 targets of the liganded genome. Using our tools, individual research groups and companies can quickly analyze private compound collections and public libraries can be progressively improved using the latest data.

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