Keywords

Drug repurposing · Therapeutic switching · Serendipitous observations · Genomic

datasets · GWAS

4.1

Historical

The sildenafil, the active ingredient in Viagra, was originally developed by Pfizer for

the treatment of hypertension and angina pectoris (chest pain due to heart disease).

The drug was meant to dilate the heart’s blood vessels by blocking an enzyme called

phosphodiesterase type 5 inhibitor (PDE-5). The discovery that sildenafil could lead

to a penile erection was germinated during clinical trials for treating hypertension,

when the nurses saw men with embarrassment lying on their abdomen to hide their

penile erections (Krishnappa et al. 2019). It appeared that the blood vessel’s dilation

was not in the heart but rather in corpora cavernosa by reducing cyclic guanosine

monophosphate (cGMP) degradation and thus increases arterial blood flow into

penile sinusoids for erection (Boolell et al. 1996a). Subsequent systematic clinical

studies on men, with erectile dysfunction without an established organic cause,

showed sildenafil to enhance the erectile response to visual sexual stimulation,

thus highlighting the important role of the drug in human penile erection (Boolell

et al. 1996b; Goldstein et al. 1998).

4.2

Introduction

Drug repurposing involves the investigation of marketed drugs or drugs that have

been discontinued in clinical trials for reasons other than toxicity concerns for new

therapeutic purposes. In comparison to drug repurposing methods, the traditional

drug discovery is laborious, time consuming, expensive, and with a low success rate

(Fig. 4.1). The striking benefit of drug-repurposing method over traditional drug

discovery is that, for an existing drug, not only preclinical information but also

various clinical profiles such as therapeutic index, pharmacokinetic (PK), pharma-

codynamic (PD), and toxicity (TD50) are already available; as a result, it reduces the

risk of failure at the terminal stage of drug development. Therefore, the drug

compound can rapidly enter terminal stage clinical trials, which involves testing of

the efficacy to treat the new disease. Due to the rapid growth of computational

methods, computing infrastructure, and the explosive large-scale growth of genomic

data such as protein-protein interactions, gene expression, and disease gene associa-

tion data, the cost of drug repurposing is dramatically decreasing. Here, we focus on

recent progress in the area of various genomic datasets (Table 4.1) which can be

exploited for developing new computational methods and identifying repurposed

drugs.

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S. Yellaboina and S. E. Hasnain