Repurposing and Drug Discovery

## In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process

*Kareti Srinivasa Rao and P. Subash*

#### **Abstract**

Repurposing "old" drugs to treat both common and rare diseases is increasingly emerging as an attractive proposition due to the use of de-risked compounds, with potential for lower overall development costs and shorter development timelines. This is due to the high attrition rates, significant costs, and slow pace of new drug discovery and development. Drug repurposing is the process of finding new, more efficient uses for already-available medications. Numerous computational drug repurposing techniques exist, there are three main types of computational drug-repositioning methods used on COVID-19 are network-based models, structure-based methods and artificial intelligence (AI) methods used to discover novel drug–target relationships useful for new therapies. In order to assess how a chemical molecule can interact with its biological counterpart and try to find new uses for medicines already on the market, structure-based techniques made it possible to identify small chemical compounds capable of binding macromolecular targets. In this chapter, we explain strategies for drug repurposing, discuss about difficulties encountered by the repurposing community, and suggest reported drugs through the drug repurposing. Moreover, metabolic and drug discovery network resources, tools for network construction, analysis and protein–protein interaction analysis to enable drug repurposing to reach its full potential.

**Keywords:** drug repurposing, protein–protein interaction, drug discovery, COVID-19, pharmacological repositioning

#### **1. Introduction**

Drug repurposing, also known as drug repositioning, is a strategy for speeding up the medicine discovery process by identifying a new therapeutic usage for an alreadyapproved drug for a different indication. One of the outcomes of polypharmacology is the increased success and applicability of drug repurposing, which is a manifestation of the transition from a single to multitarget paradigm in drug discovery [1]. COVID-19 has now been labelled a pandemic, necessitating the development of novel medicines as we move beyond containment. It is unrealistic to meet the current global crisis by developing new pharmaceuticals from the ground up because it is a lengthy

procedure. Drug repurposing is a new method in which current drugs that have been proven safe in humans are repurposed to treat diseases that are difficult to cure. While taking these repurposed medications alone may not provide a meaningful clinical advantage, strategically combining them into a cocktail could be quite useful [2].

Repositioning previously approved medications is a promising practice since it lowers the cost and length of the drug development pipeline while also lowering the risk of unexpected side effects. The ability to quickly screen candidates in silico and limit the number of prospective repositioning candidates makes computational repositioning particularly interesting. What is not obvious is how effective such strategies are at generating clinically useful repositioning hypotheses is represented in **Figure 1** [3]. The SARS-CoV-2 virus causes a respiratory infection that can lead to pneumonia. COVID-19 has a mortality rate of 2–3.5%, which rises with age and the presence of comorbidities (e.g., hypertension, cardiac insufficiency, diabetes, and asthma). By April 15, 2020, the new coronavirus has infected 2,033,406 people worldwide and killed over 130,000 people [4]. COVID-19 has depleted health systems around the world, leading countries to take drastic measures such as closing land borders and instituting social distancing regulations to halt the disease's spread [5].

The new coronavirus (SARS-CoV-2), which causes COVID-19, has swiftly become a global danger to public health and the economy [5, 6]. SARS-CoV-2, according to recent clinical reports, produces both mild, self-limiting respiratory tract infection and severe progressive pneumonia, which can lead to multiorgan failure and death. Despite the severity of some cases, no pathogen-specific antivirals are currently available to treat this infection. As a result, several studies have looked at the anti-SARS-CoV-2 activity of currently available medicines [7].

**Figure 1.** *In silico-based drug repurposing.*

*In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*

#### **2. Faster development times and reduced risks**

Attempts to speed up the development of a medication are typically accompanied by an increase in risk. Drug repositioning, on the other hand, offers a solution to the problem. Repositioning candidates have generally gone through numerous phases of clinical development and hence have well-known safety and pharmacokinetic properties, which reduces development risk. Shorter paths to the clinic are also possible because *in vitro* and *in vivo* screening, chemical optimization, toxicity, bulk production, formulation development, and even early clinical development have all been achieved in many situations. In summary, these considerations allow for the reduction of many years from the path to market, as well as major risks and costs (**Figure 2**). As a result, repositioning may provide a superior risk-to-reward ratio than other medication development tactics. These benefits have not gone unnoticed by venture capital firms looking for high value exits for their companies in the near future. Because of the strong response such firms have had from the public equity markets, it is nearly impossible for venture capitalists to invest in a therapeutics company without drug prospects in or approaching clinical trials in 2004. Indeed, repositioning allows for the rapid creation of such a pipeline, and repositioning firms are having little issue with getting venture capital.

#### **Figure 2.**

*A comparison of traditional de novo drug discovery and development versus drug repositioning. (a) It is well known that from concept to marketable medicine, de novo drug research and development takes 10–17 years [8]. The probability of success is lower than 10% [9]. (b) because repositioning candidates have frequently gone through numerous phases of development for their initial indication, several phases common to de novo drug discovery and development can be avoided, allowing for a reduction in time and risk. ADMET is an abbreviation for absorption, distribution, metabolism, excretion, and toxicity; EMEA is an abbreviation for European medicines agency; FDA is an abbreviation for Food and Drug Administration; IP is an abbreviation for intellectual property; and MHLW is an abbreviation for Ministry of Health, labour, and welfare.*

#### **3. Drug repositioning opportunities**

Drug repositioning is a potential approach that is gaining traction among governments and pharmaceutical corporations due to its critical role in decreasing time, cost, and risk in the development of treatments for cancer and other terminal diseases. As this technique became more widely known, multidisciplinary teams of researchers and scientists attempted, with varying degrees of efficiency and success, to computationally study the potential of repositioning drugs to treat other diseases and identify alternative indications, regardless of whether the drug in question was approved, withdrawn, in clinical trials, or failed. Despite the fact that drug repositioning is a relatively new technique, the traditional, costly, and risky de novo drug development process is still necessary for discovering and testing new drugs; however, incorporating some computational drug repositioning models into this process can help to move drugs forward in the development pipeline and ultimately improve drug efficiencies in clinical trials. The potential for drug repositioning to help create the critical medications needed to combat the present coronavirus outbreak cannot be overstated [10].

#### **4. Challenges and opportunities**

Traditional drug development strategies are risky, expensive, and prone to failure. As a result, drug repositioning has recently gained attention, and it expedites the release of medications for clinical usage. Drug repositioning, on the other hand, is a complicated process involving a variety of aspects, including technology, business models, patents, investment, and market demands. Despite the fact that many medical databases have been built, determining the best strategy to fully utilise huge volumes of medical data remains a challenge. New techniques for drug repositioning are urgently needed. Another problem that needs to be addressed is intellectual property (IP). IP protection for repositioning medications is minimal [11]. Some novel drug-targeted-disease connections discovered by repositioning researchers, for example, were corroborated by papers or online databases; yet, according to the law, it is difficult to seek IP protection for such associations. Some repositioned medications are unable to enter the market due to intellectual property issues. Furthermore, some repositioning attempts have to be abandoned, wasting both time and money [12]. Because the existing commercial model is serial and produces overlapping investment concerns, it is required to develop a new commercial model. Challenges accompany opportunities. An unintentional finding in the 1920s was the first example of medication repositioning. More ways of speeding up the process of drug repositioning have been proposed after nearly a century of development. As a result, medication repositioning has made significant progress. **Table 1** contains 75 examples of pharmacological repositioning culled from the extensive literature. To increase the performance of drug repositioning in this circumstance, massive machine learning techniques were applied. Experimental procedures, such as target screening approaches, have been developed in addition to computational approaches to provide direct proof of correlations between medications and diseases [11, 12, 24].

#### **5. Drug-based computational approaches**

The structure and chemical characteristics of a medicinal compound are clearly linked to its final therapeutic effectiveness. As a result, repositioning options

*In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*



*In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*



#### **Table 1.**

*Pharmacological repositioning culled from the extensive literature.*

for medicinal molecules can be investigated based on chemical similarities. The rationale for this method is based on quantitative connections between chemical structures and biological activity that are well-known (QSAR). Although identical structures in biological systems do not always act the same, computational techniques for drug repositioning can take use of the degrees of resemblance that exist. Chemical similarity techniques work by extracting a set of chemical properties for each drug in a group of medications, then clustering or creating networks based on the recovered features to relate the drugs directly to one another [25]. Simple chemical associations or looking for specific biological traits, such as known drug targets, enriched in the resulting correlations can subsequently be used to infer therapeutic repositioning prospects.

Chemical systems biology is being used to identify new drugs in a network. A unique method of modelling and predicting drug–target interactions is statistical modelling of similarities in chemical structure between medicines and possible ligands [26, 27]. Before and after modelling with chemical drug – ligand interactions, network mapping of a wide range of drugs to protein targets enabled the prediction of new targets, including primary sites of action and off-target proteins as explanations for well-known side effects, with new and unexpected drug binding revealed across major categories of proteins unrelated by sequence or structure. A number of modelling predictions were validated using binding assays, proving the method's

#### *In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*

efficacy [28]. A generated network of chemogenomic space exhibited a high level of interaction between gene families, giving tractable drug combinations the ability to act on projected targets, by integrating structure – activity data for predicted multiple target binding compounds [29]. This demonstrates how networks can be used as templates for statistical and computational modelling predictions of drug–ligand interactions, and it adds to our understanding of polypharmacology, or the particular binding of a molecule to two or more biological targets [27]. Computational tools for drug discovery are represented in **Table 2**.



#### **Table 2.**

*Tools for protein–protein interaction analysis.*

#### **6. Applications of personalised medicine and drug repositioning**

The utilisation of personalised medicine methodologies to investigate particular diseases and reposition medications for these diseases has far-reaching diagnostic and therapy implications. Both of these approaches are particularly useful for rare diseases or disease subtypes that are difficult to investigate and conduct clinical trials for due to their rarity [30]. They're also important for patients who are resistant to or have developed resistance to medicines and do not have any other options for treatment. We'll look at how customised medicine and drug repositioning methods can help in these two cases in this section.

#### **7. Orphan or rare diseases**

Any disease that affects a small percentage of the population is classified as an orphan or uncommon disease. The majority of known uncommon diseases are genetic in nature, and so they affect people for the rest of their lives. Many manifest early in life, and approximately 30% of children with rare diseases die before reaching the age of five. There is no commonly agreed-upon cut-off figure for determining whether or not a disease is rare. The Rare Disease Act of 2002, for example, defines a rare sickness as any disease or condition that affects fewer than 200,000 people in the United States, whereas in Japan, a rare disease is defined as one that affects fewer than 50,000 people. Rare diseases, on the other hand, are defined by the European Commission on Public Health as those that are life-threatening or chronically

#### *In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*

debilitating and have such a low prevalence (1 in 2000 individuals) that they require special coordinated efforts to combat. Furthermore, a sickness that is rare in one part of the world or among a specific group of people may be widespread in another. An individual uncommon disease may have a low incidence. However, the 6000 identified rare diseases collectively impact around 25 million Americans, or about 10% of the total [31]. Because rare diseases are defined by therapy availability, resource scarcity, and disease severity, they are now referred to as orphan diseases (ODs), especially since the orphan drug movement began in the United States in 1983. As a result, the United States Orphan Medication Act (1983) covers both rare and non-rare diseases for which there is no reasonable expectation that the cost of developing and commercialising a drug for such a disease in the United States will be recouped via drug sales in the United States. About 6000 rare or OD diseases have been recognised, and the National Institutes of Health's Office of Rare Diseases (ORD) keeps track of them (NIH). While some of the mentioned ODs are well-known (e.g., cystic fibrosis, Huntington's disease), the majority of people are unaware of numerous ODs with patient numbers of less than a hundred. Each year, about 250 new ODs and diseases are characterised [32]. The ODA was created to support the research and marketing of medications (orphan pharmaceuticals) for the treatment of ODs and other disorders. The ODA arose in response to the modest number of orphan medications approved in the United States in the years leading up to the ODA's approval [33]. Unfortunately, the drug research process for ODs is the same as it is for any other disease: it is extremely costly and time-consuming.

#### **8. Discussion and conclusion**

After looking at the various ways that computational drug repositioning strategies and models have been used to identify novel therapeutic interactions, we can conclude that each strategy and approach has its own set of benefits and drawbacks, and that combining different strategies and approaches often results in a higher success rate. Despite the fact that we have some excellent computational drug repositioning models, establishing robust models is still a difficult endeavour. Because of the intricacy of mapping such theoretical approaches to imitate actual organisms behaviour, as well as other difficulties such as missing, skewed, and erroneous data, one of the key challenges is bringing theoretical computing ideas into action. For example, reliable gene expression signature profiles may be difficult to define due to a variety of factors, including differences in experimental conditions (e.g., environment variables and patient age) between experiments, which can lead to data discrepancies in gene expression signatures, contributing to biased data. Furthermore, when these genes are employed as medication targets, there may not always be large changes in gene expression, which might lead to erroneous results. Furthermore, when using the chemical structure and molecular information technique, the dearth of high-resolution structural data for drug targets makes it difficult to detect potential drug-target interactions. Another issue that computational drug repositioning models face is the absence of reliable gold-standard datasets with which to evaluate their efficacy.

We offer a brief overview of the subject of computational drug repositioning, with a focus on analytically validating such methods. We cover the three methods of validation that are currently in use, as well as the challenges with consistency and essential assumptions that each of them makes. Finally, we offer an approach for increasing the validity of computational repositioning validation.

