Developing innovative approaches and processes across the pharmaceutical industry
Challenge identifier: SC1-2018
While traditional strategies in the pharmaceutical industry worked well in the past, output across the sector has remained at a stable level for the past 10 years and new methods of discovery and innovation are required to create growth. However, big data has heralded a fourth paradigm of scientific investigation. This is particularly pertinent in an area such as the pharmaceutical industry where underpinning scientific research and innovation has driven growth for decades.
According to PWC the global pharmaceutical market is estimated to be worth $1.6 trillion dollars by 2020. The importance of data analytics will continue to grow and support all aspects of the pharmaceutical industry from new drug development to marketing. All of these data and analysis developments will have an impact on the quality of patient care – which is crucial, as healthcare provider expectations regarding cost, efficacy and evidence-bases are rising.
As well as existing data sources from clinical trials and drug development there are growing data sources from genetics, wearables and healthcare systems, as well as from non-healthcare sources (e.g. social media, retail) which could be used to improve pharmaceutical processes and potentially in the development of new drugs and treatments. Data analysis techniques such as predictive modelling could also help support issues such as chronic disease management and new drug discovery.
We are particularly interested in solutions that leverage closed and shared data in the following areas:
- Personalisation of treatments – in particular supported by genomics and cheaper genetic sequencing. This lends itself to a model of outside organisations / startups providing analytical and consultancy services to established pharmaceutical businesses, providing more innovative approaches and easier access to talent.
- Supporting meta studies / big data analytics – opportunities to integrate a wide variety of datasets to support the development of new drugs, treatment approaches and simulation of drug effects.
- Internet of things / wearables – data from patient wearables can supplement data already held by companies to support business processes and regimen compliance.
- Marketing optimisation – both internal datasets and those from allied sources can be used to support the development of pharmaceutical industry marketing approaches and techniques.
Examples of data include but are not limited to:
- Data from clinical trials
- Data from fitness and activity trackers
- Public or private data from healthcare providers base in the EU
Examples of outcomes include but are not limited to:
- New apps and services
- New prediction algorithms
- New intermediary technologies to integrate data sources
- New tools and business processes to help decision making, including those making algorithms more transparent and accountable
- Registries and distributed ledger applications
- New forms of hardware
- Applications must include details on how these outcomes will be tested and evaluated during the six-months acceleration programme.
Participants will need to demonstrate how their solution:
- Provides insights and transparency new related research across the pharma industry
- Supports regulatory controls in new drug developments
- Improves patient access to and awareness of new treatments