Summer 2018
Drug Discovery World
Data-Driven Transformation In Drug Discovery
Dr Satnam Surae
Summer 2018

A review of the current approaches to data management and application within the drug development and research setting, highlighting major critical challenges and emerging solutions for organisations that are determined to harness big data and machine learning.

Data-driven companies that use integrated and advanced analytics outperform their competitors in every sector outside of the pharmaceutical industry. To compete in an increasingly- crowded commercial environment, pharmaceutical and life science companies must gain a greater understanding of the wide-ranging implications of big data and machine learning. These innovations can be applied effectively to drive drug discovery, power research and ensure a sustainable future.

Everyone in the life science sector is familiar with the productivity puzzle: research and development (R&D) spending on drug discovery is increasing, but regulatory approval of new therapeutic agents is largely in decline. Companies are investing more than ever in each new candidate molecules (a 10-fold increase since the mid-1970s), despite widespread awareness that the probability of progressing through clinical trials is less than one in 10 (1). For some diseases, such as Alzheimer’s, the figure is less than 1 in 100 (1,2).

Research shows that innovative organisations can optimise the chances of success for their clinical candidates through effective use of these data repositories (3). Efficient data storage and analysis of datasets may accelerate drug development processes. However, existing data management techniques are now struggling to deliver at the scale required to meet the rapidly-increasing quantity of scientific information produced.

As a result, pharmaceutical and biotechnology company pipelines are faltering, leaving many businesses unable to effectively manage the mounting pressure on current systems. Specialist platforms, designed to support and continuously evolve alongside drug development and research data outputs, are urgently needed to address these critical industry issues and bring companies into line with environmental demands.

Solutions that harness the rapidly-developing arena of data science will gain a greater competitive advantage. In December 2017, McKinsey described the overall impact of digital technology on R&D as “the $100 billion opportunity” (4)....

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