*Drug Repurposing - Advances, Scopes and Opportunities in Drug Discovery*

#### **Author details**

Kareti Srinivasa Rao\* and P. Subash Department of Pharmacy, Indira Gandhi National Tribal University, Amarkantak, MP, India

\*Address all correspondence to: ksrao@igntu.ac.in

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*In Silico Drug Repurposing: An Effective Tool to Accelerate the Drug Discovery Process DOI: http://dx.doi.org/10.5772/intechopen.109312*

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#### **Chapter 7**

## Drug Repurposing: Challenges and Successes in the Treatment of SARS-CoV-2

*Xolani Henry Makhoba*

#### **Abstract**

The coronavirus disease 2019 (COVID-19) outbreak resulted in an economic burden, with millions of morbidity and mortality infections, due to the unavailability of treatment and limited resources in many developing countries. Drug repurposing was among the first ways to come up with a solution to combat the COVID-19 outbreak worldwide and save lives. Drug repurposing, well-defined as investigating new hints for approved drugs or progressing formerly considered but unapproved drugs, is the main approach in drug development. It is suggested that at least 30–40% of novel drugs and biologics permitted by the US Food and Drug Administration (FDA) in 2007 and 2009 can be considered repurposed or repositioned products. Here, we discuss some of the proposed and tested drugs as tools to eliminate COVID-19, the challenges and successes of preparing for future pandemics using the drug repurposing approach, and treating other diseases.

**Keywords:** COVID-19, drug repurposing, challenges, success, preparedness for future

#### **1. Introduction**

In 2019, the whole world was hit by the outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that causes coronavirus diseases 2019 (COVID-19), with a huge economic and social burden to humankind [1]. Many people lost their household income. Many businesses were forced to shut down because of travel restrictions that were imposed to manage this novel disease. Sadly, millions of people lost their lives, and billions of cases were reported worldwide due to this disease. Unfortunately, there was no effective and affordable cure for COVID-19, resulting in the search for urgent treatment of this pandemic. Hence, pharmaceutical and academic institutions came together to develop emergency treatments. Thus, looking at existing drugs for the treatment of closely related diseases to COVID-19 was one of the proposed approaches to combat the scourge of the virus. Vaccines of tested efficacy to stop COVID-19 infection were being investigated vigorously worldwide. Currently, some specific drugs have been authorized for COVID-19, but the improvement of antivirals requires time. Hence, a faster way of treatment is done by drug repurposing [2, 3]. Drug repurposing is a promising approach in disease management because it is fast, easy, and a safe strategy to deal with everincreasing disease crises because of their previously known applications. Drugs used

for managing malaria were proposed for treatment of COVID-19. A study conducted showed that both chloroquine (CQ) and hydroxychloroquine (HCQ), which are known antimalarial medications, were found to have *in vitro* efficacy against SARS-CoV-2 [4]. Various small future studies have shown positive results. However, this outcome has not been declared worldwide, and apprehensions have been elevated due to the indiscriminate use and potential side effects. The clinicians were not in support of the usage of these medications. For example, the correct dose and duration of therapy are unknown. Another conducted study proposed that African countries have since seen low numbers of COVID-19 due to the endemic use of malarial drugs. They investigated the *in vitro* antiviral activity against SARS-CoV-2 of several antimalarial drugs. The outcome of the conducted study showed the following results: chloroquine (EC50 = 2.1 μM and EC90 = 3.8 μM), hydroxychloroquine (EC50 = 1.5 μM and EC90 = 3.0 μM), ferroquine (EC50 = 1.5 μM and EC90 = 2.4 μM), desethylamodiaquine (EC50 = 0.52 μM and EC90 = 1.9 μM), mefloquine (EC50 = 1.8 μM and EC90 = 8.1 μM), pyronaridine (EC50 = 0.72 μM and EC90 = 0.75 μM), and quinine (EC50 = 10.7 μM and EC90 = 38.8 μM) showed *in vitro* antiviral effective activity with IC50 and IC90 compatible with drug oral uptake at doses commonly administered in malaria treatment [5]. The ratio Clung/EC90 ranged from 5 to 59. Lumefantrine, piperaquine, and dihydroartemisinin had IC50 and IC90 too high to be compatible with expected plasma concentrations (ratio Cmax/EC90 < 0.05). With this data, it was then predictable that countries that generally use artesunate-amodiaquine or artesunate-mefloquine account for fewer cases and deaths than those using artemetherlumefantrine or dihydroartemisinin-piperaquine. In recent years, novel coronavirus infections have occurred occasionally in many countries worldwide. Severe acute respiratory syndrome coronavirus (SARS-CoV) arose in 2002, infecting 8,422 people and causing 916 losses during the epidemic. Middle East respiratory syndrome coronavirus (MERS-CoV) was first recognized in 2012. At the end of December 2019, a total of 2499 laboratory-confirmed cases of Middle East respiratory syndrome (MERS), including 861 associated deaths, were reported globally [6]. At the end of 2019, novel coronavirus pneumonia (NCP) appeared in Wuhan and spread speedily. The pathogen was established as a new coronavirus, publicly named COVID-19 by the World Health Organization (WHO). Proteinase is a key enzyme in CoV polyprotein processing. In recent years, research on SARS-CoV and MERS-CoV protease inhibitors has been carried out *in vitro* and *in vivo*. Lopinavir (LPV) is a proteinase inhibitor. Both peak (9.6 μg/ml) and trough (5.5 μg/ml) serum concentrations of LPV inhibit SARS-CoV [7]. LPV also blocks a postentry step in the MERS-CoV replication cycle [6]. Ritonavir (RTV) inhibits the CYP3A-mediated metabolism of LPV, thereby increasing the serum concentration of LPV. Lopinavir/ Ritonavir (LPV/r) is a combination of lopinavir and ribavirin. The antiviral activity of LPV/r is like that of LPV alone, suggesting that LPV largely drives the effect. Therefore, this review focuses on drug repurposing their success and challenges, and preparedness for future pandemics [8].

#### **2. Different types of human coronaviruses**

Coronaviruses (CoVs) are a family of viruses that cause respiratory and intestinal illnesses in humans and animals. They usually cause mild colds in people, but the emergence of the severe acute respiratory syndrome (SARS) epidemic in China in 2002–2003 and the Middle East respiratory syndrome (MERS) on the Arabian Peninsula in 2012 show they can also cause severe disease. In addition to these types of coronaviruses, the whole world has been faced with the highly transmitted type of coronavirus since

*Drug Repurposing: Challenges and Successes in the Treatment of SARS-CoV-2 DOI: http://dx.doi.org/10.5772/intechopen.111523*

December 2019. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease, was first reported in China in 2019 in Wuhan after some serious pneumonia cases were reported [9]. This disease was first referred to as the 2019 coronavirus but later as COVID-19 by the World Health Organization (WHO). Below, each type of coronavirus is explained briefly and its structure.

#### **2.1 SARS-CoV (the beta coronavirus that causes severe acute respiratory syndrome, or SARS)**

Severe acute respiratory syndrome coronavirus was first identified in Southern China around November 2002, and in 2003, it was recognized as global human treatment due to its fast-spreading conditions. For example, this disease was reported in more than 24 countries such as Asia, Europe, Northern America, and Southern America. Despite the reported cases in those areas, in 2004, no cases were reported, and the risk was relatively low [10].

#### **2.2 MERS-CoV (the beta coronavirus that causes Middle East Respiratory Syndrome, or MERS)**

Middle East Respiratory Syndrome coronavirus (MERS-CoV) was first reported in Saudi Arabia in 2012 after that reported to some other parts of the countries such as Qatar and Jordan. However, as time passed, in 2018, MERS-CoV infection cases were reported worldwide such as Asia, Europe, America, and African countries. Therefore, more than 2260 confirmed cases and 803 deaths of MERS-CoV-related disease were reported worldwide, with most cases in Saudi Arabia. This disease attracted a lot of attention in pharmaceutical and academic industries due to its high rate of human-to-human transmission and treat to human. Also, to understand its origin and pathophysiology in order to prevent it from spreading father or becoming a human pandemic. Even though health officials were dealing with a relatively new virus with different behavior, they were able to be attended to and controlled quickly, thus reducing its threat to humans [11].

#### **2.3 Human coronavirus (HCoV-NL63)**

In Holland in 2004, another novel human coronavirus (HCoV-NL63) was isolated from a seven-month-old infant suffering from respiratory symptoms. This virus has subsequently been identified in various countries, indicating a worldwide distribution. HCoV-NL63 has been shown to infect mainly children and the immunecompromised, who presented with either mild upper respiratory symptoms (cough, fever, and rhinorrhea) or more serious lower respiratory tract involvement such as bronchiolitis and croup, which was observed mainly in younger children. In fact, HCoV-NL63 is the etiological agent for up to 10% of all respiratory diseases.

#### **2.4 SARS-CoV-2 (the novel coronavirus that causes coronavirus disease 2019, or COVID-19)**

In 2019 in China Wuhan city, the first reported cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) were announced as a virus responsible for coronavirus disease 2019 (COVID-19). This disease was then reported in various parts of the world, thereafter, declared as pandemic by World Health Organization (WHO) in 2020 [12, 13]. Millions of infections and millions of fatalities were reported worldwide due to fast spreading of this disease to human-human hosts. Most countries closed their borders to slow the spread of the virus, thus affecting many economies of developed and underdeveloped countries [14]. Current evidence suggests that the virus spreads mainly between people in close contact, for example, at a conversational distance. The virus can spread from an infected person's mouth or nose in small liquid particles when they cough, sneeze, speak, sing, or breathe. Another person can then contract the virus when infectious particles that pass through the air are inhaled at short range (this is often called short-range aerosol or short-range airborne transmission) or if infectious particles come into direct contact with the eyes, nose, or mouth (droplet transmission) (WHO, 2021). Therefore, treatment to combat this disease was needed urgently. Hence, most developed countries invested many in pharmaceutical and academic institutions to foster the research and development of drugs or vaccines to treat millions of infected young and old people from different countries and ethnicities [15]. As a result, the FDA approved the use of various drugs known for treating other diseases, such as malaria. These drugs included famous malaria drugs such as chloroquine, hydroxychloroquine, and others, as shown in **Table 1**. The repurposed drugs target the entry points or strategies used by the virus to enter the human host system. For example, in **Figure 1,** the SARS-CoV-2 viral structure and proteins involved in the virus process are highlighted. Spike glycoprotein is a major role player


*Drug Repurposing: Challenges and Successes in the Treatment of SARS-CoV-2 DOI: http://dx.doi.org/10.5772/intechopen.111523*


#### **Table 1.** *List of some drugs for malarial treatment but considered for COVID-19 treatment.*

#### **Figure 1.**

*SARS-CoV-2 virus depicting the location of the nucleocapsid (N), membrane (M), envelope (E), and spike (S) protein. (Adapted from Navhaya et al., 2023 unpublished data).*

during viral entry into the human host. Therefore, chloroquine is believed to block the virus's entry into the host and inhibit its replication inside the cellular system [22].

#### **3. Impact of coronaviruses in the past and present**

Since the outbreak of the first coronavirus in 2002 (SARS-CoV-1), then the outbreak of influenza A in 2009, which was followed by the MERS-CoV, in 2019, there was an outbreak of SARS-CoV-2 which was declared a global pandemic. SARS-CoV-2 produced the highest number of infections and fatalities compared to the other coronaviruses. It only did not affect the undeveloped countries, but well-developed countries were hit the most. It, therefore, caused a lot of panic in the health system worldwide. These viruses are somehow observed to produce the same symptoms individuals infected by them, from fever, cough, and shortness of breath to sore throats (**Figure 2**). Though there has been a huge drive to develop effective treatment or management of SARS-CoV, it is important to highlight some of the drugs proposed as tools to fight this pandemic [23].

**Figure 2.** *Summary of the impact of coronaviruses and influenza in the past and present.*

#### **4. Summary of malaria and its treatment**

Malaria is one of the major causes of death, especially in underdeveloped countries. In Africa, many drugs have been approved to treat or manage malaria, such as chloroquine, hydroxychloroquine, ferroquine, and others, as listed in **Table 1** were developed. However, the outbreak of COVID-19 in 2019 became a major concern as many countries worldwide were affected. The urgent treatment of the virus was needed; therefore, drugs that were meant for the treatment or management of the disease caused by *Plasmodium* parasites were proposed for the treatment of COVID-19. Below are some of the drugs that were tested or meant for the treatment of various types of *Plasmodium* species but repurposed or proposed for the treatment of SARS-CoV-2. Drug repurposing represents an enthusiastic mechanism to use approved drugs outside the scope of their original indication and accelerate the discovery of new therapeutic options [24].

#### **5. Success, challenges, and preparedness for future treatment of COVID-19**

Major success stories in the management of the COVID-19 were seen, and evidence is the scraping of travel regulations due to the decrease in the transmissions of the disease. The emergency approval of various vaccines, such as Pfizer, messenger RNA vaccine, protein subunit vaccine, MORDENA, and Johnson and Johnson vaccine, give hope to many people. However, challenges were reported regarding the intake of the vaccine in various parts of the world due to hesitations. This resulted in the slow intake of the vaccines and the boosters. As a result, various types of SARS-CoV-2 variants were developed in various countries, such as the United Kingdom, South Africa, and the United States just to mention but a few. One of the most important things to prepare for a future pandemic is the availability of accurate information. Proper education at all levels of age, in order to prepare better for what may come [25].

#### **6. Cancer and HIV drugs repurposing for COVID-19 treatment**

Both cancer and HIV are the pauses a huge threat to human life worldwide. With cancer, it is difficult to avoid because it can be inherently detected, but with HIV, it is documented that it can be transferred from human-to-human through


#### **Table 2.**

*A list of some drugs for cancer and HIV treatment but repurposed for COVID-19 treatment.*

unprotected sex and through sharing needles with someone who has the virus in their system. There is no effective cure for both diseases; however, there are management approaches. The outbreak of COVID-19 presented the opportunity for drug repurposing from both cancer and HIV drugs to treat the pandemic. **Table 2** summarizes some drugs that are known for either cancer or HIV management but were tested for COVID-19 and were reported to be promising tools for this disease.

#### **7. Conclusions and future perspectives**

Drug repurposing is a promising tool in addressing various diseases, especially those that are still under study. The recent COVID-19 has taught us many lessons, from understanding its biology to drug development. Different types of drugs are being repurposed from the known disease to use against the treatment of coronavirus 2019. This approach has given many researchers and pharmaceutical industries to prepare for future pandemics using the same method to treat future pandemics.

#### **Acknowledgements**

This work was supported by South African Medical Research Council Self-Initiated Research (SIR) grants.

