may be enabled through integration of specific in-house generated research data with large and more diverse public data repositories from initia- tives such as UK Biobank or the 100,000 genome projects.

Machine learning: driving informed decision making The process of addressing universal data manage- ment issues and tackling common environmental difficulties has led to the successful automation and streamlining of diverse analytical processes. New machine-learning technologies allow datasets to be brought in to a truly data-driven decision- making process. These datasets may span a num- ber of varied workstreams, including mass spec- trometry, next-generation sequencing (NGS), high- throughput imaging, immunoassays and biophysi- cal assays (Figure 4). This helps to empower both the scientists and the companies they work for to ensure that those candidates that are brought for- ward to clinical trials are supported by a compre- hensive and robust pre-clinical data package and have the greatest probability of success. With the right environment, decision making

becomes faster and of higher quality. Scientists have greater clarity concerning the scientific evi- dence available to them, without needing to man- ually amalgamate disparate data sources. For example, emergence of adverse drug reactions or side-effects may be a key reason that clinical trials fail during candidate selection. Integration of diverse data assets, in combination with a variety of predictive tools, increases the likelihood of finding these side-effects at an earlier stage in development, before a new drug researches the clinical phase. McKinsey highlights this in its vision of the data-

driven digital future for biotech4. “Insights from in silico studies and analysis of

diverse datasets [will] accelerate research and early development through more informed decision making, including smoothing the repurposing of existing drugs for new therapeutic areas.” Platforms with a strong machine learning ele-

ment can identify patterns and trends, beyond those visible to the human eye. Highlighting these patterns will assist scientists in focusing on the important data, cutting down on distracting ‘noise’ or irrelevant information. Time and effort are decreased because the number of experiments required is reduced. There may also be situations when information from candidates that have previ- ously failed may inform decisions regarding the development of new candidates with similar data


profiles. This allows some candidates to ‘fail fast’ as development programmes may be cancelled at an earlier stage to avoid needless investment and analysis. The fail fast paradigm is economically critical to drug developers because higher costs are generally incurred as candidates move closer to clinical trials.

Digital transformation in data management From the bench-top to the boardroom, there are unique challenges that come as part of the inevitable digital transformation in life sciences and drug discovery. Any successful response to these challenges must be modular and continuous- ly adaptable to the unique and particular research processes and structures of an individual organisa- tion as it grows. New platforms are now meeting the needs of

research-intensive organisations in the life sciences sector, leveraging cutting-edge IT concepts such as the cloud, DevOps and software-as-a-service. The most successful are employing machine learning and artificial intelligence (AI) technologies. These platforms are already making an impact, with immediate benefits for researchers, IT departments and management departments; improving produc- tivity exponentially. Security is another pivotal consideration for data

management platforms and should not be imple- mented as an after-thought. Standard security certi- fications such as ITIL, ISO:9001 and ISO:27001 should be sought. Compliance with GDPR is also critical when dealing with personally identifiable data, whereas relevant GxP/FDA/EMA regulations for data analysis or processing are only needed when the output of these data platforms contributes directly to manufacturing processes, companion diagnostics or clinical trial design/applications.

Future perspectives and powerful partnerships Innovative data management platforms enable huge and diverse datasets to be structured, stored and analysed effectively. Drug developers who fully embrace machine learning and AI technologies will make rapid, impactful and significant leaps in their research. Leveraging these platforms will lead to the emergence of novel data patterns and ideas that inspire new approaches to data interrogation and research. This will, in turn, reveal fresh scientific challenges that may advance the fields of medicine and biotechnology in unexpected and exciting ways that would not have been previously possible. Not all pharmaceutical and biotechnology

Drug Discovery World Summer 2018

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