At the 1898 inaugural International Urban Planning conference in New York, there was one pressing topic on the agenda. With reliance on horses as the primary mode of transport, emerging cities were facing a crisis- an unmanageable volume of horse manure. To be specific, nearly 500 tonnes of horse manure needed to be collected and removed from the streets of New York every single day. Dejected planners left the meeting without answers to the problem. Yet, almost overnight, this problem was resolved by Henry Ford’s motor car, in a turn of events that the planners attending that important meeting would have been unable to predict at the time. This analogy, I believe, applies neatly to the new paradigm of data-powered drug development and personalised medicine which now seems almost in our grasp but was once firmly out of reach. The impact of new data sources, availability of ‘real-world’ data, powerful computational methods such as artificial intelligence and machine learning, combined with demand from individuals for better control over their health and wellbeing, will, in the future lead to a transformation of the healthcare landscape that is probably unimaginable to us now. In recent years, we have seen a drive towards innovative methods such as basket trials, synthetic controls and seamless designs along with a commitment to investigate the potential of real-world evidence to complement randomised controlled trials. With this transformation, vendors, sponsors and other stakeholders involved in delivering healthcare will need to cultivate agility to respond to new opportunities and challenges. As Simon Sinek discusses in his excellent book The Infinite Game, since business is a ‘game’ with no rules, and no end, organisations need to develop an ‘infinite’ outlook to enable innovation and thrive in an ever-changing world. This principle holds especially true in the drug development and healthcare environment where new approaches are so desperately needed to benefit the lives of patients with new medicines.
As we look ahead to 2020 and beyond, in this blog, I will share a few thoughts on some of the key opportunities, barriers and outsourcing outcomes that may play out through the digital revolution.
Personalised medicine
In Matthew Syed’s book Rebel Ideas, he shares the story of how in the 1950s United States Airforce pilots were having difficulties controlling their planes. It turned out that the cause was not pilot error but the fact that the cockpit had been designed for the ‘average pilot’. With the help of Lieutenant Gilbert Daniels, they discovered that fewer than 3.5 per cent of the pilots had measurements that fell within this average and so the cockpits were not suitable for the majority of their workforce. To resolve the problem, they ultimately redesigned cockpits to make them adjustable to the individual’s dimensions.
This analogy extends to the pharmaceutical industry, where, for almost a generation, we have been discussing how we can move away from averages and towards personalised medicine and therapies designed with specific patients in mind. Now, with the application of data science, we may finally have the tools within our reach to be able to stratify patients more precisely and gain a much better handle on an individual’s propensity to respond to a specific drug. Advances in this area have the potential to significantly improve the effectiveness of treatments and will rely heavily on the skills that statisticians and data scientists can bring to the table.
Virtual trials
Virtual trials are currently generating a great deal of industry traction thanks to their potential to improve efficiency, access more geographically dispersed patients, and strengthen patient-centricity. In essence, the virtual trial takes advantage of new technologies and platforms to allow patients to be home-based during the various stages of a clinical trial. In my view, this development may make the feasibility of trials more complicated, but, as we get a better understanding of the individual needs of patients and use diagnostic data, we will be able to drill down more precisely into subpopulations. Then, by using the virtual trial framework, we will be able to better pinpoint the right patients in feasibility for the specific requirements of the trial. Data science and statistics will be essential as part of this delivery model to help identify the right patients as well as interpret the data generated by the trial.
Technology players forging a new role in healthcare
The role of Silicon Valley giants Google, Amazon and Apple in forging the future of healthcare is beginning to play out, but the full extent of the position they may fulfil is still unclear. In 2019 there were several news stories about deals, acquisitions and strategic moves by these technology companies as they expand their digital health market footprint. Some examples of these developments include Google Health’s deal with 5 NHS trusts following its acquisition of UK AI firm Deep Mind, Apple’s acquisition of digital health firm Tueo Health, and Amazon’s launch of a virtually-centred primary care program for its employees in Seattle.
In my view, there is potential for new technology players to have a very positive influence on innovation and efficiency in the pharmaceutical and healthcare industries, assuming that consumer concerns about data privacy can be addressed.
Role of CROs in Supporting drug Development
Against the backdrop of personalisation in healthcare, we may also see a change of emphasis for CROs as well as sponsors in the type of clinical trials that we will run. With an increasing focus on smaller patient populations and more ‘seamless’ drug development pathways, we will likely see sponsors and CROs working on a higher volume of smaller trials. As trial planners seek to gather more insight, earlier, to more precisely inform the next stage of development CROs will need to be agile, flexible and responsive to meet tight timelines and juggle multiple small projects. They will also be increasingly relied upon to provide understanding and interpretation of diverse data sources. As the role of biotech companies in driving innovation increases, CROs that can play well with virtually structured companies and teams, and communicate and engage at a high level will likely thrive.
Overcoming Obstacles
One of the significant obstacles we, as an industry, need to overcome to realise the power of digitisation and data science is the lack of trust and fear around the use of data. Better scientific communication has a vital role to play in providing valuable context about how data is going to be used, its purpose and the potential impact of a particular project. Trust is also fundamental from a regulatory standpoint- while regulators have shown themselves to be eager to support innovations in drug development practice, we need to provide transparent, robust methodologies that will underpin novel approaches to drug development. The issue of data ownership plays directly into the theme of trust, and I believe that in the future it is likely that patients and individuals, in general, will have more control and empowerment over their data. This control may extend to individuals being able to opt-in and out of specific applications of data.
New opportunities for statisticians and data scientists
I believe that advanced analytical technologies and use of emerging data sources will generate more new and exciting opportunities in the pharmaceutical industry. Despite automation and computational advancements, there is still an essential human role required to process, interpret and understand the findings that AI can expedite. Indeed, as the demand for data science skills increases across all verticals, schools and universities will have an essential role to play in equipping the next generation of talent for the digital revolution. To underpin innovation in healthcare and pharma, providing exceptional education in statistics, data science, and biology at various levels will be key. Another factor is that the healthcare and pharmaceutical industry will continue to be in fierce competition technology, finance and consumer industries for the best analytical talent. In the future, I would like to see the pharma industry working to carve out new career pathways for market entrants in analytical disciplines, particularly for applied data science roles. These initiatives might take the form of modern apprenticeships, overseen by universities, within businesses and other professional organisations.
Analytical professionals and data scientists have an instrumental role to play as we work to build a brighter future for healthcare, the pharmaceutical industry and for patients. In the light of these trends, we can expect to see the influence of statisticians and data scientists increase further as stakeholders from across healthcare aim to harness the power of new data sources to generate better outcomes for patients. The ability to take data, derive meaning from it, and importantly communicate these insights in a way that is understandable to a range of stakeholders will be ever more sought after in our data-driven world.
References
Sinek, Simon (2019). The Infinite Game. Portfolio/Penguin.
Syed, Matthew (2019). Rebel Ideas: The Power of Diverse Thinking. John Murray Press