#### **Author details**

Xolani Henry Makhoba Department of Biochemistry, Microbiology and Biotechnology, University of Limpopo, Sovenga, South Africa

\*Address all correspondence to: xolani.makhoba@ul.ac.za

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Drug Repurposing: Challenges and Successes in the Treatment of SARS-CoV-2 DOI: http://dx.doi.org/10.5772/intechopen.111523*

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*Drug Repurposing: Challenges and Successes in the Treatment of SARS-CoV-2 DOI: http://dx.doi.org/10.5772/intechopen.111523*

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### **Chapter 8**

## Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management of Cancer – Prospects in Drug Repurposing

*John Oluwafemi Teibo, Chioma Ejiro Irozuru, Titilade Kehinde Ayandeyi Teibo, Olabode Ebenezer Omotoso, Ahmad O. Babalghith and Gaber El-Saber Batiha*

#### **Abstract**

Paclitaxel (Taxol) is potent natural anticancer drug that has evolved over the years. It has been useful in the management of many cancers. Hence, this review aims to appraise the pros and cons of paclitaxel in the management of cancers using literature. Paclitaxel acts by obstructing mitotic spindle formation attributed to clampdown of mitotic clampdown hence arresting the cell cycle at the G2/M phase. Some of the notable side effects of paclitaxel usage include: hair loss, numbness, bone marrow suppression, muscle pain, allergic reactions, diarrhea, etc. Among the mechanism of paclitaxel resistance are P-glycoprotein efflux pumps, mutation in tubulin and alterations in binding regions of β-tubulin, altered function of cytokine expression as well as apoptotic Bcl-2 and p53. Combination of paclitaxel with cisplatin clearly improves the duration of progression-free survival and of overall survival of breast cancer. Paclitaxel which is a valuable natural anticancer drug seems promising in the management of non-cancer diseases such as COVID-19, renal and hepatic fibrosis, inflammation, skin disorders, axon regeneration, limb salvage, and coronary artery restenosis. With the advancement of technology, it is expected that the biosynthesis, chemo-resistance as well as its targeted delivery would unfold and perhaps open new uses and vista to the old drug of about five decades ago.

**Keywords:** paclitaxel, cancer management, mechanism of action, resistance, repurposing

#### **1. Introduction**

One of the World Health Organization (WHO) list of essential medicine is paclitaxel which is also known as Taxol and belongs to the taxane family (**Figure 1**). It's an approved drug used to treat some cancers which include: breast, ovarian, lung, esophageal, cervical among other, it has a total market value of over \$1 billion per year [1, 2]. Some of the notable side effects of paclitaxel usage include: hair loss, numbness, bone marrow suppression, muscle pain, allergic reactions, diarrhea, etc. [3].

One of the remarkable natural anticancer drugs—paclitaxel was first extracted from the Pacific yew tree, *Taxus brevifolia* in 1971. The yield from the bark of the yew tree was 0.01–0.05% which was low and this prompted the search for alternative means of synthesis which range from microbial fermentation, chemical synthesis, tissue, and cell culture [4]. It acts by obstructing mitotic spindle formation attributed to clampdown of mitotic clampdown hence arresting the cell cycle at the G2/M phase [5].

Among the mechanism of paclitaxel resistance are P-glycoprotein efflux pumps, mutation in tubulin and alterations in binding regions of β-tubulin, altered function of cytokine expression as well as well as apoptotic Bcl-2 and p53 [6]. Combination of paclitaxel with cisplatin clearly improves the duration of progression-free survival and of overall survival of breast cancer [7].

Earlier development shows that the combination of nab-paclitaxel and gemcitabine significantly improved the survival of patients with metastatic pancreatic cancer [8]. Recently, low-dose paclitaxel seems promising in treating non-cancer diseases, such as skin disorders, renal and hepatic fibrosis, inflammation, axon regeneration, limb salvage, and coronary artery restenosis. Future studies would help to understand the mechanisms underlying these effects in order to design therapies with specificity [9].

Nanocarrier systems including nanoparticles, liposomes, micelles, bioconjugates, and dendrimers have been employed in order to improve paclitaxel solubility and eliminate undesired side effects [10].

In the review, we examined the history, synthesis and biosynthesis of paclitaxel and also highlight the usage in the treatment of various cancers. We also presented the mechanism of action, combination with other drugs and well as the side effects and

**Figure 1.** *Structure of paclitaxel.*

*Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management… DOI: http://dx.doi.org/10.5772/intechopen.109155*

mechanisms of resistance. Hence, we concluded and provided future directions on paclitaxel with increasing evidence in the management of other disease other than cancer.

#### **2. Materials and methods**

#### **2.1 Literature search**

Literature search was done across many databases such as Google Scholar, PubMed, Embase, and Scopus using the keywords "Paclitaxel" and "Cancer." A lot of research article was obtained as this area has been explored for the past 50 years. Preprints that have not been peer-reviewed; non-cancer studies were excluded as well as gray literature. This was filtered with abstract, title and full text to identify relevant articles that can be integrated to assess the appraisal of paclitaxel (Taxol) in the management of cancers. The other articles were excluded by abstract, title or full text after the authors have read the abstract or articles and discovered the articles do not adhere to the objectives of our review.

#### **3. History and synthesis**

Paclitaxel has been previously extracted from the bark of the Pacific Northwest yew tree which is one of eight varieties of Taxus species specifically the *Taxus brevifolia* [1, 11]. The yew tree has been historically used in the production spear points and other weapons, household implements and diverse tools [12, 13]. The active compound in paclitaxel was identified by Mansukh Wani and Monroe Wall in 1971 [14–16]. The drug was also selected that same year by the NCI as a candidate for preclinical development and took the crucial step of entering into an agreement with the National Forest Service to ensure a harvest of the yew [17, 18]. A breakthrough in the development of paclitaxel occurred in 1979 when Dr. Susan Horwitz at Albert Einstein Medical College in New York identified the drug's unique mechanism of action as a promoter of microtubule assembly and its cytostatic activity on many types of tumors, thus increasing scientific interest in studying the drug [19, 20]. According to the National Cancer Institute (NCI), the *Taxus brevifolia* has a very poor Taxol content of only about 0.06% in the bark making it incapable of meeting the market and research's needs [21, 22]. It was then concluded that the slow growth rate and high cost of production of paclitaxel made production impractical, non-environmentally conscious and financially burdening resulting in its insufficiency as a natural source of paclitaxel [23, 24]. The isolation of Taxol from endophytic fungus was also used to produce Taxol [11, 25]. This is done by the chemical conversion of 10-deacetylbaccatin-III to Taxol using synthetic and semi-synthetic methods [25]. Fermentation is also used to produce paclitaxel from microorganisms but it produces a small yield of between 24 ng and 70 μg per liter and it is very unstable [26].

#### **3.1 Biosynthesis**

Paclitaxel can be synthesized from the isoprenoid precursors, including IPP (isopentenyl pyrophosphate) and its isomer DMAPP (dimethylallyl pyrophosphate) utilized by organisms in the biosynthesis of terpenes and terpenoids, which can be

produced through the MVA (mevalonate) pathway and the MEP (methylerythritol phosphate) pathway [4] as shown in **Figure 2**.

#### **3.2 Mechanism of action**

Paclitaxel is a chemotherapeutic drug functioning as a mitotic inhibitor that is used to treat common cancers [21, 25]. Paclitaxel is known to be the earliest microtubule-stabilizing agent that is able to arrest the cell cycle in the G2/M phase and also promote apoptotic cell death [27, 28]. In preclinical *in vitro* studies, Taxol with concentrations as low as 0.05 μmol/L have been shown to promote microtubule assembly by decreasing the lag time for the microtubule assembly, and also to shift its equilibrium in favor of microtubule formation [29, 30]. It performs this role by interrupting the normal function of microtubule growth by hyper-stabilizing the structure, preventing the dissociation of microtubules, blocking cell cycle progression, preventing mitosis, and inhibiting the growth of cancer cells [31, 32]. In essence, Taxol reduces the concentration of tubulin that is needed for the assembly of microtubule in the presence or absence of factors that are usually essential for this function, such as exogenous GTP or microtubule-associated proteins [33]. Microtubules treated with Taxol are known to be stable even after a short period of treatment with calcium or low temperatures, conditions that easily promote disassembly [27]. This unusual stability results in the inhibition of the normal dynamic reorganization of the microtubule network [34]. Specifically, paclitaxel binds to the Taxol-binding domain of the β subunit of tubulin which is the "building block" of microtubules, and the binding of paclitaxel locks these building blocks in place preventing their depolymerization [35]. The complex compound formed (microtubule/paclitaxel) is unable to disassemble, thus reducing the critical concentration of the assembled tubulin subunits and increases the percentage of assembled tubulin subunits (shortening and lengthening) blocking the progression to mitosis [2]. As an anticancer drug, the microtubules in the prophase stage forms a spindle that pulls the chromosomes away from the equator to the poles [36]. During later stages, they depolymerize and the spindle structure dissolves [29] and the exposure to cold temperatures and calcium ions can also trigger depolymerization of microtubules [37]. The binding site of paclitaxel has been shown to be different from that for guanosine triphosphate, vinca alkaloids, colchicine, or

**Figure 2.** *Biosynthetic pathway of paclitaxel [4].*

#### *Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management… DOI: http://dx.doi.org/10.5772/intechopen.109155*

podophyllotoxin and is present on the microtubule rather than tubulin dimers [38]. The mechanism of action of paclitaxel has been proposed on the basis of its effective action as a chemotherapeutic drug for different types of cancers.

#### **3.3 Repurposing of paclitaxel for possible therapeutic outcomes**

Drug repurposing has become an economical as it saves money and time, it also overcomes development risk associated with new drugs. Great benefits that exist with drug repurposing has already been outlined especially the knowledge of the mechanism of actions of the drugs that has been studied using new methods such as genomic expression and *in vitro* drug screening and target verification. Paclitaxel has been studied to show its effects in different types of cancers. Currently, new studies have also shown its involvement in non-cancer diseases also such as fibrosis. Zhang et al. [9] indicated that signal transducer and activator of transcription 3 (STAT3) were reduced in mice and *in vitro* in a dose dependent manner. They hypothesized that the administration of low doses of paclitaxel administration, may block the STAT3 (signal transducer and activator of transcription 3). This singular activity is responsible the attenuation of fibrous that has unilateral ureteral obstruction.

#### **3.4 Appraisal of treatment effects/success**

Rowinsky and collaborators [25] reported an excellent review on the preclinical and early clinical trials with paclitaxel and observed that 30% of the patients with ovarian achieved a complete remission [39]. Shortly after the original report of activity in ovarian cancer, three additional clinical trials provided confirmation that responses are observed (mostly partial remissions) in 20–50% of the patients with this disease.

Sparano et al. [40] experimentally depicted that paclitaxel significantly improves overall survival. There was also a 32% reduction in the hazard ratio for death afforded by weekly paclitaxel which was observed in a similar administration of anthracyclinecontaining chemotherapy. Sparano et al. [40] results are in consonant with studies of metastatic breast cancer that demonstrated a beneficial administration of paclitaxel weekly. Zhu et al. [41] explained in his journal that paclitaxel in combination with immunotherapy can increase the efficacy of treatment against breast cancer by inhibiting the normal function of Tregs and thus reversing the immune escape of tumors.

O'Shaughnessy et al. [42] performed a randomized clinical trial including 139 patients and suggested that paclitaxel in combination with alisertib improves progression-free survival observed in patients with ER-positive, ERBB2-negative or triple-negative metastatic breast cancer which had been pretreated with endocrine therapy. Markman et al. [43] evaluated the activity of single agent weekly paclitaxel in patients with both platinum and paclitaxel (delivered every 3 weeks)—resistant ovarian cancer. Forty-eight patients with platinum and paclitaxel-resistant ovarian cancer received single agent weekly paclitaxel (80 mg/m2 /week). It was observed that the weekly administration of paclitaxel can be a useful management approach in women with both platinum and paclitaxel (given every 3 weeks)-resistant ovarian cancer.

#### **4. Combinations with other drugs**

Studies have shown that combination chemotherapy produces faster response rates and longer progression-free survival than single agents [39, 44, 45]. They

remain the mainstay of therapy for patients with advanced breast cancer, and these regimens often include the anthracycline doxorubicin. When paclitaxel and MG1 were combined experimentally, their combination improved the efficacy of all of the breast cancer models tested, demonstrates greater efficiency in murine tumor models, greater tumor killing in vivo and thus is a promising alternative approach for the treatment of patients with refractory breast cancer [39]. Kawiak et al. [46] experimental study indicated that plumbagin increases the sensitivity of breast cancer cells to paclitaxel. The role of ERK (a component of mitogen-activated protein kinases that controls cell proliferation and survival) in plumbagin-mediated sensitization of breast cancer cells to paclitaxel was shown through the enhancement of the synergistic effect between compounds in cells with decreased ERK expression. These results imply that plumbagin can inhibit the activation of ERK in breast cancer cells and this plays a vital role in the sensitization of cells to paclitaxel-induced cell death [46].

Elserafi et al. demonstrated experimentally that the combination chemotherapy of paclitaxel and cisplatin provided similar response rate, lower toxic effect and overall survival when compared sequentially and in combination [44]. Also, Steuer et al. [45] showed that the combination of carboplatin-paclitaxel had a more favorable toxic-effect profile when compared to the combination of cisplatin-etoposide [45]. An experiment was carried out by Shroff et al. to evaluate the association between progression-free survival and the addition of nanoparticle albuminbound (nab)-paclitaxel to gemcitabine-cisplatin for the treatment of patients with advanced biliary tract cancer. The result indicated that the treatment with nabpaclitaxel in addition to gemcitabine-cisplatin prolonged median progression-free survival, response rate and overall survival when compared to controls treated with gemcitabine-cisplatin alone [47, 48].

