Last month, three life sciences giants announced new or deepening partnerships with IBM, to capitalise on IBM’s supercomputer, ‘Watson’, an artificially intelligent computer system. There has been a lot of buzz recently about how big data can be meaningfully applied in the healthcare setting to assist with product development and disease treatment. The European Medicines Agency has recognised the potential of exciting opportunity of exploiting big data with the capability of significantly contributing to the way the benefit-risk of medicines is assessed over a product’s lifecycle. The flurry of recent announcements indicate that we are entering into the new era of cognitive computing that is capable of re-engineering product development and improving assessment of performance of medicines or healthcare products on the market.

Watson

IBM’s supercomputer, nicknamed ‘Watson’, made headlines in 2011 for beating two of the gameshow Jeopardy’s greatest human champions. It did so by making use of cognitive computing technology, which involves teaching computers to process large amounts of information in a way similar to how humans think, with the capability to analyse and interpret data in various formats including those that are unstructured.

Since its days of gameshow glory, Watson has advanced its offerings to include applications that are specific to healthcare. Following the launch of Watson Health in 2015, IBM has lined up several large pharmaceutical companies to partner with in using and developing the new technology.

  • Medtronic and Johnson & Johnson were quick off the mark to enter into Watson Health partnerships with IBM, announcing in April 2015 that the technology would be used to personalise diabetes management solutions using data collected from Medtronic’s devices and to set up mobile-based coaching systems for pre- and post- operative patient care for Johnson.
  • In September 2015, Teva Pharmaceuticals became the first pharmaceutical company to deploy the IBM Watson Health Cloud as a mechanism for building global eHealth solutions designed to address complex and chronic conditions such as asthma, pain, migraine and neurodegenerative diseases. In addition, the partnership envisages making use of big data and machine learning technology to create disease models and advanced therapeutic solutions. An expansion of the partnership was announced on 26 October 2016 and will focus on the discovery of new treatment options and on improving chronic disease management. In particular, the expanded partnership seeks to enable the delivery of novel therapies by repurposing existing drugs as new treatment options.
  • Novo Nordisk signed up to an IBM Watson Health Cloud partnership in December 2015, in a bid to launch a digital platform to help manage patients’ diabetes by way of a real-time analysis of data-uploads concerning patients’ blood sugar levels, food intake and medicine usage.
  • On 1 November 2016, Celgene and IBM announced a partnership aimed at facilitating and improving pharmacovigilance through the creation of a cloud-based drug evaluation platform, to be run on Watson Health Cloud.
  • GlaxoSmithKline (GSK) announced a collaboration with IBM Watson in June 2016, which aims to allow GSK consumers to ask questions via voice or text directly through GSK’s online ads. Watson will then generate a personalised response for delivery to the consumer. On 6 October 2016, GSK announced that the use of Watson’s interactive functionality had been launched in respect of the company’s Theraflu brand.

Although Watson has featured prominently among the recently announced cognitive computing applications, it is not the only player. Notably, both GSK and Sanofi have recently announced joint ventures with Verily Life Sciences, to develop bioelectronic medicines and comprehensive diabetes management platforms.

What is abundantly clear, is that the recent flurry of announcements on partnership and joint venture between life sciences companies and technology companies is an indication of the industries’ voracious appetite for big data analytics to improve efficiency and reduce costs in research and development as well as timely access to new products.