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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

ABSTRACT: The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

SUBMITTER: Himmelstein DS

PROVIDER: S-EPMC5640425 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

Himmelstein Daniel Scott DS Lizee Antoine A Hessler Christine C Brueggeman Leo L Chen Sabrina L SL Hadley Dexter D Green Ari A Khankhanian Pouya P Baranzini Sergio E SE

eLife 20170922

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public ...[more]

PMID: 28936969

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Project description:The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Project description:Background: Dementia has a significant impact on quality of life of older individuals. Impaired proteostasis has been implicated as a potential cause of dementia, that can be therapeutically targeted to improve patient outcomes. This review aimed to collate all current evidence of the potential for targeting proteostasis with repurposed drugs as an intervention for age-related dementia and cognitive decline. Methods: PubMed, Web of Science and Embase databases were searched from inception until 4th July 2017 for studies published in English. Interventional studies of repurposed proteostasis-modifying drugs in Alzheimer's disease (AD), Parkinson's disease (PD), Lewy Body disease, vascular dementia, and cognitive aging, in either animal models or humans with change in cognition as the outcome were included. The SYRCLE and Cochrane tools were used to assess risk of bias for included studies. Results: Overall 47 trials, 38 animal and 9 human, were isolated for inclusion in this review. Drugs tested in animals and humans included lithium, rapamycin, rifampicin, and tyrosine kinase inhibitors. Drugs tested only in animals included Macrophage and Granulocyte-Macrophage Colony Stimulating Factors, methylene blue, dantrolene, geranylgeranylacetone, minocycline and phenylbutyric acid. Lithium (n = 10 animal, n = 6 human) and rapamycin (n = 12 animal, n = 1 human) were the most studied proteostasis modifying drugs influencing cognition. Nine of ten animal studies of lithium showed a statistically significant benefit in Alzheimer's models. Rapamycin demonstrated a significant benefit in models of vascular dementia, aging, and Alzheimer's, but may not be effective in treating established Alzheimer's pathology. Lithium and nilotinib had positive outcomes in human studies including Alzheimer's and Parkinson's patients respectively, while a human study of rifampicin in Alzheimer's failed to demonstrate benefit. Microdose lithium showed a strongly significant benefit in both animals and humans. While the risk of bias was relatively low in human studies, the risk of bias in animal studies was largely unclear. Conclusion: Overall, the collective findings support the hypothesis that targeting proteostasis for treatment of dementia may be beneficial, and therefore future studies in humans with repurposed proteostasis modifying drugs are warranted. Larger human clinical trials focusing on safety, efficacy, tolerability, and reproducibility are required to translate these therapeutics into clinical practice.

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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

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Systematic integration of biomedical knowledge prioritizes drugs for repurposing.

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