A combination of chemotherapeutic drugs doxorubicin and paclitaxel are known to be active in the treatment of advanced breast cancer. However, earlier studies indicated that this combination had a high incidence of congestive heart failure which was caused by increased exposure to doxorubicin and its metabolite doxorubicinol [49, 50]. Limitations of the paclitaxel-doxorubicin-cisplatin (TAP) regimen in the treatment of endometrial cancer include tolerability and cumbersome scheduling [51, 52]. In a phase 3 study of the efficacy and safety of the albumin-bound paclitaxel (nab-paclitaxel) plus gemcitabine versus gemcitabine monotherapy in patients with metastatic pancreatic cancer, the combination drug significantly improved overall survival, progression-free survival, and response rate [53, 54]. Combination therapy resulted both in a superior overall response rate and a superior time to treatment failure, two frequent measures of efficacy in metastatic chemotherapy trials [55].

#### **5. Side effects/toxicity in organs**

Traditional paclitaxel has a very poor solubility in water, and their solvents are likely to cause serious adverse effects [56]. There is evidence in the literature to suggest that paclitaxel effects are concentration-dependent. Adverse effects associated with paclitaxel administration include the peripheral neuropathy, hypersensitive reactions, myelosuppression, hepatotoxicity, bradycardia, cardiotoxicity, myalgias, hypotension, diarrhea, arthralgias, nausea, mucositis, gastrointestinal toxicity, and alopecia [57].

*Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management… DOI: http://dx.doi.org/10.5772/intechopen.109155*

#### **5.1 Myelosuppression**

A major dose-limiting side effect of the administration of paclitaxel is myelosuppression which is known as bone marrow suppression that results in the decrease in the production of blood cells [57]. Specifically, paclitaxel administration results in grade IV leukopenia and neutropenia in about 26 and 68% of patients, respectively [58].

#### **5.2 Hypersensitivity reactions**

Hypersensitive reactions are mostly encountered either during or shortly after infusion with paclitaxel and the onset is usually very rapid, and seen within a few minutes of starting the infusion [59, 60]. Studies have shown that the solvent for paclitaxel (Cremophor EL®, castor oil vehicle) plays a very crucial role in hypersensitivity reactions such as anaphylactoid hypersensitivity reactions, abnormal lipoprotein patterns, hyperlipidemia, aggregation of erythrocytes and peripheral neuropathy which has been mediated by kinetic interference [61–63].

#### **5.3 Neuropathy**

Paclitaxel is known to cause weakness, cold sensitivity, numbness, pain from muscle and nerve damage to the hands and feet. Higher doses of paclitaxel are associated with an increased incidence of neuropathy, in fact, grade 3 or 4 neutropenia was observed in 68% [64, 65]. The effect of paclitaxel on microtubule assembly and disassembly reduces the normal axonal transport system leading to a length-dependent sensorimotor axonal neuropathy [66].

#### **5.4 Renal and hepatic toxicities**

Renal as well as hepatic toxicities are also a clinical concern in the administration of paclitaxel because they may compromise essential organ functions, impair renal excretion and reduce metabolism which lead to increased risk of other severe adverse effects [67]. This toxicity may be related to germline variations, such as singlenucleotide polymorphisms (SNPs) in genes that affect the pharmacokinetics and/or pharmacodynamics of paclitaxel [68].

#### **5.5 Myalgias and arthralgias**

Paclitaxel causes a syndrome characterized by diffuse myalgias and arthralgias, which can be resistant to opioids and other pain medications. Patients have reported pain that typically starts between day 2 and day 7 of administration and peaks on days 3–4 but remains consistent in intensity and duration with continuation of drug administration [58].

#### **5.6 Dermatological adverse effects**

Photosensitivity, pustular eruptions, folliculitis, extravasation, dorsal hand-foot syndrome, hair and nail changes, fixed erythrodysesthesia and also pigmentary changes are all caused by prolonged administration of paclitaxel [61].

#### **6. Resistance**

Drug resistance still remains the fundamental limiting factor to achieving cures to patients with cancer. Paclitaxel has been established as the first-line chemotherapeutic treatment drug for breast cancer [69, 70]. Mechanisms of drug resistance include overexpression of P-glycoprotein efflux pump, alterations in binding regions of β-tubulin and tubulin mutations, reduced function of significant apoptosis proteins (such as Bcl-2 and p53), alterations in cytokine expression (such as Interleukin-6), paclitaxel detoxification mediated by CYP [6], altered expression of regulatory proteins. These proteins include keratin 17 (KRT17) in cervical cancer cells, which may increase cell migration and PTX survival, or fibronectin type III domain-containing protein 5 (FNDC5), which could promote paclitaxel sensitivity by inhibiting NF-κB/MDR1 signaling in NSCLC [71], as well as microtubule specific effects with mutated β-tubulin, varied levels of β-tubulin isotypes, and chemical modification of tubulin [72]. Experimentally using indirect immunofluorescence and electron microscopy, acquired Taxol resistance in Chinese hamster ovary cell lines possessed altered α-tubulin or β-tubulin and required Taxol in the medium for normal growth have demonstrated that these resistant cells have mutations in tubulin, resulting in impaired microtubule assembly. In essence, continuous exposure to Taxol is required for polymerization to proceed normally, thereby promoting the formation of functional microtubules.

The ABC transporters are well known to be energy dependent transporters that exist across the cell membrane and transfer substrate across the cells using hydrolysis of ATP [32, 72]. Increased expression of ABC transporters such as ABCB1, ABCB4, and ABCG2 mRNA resulted in efflux of anticancer drug paclitaxel (pumping drug out the cell), leading to reduction in their efficacy and development of multidrug resistance (MDR) cells [73]. ABCB1 belongs to ABC transporter family and encodes a membrane protein P-glycoprotein, which is a well-known efflux pump responsible for Multi drug resistance [74]. Cells resistant to paclitaxel showed cross-resistance to other hydrophobic drugs and exhibited increased level of P-glycoprotein [64].

#### **7. Conclusion and future direction**

Plant-based medicines has shown potent anti-cancer, anti-diabetic, anti-viral and neuroprotective effect [75–77]. Notable pros of paclitaxel's have been its usage in many cancers, high success rate from preclinical and clinical trial data, its combinatorial properties with other drugs. Also, it has also been shown to be promising in treating non-cancer diseases such as renal and hepatic fibrosis, inflammation, skin disorders, axon regeneration, limb salvage, and coronary artery restenosis [9]. Further research would be needful to show insight to the mechanistic mode of action in various diseases processes. It was recently reported by [78] that through proteinprotein network analysis (bioinformatic and proteomics data analysis). Paclitaxel was the most potent candidate showcasing anti-cancer as well as anti-viral property. More wet lab research is needed to validate and enhance its repurposing strategy.

Also, notable cons about paclitaxel that would be improved in the incoming years include: utilization of biotechnology to improve biosynthesis of paclitaxel will unfold with improved technologies and technological application. Overcoming the chemo-resistance associated with paclitaxel would enhance its usage in many other diseases as well as novel combination with other drugs/therapies will uncover faster response and survival in patients. Modification with targeted deliveries like novel

*Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management… DOI: http://dx.doi.org/10.5772/intechopen.109155*

liposomes and magnetic particle preparations would ensure prompt pharmacological action. Alternating the formulation approach to minimize its toxicity as a result of Cremophor. This assessment of the pros and cons of paclitaxel is discussed in this chapter (**Figure 3** below).

**Figure 3.** *Overview of the pros and cons of paclitaxel in cancer management.*

Researchers envisages more development and improvement in the near future for synthesis, overcoming chemo-resistance, combination with other drugs and repurposing and application in non-cancer diseases of the compound extracted from the bark of pacific yew tree some five decades ago.

#### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

John Oluwafemi Teibo1 \*, Chioma Ejiro Irozuru2 , Titilade Kehinde Ayandeyi Teibo3 , Olabode Ebenezer Omotoso2 , Ahmad O. Babalghith4 and Gaber El-Saber Batiha<sup>5</sup>

1 Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil

2 Department of Biochemistry, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Nigeria

3 Department of Maternal-Infant and Public Health Nursing, College of Nursing, University of São Paulo Ribeirão Preto, São Paulo, Brazil

4 Medical Genetics Department, College of Medicine, Umm al-qura University, Makkah Saudi Arabia

5 Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, AlBeheira, Egypt

\*Address all correspondence to: johnteibo@usp.br

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Perspective Chapter: Appraisal of Paclitaxel (Taxol) Pros and Cons in the Management… DOI: http://dx.doi.org/10.5772/intechopen.109155*

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#### **Chapter 9**

## Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role of in silico Techniques

*Manisha Kotadiya*

#### **Abstract**

Natural products and their derivatives are the most promising and prolific resources in identifying the therapeutic small compounds with potential therapeutic activity. Nowadays, working with herbal or natural products can be boosted by collecting the data available for their chemical, pharmacological, and biological characteristics properties. Using in silico tools and methods, we can enhance the chances of getting a better result in a precise way. It can support experiments to emphasis their sources in fruitful directions. Though due to their limitations with respect to current knowledge, quality, quantity, relevance of the present data as well as the scope and limitations of cheminformatics methods, herbal productbased drug discovery is limited. The pharmaceutical re-profiling is done with the main objective to establish strategies by using approved drugs and rejected drug candidates in the diagnosis of new diseases. Drug repurposing offers safety lower average processing cost for already approved, withdrawn drug candidates. In silico methods could be oppressed for discovering the actions of un-investigated phytochemicals by identification of their molecular targets using an incorporation of chemical informatics and bioinformatics along with systems biological approaches, hence advantageous for small-molecule drug identification. The methods like rulebased, similarity-based, shape-based, pharmacophore-based, and network-based approaches and docking and machine learning methods are discussed.

**Keywords:** docking, molecular simulation, bioinformatic tools, machine learning, target identification, databases

#### **1. Introduction**

Herbal or natural products and their derivatives have an ancient status in using them as traditional medicines to treat ailments and various diseases. However, in today's era, they become a prolific resource for identifying small therapeutic molecules as an inspiration. With regard to it, around two-thirds of small-molecule medicines or drugs approved in between 1981 and 2019. William C. Campbell, Satoshi Omura, and

Youyou Tu got noble prize for the discovery of two natural products such as **avermectin and artemisinin**, and they are used in the treatment of parasitic diseases caused by parasites [1].

Due to these evolutionary processes, natural compounds consist of various biological activities in different races. Because of these characteristics, the wide range of products from natural resources are identified as privileged structural molecules [2, 3]. They are highly diverse with respect to structure, pharmacological, and physiological properties. Some are having good ADME and physicochemical properties, and some are clearly beyond and generally recognized as small drug-like chemical space [4–6]. Almost all phytochemicals and other compounds from natural origin have complex molecular structure with respect to their 3D molecular shape, geometry, stereochemistry, ring complexity, and conformations like more number of rotatable bonds and absence of aromaticity [7–9]. This includes numerous basic obstacles to 3D cheminformatics methodologies, which is why the creation of force fields and algorithms for the prediction and identification of protein-bound conformations of such complex compounds remains the most actively pursued research period in cheminformatics and bioinformatics [10–15].

In silico methods can contribute to natural product small-molecule discovery and can also became a backbone to experimentalists throughout the lead identification [16–19]. They are not only used for identifying bioactive molecule but also used to prioritize material for testing [20, 21]. In silico methods are also adopted as follows:

i.data curation and dereplication,

ii. chemical space analysis, visualization, and comparison,

iii. accentuation of product-likeness,

iv.prediction of ADME properties and safety profiling.

A high-performance computer facility on-site is no longer required. Calculations may now be conducted at extremely large sizes in the cloud at a cheap cost and complexity. Simply paying software license fees is a significant cost component that has steadily climbed in recent years. Simultaneously, we are seeing an increase in the number of sophisticated open-source tools, similar to what has been widely used in the area of bioinformatics. Some of the best softwares in this category are as follows:

i.RDKit and CDK [22, 23]

ii.KNIME [24, 25] (an open-source analytics platform), and

iii.Scikit-learn (an open-source Python module for machine learning) [26]

This summarizes the methods and in silico tools for repurposing to provide a concise but comprehensive overview of the scope and limitations for herbal or other natural origin-based drug discovery in a format that is accessible to researchers from different areas with an interest in drug discovery. The conversation covers a huge number of methods in cheminformatics, bioinformatics as well as data resources relevant to natural product-based drug discovery.

*Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

#### **2. Herbal/natural product databases and computational methods/tools**

Most databases also provide free bulk download, allowing for virtual screening and other uses. According to these studies, the total number of natural compounds whose structures can be obtained via bulk download from free databases exceeds 250 k, approaching 300 k. Unfortunately, many databases have a brief half-life; just a handful are sustainably managed and under continuous improvement. Data quality is always an issue, but when it comes to phytoconstituents, extra care should be taken, especially when integrating the data with computational methods that rely on correct depiction of 3D molecular structures. This is because of that stereochemical information on phytocompounds is frequently erroneous. Virtual databases can be distinguished into following:


Super Natural II [27] is the most comprehensive free database, with over 325 k substances. The database may be queried using a chemistry-aware online interface; however, mass download is not supported. A handful of the best free, downloadable materials are described below:


#### **3. In silico analysis, physicochemical studies, and structural properties of natural products**

Computational chemistry has been playing a key role in the characterization of compounds by their physicochemical and structural properties. Phytocompounds

cover a much lots of chemical space than synthetic [34]. The structural uniqueness (and complexity) of some phytocompounds and other natural compounds from other sources could allow them to target macromolecules. They are on average heavier and more hydrophobic than synthetic drugs and synthetic, drug-like compounds. Their structural complexity is also often higher, in particular with regard to stereochemistry (commonly quantified by the number of chiral centers [35], the number of fractions of Csp3 atoms, and or the number of bridge head atoms in ring systems and 3D molecular shape) [36]. All natural compounds show an enormous diversity of ring systems, in particular of aliphatic systems. One study showed that 83% of core ring scaffolds of natural products are absent in commercially available screening databases [37]. Compounds from natural sources from different kingdoms have distinct physicochemical and structural properties. For example, natural compounds with macrocycles or long aliphatic chains are more commonly to marine species than terrestrial species. Bacteria also manufacture a large number of macrocyclic natural chemicals. Natural compounds have a large number of heteroatoms and, as a result, a wide range of functional groups [38]. Computational Methods for Assessing the Institutional Variety of Herbal Compounds are unparalleled in terms of the structural diversity, which is expressed on a fragment level [39]. The majority of studies comparing the diversity of compounds with that of chemical drugs use the idea of biochemical structures (scaffolds) presented by Bemis and Murcko [40]. A powerful tool for the intuitive, visual analysis of the structural diversity of sets of compounds is Scaffold Hunter [41]. Some of the methods are enlist as below:

The open-source, Java-based program has a graphical interface and different clustering techniques.

