Informatics
improvements in algorithms have transformed the AI landscape. A recent Harvard Business Review Article6 states
that “In the sphere of business, AI is poised to have a transformational impact, on the scale of earlier general-purpose technologies… Although AI is already in use in thousands of companies around the world, most big opportunities have not yet been tapped.” In addition, a recent Forbes article7 cites “The
necessity to start embracing AI technologies and revamping human resource strategies to create data science-driven interdisciplinary teams has become a matter of the future business sustainabil- ity for biopharma organisations.”
Biopharma opportunities in AI A crowd-sourced, multi-author paper entitled ‘Opportunities and Obstacles for Deep Learning in Biology and Medicine’8 was created by a broad list of contributors (many academic) from around the world. The paper highlights a number of opportu- nities and challenges in applying deep learning to biology and medicine. It examines applications of deep learning to a variety of biomedical problems in particular: (i) patient classification, (ii) funda- mental biological processes and (iii) treatment of patients. The conclusion was that the future would see “deep learning powering changes at the bench and bedside with the potential to transform several areas of biology and medicine”. Discussion at the meeting revealed that many
technology-based companies were highlighting their AI capabilities as the market quickly respond- ed to the enthusiasm for the potential of AI, and in particular to the use of AI in life science R&D and healthcare. More than 30 relevant examples (see Table 1) were quickly identified, but it was widely recognised that any list of organisations active in the evolving R&D IT/healthcare AI landscape would be rapidly evolving. Table 1 illustrates that there are many opportu-
nities for the application of AI across the life sci- ence R&D/healthcare pipeline. However, the timescales in which they might create benefit varies. Importantly, there are today many near- term opportunities for AI that are not as yet adopt- ed broadly, but that have clear benefits for bio- pharmaceutical R&D. Examples might include:
l Image analysis and phenotypic screening of drug candidates. l Drug repositioning and competitive intelligence though data integration. l Clinical trial cost and timeline improvement (eg
Drug Discovery World Spring 2018
protocol authoring, patient recruiting, site moni- toring and risk assessment have already been implemented in commercial products and services from CROs). l Cost savings in pharmacovigilance and regulato- ry reporting (eg through Robotic Process Automation [RPA]).
What are the implications of AI for the biopharmaceutical industry? The 60 or so participants at the meeting were organised into five different discussion groups to consider the actions that biopharmaceutical com- panies would need to take to be in a position to derive advantage of this emerging AI technology. Each group considered one of the following five perspectives viz: Skills, Data, Organisation, Infrastructure and Metrics.
Skills Leveraging AI in biopharma required the IT func- tion, and the R&D IT groups, to have a strong foundation of the traditional skills and the willing- ness, flexibility and capability to acquire new skills. As illustrated in Figure 1 these skills span science, mathematics and technology. There are also foundational capabilities required
that are cultural and cross-disciplinary and include a process to innovate rapidly and to assess value from ‘placed bets’ in the quickly-changing AI land- scape. The MIT Sloan article entitled ‘How Innovative is Your Company’s Culture’9 provides practical guidance for assessing a corporate culture and highlights that an innovative culture rests on a foundation of six building blocks, viz: resources, processes, values, behaviour, climate and success. The adoption of AI follows a similar trajectory
as with other technology innovations (reference the Gartner Hype Cycle10). There are many examples and articles on innovation management processes which generally focus on:
l Knowing what problem it is one needs to solve. l Establishing success criteria. l Experimenting rapidly – either to fail quickly or to demonstrate value. l Scaling up successful experiments.
The Global Innovation Management Institute
describes one ‘Rapid Iterative Experimentation Process’ (RIEP – pronounced ‘reap’) in the article ‘Rapid, Iterative Experimentation Process – a Lean Startup-style Approach to Innovation’11. Sanjoy Ray at Merck has been an influential voice on this topic in pharma, describing a hypothesis-driven
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