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.
Abstract
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.
Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to ...compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online tool http://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1,852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections.
The disciplines of small molecule drug discovery and chemical genetics are highly related in that they both study the effect of small molecules on biological systems, but also differ in intention. ...Drug discovery aims to find disease mitigating substances, while chemical biology ultimately aims to increase our knowledge of biology. Despite this difference however, both chemical genetics and targeted drug discovery have focused on selectivity of small molecules to assess the quality of their molecules and infer likelihood of success. The logic for pursuing selectivity in chemical genetics is that a truly specific in that it binds to a single protein, the phenotypic changes observed when using this molecule can be attributed to the function of this protein. In this case, selectivity can be seen as a runner-up or best alternative of specificity. In drug discovery, selectivity became key as the practice of targeted drug discovery, which approaches therapeutics from a ‘one disease, one gene, one drug’ angle. In targeted drug discovery, selectivity is regarded as indicative for the likelihood of side-effects associated with therapeutic drugs – the more selective a drug candidate, the lower the likelihood of a no side-effect effective therapeutic. This thesis focuses on various aspects of small molecule selectivity. Throughout the chapters I review how selectivity of small molecules became a concept that was important to medicinal chemists and molecular biologists in drug discovery. I assess the selectivity of currently available small molecules in a data-driven manner. I develop metrics that accurately describe diverse biological properties of small molecules in a data driven fashion and use this to improve compound selection for initial screening of small molecules as well as biological conclusions drawn from these studies. Ultimately, I investigate whether the lack of specificity often found in approved therapeutic drugs could be advantageous to their efficacy for which I compare drug targets that are inhibited concomittedly to the modular patterns in genomic functional redundancy. Lastly, in the final chapter, I propose how the findings described in this thesis can be incorporated in future studies in both chemical genetics and drug discovery.
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