Scaffold Hunter is based on the concept of molecular scaffold hierarchical representation and categorization ("scaffold tree").

An early prototype of this instrument served as the foundation for the structural categorization of bioactive substances (SCONP), a technique for mapping compounds' chemical space [42].

Principal component analysis (PCA) is one of the most widely used approaches for modeling the chemical space [43], which projects high-dimensional data into a low-dimensional space for improved interpretability, while keeping information loss to a minimum. Natural compounds have been used in several studies for mapping the chemical space of small molecules [44], for mode of action prediction and for the analysis of structure-activity relationships. Despite significant variations in chemical structure, these studies reveal a high degree of similarities between natural substances and synthesized pharmaceuticals in terms of pharmacophore characteristics [45]. T-distributed algorithms are another effective way for reducing dimensionality.

Stochastic Neighbor Embedding (t-SNE) [46], as well as the recently announced Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) [47], generates plots in which comparable things are clustered together and dissimilar ones are represented by distant points. Although t-SNE can provide graphics that seem to be superior to those produced by PCA, the approach does not really scale well with dataset size.

UMAP is theoretically similar to t-SNE and yields comparable results, but it is quicker. Medina-research Franco's group has been working on many techniques for such intuitive characterization, visualization, and comparison of chemical collections, with an emphasis on their databases producing similar result faster (**Figure 1**). *Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

#### **Figure 1.**

*Examples of approved drugs and interaction of drugs to their target proteins: (A) (−)-galantamine, an ACEs inhibitor used for the treatment of Alzheimer's disease (PDB: 1DX6), (B) tacrolimus, a macrocyclic immunosuppressant (PDB: 1FKF) and (C) chenodeoxycholic acid for the treatment of hypocholesterolaemia (PDB: 6HL1) [39].*

#### **4. Computational methods for the analysis of natural compounds and their drug-likeness prediction**

Computational tools are able to discriminate natural compounds and natural-like compounds from synthetic compounds with high. Accuracy, and they are also able to quantify the natural compound-likeness of compounds. As such, they are frequently used in compound design, library design, natural compound selection (and their derivatives and analogs) among heterogeneous compound collections, and compound prioritizing [48]. The Natural Products-Likeness Score created by Ertl is one of the most well-established techniques [49]. This score measures the chemicals based on the resemblance of their fragment from those of existing natural compounds using Bayesian statistics. The Natural Product-Likeness Score has been re-implemented with certain changes in various tools and platforms [50]. Additional techniques include a theoretically comparable method based on extended connectivity fingerprinting (ECFPs) and a **rule-based approach** [50].

More recently, we developed Natural Product-Scout, a tool for identifying NPs and NP-like compounds in large sets of molecules. Arbitrary forest classification techniques are trained and tested database of known biologically active compounds.

On a sample test set, a classifier based on Molecular ACCess System keys achieved an area under the characteristics curve (AUC) of 0.997 as well as the Matthews correlation coefficient (MCC) of 0.960. Similarity maps are used by NP-Scout to identify locations in a compound that help to the identification of a compound as NP and synthetic chemical (**Figure 2**). NP-Scout may be accessed via a free online service [52].

Recently, the Natural Compounds Molecular Fingerprint (**NC-MFP**) was presented as a novel method of defining the structural properties of natural compounds in term of the scaffold and fragments they are made up of [53]. It has been

**Figure 2.** *Similarity maps of (A) vorapaxar and (B) empagliflozin [51].*

demonstrated that the NC-MFP outperforms existing fingerprints in distinguishing natural substances from manufactured ones.

#### **5. Computational identification of natural products likely to interfere with biological activities**

Computational techniques have a robust track record in the identification of bioactive natural origin compounds.

For their research, they used a wide range of virtual screening methods, from simple, fast methods based on 2D molecular fingerprint similarity to more complex.

Machine learning algorithms have lately become a standard in screening for pharmacological active natural compounds [54].

The structural properties of many NPs, including such greater levels of conformational flexibility, the complexity of about their shapes and ring system (particularly macrocycles), inadequacies of molecular force fields primarily model defined for synthetic substances, and uncertainly related to protonation states, tautomerism, and oxidation states pose particular challenges to 3D virtual screening techniques. One method for reducing the spatial structure of natural substances is to eliminate sugars and sugar-like substances that are not required for bioactive components on a target of interest [55]. This can be done, for example, by use of defined (**SMARTS**) patterns. Given the sparsity of available structural data, docking of natural compounds to the structures of macromolecules can pose a profound challenge. This is because of **docking algorithms** and scoring functions are highly sensitive even to very small changes in 3D structure such as commonly induced by ligand binding method (including solvent effects). However, also this hurdle may be overcome by the prudent use of homology modeling techniques, induced fit docking approaches, and molecular dynamics simulations. In case of extremely flexible proteins, docking against multiple, protein structures ("**ensemble docking**") may be a good way onward (not only for screening but also for binding area prediction) [56, 57]. Diligence and patience will certainly be required and, above all, checks of the plausibility of a hypothesis using all available information can help to piece the puzzle together. More often than in virtual screening, docking algorithms produce good results in binding mode prediction [58]. Provided that the natural compounds of interest is not excessively

#### *Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

large or flexible (as a rough guide, not exceeding 35 heavy atoms or eight rotatable bonds), that the ligand binding site is well defined (i.e., not overly shallow, not solvent-exposed), and that the interaction between the binding partners involves two or more directed interactions, and there is a good chance that a satisfactorily accurate binding pose can be obtained that offers crucial insights for the development of optimization strategies. Binding posture prediction is more practicable than virtual screening, since that allows researchers to ignore the most difficult component of docking, which is grading compounds based on their ligand binding, and it allows them to focus their efforts on a single ligand-target combination. Docking, particularly in the context of NP research, allows for the rationalization of stereoselectivity in ligand binding (and other processes, such as metabolism). The significance of incorporating accurate conformational information with 3D techniques, particularly docking, cannot be emphasized. In the following paragraphs, we will examine several exemplary investigations in which virtual screening has been effectively used to identify bioactive chemicals. Using katsumadain A, a diarylheptanoid inhibiting influenza neuraminidase, as a template for 3D molecular shape-based screening, a number of structurally distinct NPs were identified that inhibit the viral enzyme with IC50 values in the sub-micromolar to low-micromolar range (for example, artocarpin (1), which is depicted in **Figure 3**) [59]. In another study, pharmacophore-based virtual screening was combined with a shape-based approach in order to identify activators of the G protein-coupled bile acid receptor 1 **(GPBAR1**) [51]. In addition to several NP databases, a collection of synthetic compounds was screened. Among the 14 selected NPs, eight (57%) obtained a measured receptor activation of at least 15% at 20 μM concentration.

Two of these compounds, (1) farnesiferol B (2) and microlobidene (3), are based on molecular scaffolds that had not yet been associated with GPBAR1 modulation. Both compounds were reported to have EC50 values of approximately 14 μM. Among all 19 selected compounds, only two were active (applying the identical activity threshold).

#### **Figure 3.**

*Natural compound and their derivatives identified by virtual screening method.*

#### **6. In silico prediction of the therapeutic targets of natural products**

Identifying the receptors of small compounds is critical for assessing the pharmacological activity and safety of drugs, as well as their future development. However, the method of action of a significant percentage of marketed medications is uncertain or very loosely understood.

**Target prediction in silico** is a large-scale use of virtual screening [60], in which one, many, or even several molecules are assessed against the broadest collection of macromolecules conceivable. A number of techniques including models have been released in recent years [61], and they have emerged as valuable tools in earlier drug discovery. The majority of target prediction strategies are ligand-based, which is connected to the issues with docking and structure-based methods.

**Ligand-based methodologies** range from simple similarity-based approaches to advanced machine learning as well as network-based approaches. Surprisingly, despite the variety of computer tools for target prediction available today, our understanding of their utility under real-world situations remains restricted [62]. This is largely due to the (in practice) exorbitant expenses associated with the practical, scientific, proactive evaluation of such models, but it is also due to the often used inadequate, superficial retrospective validation techniques [63]. To the best of our knowledge, the only computational technique that has received rigorous experimental validation is the well-established Similarity Ensemble Approach (SEA) [64]. One may argue that testing models using current data tends to an exaggeration of how effectively a model would perform in real-world scenarios.

It is more likely that **phenotypic test** readouts with different types of cells or information for structurally similar drugs would be obtained. By merging all available information, some false-positive predictions are likely to be eliminated, leaving many fewer prospective targets to be studied experimentally. In a recent in-depth study of the behavior and scope of a similarity-based approach and a machine learning approach for estimating the targets of small molecules, we display that the reliability of either approach's predictions is strongly influenced by the structural relationship between the compounds of interest and compounds represented in the training set. This issue must be carefully examined while working with natural substances, considering that target prediction algorithms are largely intended for, and with natural chemicals, given that target prediction algorithms are largely built for and trained on synthetic compound measurement data. In the same investigation, we discovered that, surprisingly, the similarity-based strategy outscored the machine learning technique using the already available data. While a meaningful correlation of these two methods should be approached with caution for several reasons, the results indicate that the basic similarity-based strategy is a solid choice, particularly when model interpretability is considered. This is also shown in the high performance of other well-known, similarity-based models, like Swiss Target Prediction [65].

The majority of the compounds differ structurally from more common, synthetic chemicals that account for the majority of the observed activity data. More complicated similarity-based approaches that examine molecules based on their 3D molecular structure are supposed to identify such distant structural similarities, but how effectively these methods would function in practice was unknown until recently. We investigated the capability of ROCS [66].

ROCS, a premier shape-based screening engine which also takes chemical feature distributions into consideration, was used to discover the biomolecules targets of

#### *Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

"complex" molecules using a knowledge and understanding of "non-complex" molecules with measured bioactivity data [67].

We designated molecules as "complex" for this work if they are either (extremely) large in size (45 to 55 heavier atoms) or macrocyclic. We classified compounds as "non-complex" when they were tiny in size (15 to 30 heavy atoms). A collection of 28 pharmacologically important targets were investigated. A diversified set of 10 complicated small molecules was created automatically for each one of the targets. Each of these molecules had a single low-energy conformation that was used as a query for ROCS screening against a multi-conformational knowledge base. The knowledge base has 3642 targets and 272,640 non-complex molecules. This study discovered that ROCS accurately rated at least one known target in the top 10 spots (out of 3642) for up to 37% of the 280 complicated small compounds used as queries. This result is amazing given the dissimilarity of the queries and compounds in the knowledge base. It suggests that target prediction is achievable for a large number of difficult complicated compounds. It should be noted that, in many circumstances, researchers will be able to significantly limit the number of target candidates based on specialist knowledge and accessible information. There were at least 31 identified complex molecules and natural product-like molecules among the 280 complicated small molecules. The top-10 rate of success for these compounds was lower (23% vs. 37%). This is due to the fact that the median Tanimoto coefficient between the complex NP (or NP-like substance) and the nearest simple molecules in the knowledge base is only 0.13. For pairings of compounds with such a minimal degree of similarity, it is reasonable to predict that the respective binding interaction possess will be unique, which is normally outside the reach of ligandbased approaches.

In addition to 3D similarity-based techniques, 3D pharmacophore-based methodologies are commonly utilized for prediction of target protein in the context of natural substances research. A profiling investigation, for example, evaluated secondary metabolites extracted from the medicinal plant Ruta graveolens against a battery of over 2000 pharmacophore models spanning over 280 targets.

**Arborinine** was found as an antagonist of Angiotensin-converting enzymes (ACEs) (measured IC50 = 35 M) results from in silico screening, among many other bioactive chemicals and interactions. Machine learning-based methods for natural chemical target prediction have sparked the greatest attention in recent years. Some of examples for online tools are given below:

i.SPIDER,

ii.TIGER, and

iii.Starfish

Spider employs self-organizing maps in conjunction with "fuzzy" chemical descriptors, allowing it to be extended to NPs. The model proved useful in identifying 5-lipoxygenase, peroxisome proliferator-activated receptors, steroid receptors, prostaglandin E2 synthase 1, and Farnesoid X receptor as therapeutic targets of the archazolid A, and it accurately predicted prostanoid receptor 3 as a molecular target for doliculide, which is a 16-membered depsipeptide [68]. SPIDER has effectively discovered the targets of other fragment-like natural compounds, such as Sparteine, for which the kappa opioid receptor, p38 mitogen-activated protein kinase, and muscarinic and nicotinic receptors

were clinically verified as targets [3]. DL-goitrin, whose targets have been experimentally proven to be receptor pregnane X and the cholinergic receptor,

Graveolinine acts on cyclooxygenase-2, serotonin 5HT2B receptors were clinically verified as targets, isomacroin acts on adenosine A3, and platelet growth factor receptors were clinically identified as targets.

DEcRyPT uses random forest regression to build a revised list of possible macromolecule targets based on predictions obtained from spider, the Target-Drug Relationship Predictor. DEcRyPT was used to successfully identify 5-lipoxygenase for which ortho-naphthoquinone-lapachone is well-known substrate. Lapachone hydroquinone was shown as inhibitor of 5-lipoxygenase.

TIGER is thematically connected to SPIDER. However, it utilizes updated Cats descriptors and employs a different technique for assessing expected targets. The, glucocorticoid, Orexin as well as cholecystokinin receptor were effectively discovered as therapeutic hit for marine NP (±) marinopyrrole A by TIGER. Among other proteins, the model correctly predicted estrogen receptors and as binding biomolecule of the stilbenoid resveratrol [69]. Starfish is a stacked ensemble approach for target prediction trained on synthetic compounds.

