Can data drive more successful R&D in life sciences?

The demand for new, personalized, trusted medicines and therapies delivered quickly and cost-effectively is increasing globally – especially during a global pandemic.

As a civilization, we are arguably in a historically unprecedented position, with greater understanding of illness and disease and more patient data available than ever before. But the route to market for medicines has not changed significantly in a number of years, and the status quo presents numerous obstacles to opportunity.

Digitalization in healthcare is impacting the entire value chain and is generating large amounts of heterogenous data – but often it is not sufficiently useful or available to be harnessed.

For example, the fight against cancer has delivered explosive growth in the volume and richness of both proprietary and publicly available data that links the human genome with the molecular basis of cancer and the efficacy of drug-like molecule cancer treatments. However, these datasets are highly varied, often unstructured, and generated by different tools and techniques, resulting in data quality and consistency issues. Clinical oncologists need to be able to search, filter, and interactively explore a unified view across the full scope of this data in a single, intuitive platform.

Similarly, life sciences and R&D professionals spend a large amount of time examining clinical trial notes, patient data sets, and documents aiming to identify key issues (e.g., adverse drug effects) and getting an overall view on the document content. These documents and medical records are highly complex and unstructured, often leading to an information overload and inaccurate or missing information. Oncologists need help in driving insights and understanding patterns from unstructured documents.

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