The missing link between clinical endpoints and drug targets in depression

Oscar Della Pasqua; Gijs W. Santen; Meindert Danhof

doi:10.1016/j.tips.2009.12.004

The missing link between clinical endpoints and drug targets in depression

Trends in Pharmacological Sciences ◽

10.1016/j.tips.2009.12.004 ◽

2010 ◽

Vol 31 (4) ◽

pp. 144-152 ◽

Cited By ~ 11

Author(s):

Oscar Della Pasqua ◽

Gijs W. Santen ◽

Meindert Danhof

Keyword(s):

Drug Targets ◽

Missing Link ◽

Clinical Endpoints

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Zika virus drug targets: a missing link in drug design and discovery – a route map to fill the gap

RSC Advances ◽

10.1039/c6ra12142j ◽

2016 ◽

Vol 6 (73) ◽

pp. 68719-68731 ◽

Cited By ~ 18

Author(s):

Pritika Ramharack ◽

Mahmoud E. S. Soliman

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Viral Replication ◽

Zika Virus ◽

In Silico ◽

Drug Targets ◽

Missing Link ◽

Potential Inhibitors

This review depicts anin silicoroute map for ZIKV drug discovery, thus revealing novel potential inhibitors of viral replication.

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ChemInform Abstract: Zika Virus Drug Targets: A Missing Link in Drug Design and Discovery - A Route Map to Fill the Gap

ChemInform ◽

10.1002/chin.201639220 ◽

2016 ◽

Vol 47 (39) ◽

Author(s):

Pritika Ramharack ◽

Mahmoud E. S. Soliman

Keyword(s):

Drug Design ◽

Zika Virus ◽

Drug Targets ◽

Missing Link

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Targeted Therapies in Adult B-Cell Malignancies

BioMed Research International ◽

10.1155/2015/217593 ◽

2015 ◽

Vol 2015 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Jean-François Rossi

Keyword(s):

B Cell ◽

B Lymphocytes ◽

Drug Targets ◽

Cell Communication ◽

New Drugs ◽

Activation Process ◽

Cellular Mechanisms ◽

B Cell Malignancies ◽

Clinical Endpoints ◽

Clinical Consequences

B-lymphocytes are programmed for the production of immunoglobulin (Ig) after antigen presentation, in the context of T-lymphocyte control within lymphoid organs. During this differentiation/activation process, B-lymphocytes exhibit different restricted or common surface markers, activation of cellular pathways that regulate cell cycle, metabolism, proteasome activity, and protein synthesis. All molecules involved in these different cellular mechanisms are potent therapeutic targets. Nowadays, due to the progress of the biology, more and more targeted drugs are identified, a situation that is correlated with an extended field of the targeted therapy. The full knowledge of the cellular machinery and cell-cell communication allows making the best choice to treat patients, in the context of personalized medicine. Also, focus should not be restricted to the immediate effects observed as clinical endpoints, that is, response rate, survival markers with conventional statistical methods, but it should consider the prediction of different clinical consequences due to other collateral drug targets, based on new methodologies. This means that new reflection and new bioclinical follow-up have to be monitored, particularly with the new drugs used with success in B-cell malignancies. This review discussed the principal aspects of such evident bioclinical progress.

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Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes

International Journal of Genomics ◽

10.1155/2018/8124950 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Runyu Jing ◽

Yu Liang ◽

Yi Ran ◽

Shengzhong Feng ◽

Yanjie Wei ◽

...

Keyword(s):

Machine Learning ◽

Drug Targets ◽

Ensemble Methods ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Complete Coverage ◽

Sequencing Platform ◽

Clinical Endpoints ◽

Disease Related Genes ◽

Stable Scheme

In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. In addition, many studies commonly use machine learning methods to evaluate genetic datasets to identify potential disease-related genes and drug targets, but to the best of our knowledge, the information associated with the selected gene set was not thoroughly elucidated in previous studies. To identify a relatively stable scheme for modeling limited samples in the gene datasets and reveal the information that they contain, the present study first evaluated the performance of a series of modeling approaches for predicting clinical endpoints of cancer and later integrated the results using various voting protocols. As a result, we proposed a relatively stable scheme that used a set of methods with an ensemble algorithm. Our findings indicated that the ensemble methodologies are more reliable for predicting cancer prognoses than single machine learning algorithms as well as for gene function evaluating. The ensemble methodologies provide a more complete coverage of relevant genes, which can facilitate the exploration of cancer mechanisms and the identification of potential drug targets.

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