5.2.2

In Silico Approaches

The in silico approach has garnered more attention in the recent decade. This

approach requires a computer and available databases with drug, disease, or pathway

information. This approach comprises many different types of methods, such as

network analysis, data mining, ligand/structure based, and molecular docking

(March-Vila et al. 2017). Network analysis allows for the modelling of functional

similarities between drugs, proteins, genes, and other biological systems (Tuerkova

and Zdrazil 2020). Networks can be classified into two categories—homogenous

and heterogenous. A homogenous network is defined as protein-protein interaction

networks that can be used to identify drug targets involved in multiple pathways,

whereas a heterogeneous network incorporates different information, such as geno-

mics, proteomics, and metabolic pathways to create a multilayer relationship model

(Xue et al. 2018).

Data mining allows the generation of novel hypotheses through a method known

as the “ABC model” discovered by Swanson (Weeber et al. 2005). In this model, it

states that if A and B are related and B and C are related, then it can be hypothesized

that A and C are indirectly related. This model is said to be a pioneer in literature-

based discovery (LBD) (Kim et al. 2016). One example of DR through LBD is

pirlindole (BVA-201), where a new indication to treat MS was found when it was

initially used as a chronic treatment of depression and anxiety disorders (Lekka et al.

2011). There has been an increase in text mining tools with the development of

natural language processing (NLP) techniques. A summary of the tools and respec-

tive descriptions can be found in this review by Xue et al. (2018). Using the network

analysis approach, human immunodeficiency virus (HIV) protease inhibitors were

observed to inhibit the phosphoinositide 3-kinase (PI3K)/Akt pathway, a pathway

that is activated in many types of cancer. As a result, nelfinavir, a HIV protease

inhibitor, is undergoing clinical trials to be repositioned as an anticancer agent

inhibiting Akt (Gills et al. 2007).

Molecular docking is a method that visualizes the binding of a drug inside a three-

dimensional target structure. In 2001, another method known as “inverse docking”

was proposed to investigate one drug against multiple protein binding sites (Li and

Jones 2012). In a study by Kumar et al., molecular docking was used to screen a

library of available antipsychotic drugs and found that benperidol interacted with

different target proteins involved in Alzheimer’s disease (AD), showing its potential

as a possible candidate for treating the disease (Kumar and Kumar 2019).

The computational approach is still growing, and more methods that are consid-

ered “newer” and more advanced are being utilized. The sections below will dive

deeper into the more current computational approaches focusing on the genomic and

network approaches. Fig. 5.2 below also provides a brief summary of various

computational methods and their strategies.

5

Genomic Approaches for Drug Repositioning

55