4.2.5

Drug Induced Gene Expression Signatures

High-throughput screening of drugs has been greatly enhanced by the development

of computational methods and various genomic resources such as connectivity map

(CMap) (Lamb et al. 2006) and the Library of Integrated Network-Based Cellular

Signatures (LINCS) (Keenan et al. 2018). CMap and LINCS are large-scale gene

expression databases based on drug perturbation of many cultivated cell lines. Both

datasets serve as reference datasets for drug perturbation profiles of thousands of

chemical compounds. These large scale data resources provide an important plat-

form to characterize signatures of gene expression changes induced by drugs and

small molecules. Such signatures of drug perturbation have been used to identify the

interactions, similarities, or dissimilarities among drugs, diseases, genes, and

pathways.

4.3

Major Techniques of Drug Repurposing

Advanced high-throughput technologies in proteomics, gene expression, sequenc-

ing, and genome-wide association studies have been generating large amounts of

data on protein-protein interactions, gene expression, and disease gene interactions.

In addition to docking-based methods (Kumar et al. 2019; Hasnain et al., US 2020/

0188477 A1, 2020), there were several genomics and cheminformatics-based

computational methods developed for drug repurposing by exploiting aforemen-

tioned datasets. Here, we discuss some of the computational methods.

4.3.1

Connectivity Map

The connectivity map (CMap) and Library of Integrated Network-Based Cellular

Signatures (LINCS) are comprehensive, large-scale drug perturbation databases

containing transcriptomic profiles of dozens of cultivated cell lines treated with

thousands of bioactive chemical compounds serving as reference databases for

drug-induced gene depression signatures (Lamb et al. 2006; Subramanian et al.

2017). The resource can be used to find connections among small molecules sharing

a common mechanism of action, diseases, and physiological processes. Particularly,

the reference data resource can be used in drug discovery to find out the small

molecules which could possibly suppress or reverse the disease-induced gene

expression signature based on anticorrelation between small molecule-induced

gene expression and disease-induced gene expression signature of interest. Several

groups have used the cMap drug discovery feature to identify the potential candidate

drugs for various diseases such as cancer and Crohn’s disease to name a few (Cheng

et al. 2014; Dudley et al. 2011; Kwon et al. 2020). The gene expression was obtained

from Gene Expression Omnibus (GEO). The disease gene expression signatures

were identified by differential expression analysis of genes between the disease

affected (Crohn’s disease and ulcerative colitis) and healthy control samples. The

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Computational Methods for Drug Repurposing

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