Gene Signature-Based Drug Repositioning

*Zhilong Jia, Xinyu Song, Jinlong Shi, Weidong Wang and Kunlun He*

## **Abstract**

With the advent of dynamical omics technology, especially the transcriptome and proteome, a huge amount of data related to various diseases and approved drugs are available under multi global projects or researches with their interests. These omics data and new machine learning technology largely promote the translation of drug research into clinical trials. We will cover the following topics in this chapter. 1) An introduction to the basic discipline of gene signature-based drug repurposing; 2) databases of genes, drugs and diseases; 3) gene signature databases of the approved drugs; 4) gene signature databases of various diseases; 5) gene signature-based methods and tools for drug repositioning; 6) new omics technology for drug repositioning; 7) drug repositioning examples with reproducible code. And finally, discuss the future trends and conclude.

**Keywords:** transcriptome, databases, drug repurposing, mode of action, reproducible study

## **1. Introduction**

Drug repositioning is to identify new indications of the approved drugs. It has lower risk, less human resources, lower cost, and shorter developmental period, compared with traditional drug development. Sir James Black, a Nobel Prize laureate, originally stated that "The most fruitful basis for the discovery of a new drug is to start with an old drug", largely promoting the concept of drug repositioning [1]. There are huge examples of drug repositioning as described in the book. Multinational pharmaceutical companies, such as AstraZeneca and GSK, also showed their great interest in drug repurposing approaches [2, 3].

In this chapter, we focus on gene signature-based drug repositioning. The idea could date from 2000 year. Hughes et al. built a prototypical library of the microarray-based gene expression signatures of Yeast with about 300 diverse gene mutations and the treatment of 13 drugs with known molecular targets by keeping other experimental conditions consistent [4]. They identified a new target of the drug dyclonine by comparing the signatures of genes and drugs via pattern matching [4]. This article opened a door for gene signature-based drug repositioning [5].

A comprehensive gene signature library of genes, diseases and perturbations plays a fundamental role in gene-signature-based drug repositioning. From the genes' view, the knocking down, knocking out, knocking in genes could be achieved to represent the expression signatures of genes with the advances of molecular biology, especially the emergence of the RNAi and CRISPR/Cas9 technology [6].

From the diseases' view, modeling disease in a cell or animal experimental assay would make it possible to produce the gene signatures of various diseases via the quantification of molecular phenotypes. It should be noted that modeling various diseases in parallel and high throughput ways are relatively difficult so far as the condition of modeling various diseases is disease-specific or unclear due to the complexity and our little understanding of some diseases. However, with the development of the pathogenesis of various diseases, it will be efficient to model cellular and animal models of various diseases by magic genome editing using CRISPR/ Cas9 technology [7].

Finally, from a drugs' view, there are thousands of approved drugs available so far. Lots of the bioactive compounds, besides the approved drugs, were also tested to obtain their gene signatures. Particularly, the connectivity map (CMap) [8] and Library of Integrated Network-based Cellular Signatures (LINCS) program [9, 10] largely promoted the rapid development of drug repositioning as they provided a huge of gene signatures of drugs and compounds freely available to the scientific community.

The core principle of gene signature-based drug repositioning is that the candidate drugs should revert the gene signature of the disease of interest, which is changed by the disease, compared with the controls (**Figure 1**). The reversion could be characterized by anti-correlation, distance, similarity and metrics produced by machine learning models. A derivative principle is that the similarity of two drugs could reveal similar indications of the two drugs. In detail, if drug A could be used to treat disease C, and the other drug B is similar to drug A based on their gene signatures, then drug B could also be used to treat disease C. This idea should come from chemoinformatics as the principle that similar drugs based on chemical structures should have similar functions is widely used in the field of drug research and development, especially the development of me-too drugs [11]. Importantly,

#### **Figure 1.**

*The core idea of gene signature-based drug repositioning. Drug repositioning tools search the gene signatures of a drug library to identify which signature is "opposite" to the gene signature of disease, reverting the state of disease to the healthy state.*

#### *Gene Signature-Based Drug Repositioning DOI: http://dx.doi.org/10.5772/intechopen.101377*

several researchers have developed or detailed this principle from different perspectives, making this idea efficient to implement and use.

The gene signatures are the molecular phenotype, revealing the molecular landscape of genes, diseases or drugs. In general, the gene signatures are the expression profiles or changes of RNA measured by RNASeq-based transcriptome via microarray, Next-Generation Sequencing or Third-Generation Sequencing [5, 8]. More broadly, the gene signature could be the abundance profiles or changes of proteins qualified by the antibody-based or tandem mass spectrometry (MS/MS) based proteome. The reason why is that the principle of gene signature-based drug repositioning is suitable to any molecular phenotype, such as the transcriptome and proteome. Moreover, in machine learning models, the tabular data of transcriptome and proteome is similar to a great extent as they are features of samples in a highlevel and united view.

In summary, with the rapid advance of various omic technology, a huge amount of public available omic data related to molecules, drugs, diseases and genes, computational resources and efficient deep learning algorithms make the field of drug repositioning vigorous. There will be increasing therapeutic applications of drug repositioning. In the following sections, we will introduce the databases related to genes, pathways, drugs and diseases, providing the resources for gene signature-based drug repositioning, then describe key tools for web servers for drug repositioning with a highlight on the new powerful and easy-to-use methods, show examples for drug repositioning for several diseases with reproducible code, convenient to the readers to follow. Finally, we will summarize the ongoing challenges, unmet needs, future trends and conclude.
