Informatics


Figure 3


Representation of workflows in a typical R&D laboratory


of new drug targets or improve the quality of clin- ical trial protocols through stratification of patient cohorts. However, use of patient information in this setting (even when anonymised) is subject to strict data protection and ethical regulations that usually require permission to be given on a case- by-case basis. This cumbersome approach pre- cludes routine integration of these data and analy- ses within R&D activities. These issues are not unique to the pharmaceutical and biotechnology sectors; other industries, such as finance and bank- ing, have also been required to adapt to complex, additional compliance obligations. This represents an important and timely opportunity for compa- nies within the life sciences sector to learn from the experiences of other industries and identify solu- tions that can be successfully applied within the drug discovery and development environment. In a typical busy research laboratory, workflows


usually span several busy departments. Data from individual experiments are often downloaded to local laptops, or networks, in the form of large Excel spreadsheets. At each stage, the data must be analysed. Each analysis may need to be performed manually over several days and is subject to human error (Figure 3). Complexity is compounded by variation in the way that data are structured (or even named) across a workflow. Breakthroughs can occur when trends, patterns and relationships are identified within the data. Unfortunately, inconsistencies and inefficiencies within existing systems mean that data, which should be an asset, becomes an impenetrable liability. Given the size of many research organisations and the scale of work undertaken within each department, it is easy to see how productivity and quality may be improved by data management systems that offer consistency and a defined structure.


70


Creation of one common platform that aggre-


gates, structures and digitalises workflows across an organisation can unlock the potential of data, providing easy access and opportunities for greater and more productive cross-department collaborations. Once data is properly structured, analyses can be customised and performed at the touch of a button.


Embracing a data-based revolution Specialist expertise that has traditionally only been available to universities and other academic insti- tutions, is now emerging through a new generation of start-up companies . Companies that harness this academic knowledge are poised to make break- throughs, not just in pipelines and processes, but in the fundamental way that businesses control, shape and steer data flow through their entire organisation. These changes are so radical and happening so fast that companies will need to adopt an entirely new perspective concerning their understanding of the data they produce and the way that it is applied within the research or com- mercial environment. R&D-focused organisations within the pharma-


ceutical and biotechnology industries are placing greater emphasis on becoming more literate with big data technologies. Large corporations, such as Novartis, are reinventing themselves as ‘medicines and data science’ companies. This has raised the profile and importance of bioinformaticians within organisations who have traditionally provided ser- vice roles for biologist colleagues concerning data analysis, statistics and pipelining of tools. The value of bioinformaticians, especially those with an understanding of the challenges of commercial R&D, is increasing in line with the rapidly-evolv- ing demands of the environment. Bioinformatics is


Drug Discovery World Summer 2018


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72  |  Page 73  |  Page 74  |  Page 75  |  Page 76  |  Page 77  |  Page 78  |  Page 79  |  Page 80