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Hyderabad: It takes at least a decade or even more to develop and launch a novel drug from a ‘bench to bedside’ in India. By the time the drug reaches the patients after the initial discovery, drug developers would have already burned through a substantial chunk of financial investment, human resources and above all, decades of time spent in research and development.

Apart from time and expenditure, another major challenge in taking up cutting-edge research to develop new drugs is the risk involved, as there is no guarantee that the new drug developed will prove to be a blockbuster in the market.

Such inherent difficulties in drug development have literally spawned drug repurposing or repositioning, which now has almost become mainstream research. To hasten the development of therapeutics, researchers worldwide have started to look into the existing compounds, chemicals and molecules and explore their potential in the treatment of diseases. In fact, almost all the major Council of Scientific and Industrial Research (CSIR) funded research institutions in the country are pursuing drug repurposing in a big way.

In the last few years, taking the existing drugs in the market and exploring their potential to treat or cure other diseases has gone fully mainstream. According to data available from various sources, at present, 30 per cent to 35 per cent of the drugs and even vaccines that are approved by the US Food and Drug Administration (USFDA) are repositioned or repurposed.

What is drug repurposing

Drug repurposing or repositioning is a technology wherein existing drugs are used to treat other diseases, quite often novel and emerging diseases. So, Sildenafil citrate, the active ingredient meant for the treatment of hypertension and chest pain, now has become a treatment for erectile dysfunction (manufactured by Pfizer with the brand name Viagra).

Another drug that is universally considered a poster child for the concept of drug repurposing is Thalidomide. The drug that was first used as a sedative for pregnant women, later become therapeutic for the treatment of leprosy. A few years ago, Thalidomide in its new avatar has become a drug to treat cancer among patients with multiple myeloma.

In Hyderabad, researchers at the Centre for Cellular and Molecular Biology (CCMB) recently found that the drug Bazedoxifene, widely used for the treatment of post-menopausal symptoms and osteoporosis among women, now has the potential to treat malaria. The city-based Indian Institute of Chemical Technology (IICT) had repurposed Favipiravir, an anti-influenza medicine, and developed a cost-effective way to process to develop Favipiravir and transferred the technology to noted drug manufacturing company — Cipla.

Another classic instance of success in drug repurposing is aspirin, which is widely used to treat pain, fever and inflammation. Later on, researchers figured out that aspirin could also be used against cardiovascular diseases. Further research on the potential of aspirin revealed that it could also be utilised for cancer prevention, central nervous disorders, diabetes and even beneficial against certain kinds of pulmonary infections.

AI in drug repurposing

To quickly identify existing drugs that have the potential to be repurposed, researchers increasingly are utilising AI and electronic health records of patients for drug development. Such a technique of leveraging AI is cheaper, faster and less time-consuming. Recently, researchers and AI scientists from Ohio State University developed a machine-learning system that analyses massive amounts of patient data and come-up with existing medicines that can be utilised to improve outcomes for other diseases.

The study, which was published in 2021 in Nature Machine Intelligence, was aimed at developing an AI-based system to speed up the process of identifying ‘ideal candidates’ or medicines that can be utilised against other diseases. While taking-up such analysis manually might take a lot of time and even some luck, the presence of an AI-based system ensures that the massive amounts of patient data is analysed quickly and the suggestions are accurate.

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Author: Howard Caldwell