As a component of the development process, various machine learning methods were investigated. The authors determined the optimum stacking strategy by feeding molecular fingerprints into k-nearest neighbor's model and a random forest model. The probability predicted by such models in which each of the therapeutic targets are employed as input for a logistic regression-based meta-classifier (level 1). On a test set of NPs, the stacking technique performed much better than the separate models (ROC AUC 0.94; BEDROC score 0.73). Network techniques for predicting biological targets of natural chemicals have also been published. Cheng and colleagues, for example, created statistical models in order to bind natural compounds to cancer targets and their protein involved in disorders like aging. Neural networks system was recently trained on clinical indication data and applied to discover favored molecular scaffold in natural products. Based on these models' predictions, a unique template database for 100 indications were created, which may be used as a preliminary step for NP-based drug development. The reader is directed to reference for further information on this subject. Natural compounds that are likely to disrupt with biological experiments can be identified computationally. The proclivity of compounds to interfere with biological assays remains a significant challenge in compound screening experiments. The flavonoid quercetin, a well-known pan assay interference compound, exemplifies the scope of the issue: since about 28 July 2020, and the PubChem Bioassay repository identified quercetin as conclusively bioactive in over 800 separate bioassays, representing a hit rate of more than 50%. The most typically seen method of test interference is aggregation formation, which happens under certain assay circumstances. Covalent binding, redox cycling, interference with spectroscopy assay, metal chelation, membrane rupture, and breakdown in buffers are further significant processes [70].

#### **7. Computational identification of natural products likely to interfere with biological assays**

The development of computer techniques to address this challenge has been gradual. Until recently, the tools available to users comprised numerous rule sets, a few similaritybased techniques, and a statistical method. The most well-known method and widely

*Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

used rule set is pan assay interference components (pains) rule set. Despite the unambiguous declarations of its creators, operators of the PAINS rules set all too frequently overlook the significant drawbacks of its scope, applicability, and trustworthiness. Other relevant rule sets here include rapid elimination of swill rules as well as a set of rules generated from an Nuclear magnetic resonance-based approach in detecting tiny compounds that give false-positive test results owing to interaction (ALARM NMR) [71]. Aggregator Advisor is a useful similarity-based technique that identifies compounds with similar structural structures. Aggregator Advisor is a handy similarity-based technique that indicates compounds that have a close structural affinity to identified aggregators based on molecular scaffolds.

Hit Dexter 2.0 is the second generation of a series of machine learning models meant to identify compounds that are likely to exhibit prolific hitter behavior in primary screening and/or confirmatory dose-response tests, independent of the underlying (interference) mechanism. All of these methods are generated from databases dominated by synthetic chemicals. As we demonstrate in our work on Hit Dexter 2.0, the training set, although comprising of around 250 k compounds, covers just a tiny proportion (approximately 15%) of the active compounds with molecules that are structurally related to the model to make credible predictions. This means that, once again, discretion is required when employing any of these techniques, specifically in the area of NPs.

#### **8. In silico prediction of ADME and safety profiles of natural products**

The biodistribution and safety characteristics of NPs are frequently a source of difficulty in NP-based drug development. The hERG channel (whose blockage has been associated with potentially deadly cardiac arrhythmia), cytochrome P450 enzymes (which can induce drug-drug interactions and toxicity), and P-glycoprotein are some of the most well-known anti-targets tackled by NPs (an efflux pump with broad substrate specificity that can effectively cause drug resistance). A wide range of computational models (e.g., pharmacophore models, statistical models, docking machine learning models, etc.) are also used to handle these and many additional anti-targets and end points. However, because of the data available, these and many other in silico methods are tested/tested using substances that are mainly of synthetic origin. For example [72, 73],



#### **Table 1.**

*Available software for in silico drug repurposing.*

#### **9. Conclusions**

NPs provide remarkable hurdles to both experimentalists and theorists, yet data on recently approved small-molecule medications demonstrate that NP research is worthwhile and can deliver useful, new therapeutics. Modern in silico approaches can contribute significantly to the speeding and non-risking of natural drug development. However, model applicability must be carefully monitored, especially when dealing with NPs, because computational approaches are often created for and trained on data for synthesized chemicals. Unfortunately, even recently established models sometimes lack rigorous definitions of the application area and do not appropriately notify users about compounds with unreliable predictions. Researchers, in fact, may be attracted to use one of the numerous free, user-friendly web applications. Obviously, the idea holds true for these web applications as well: in the absence of solid indications of the trustworthiness of individual forecasts, these estimates are not to be believed. Given the renewed interest in NP research, the increasing availability of biological, chemical, advances in algorithms, and structural data, and improvements in algorithms, modeling techniques, as well as computing capability, the future will see the sustained connectivity of computational techniques in natural compound-based drug development pipelines.

#### **Conflict of interest**

Authors declare that there is no conflict of interest.

*Drug Repurposing: Scopes in Herbal/Natural Products-based Drug Discovery and Role… DOI: http://dx.doi.org/10.5772/intechopen.109821*

### **Author details**

Manisha Kotadiya Department of Pharmaceutical Chemistry, S.M. Shah Pharmacy College, Mehemdabad, Gujarat, India

\*Address all correspondence to: manishakotadiya3@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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#### **Chapter 10**

## Computational Approaches in Drug Repurposing

*Christabel Chikodi Ekeomodi, Kingsley Ifeanyi Obetta, Mmesoma Linus Okolocha, SomtoChukwu Nnacho, Martins Oluwaseun Isijola and InnocentMary IfedibaluChukwu Ejiofor*

#### **Abstract**

Drug repurposing is a term applied to finding a new therapeutic and pharmacological indication for an existing drug molecule with a known indication. Repurposing existing drugs to treat both rare and widespread ailments is more and more compelling due to the use of less risky compounds, which may result in lower entire development costs and quicker development timelines. This is due to the high attrition rates, high cost, and slow new drug discovery and development pace. The introduction of computational techniques and their advancements in drug design, discovery, and development has provided a platform for scientists to kick-start drug repurposing with ease. Computational approaches have provided rationality in drug repurposing, reducing the chances of failure in drug repurposing attempts. In this chapter, we present techniques for drug repurposing that are both conventional and computational, talk about the difficulties faced by scientists who attempt drug repurposing, and suggest creative solutions to these difficulties to help drug repurposing reach its full potential.

**Keywords:** drug, repurposing, computational, diseases, in-silico

#### **1. Introduction**

Drug repurposing simply means the science and technology of assigning new indications to exist molecules or medications with known therapeutic usage and safety profiles, most stemming from serendipitous discoveries [1]. According to the drug bank library of drug molecules, there are 4302 approved drugs [2–5]. Though these drugs have been classified based on the target enzymes and pharmacological/ therapeutic effects, they might still have the potential to activate or inhibit other enzymatic pathways, leading to different impacts on the body. Drug repurposing is all about utilizing and studying other possible enzymatic pathways or effects an already known drug can activate or inhibit, leading to pharmaceutical or pharmacological importance.

The traditional method of developing drugs is time-consuming and expensive; repurposing known drugs is a viable and promising alternative [6]. Developing a new drug

involves studying its effectiveness, toxicity, pharmacokinetic, and pharmacodynamic profiles in cell- and animal-based investigations and its effectiveness and safety in humans in clinical trials. It typically takes 13 years and 2–3 billion dollars to develop a new drug from bench to bedside [7]. Drug discovery and development is a less attractive business for funding because of the rising costs and length of time. On the other hand, drug repurposing aims to identify new medical uses for an approved or experimental drug. Clinical trials can be hastened because the drug's dosing and safety have been thoroughly investigated, considerably cutting the time and money needed for development [8].

Due to the high rates of illness and death associated with certain emerging diseases, repurposing drugs may be the most effective approach for addressing these

*Computational Approaches in Drug Repurposing DOI: http://dx.doi.org/10.5772/intechopen.110638*

conditions. When there is an urgent need to develop new medications and treatments during an outbreak, such as was the case with the COVID-19 pandemic, the strategy of quickly repurposing existing drugs has a significant advantage as it has the potential to identify medications that could be used to address the situation [9].

With current R&D costs, developing de novo drug therapies for more than 8000 rare diseases is inconceivable; nevertheless, drug repositioning, based on finding hidden associations or building connections between a drug and disease, holds promise for orphan drug disease therapy [10]. Furthermore, evaluating approved medications to determine new indications assists pharmaceutical companies in extending the patent life of drugs through application to adjacent diseases and in protecting IP against competitors [1].

Compared to the conventional drug development procedure, as shown in **Figure 1**, the advent of computation methods in medicinal research has provided a lesser expensive and less time-consuming approach to finding other disease conditions that can be treated using already approved or experimental drugs.

#### **2. Repurposing principles**

There are typically two main repositioning principles for drugs. First, because many diseases are interdependent, medications for one condition may also be effective for treating other disorders. Second, because medicines are naturally confusing, they can be linked to various targets and pathways. According to the source of the findings, drug repositioning research can be divided into two groups:


#### **3. Repurposing techniques**

*In-silico* approaches using existing data to find new drug-disease linkages and experimental screening approaches are the two main categories of systematic repurposing techniques.

#### **3.1 Experimental screening approaches**

Experimental screening approaches are used as a source of hits for drug discovery and drug repurposing, with notable differences in their application and outcomes. Searches in drug discovery programs are typically done for de novo candidate hits, fuelled by an HTS campaign, which requires highly specialized screening facilities and compound libraries containing several million compounds. Repurposing programs focus on advanced known molecules that either approved or failed with some knowledge of their safety or MoA available, led by in-depth screening and with smaller compound libraries. Typically approved compound libraries containing 500–2000 compounds and a similar number of existing but unapproved compounds are thought to be available.

#### **3.2** *In-silico* **approaches**

*In-silico* repurposing techniques analyze data already in existence using sophisticated analytical methods to discover new possible connections between a drug and a disease [1]. The capacity to predict the conformation of small-molecule ligands inside the proper target binding site with a high degree of accuracy makes molecular docking one of the most commonly used in silico processes; after the creation of the first algorithms in the 1980s, molecular docking became a crucial tool in drug discovery. For example, investigations can be conveniently performed involving important molecular events, including ligand binding modes and the corresponding intermolecular interactions that stabilize the ligand-receptor complex. Furthermore, molecular docking algorithms execute quantitative predictions of binding energetics, providing rankings of docked compounds based on the binding affinity of ligand-receptor complexes [11]. Identifying the most likely binding conformations requires two steps: (i) exploration of an ample conformational space representing various potential binding modes; (ii) accurate prediction of the interaction energy associated with each of the predicted binding conformations. Molecular docking programs perform these tasks through a cyclical process in which the ligand conformation is evaluated by specific scoring functions [11].

*In-silico* approaches can broadly be divided into molecular techniques and realworld data approaches.

#### *3.2.1 Molecular approaches*

The molecular approaches are based on understanding drug activity and disease pathophysiology. They are often powered by large-scale molecular data known as omic data, including genomic, transcriptomic, or proteomic data and data based on drug targets and chemical structure. Due to the availability of datasets on drugs and diseases, as well as the robustness and reproducibility of the data, transcriptomics, and genomics are the two data types most widely used to support drug repurposing [12]. Transcriptomics studies the expression levels of thousands of genes, often accomplished by quantifying RNA using RNASeq or gene expression microarrays. One approach to using transcriptomics for drug repurposing is based on the idea that reversing gene expression signatures may result in a clinical benefit [1].

#### *3.2.2 Real-world data approaches*

The Real-world data approach focuses on identifying unknown and sometimes unexpected relationships between drugs and diseases or their symptoms. They are data based on individuals' health, habits, and behavior captured without environmental intervention or bias introduced by data collection methodologies [1]. The real-world data approaches include network-based drug repurposing, ligand-based drug repurposing, structure-based drug repurposing, and machine-learning techniques [13].

#### *3.2.2.1 Network-based drug repurposing*

Network-based computational biology has become more prevalent in recent times. It integrates the relationship between biological molecules into networks to discover newly discovered properties at the network level and investigate how cellular systems induce different biological phenotypes under other conditions. A network can be represented as a connected graph in the network pharmacology framework, with

#### *Computational Approaches in Drug Repurposing DOI: http://dx.doi.org/10.5772/intechopen.110638*

each node representing either an individual molecular entity, its biological target, a modifier molecule within a biological process, or a target pathway, and each edge representing either a direct or indirect interaction between two connected nodes. An instance of this approach was demonstrated in 2009 by Hu and Agarwal, who utilized publicly available gene expression profiles from NCBI Gene Expression Omnibus (GEO) to construct a network that showed the similarity between different diseases. They then integrated this network with molecular profiles and knowledge of drugs and drug targets, which enabled them to identify opportunities for drug repositioning, as well as to propose molecular targets and mechanisms underlying drug effects [14]. In 2012, Jin et al. also devised a new method for repurposing drugs for cancer therapeutics that takes advantage of off-target effects that may affect critical cancer cell signaling pathways [15]. A hybrid model composed of a network component called cancer-signaling bridges and a Bayesian factor regression model was used to identify off-target effects of drugs on signaling proteins [13]. The main limitation of network-based approaches is that many biological aspects of the disease still need to be discovered, and network-based approaches may fail to produce promising drug candidates; also, biological elements interact with one another to form a complex system. As a result, this class of methods may have more practical effects [16].

#### *3.2.2.2 Ligand-based drug repurposing*

Ligand-based approaches are evaluated because similar compounds have similar biological properties. These methods have been widely used in drug repurposing to analyze and predict the activity of ligands for new targets. The number of publicly accessible compound records (more than a hundred million provided only by PubChem) is far greater than the number of deposited protein crystal structures (as of today, less than 150,000 in the Protein Data Bank) [17, 18]. Ligand-based methods rely on the chemical space coverage of already-known molecules. Deep learning and multi-task learning have been successfully used in ligand chemogenomic benchmark studies. When target and drug similarities were considered, the algorithm better predicted new drug-target associations. Machine-learning approaches play an essential role in silico Chemogenomics [13].

