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Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery.

Cell chemical biology | 2017

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade.

Pubmed ID: 28434878 RIS Download

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This is a list of tools and resources that we have found mentioned in this publication.


edgeR (software resource)

RRID:SCR_012802

Bioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.

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Qvalue (software resource)

RRID:SCR_001073

R package that takes a list of p-values resulting from the simultaneous testing of hypotheses and estimates their q-values. It is designed to measure the proportion of false positives when a test is significant. The software is capable of generating plots for visualization. It can be applied to problems in genomics, brain imaging, astrophysics, and data mining.

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