{"id":33377,"date":"2018-11-13T16:36:25","date_gmt":"2018-11-13T21:36:25","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/enabling-patients-to-get-innovative-treatments-faster-through-machine-learning-at-gsk\/"},"modified":"2018-11-13T16:36:25","modified_gmt":"2018-11-13T21:36:25","slug":"enabling-patients-to-get-innovative-treatments-faster-through-machine-learning-at-gsk-2","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/enabling-patients-to-get-innovative-treatments-faster-through-machine-learning-at-gsk-2\/","title":{"rendered":"Enabling patients to get innovative treatments faster through machine learning at GSK"},"content":{"rendered":"

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Healthcare today faces\u00a0extraordinary challenges with aging population and increasing burden of chronic diseases in developed countries in addition to growing demand from the middle class in emerging countries. At the same time the pharma industry is facing a significant challenge in terms of R&D productivity[1]<\/a>. WhileR&D expenditures have been going up, the number of drugs approved per year has been largely decreasing over the past 50 years.[2]<\/a><\/p>\n

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The challenge of R&D productivity at GSK<\/strong><\/p>\n

GSK, a global pharmaceutical company, is facing this very challenge. GSK spent GBP 3.9B in 2017[3]<\/a>– close to 20% of their operating expenses – in R&D. Increasing productivity is thus critical in order to maximize value for shareholders and ultimately enable patients to get innovative treatments faster.<\/p>\n

GSK led by its top R&D executive (John Baldoni) and CEO (Emma Walmsley) is probably the most active[4]<\/a>of all pharma companies in applying artificial intelligence and machine learning (AI\/ML) to the challenge of R&D productivity. Their journey started in 2013 with three core initiatives.<\/p>\n

1 – Shared a bold vision<\/strong><\/p>\n

In 2013, Witty then CEO and Baldoni, seeing what happened in other industries, realized that machine learning would transform pharma. In 2014, Baldoni officially shared his bold vision and an ambitious target with the rest of the R&D organization: \u201cWithin 3 years, I want to go from a drug target to a molecule in 12 months. That takes 5 to 6 years now. That\u2019s what success looks like.\u201d[5]<\/strong><\/a><\/em>\u00a0Following this, GSK reorganized itself with the creation of an in-house artificial intelligence unit. The first of its kind in the pharma industry.<\/p>\n

2 – Leveraged partners to experiment<\/strong><\/p>\n

Not having the ML\/AI capabilities in-house, Baldoni leveraged external partners to innovate and develop ML\/AI applications. Baldoni\u2019s team built partnerships with innovative startups – including Exscientia[6]<\/a>(announced in July 2017) and Insilico Medicine[7]<\/a>(Aug 2017) – to discover novel biological targets and pathways. GSK also worked with Google on applying AI to drug discovery. In July 2018, a paper in PLOS One described how researchers from the two firms developed a machine learning algorithm to identify protein crystals.<\/p>\n

3- Structured GSK\u2019s internal data<\/strong><\/p>\n

GSK also understood that data would be key to its future. The R&D department had close to 200 different databases. Answering any question required the scientists to search ~20 databases.Thus, GSK piloted consolidating data with Palantir\u2019s help. They then created an internal R&D Data Centre of Excellence to bring additional data sources together. This resulted in a faster drug discovery process.<\/strong><\/p>\n

These three initiatives resulted in increased organizational focus and shorter development timelines. However, two other initiatives could further enhance current efforts both in the short-term (2018-20) and longer term (beyond 2020).<\/p>\n

Looking ahead<\/strong><\/p>\n

1 – Strengthen the Culture of Experimentation<\/strong><\/p>\n

In the short-term, GSK should focus on changing their culture to encourage experimentation. Pharma is heavily regulated and requires large investments which historically has inhibited cultures of \u2018trial and error\u2019. GSK should take example on IDEO[8]<\/a>with their unique culture and their three-step ideation process. Using prototypes and qualitative user research would help GSK develop ML\/AI applications faster and better meet the needs of their researchers.<\/p>\n

2 – Formalize the development process for ML applications through stage-gate<\/strong><\/p>\n

In the meantime, GSK should also develop a stage-gate process. Like Indigo[9]<\/a>, this would help better allocate resources and scale ideas in a more efficient way. Today, GSK is relying heavily on external partners for its development of ML\/AI applications. This result in a costly development. The use of a stage-gate process could increase focus and enable the organization to develop machine learning applications in-house in the longer term.<\/p>\n

Questions in suspense<\/strong><\/p>\n

To conclude ten years from now, drug discovery will be largely driven by machine learning. It will enable GSK to predict in silico a molecule\u2019s likelihood of success thus lowering the costs of bringing a new drug to market. However, after 5 years of strategic focus on artificial intelligence a few questions remain open for GSK:<\/p>\n