#### *3.2.2.3 Structure-based drug repurposing*

Structure-based similar protein structures increase the likelihood of performing similarly and identifying related ligands. Protein comparison is a technique used in medication repurposing to find secondary targets for a medicine that has already been licensed [19]. Proteins can be compared on a broad scale based on how similar their sequences are. The kinome is the most often-used example of a phylogenetic tree constructed using protein sequences [20]. In this tree, proteins from the same family are more likely to detect substrates or ligands that share similar functions, as in the case of dual inhibitors of the EGFR and ErbB2 receptors for an epidermal growth factor [21]. Sequence alignments work best when proteins have a high level of sequence identity. In contrast, local protein comparison works better when proteins share a low level of sequence identity to uncover unknown targets of known ligands [22]. It has become more crucial to compare protein binding sites to find local similarities [19]. This process is frequently followed by computing several descriptors that help determine a similarity score to locate cavities on the protein surface and compare binding sites. It is important to note that, when available, ligand binding modes are a valuable

tool for finding new targets. Putting a focus on target-ligand interactions is one method of modeling molecular recognition. Several techniques, like structure-based pharmacophores or interaction fingerprints, can accomplish this. When the proteinligand complex's structure is unknown, one can predict hot spots in the binding site using computational approaches [23]. The viability of crystallographic structures of protein-ligand complexes is a prerequisite for structure-based techniques. The level of specificity that can be used to represent a binding site depends on resolution and sensitivity to atomic coordinates. While a protein's static model can be seen in its crystallographic structure, conformational variations can cause the appearance of additional pockets [13].

#### *3.2.2.4 Machine learning approaches*

Although machine learning methods produce better prediction models, they are more data-dependent. Combining machine learning methods and other techniques can make an effective treatment plan for COVID-19 [16]. The general approach has been to fuse the structure-based and ligand-based screening methods with AI algorithms to build prediction models. AI and ML algorithms like deep learning, support vector machine (SVM), random forest (RF), Naive Bayesian, and neural networks have been extensively used for high throughput screening with lots of dataset molecules. In recent years, the development of next-generation computational methods using Artificial Intelligence (AI), Machine Learning (ML), and network medicine approaches has positively impacted the different stages of drug development [24].

#### **4. Success stories in computational drug repurposing**

The field of data science is blooming, and its role in detecting potential candidates for drug repurposing has yet to be explored. There are various approaches to drug repurposing, but the computational approach is unique in the way it utilizes neither in-vivo nor in-vitro techniques. It is known as in-silico drug repurposing—an expediting, cost-friendly, and reliable process [25]. This method relies heavily on data from diverse sources like electronic health records (EHRs) comprising disease diagnoses, lab test results, medical prescriptions, genetic data from biobanks, chemogenomic data, and proteomic data [26]. These data sources, when collated and analyzed, are then capable of producing valuable insights. A few instances:

Given widespread tuberculosis and its extensive resistance mechanisms to current anti-infective treatment, Kleandrova et al. performed a study on computational drug repurposing for antituberculosis therapy by creating a multi-condition model based on quantitative structure–activity-relationship (QSAR) [27]. This sought to find potential antituberculosis agents capable of acting as inhibitors of multiple strains of the bacteria. The model utilized a combination of perturbation theory concepts and machine learning techniques to screen large data repositories for chemical structures with the potential to inhibit Mycobacterium tuberculosis, the causative organism. The dataset comprised 8898 agency-regulated chemicals, including investigational and FDA-approved drugs. After that, stipulated metrics were used to rank these agents, with priority given to those exhibiting the highest values. Top of the list was macozinone, BTZ-043, and niclosamide, but niclosamide is a popularly known antihelminthic. This drug is believed to have anti-parkinsonian, anti-diabetic, and antiviral properties [28]. It is also important to mention that through computationally

*Computational Approaches in Drug Repurposing DOI: http://dx.doi.org/10.5772/intechopen.110638*

identifying drugs that can increase the mRNA expression of downregulated genes in hepatocellular carcinoma (HCC) and decrease the mRNA expression of upregulated genes, the antitumor activity of niclosamide and its ethanolamine salt (NEN) was discovered. The antiproliferative activity of niclosamide and NEN in different HCC cell lines and primary human hepatocytes was then evaluated in vitro. This was further confirmed by in vivo testing against two mouse models (genetically induced liver tumors and patient-derived xenografts [PDXs]) for HCC to show a substantial reduction in the cancer progression after oral administration of NEN compared to niclosamide [29].

Similarly, Zhang et al. performed thorough data mining to identify drugs with anti-Alzheimer properties [30]. Their study revealed seven drugs inhibiting acetylcholinesterase, a known drug target of most anti-Alzheimer conventional medicines. These drugs, which have never been used in the management of Alzheimer's, can be used in the future for cognitive deficiency therapy in patients with the disease. Zhang et al. previously conducted an identical study for drugs that can be used for antidiabetic treatment [31]. Using data mining and pathogenesis information, their study repurposed 58 drugs, out of which nine were prioritized for having higher potential in treating diabetes. Among these nine drugs were four (diflunisal, nabumetone, niflumic acid, and valdecoxib) used in rheumatoid arthritis, osteoarthritis, and pain management. Connectivity map analysis showed that cells treated with these four drugs had similar gene expression as cells treated with conventional anti-diabetic medications like metformin and glimepiride. Evidence from Koren et al., 2019 also suggests that a different class of drugs, the alpha-1 adrenergic antagonists, might have a potential impact on diabetes control [32]. These success stories, though sparse in their numbers, hold a promise for the future. Diseases like diabetes often last for a lifetime, and an estimated 400 million people [33] worldwide suffer from it; therefore, integrating the results of this expediting approach to drug discovery into clinical practice will revolutionize modern medicine.

#### **5. Limitations**

Drug repurposing by pharmaceutical companies faces many challenges. There is a need to create a business model to support the use of existing molecules as therapeutics for new indications and repurposing drug pathways. There is also a need to demonstrate the effectiveness and recover the investment required to bring recycled products to market [1]. Furthermore, this methodology is based on structural files and cannot be used immediately when identifying a new or orphan target [24]. This is because a more extensive collection of records may not be achieved since there is no defining identifier to connect data [16]. This can be seen in the Artificial Intelligence, Machine Learning, and network medicine approaches of computational drug repurposing, which require large amounts of data to train models. Lack of access to structured, standardized data related to analytics and clinical trials can impair the tool's predictive ability. Furthermore, the majority of developed models are local models; that is, they are specific to one problem, and there is no global model or suite that helps in solving or querying the wide range of problems drug discovery teams may encounter [24].

All computational-based drug repurposing methods heavily depend on data. Existing databases pose lots of challenges for researchers. The volume of data in some databases needs to be increased to generate a suitable model, and there is no determinant identifier to connect data to collect more comprehensive datasets. Data descriptions could be clearer, making it easier to understand them. The databases also contain data for a specific purpose rather than complete data. Lastly, introducing new Active Pharmaceutical Compounds (API) commands has made them difficult to learn and use. Existing databases have some limitations that can be overcome using software engineering techniques [16]. In terms of improving efficacy and reducing the time and cost of a drug discovery project, computational-based approaches may produce more acceptable results than others. Every computational drug repurposing method has advantages and disadvantages and heavily depends on data [16].

The computational approach is auspicious and effective in other domains. Natural language processing, for example, has proven helpful in translation, spell-checking, and other applications. However, AI/ML-based techniques necessitate a large amount of data to train the models. The inaccessibility of structured and standardized data associated with assays and clinical trials may jeopardize the tools' predictive ability. Furthermore, most developed models are local, which means they are specific to one problem. No global model or suite can help resolve or query a wide range of issues that a drug discovery team may frequently encounter [24].

#### **6. Opportunities in the computational repurposing of drugs**

Although sciences and technology have progressed rapidly, de novo drug development has been costly and time-consuming over the past decades. Given these circumstances, "drug repurposing" (or "drug repositioning") has appeared as an alternative tool to accelerate the drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked medications in the U.S [34]. Even though the application of computational methodologies to drug repurposing has yielded some positive results and has been propounded to repurpose drugs on a large scale by utilizing available high-throughput data, due to the failure of the current drug regimen, many more diseases need urgent attention in terms of new drug therapies. There are increasing number of deaths from Neglected tropical diseases. The World Health Organization (WHO) describes neglected tropical diseases (NTDs) as a diverse group of communicable diseases that prevail in tropical and subtropical conditions [35]. Neglected Tropical Diseases include Buruli ulcer, Chagas disease, dengue and chikungunya, dracunculiasis (Guinea-worm disease), echinococcosis, foodborne trematodiases, African human trypanosomiasis (sleeping sickness), leishmaniasis, leprosy (Hansen's disease), lymphatic filariasis, mycetoma, chromoblastomycosis, and other deep mycoses, onchocerciasis (river blindness), rabies, scabies, and other ectoparasitoses, schistosomiasis, soil-transmitted helminthiases, snakebite envenoming, taeniasis/cysticercosis, trachoma, and yaws and other endemic treponematoses [35]. According to WHO, NTDs cause about 200,000 deaths yearly [35]. A person may become severely disabled, disfigured, blind, or malnourished after contracting an NTD and frequently acquire multiple NTDs at once. If new drugs have to be developed for these conditions through conventional means, many deaths must have been recorded before the drugs get to market.

According to Nigeria Centre for Disease Control (NCDC), in 2021 and 2022, Cholera killed more people in Nigeria than COVID-19 [36]. Even though there are standard treatment guidelines for this condition, the death rate keeps rising. Globally, lives are being lost from different types of cancers, even with all the treatments currently available. Lives are also being lost from various other diseases affecting

mankind. Computational drug repurposing will go a long way in providing within a short time a possible better treatment and management options for these diseases and all other diseases challenging mankind.

#### **7. Conclusion**

The utilization of existing drugs to identify other potential therapeutic indications can be done more quickly and with less expense through computational drug repurposing. This approach is facilitated by the use of protein and chemical databases, which have been developed to support computational techniques. These databases enable existing drugs to be acquired in the necessary formats for computational studies. There is now a broad selection of available computational tools, with more currently under development, which can help to advance the application of computational approaches in drug repurposing. With access to these tools and databases, any researcher with an interest in this area can begin to explore drug repurposing. The effective use of computational drug repurposing has the potential to improve treatment and management options for a wide range of diseases affecting humanity.

#### **Acknowledgements**

The authors express their gratitude to the CURIES research group, whose support and encouragement have been invaluable.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Christabel Chikodi Ekeomodi\*, Kingsley Ifeanyi Obetta, Mmesoma Linus Okolocha, SomtoChukwu Nnacho, Martins Oluwaseun Isijola and InnocentMary IfedibaluChukwu Ejiofor Faculty of Pharmaceutical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

\*Address all correspondence to: cc.ekeomodi@stu.unizik.edu.ng

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[34] Park K. A review of computational drug repurposing. Translational Clinical Pharmacology. 2019;**27**(2):59-63. DOI: 10.12793/tcp.2019.27.2.59

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#### **Chapter 11**

## Role of Drug Repurposing in Sustainable Drug Discovery

*Shanta Bhar*

#### **Abstract**

The contemporary global drug discovery scenario, in spite of several technological advances, is heavily ridden with multiple challenges of a dynamic regulatory system, escalating costs from bench to bedside investigational drugs, the increased probability of withdrawal after launch, and over-stretched timelines from discovery to approval, among others. Drug repurposing/repositioning/re-profiling/re-tasking is an effective and practical complimentary method for the selection of alternate therapies for approved, shelved, discontinued/abandoned, and investigational drugs or new chemical entities, with the parallel study of new metabolic pathways and/or protein targets. Such an approach encompasses multipronged benefits of redundant preclinical testing, toxicity evaluation, and formulation studies, based largely on serendipity. In recent years, approaches have been driven by artificial intelligence (AI) and machine learning, and bioinformatics have opened up new vistas in drug re-profiling acceleration. Increasing protocols to club the shared mechanisms among structurally diverse/dissimilar drugs include pathway analysis, phenotypic screening, signature matching, related disease genes, binding assay studies, molecular docking, and clinical data monitoring. All in all, repositioning of abandoned/investigational/existing drugs or new chemical entities for other therapeutic indications could enhance the overall productivity of the pharmaceutical industry while paradigmatically shifting the focus from new drug discovery to the optimization of available resources.

**Keywords:** innovation, repurposing, big data, pharmacological analysis, pathway matching

#### **1. Introduction**

The overall global population exposed to regular medicines has increased twofold over the past few decades. To complement this, the average life expectancy has increased considerably, and newer, lesser understood diseases are on the rise. Unfortunately, the rather long timelines of drug design and approval (10-12 years), increased rates of USFDA failure/recall after launch, escalating resources for new drug discovery and development, and a paradigm shift toward green chemistry have, in totality, rendered the conventional drug discovery process largely wanting for alternative backup plans.

According to the Brundtland Commission of the United Nations: "Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs" [1]. The Sustainable Development Goals (SDGs) and the One-Health approach are analogous initiatives [2]. The various alternative ways of making drug discovery sustainable are: signature mapping, pathway matching, in silico screening and molecular docking, genetic association, retrospective analysis of clinical data, drug repurposing, high-throughput screening, and so on.

Out of all the options available for introducing sustainability in drug discovery and development, drug repositioning, also known by its alternative names of repurposing, re-profiling, or re-tasking, is the strategy of choice as the advantages far outweigh the challenges encountered in offering a drug for a new medical indication, totally distinct from its original scope.

The most important reason for the failure/withdrawal of an approved drug is addressed adequately: the potentially repurposed drug would have been in preclinical models and early-stage human clinical trials (phase I & II), thereby justifying its safety; thus, subsequent efficacy trials would be more predictable.

