Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs

Womack, Finn; McClelland, Jason; Koslicki, David

doi:10.1101/765305

Cited by 11 publications

(14 citation statements)

References 66 publications

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“…An alternative approach taken to decide classification threshold based largely on the known class distribution. 3 The knowledge graph provides an incredible amount of biological information, and we only used an incredibly small amount of this information. Each edge in the graph was weighted according the absolute occurrence of this relationship appears in scientific literature.…”

Section: Discussion Methodologymentioning

confidence: 99%

“…Systems pharmacology and network medicine (NM) approaches to drug discovery and drug repurposing have proved efficient methods to highlight potential drug candidates. 2,3,4 NM treats biological networks as heterogeneous information systems; correlating network topology and node properties with biological processes, functions, pathways and interactions. From a systems biology point of view, a disease can be seen as a selection of genes within a network, whose misregulation culminates in changes in biological processes and pathways.…”

Section: Introductionmentioning

confidence: 99%

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A Network Polypharmacological Approach to Combinatorial Drug Repurposing for Diffuse Intrinsic Pontine Glioma

Maclean

Nazarian

Pantziarka

et al. 2020

Preprint

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Despite five decades of clinical investigations, there is currently no effective treatment for children diagnosed with Diffuse Intrinsic Pontine Glioma (DIPG). We now understand that DIPGs share the same histone 3 mutation and fatal prognosis as other diffuse midline gliomas (DMGs), which led to the introduction of a new entity referred to as DMG, H3 K27M mutant. Indeed, therapeutics indicated for other brain neoplasms have proven ineffective for DIPGs. We posit that by using a polypharmacological approach to determine drug combinations that target distinct mechanistic pathways of DIPG, it is more likely that an efficacious treatment will be developed. Using FDAapproved drugs as an indication for viability of a drug pair for combinatorial use, we developed an embedding-based link-prediction model trained on a drug-disease regulation network, to predict the complementary relationship between a drug pair. To further understand the regulatory mechanisms of these potential drug combinations, we explored the regulatory action of these drugs upon statistically significant biological processes and molecular functions that are in turn misregulated in the disease state of phylogenetically similar gliomas to DIPG. Next, we constructed a regulatory network of these regulators illustrating a negative correlation between network proximity and overlap of direct targets, regulators, pathways and biological processes. We replicate research that finds an optimal distance between drugs in this regulatory network for both FDA-approved and synergistic drugs, although we deem the embedding model more performant. Finally, we highlight the individual importance of all disease-host protein regulators in notable pathways, by constructing protein-protein interaction networks for each pathway and calculating the centrality of each regulatory gene. We believe this newly formed knowledge of drug pairs and their regulatory pathways, will help researchers in identifying an efficacious treatment for this disease.

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Section: Discussion Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Network Polypharmacological Approach to Combinatorial Drug Repurposing for Diffuse Intrinsic Pontine Glioma

Maclean

Nazarian

Pantziarka

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…We encourage predictions to be analysed only on the context of the relative list of regulators for that disease. An alternative approach taken to decide classification threshold would be based largely on the known class distribution [2].…”

Section: Discussionmentioning

confidence: 99%

A Network Medicine Approach to Drug Repurposing for Chronic Pancreatitis

Golden

2020

Preprint

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Despite decades of clinical investigations, there is currently no effective treatment for patients diagnosed with Chronic Pancreatitis (CP). Computational drug repurposing holds promise to rapidly identify therapeutics which may prove efficacious against the disease. Using a literature-derived knowledge graph, we train multiple machine learning models using embeddings based on i) the network topology of regulation bipartite networks, ii) protein primary structures and iii) molecule substructures. Using these models, we predict approved drugs that down-regulate the disease, and assess their proposed respective drug targets and mechanism of actions. We analyse the highest predicted drugs and find a diverse range of regulatory mechanisms including inhibition of fibrosis, inflammation, immmune response, oxidative stress and calcium homeostasis. Notably, we identify resiniferatoxin, a potent analogue of capsaicin, as a promising repurposable candidate due to its antiinflammatory properties, nociceptive pain suppression, and regulation of calcium homeostatis (through potentiation of mutant cystic fibrosis transmembrane conductance regulator (CFTR)). Resiniferatoxin may also regulate intracellular acinar Ca2+ via agonism of transient receptor potential vanilloid subfamily member 6 (TRPV6). We believe the potential of this repurposable drug warrants further in silico and in vitro testing, particularly the affect of the TRPV6 agonism on disease pathogenesis.

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“…Typically, careful integration of databases can take months and even years, and oftentimes the integration workflow is quite specific for the knowledge base at hand [30], [31].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, from a technical standpoint, it is of paramount importance that we have publicly available mechanisms and workflows for real-time integration of heterogeneous information. Typically, careful integration of databases can take months and even years, and oftentimes the integration workflow is quite specific for the knowledge base at hand [30], [31].…”

Section: Introductionmentioning

confidence: 99%

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

Zahoránszky-Kőhalmi

Siramshetty

Kumar

et al. 2020

Preprint

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Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availability: https://neo4covid19.ncats.io . Keywords: SARS-CoV-2, COVID-19, network pharmacology, graph database, Neo4j, data integration, drug repositioning

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Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs

Cited by 11 publications

References 66 publications

A Network Polypharmacological Approach to Combinatorial Drug Repurposing for Diffuse Intrinsic Pontine Glioma

A Network Polypharmacological Approach to Combinatorial Drug Repurposing for Diffuse Intrinsic Pontine Glioma

A Network Medicine Approach to Drug Repurposing for Chronic Pancreatitis

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

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