Pharmaceutical companies need to adopt technologies such as AI and advanced analytics, to not only improve efficiencies and reduce costs, but also to adapt a more patient centric business models, says a new FICCI-KPMG paper on artificial Intelligence and advanced analytics in pharmaceuticals.
Globally, there is pressure to reduce costs and demonstrate greater value, swinging from treatment to prevention and personalised treatments, which is posing a challenge to the overall business model of the pharmaceutical companies. AI and advanced analytics can process large amount of complex, both structured and unstructured data at a rapid pace to generate actionable insights and thereby reduce costs, to improve time to market and gain competitive strength in the market place. While major global companies are investing heavily in this sector, it is only recently that pharmaceutical companies have started to demonstrate significant interest in AI application for various R&D (research and development) and supply chain needs, observed the report.
AI in pharmaceutical industry can transform processes from the initial R&D level to the after-consumption stage from R&D, drug dosage and safety, manufacturing, supply chain and commercialisation and for regulatory approvals, said the knowledge paper, which estimates AI in drug dosage error reduction alone can save $16 billion by 2026.
It said pharma companies are increasingly collaborating with technology companies, especially start-ups in this area. Atomwise, a start-up which established the use of deep neural networks for drug design, raised $45 million investment to develop its AI-driven drug discovery technology. GlaxoSmithKline signed a $43 million drug discovery collaboration with Exscientia, a UK based AI-based start-up, to identify small molecules for ten selected targets across undisclosed therapeutic areas. Pharma companies such as GSK, Sanofi, Takeda Pharma and Merck have entered into various partnerships with AI start-ups.Sanofi and Exscientia entered into $283 million strategic research collaboration agreement aimed at developing new therapies for diabetes and other metabolic diseases. Drug developers are relying on AI for multiple uses: to develop better biomarkers; to identify drug targets; to design new drugs as well as for re-purposing drugs i.e. finding new uses for existing drugs or late-stage drug candidates. An increasing need is also being felt for pharmaceutical companies to explore coming together to share data and look for patterns for precision medicines. Applications in pharmaceutical R&D will include identification of new target molecules from a repository, experimenting with the drug or device using technologies such as 3D printed molecules and lab-on-chips and pre and post approval monitoring.
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