In addition, drug development timelines can be sufficiently squeezed, due to nonrepetition of preclinical testing, safety assessment, and/or formulation development. Most significantly, it is the most economically lucrative of all the sustainable strategies employed, as mandatory investment is marginal. The regulatory and phase III costs can involve substantial savings in preclinical, phase I, and phase II expenditure.

Since the commencement of drug repurposing concept, it was rational but serendipitous; that is, an approved drug or one in clinical trials was studied for its contraindications, off-target reactions, and/or an enhanced on-target response, patented and groomed for repurposed launch. Most success stories of relaunch in a new therapeutic avatar have indeed relied hugely on serendipity, rather than on a structured and wellplanned approach. Such examples include aspirin, minoxidil, sildenafil, thalidomide, celecoxib, rituximab, raloxifene, fingolimod, dapoxetine, topiramate, and ketoconazole, among others.

However, with several technological advancements, in the form of various approaches, drug repurposing in general, and specifically for rare diseases, comprising a databank of over several thousand (~7000), with 95-96% of these having no approved therapeutic agent, primarily because of unknown disease pathophysiology, contemporary drug repurposing abounds in opportunities to fill up the unmet medical space.

As we shall see further, the trade-off between challenges posed by drug repurposing and its many advantages is also laced with limitations of collation, integration, analysis, and interpretation of big data, of biomedical, clinical, pharmacological, or sequencing type.

#### **2. Types of sustainable approaches to drug discovery**

The many approaches toward sustainable drug discovery are listed below (**Figure 1**):

#### **2.1 Signature mapping**

In a broad sense, this process is based on the relative comparison of certain distinct features, thus referred to as the 'signature' of one drug against another, disease

*Role of Drug Repurposing in Sustainable Drug Discovery DOI: http://dx.doi.org/10.5772/intechopen.110621*

**Figure 1.** *Sustainable drug repurposing approaches.*

pathway comparison, or clinical phenotype matching [3, 4]. This signature pattern of two comparable drugs is generally assigned from three databanks:


For example, two drugs were identified using drug–disease similarity approach using the correlation between the gene expression signature of the drug and that of the disease, prednisolone, and topiramate.

#### **2.2 Pathway/network matching**

Genetic, protein, or disease data can aid the identification of repurposing targets. In some disease pathways, the relevant genes are not druggable as targets. This is when a pathway-based comparison of genes that are either downstream or upstream or a genome-wide association studies (GWAS)-related target may be used for repurposing issues [5]. Network analysis uses available information on gene expression

patterns, disease pathology, protein interactions, or GWAS data for construction of a drug or disease network to further potential repurposing candidates.

For example, pranlukast, an asthma drug/leukotriene receptor 1 antagonist, and a phosphodiesterase inhibitor amrinone are used for congestive heart failure.

#### **2.3 High-throughput screening**

Or phenotypic screening of compounds, it is applied to in vitro or in vivo disease models can reveal potential drug candidates for clinical evaluation.

For example, the discovery of disulfiram, used against alcohol abuse, turned out to be a selective antineoplastic agent, with proven research using genome-wide gene expression studies.

#### **2.4 In Silico screening and molecular docking**

It is a computational approach based on structure similarity to predict binding site complementarity between a ligand (for example, a drug) and a therapeutic target (typically, a protein receptor).

For example, molecular docking studies identified mebendazole, an anti-parasitic drug and inhibitor of vascular endothelial growth factor receptor 2 (VEGFR2), and also a mediator of angiogenesis. This was validated with experiment studies.

#### **2.5 Genetic association**

Genes that assumed to be associated with a disease mechanism should be considered as potential drug targets about which a small molecule ligand can be designed.

#### **2.6 Retrospective analysis of clinical data**

Large volumes of old, retrievable clinical data including hospital records, clinical trial data, and post-marketing surveillance data can be tapped, compiled, integrated, and analyzed. Such databases comprise both structured and unstructured data on patient response and outcomes.

For example, terbutaline sulfate, an anti-asthmatic drug candidate, was arrived at for the treatment of amyotrophic lateral sclerosis (ALS), from the retrospective clinical data analysis.

#### **3. Drug repurposing**

#### **3.1 Challenges to overcome**

#### *3.1.1 Legal and commercial challenges*

Some developing countries like India, the Philippines, Taiwan (Province of China), the Andean Pact Latin American countries (Bolivia, Colombia, Ecuador, and Peru), and Vietnam do not allow the granting of a method-of-use patent for a second or alternate medical use of an approved drug, whereas several developed nations allow second medical use patents defined as fiction of novelty. In some other cases,

possible repurposing may have been discussed/covered/reported in the original literature or may be part of its non-registered uses. The international TRIPS Agreement allows flexibility to the individual signatory nations as also to their courts on the refusal/approval of second medical use patents.

The European Patent Office (EPO) does not allow a second medical use patent if it is merely noted that the drug exhibits selective binding to another receptor; yet if the claim focuses on the end result of the drug function, mentioned categorically as "any condition susceptible of being improved or prevented by inhibition or enhancement of a specific enzyme activity," the patent is granted/approved provided that it is validated/supported by experimental data, which is also disclosed in the patent application specifications. Any information related to drug re-profiling may not be controlled by intellectual property rights; however, if in the public domain, it cannot attract novelty, thus ceasing to be patentable material. Furthermore, proof-of-concept studies also need to be validated by controlled clinical trials.

Toggling the bioisosteres of existing off-patent drugs by keeping their pharmacophore intact would defeat the purpose of repurposing, as it would give rise to a new entity. Same dosage and/or formulation for off-patent drugs being re-profiled would not be legally patentable. Therefore, offering a lower dosage form than in use or an alternate formulation would be key to its re-profiling.

#### *3.1.2 Retrieving data*

With respect to shelved/abandoned/withdrawn drugs, a major hurdle is accessibility to clinical trials data that only its discovering organization may be privy to. Although a shelved drug is capable of reinventing itself as a lower dose repurposed opportunity, the pharma industry, on an average, does not resort to sharing its list/ portfolio of shelved drugs, especially if the therapy area of repurposing does not cater to its organization's disease portfolio.

In another scenario, where there is a collaborative interface between industry and academia, this problem can only be facilitated by CDA (Confidentiality Disclosure Agreement) and compound sharing. Dealing with abandoned drugs, or those which have been outlived by their competitors, another challenge is posed by the availability of a suitable vendor.

#### *3.1.3 Limited repurposing space*

As more and more approved drugs are finding their way into repurposed therapeutic territories, it may seem that the druggable space for repurposing is exhausting itself out. In such a scenario, a prudent strategy would be to club drugs for combination therapy, as also to discover new pathways for their effective applications. This is visible more so in the field of infectious diseases, by employing the nifurtimox–eflornithine combination therapy for second-stage African trypanosomiasis [6]. Another avenue to expand the re-profiling horizon is personalized medicine.

#### **4. Early examples of successful drug repositioning**

The first example of drug repositioning is aspirin or acetylsalicylic acid (**Figure 2**). In 1899, Bayer marketed it as an analgesic, while at the turn of the century, in the 1980s, it was repositioned as an antiplatelet aggregation drug [7].

**Figure 2.** *Aspirin/AcetylSalicylic acid.*

During the last millennium, drug repurposing successes of sildenafil, valproic acid, and minoxidil were charted by analyzing their known pharmacology in a particular domain (vis-a-vis the adverse effects) to resolve a pressing clinical problem from another therapeutic area, not relying entirely on serendipity [8].

Sildenafil (formulated as its citrate) (**Figure 3**), originally introduced by Pfizer, as an antihypertensive medication, was later repurposed for erectile dysfunction (TM Viagra) based on prior clinical experience and went on to become a blockbuster drug [9]. Moreover, as a reversible inhibitor of phosphodiesterase type 5 (PDE5), it has also been approved for pulmonary arterial hypertension (PAH) intervention under the brand name of Revatio® [10].

Valproic acid (N-dipropylacetic acid) (**Figure 4**) was discovered by Meunier and Carraz in the year 1967. On the revelation of its anticonvulsant properties, it was a popular drug widely used in epilepsy and bipolar disorder, formulated as sodium valproate. Valproic acid (VPA) has so far been the drug of choice for epilepsy and other neurological disorders since the past 66 years. Ongoing research has indicated the potential of VPA as an antineoplastic agent, partly due to its role in the inhibition of histone deacetylases, thus modulating the expression of genes and affecting changes in the cell cycle, differentiation, and subsequently apoptosis. Over and above inhibiting histone deacetylases, VPA enhances RNA interference, activating histone methyl-transferases, or suppressing the activation of transcription factors [11].

Minoxidil (**Figure 5**) was originally developed in the late 1950s, by The Upjohn Company, now Pfizer, with hopes of treating ulcers; however, it failed to treat gastric issues, and instead was shown to be a vasodilator and a potassium channel opener, hyperpolarizing the cell membranes. Two decades later, in 1979, minoxidil was repurposed for arterial hypertension [12]. During clinical trials, it was observed that unwanted hair growth was an adverse side effect, and thus, subsequently, a topical minoxidil formulation was evaluated to treat hair loss. FDA approved topical minoxidil in 1988 for androgenetic alopecia and alopecia areata, and today, generic minoxidil is sold over the counter (OTC) as Regaine Topical Solution 2%, for men and women [13].

Another drug, thalidomide, originally introduced as a sedative in the year 1957, was found to induce severe skeletal birth defects in newborn children whose mothers were administered this drug in the first pregnancy trimester; thus, after about 4 years, it was withdrawn and further repurposed for erythema nodosum leprosum (ENL) (1964), and multiple myeloma (1999) was based on serendipity [14]. The two indications were distinct of each other, and decades apart from each other. The

**Figure 4.** *Sodium valproate.*

successful repurposing of thalidomide also led to the discovery, development, and approval of other highly successful derivatives, notably lenalidomide (Revlimid, Celgene) (**Figure 6**).

This strategy is especially very pertinent when applied to rare and neglected diseases and disorders, and orphan drugs, as is noticed in the drug portfolio of DNDi (Drug for Neglected Diseases initiative) repurposed NCE candidates undergoing clinical trials, including fexinidazole, fosravuconazole, Ambisome™, and miltefosine, fexinidazole being the first oral-only drug, for advanced-stage sleeping sickness [15], with a small fraction of the estimated investment for a *de novo* drug. Rare and orphan diseases often have unknown or poorly characterized metabolic pathways/ pathophysiology where computational approaches for predictive repurposing result in large-scale genome sequencing data analysis for the identification of genetic variation/s contributing to the disorder and expediting the re-profiling of existing drugs targeting the relevant protein/s [16].

During the past decade, drug repurposing as well as drug discovery is hugely complemented by artificial intelligence/machine learning methods to squeeze the drug discovery timelines while maintaining consistency in the systematic retrieval, *Drug Repurposing - Advances, Scopes and Opportunities in Drug Discovery*

**Figure 6.** *Thalidomide and its derivatives, Lenalidomide, Pomalidomide, and BTX306.*

analysis, and application of big data. Notable approaches in this area are systematic analysis of clinical trial data, molecular similarity approximations, signature matching of targets, transcriptomic and proteomic data, and structure-based virtual screens.

#### **5. Unusual case studies**

#### **5.1 Dapoxetine**

Dapoxetine, discovered by Eli Lilly, as a selective serotonin reuptake inhibitor (SSRI), was primarily discontinued by Eli Lilly as an adjunct therapy for analgesia. Later on, it was repurposed as an antidepressant with fluoxetine. But short half-life of the compound and the rapid onset did not permit daily dosage, an imperative criterion for any supplementary antidepressant, and thus, it fizzled out.

Finally, dapoxetine's rapid onset and short half-life were considered to be a pharmacokinetic advantage for the therapy of premature ejaculation, which prompted patenting the findings and validating with phase II proof-of-concept studies, and after changing hands, with a new method-of-use patent, dapoxetine (now a part of Johnson & Johnson portfolio) is now in phase III clinical development for premature ejaculation.

#### **5.2 Thalidomide**

As noted previously, after severely unfortunate fallouts, thalidomide made a grand role reversal. Originally marketed as a sedative in 1957 in Germany and England, Thalidomide created unforeseen complications in pregnant women facing morning sickness. It was subsequently repurposed as a drug of choice to treat the erythema nodosum laprosum (ENL), an agonizing inflammation caused due to leprosy whereby painfully large boils often lead to blindness.

#### **6. Future outlook**

Currently, almost a third of approvals are repurposed drugs. This clearly indicates the success of several repurposed drugs. New avenues for repositioning can emerge from increased collaboration between pharmaceutical industry and allied sectors of academia, with priority awarded to orphan diseases, rare and neglected disorders, and synergistic drug combinations of repurposed drugs, in such cases as metformin, where the efficacy of the original drug continues unabated. Rare and neglected diseases are not usually profitable for pharmaceutical giants, and their involvement in terms of corporate social responsibility, which endeavors to bridge the gap between profit margins and overall societal welfare, can also add up to generate awareness about several lesser-known disorders. These can be heavily incentivized, parallelly by governmental agencies/organizations, to sustain the equilibrium between commercial viability and redressal of new therapeutic solutions for marginalized diseases.

To supplement this, personalized/precision medicine will add to newer information regarding the characterization and classification of disease pathways, leading to a better understanding of repositioning old/abandoned drugs for diverse therapeutic areas, revitalizing drug re-profiling even further. Contraindications/adverse reactions relating to a prior allotted pharmacokinetic metabolism can assist and enhance our knowledge of expected drug reactions. Sustainable drug re-profiling is also complimented by advances in technology such as artificial intelligence and machine learning, which are instrumental in the expedition of large-scale extraction and integration of heterogeneous data, comprising a mixed bag of imaging, HTS data, DMPK profiles, clinical trials records, and electronic health reports and records, to name a few.

Thus, *de novo* drug discovery and drug repositioning can support each other to make pharmaceutical innovation more sustainable in terms of resources, time, and setbacks.

#### **Author details**

Shanta Bhar Indian Institute of Information Technology, Guwahati, India

\*Address all correspondence to: shanta.bhar@gmail.com

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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### Section 3
