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Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.

ABSTRACT: Computation-based drug-repurposing/repositioning approaches can greatly speed up the traditional drug discovery process. To date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for epithelial ovarian cancer (EOC) have not been undertaken. Here, we present a novel drug discovery strategy that combines a computational drug-repositioning system (DrugPredict) with biological testing in cell lines in order to rapidly identify novel drug candidates for EOC. DrugPredict exploited unique repositioning opportunities rendered by a vast amount of disease genomics, phenomics, drug treatment, and genetic pathway and uniquely revealed that non-steroidal anti-inflammatories (NSAIDs) rank just as high as currently used ovarian cancer drugs. As epidemiological studies have reported decreased incidence of ovarian cancer associated with regular intake of NSAIDs, we assessed whether NSAIDs could have chemoadjuvant applications in EOC and found that (i) NSAID Indomethacin induces robust cell death in primary patient-derived platinum-sensitive and platinum- resistant ovarian cancer cells and ovarian cancer stem cells and (ii) downregulation of ?-catenin is partially driving effects of Indomethacin in cisplatin-resistant cells. In summary, we demonstrate that DrugPredict represents an innovative computational drug- discovery strategy to uncover drugs that are routinely used for other indications that could be effective in treating various cancers, thus introducing a potentially rapid and cost-effective translational opportunity. As NSAIDs are already in routine use in gynecological treatment regimens and have acceptable safety profile, our results will provide with a rationale for testing NSAIDs as potential chemoadjuvants in EOC patient trials.

SUBMITTER: Nagaraj AB

PROVIDER: S-EPMC5799769 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.

Nagaraj A B AB Wang Q Q QQ Joseph P P Zheng C C Chen Y Y Kovalenko O O Singh S S Armstrong A A Resnick K K Zanotti K K Waggoner S S Xu R R DiFeo A A

Oncogene 20171002 3

Computation-based drug-repurposing/repositioning approaches can greatly speed up the traditional drug discovery process. To date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for epithelial ovarian cancer (EOC) have not been undertaken. Here, we present a novel drug discovery strategy that combines a computational drug-repositioning system (DrugPredict) with biological testing in cell lines in order to rapidly identify novel dru ...[more]

PMID: 28967908

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Project description:Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein-protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates' therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

Project description:BackgroundDespite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.MethodsHere, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.ResultsWe identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.ConclusionsThis study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Project description:PurposeEsophageal squamous cell cancer (ESCC) is a deadly malignant tumor characterized by an overall 5-year survival rate below 20%, with China accounting for approximately 50% of all cases worldwide. Our previous studies have demonstrated that high integrin-linked kinase (ILK) expression plays a key role in development and progression of ESCC both in vitro and in vivo. Here, we employed the drug repurposing approach to identify a novel FDA-approved anticancer inhibitor against ILK-induced tumorigenesis and progression.MethodsWe screened the ZINC15 database and predicted the molecular docking ability among FDA-approved and publicly available drugs to ILK and then performed computational docking and visual inspection analyses of the top 10 ranked drugs. Two computer-based virtual screened drugs were evaluated in vitro for their ability to directly bind purified ILK by surface plasmon resonance. Cytotoxicity of the two candidate drugs was validated in vitro using CCK-8 and LDH assays.ResultsWe initially selected the top 10 compounds, based on their minimum binding energy to the ILK crystal, after molecular docking and subjected them to further screening. Taking the binding energy of -10 kcal/mol as the threshold, we selected two drugs, namely, nilotinib and teniposide, for the wet-lab experiment. Surface plasmon resonance (SPR) revealed that nilotinib and teniposide had equilibrium dissociation constant (KD) values of 6.410E - 6 and 1.793E - 6, respectively, which were lower than 2.643E - 6 observed in ILK-IN-3 used as the positive control. The IC50 values for nilotinib and teniposide in ESCC cell lines were 40 μM and 200-400 nM, respectively. Results of the CCK-8 assay demonstrated that both nilotinib and teniposide significantly inhibited proliferation of cells (P < 0.01). LDH results revealed that both drugs significantly suppressed the rate of cell death (P < 0.01).ConclusionThe drug repositioning procedure can effectively identify new therapeutic tools for ESCC. Our findings suggest that nilotinib and teniposide are efficacious inhibitors of ILK and thus have potential to target ILK-mediated signaling pathways for management of ESCC.

Dataset Information

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.

Publications

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment.

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