Integrating Research Data for Drug Efficiency in the Indian Pharma Landscape

The average cost to develop a new drug is approximately 2.6 billion, according to a study by the Tufts Center for the Study of Drug Development. This cost may be reduced by up to 30 per cent through the integration of AI/ML and data analytics, which would result in substantial savings for India's pharmaceutical industry and poised to strengthen its global competitiveness and contribute to a healthier world.

- Dr Mary Donlan

The Indian pharmaceutical industry has emerged as a global powerhouse, catering to over 60 per cent of the world's vaccine demand and ranking 3rd worldwide in drug production volume. However, to sustain this competitive edge, the sector must prioritise efficiency, innovation, and process optimisation across the drug development lifecycle. Driving this necessity is the transformative phase the Indian pharmaceutical industry is experiencing, fueled by technological advancements that enhance research capabilities and drug efficiency. Among these pivotal technologies are generative AI, AI/ML, and data analytics, which offer unprecedented opportunities to revolutionise drug development and boost efficiency. The integration of research data in the Indian pharma landscape, a critical step in leveraging these technological advancements will impact drug efficiency, cost reduction, regulatory harmonisation, and global competitiveness, while also addressing the associated challenges and strategic implications.

Enhancing Formulation Research through Data Integration

Formulation research, which determines the bioavailability, stability and efficacy of a medicinal product, plays a key role in the development of these products. Historically, data on product formulation have been fragmented across different systems, making it difficult to collaborate and slowing down the pace of market entry. Pharma companies can streamline the exchange of information, enabling researchers to make data driven decisions and accelerate the development of drugs, by integrating formulation data into a central electronic laboratory notebook (ELN).