With nearly one-fifth of the global supply chain and 60% of the worldwide vaccine supply, the Indian pharmaceutical industry is a dominant global player. India has the highest number of US Food and Drug Administration-approved pharmaceutical plants outside of the US, and pharmaceutical exports from India accounted for nearly 30 billion US dollars last year. The pharma sector, including biotechnology, vaccines, biologics and generic drugs, forms an important component of the country’s economy, job creation and exports.
The biological active component of a drug is known as active pharmaceutical ingredient (API). With over 70% of the APIs imported from China, India’s dependence on China is well known. Although some modest efforts have been initiated to minimise this, it has not changed the balance of power in the API industry in terms of scale and cost.
The biggest challenge to the Indian pharma sector, however, is not its dependence on China for APIs or producing the bulk of global vaccines and generic drugs but the emergence of Artificial Intelligence (AI). Can India sustain its growth in the sector, let alone lead? Can it adopt new technology to remain a relevant and dominant global player in the sector?
The process of drug discovery and development takes billions of US dollars in investment, time (more than a decade) and is mired with high rates of failure, toxicity, lack of desired effects in humans, and adverse effects. These factors make the prices of new and novel drugs very high for patients and the government that subsidises them.
AI promises to change all that by reducing the discovery and development time and increasing the chance of drugs working. Although the fundamentals of AI are decades old, the emergence of generative AI and the development of large language models (LLMs) has redefined the landscape. In the AI space, the availability of a large number of sequences, structures and functional data on biological molecules, along with the power of graphics processing units (GPUs) providing the backbone of computing power, has made it possible to design better drugs faster with a fraction of the cost.
One of the recent examples is the development of AlphaFold3, the primary developers of which were awarded the Nobel Prize for Chemistry last year, that not just predicts the structures of proteins from their primary sequence but also their interactions with deoxyribonucleic acid (DNA), (ribonucleic acid) RNA, small molecule ligands, ions and other proteins.
AI has shown promise in drugs and the development of other therapeutics against multiple disease conditions. Although no drugs exclusively discovered and developed using AI methods have reached the market yet, several in the late phases of clinical trials are in the pipeline.
For example, the ISM001-055, an AI-developed drug for idiopathic pulmonary fibrosis, and BenevolentAI-001, a drug against rheumatoid arthritis, by companies Insilico Medicine and BenevolentAI, respectively, are in their phase-2 trials. Perhaps one of the most promising examples – highly relevant to India – came from David Baker (a co-recipient of last year’s Nobel Prize in Chemistry) and his co-workers, who designed proteins from scratch using AI models. Their approach, which required limited resources, was significantly faster and produced proteins with high stability and strong binding affinity to their target, three-finger toxin – a highly potent component of venom from several cobra-family snakes. Given that the current method of snakebite treatment relies on antivenoms derived from the blood of immunised animals (mainly horses) – a time-intensive, expensive approach with limited efficacy against specific snake toxins – this development holds tremendous potential.
Returning to Indian pharma and its edge in the global marketplace, here is a comparison. The cumulative value of the licensing deals from various Chinese biotech companies for novel drugs to global pharma exceeded the total export value of the entire Indian pharma industry last year, indicating what is yet to come.
The competitive advantage of the Chinese companies comes not just from the use of skilled manpower (many of whom are trained at the best places in the US and Europe) and ease of conducting clinical trials with a better and streamlined regulatory environment, but a preeminent role in developing and using of technology, such as AI, in the process of drug development. Deepseek’s rise as an alternative to the models developed by much larger companies in the US with better financial resources is a case in point. Pharma is no longer immune to technology development.
When the price of novel drugs comes down due to significant savings in development time, with a higher rate of success due to the use of AI, will the Indian pharma industry be able to hold on to its cost arbitrage advantage? The answer is obvious. The industry needs to embrace innovation with technology towards novel drug development with an infusion of substantial financial resources to remain relevant and competitive in the global marketplace.
Binay Panda is a Professor at the Jawaharlal Nehru University, New Delhi.
