Informatics


Data-driven transformation in drug discovery


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.


By Dr Satnam Surae D


ata-driven companies that use integrated and advanced analytics outperform their competitors in every sector outside of the


pharmaceutical industry. To compete in an increas- ingly-crowded commercial environment, pharmaceu- tical 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 develop- ment (R&D) spending on drug discovery is increasing, but regulatory approval of new thera- peutic 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 (Figure 1)1. For some diseases, such as Alzheimer’s, the figure is less than 1 in 1001,2. Research shows that innovative organisations


can optimise the chances of success for their clini- cal candidates through effective use of these data repositories3. Efficient data storage and analysis of datasets may accelerate drug development process- es. However, existing data management techniques are now struggling to deliver at the scale required


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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 man- age the mounting pressure on current systems. Specialist platforms, designed to support and con- tinuously 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 competi- tive advantage. In December 2017, McKinsey described the overall impact of digital technology on R&D as “the $100 billion opportunity”4. “As we look toward the future of R&D 10 years


ahead, we glimpse an entirely new vista: a world where drug discovery is driven by machine learning and advanced analytics mining large data sets, enabling us to understand and visualise interaction with targets and to predict in silico a molecule’s likelihood of success and of reaching approval in the market.”


Meeting the challenge of compliance and complexity Scientists have turned to ever more sophisticated research technologies to improve their chances of


Drug Discovery World Summer 2018


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