Dosage de l'alpha-1-foetoprotéine sérique par électroimmunodiffusion sur agarose

Borel-Giraud, N.; A, Maillavin; Vernet-Nyssen, M

doi:10.1016/0009-8981(76)90521-0

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Therefore, Hedgehog pathway inhibitors such as GDC-0449 and AAG are strongly associated with this GO term [99, 100]. Based on recent publications, the half-lives of these drugs are longer than a day (more than 7 days and 5 days for GDC-0449 and AAG, respectively) [100, 101]. Unlike the single enriched GO term, more KEGG pathways were associated with the metabolism of drugs whose half-lives were greater than 24 hours.…”

Section: Resultsmentioning

confidence: 99%

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

et al. 2016

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A drug’s biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

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