EconPapers    
Economics at your fingertips  
 

A joint modeling approach for uncovering associations between gene expression, bioactivity and chemical structure in early drug discovery to guide lead selection and genomic biomarker development

Perualila-Tan Nolen (), Shkedy Ziv, Kasim Adetayo, Talloen Willem, Verbist Bie, Göhlmann Hinrich W.H. and Consortium Qstar
Additional contact information
Shkedy Ziv: Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Center for Statistics, Hasselt University, 3590 Diepenbeek, Belgium
Kasim Adetayo: Wolfson Research Institute for Health and Wellbeing, TS17 6BH Durham University, UK
Göhlmann Hinrich W.H.: Janssen Pharmaceutica NV, 2340, Beerse, Belgium
Consortium Qstar: http://qstar-consortium.org

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 4, 291-304

Abstract: The modern drug discovery process involves multiple sources of high-dimensional data. This imposes the challenge of data integration. A typical example is the integration of chemical structure (fingerprint features), phenotypic bioactivity (bioassay read-outs) data for targets of interest, and transcriptomic (gene expression) data in early drug discovery to better understand the chemical and biological mechanisms of candidate drugs, and to facilitate early detection of safety issues prior to later and expensive phases of drug development cycles. In this paper, we discuss a joint model for the transcriptomic and the phenotypic variables conditioned on the chemical structure. This modeling approach can be used to uncover, for a given set of compounds, the association between gene expression and biological activity taking into account the influence of the chemical structure of the compound on both variables. The model allows to detect genes that are associated with the bioactivity data facilitating the identification of potential genomic biomarkers for compounds efficacy. In addition, the effect of every structural feature on both genes and pIC50 and their associations can be simultaneously investigated. Two oncology projects are used to illustrate the applicability and usefulness of the joint model to integrate multi-source high-dimensional information to aid drug discovery.

Keywords: bioactivity; biomarkers; chemical structure; joint model; transcriptomic (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1515/sagmb-2014-0086 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:291-304:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/sagmb

DOI: 10.1515/sagmb-2014-0086

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2021-05-07
Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:291-304:n:2