Early repositioning through compound set enrichment analysis: a knowledge-recycling strategy

Guanabenz Acetate Induces Endoplasmic Reticulum Stress-Related Cell Death in Hepatocellular Carcinoma Cells

Background

Development of chemotherapeutics for the treatment of advanced hepatocellular carcinoma (HCC) has been lagging. Screening of candidate therapeutic agents by using patient-derived preclinical models may facilitate drug discovery for HCC patients.

Methods

Four primary cultured HCC cells from surgically resected tumor tissues and six HCC cell lines were used for high-throughput screening of 252 drugs from the Prestwick Chemical Library. The efficacy and mechanisms of action of the candidate anti-cancer drug were analyzed via cell viability, cell cycle assays, and western blotting.

Results

Guanabenz acetate, which has been used as an antihypertensive drug, was screened as a candidate anti-cancer agent for HCC through a drug sensitivity assay by using the primary cultured HCC cells and HCC cell lines. Guanabenz acetate reduced HCC cell viability through apoptosis and autophagy. This occurred via inhibition of growth arrest and DNA damage-inducible protein 34, increased phosphorylation of eukaryotic initiation factor 2α, increased activating transcription factor 4, and cell cycle arrest.

Conclusions

Guanabenz acetate induces endoplasmic reticulum stress–related cell death in HCC and may be repositioned as an anti-cancer therapeutic agent for HCC patients.

A Review of Drug Repositioning Based Chemical-induced Cell Line Expression Data

Drug repositioning is an important area of biomedical research. The drug repositioning studies have shifted to computational approaches. Large-scale perturbation databases, such as the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures, contain a number of chemical-induced gene expression profiles and provide great opportunities for computational biology and drug repositioning. One reason is that the profiles provided by the Connectivity Map and the Library of Integrated Network-Based Cellular Signatures databases show an overall view of biological mechanism in drugs, diseases and genes. In this article, we provide a review of the two databases and their recent applications in drug repositioning.

VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization

BACKGROUND

Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.

METHOD

We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions.

RESULTS

VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of "small sample size" regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time.

CONCLUSION

In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions.

"Omics"-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the "data-heavy" outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences-genomics, transcriptomics, proteomics and metabolomics-have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

Drug repositioning for gynecologic tumors: a new therapeutic strategy for cancer

The goals of drug repositioning are to find a new pharmacological effect of a drug for which human safety and pharmacokinetics are established and to expand the therapeutic range of the drug to another disease. Such drug discovery can be performed at low cost and in the short term based on the results of previous clinical trials. New drugs for gynecologic tumors may be found by drug repositioning. For example, PPAR ligands may be effective against ovarian cancer, since PPAR activation eliminates COX-2 expression, arrests the cell cycle, and induces apoptosis. Metformin, an antidiabetic drug, is effective for endometrial cancer through inhibition of the PI3K-Akt-mTOR pathway by activating LKB1-AMPK and reduction of insulin and insulin-like growth factor-1 due to AMPK activation. COX-2 inhibitors for cervical cancer may also be examples of drug repositioning. PGE2 is induced in the arachidonate cascade by COX-2. PGE2 maintains high expression of COX-2 and induces angiogenic factors including VEGF and bFGF, causing carcinogenesis. COX-2 inhibitors suppress these actions and inhibit carcinogenesis. Combination therapy using drugs found by drug repositioning and current anticancer drugs may increase efficacy and reduce adverse drug reactions. Thus, drug repositioning may become a key approach for gynecologic cancer in drug discovery.