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LABORATORY INFORMATICS


operating system? Graeme Dennis, commercial director at preclinical pharma, IDBS, says we should move away from traditional definitions of informatics software


Are we ready for a data


Scientific software vendors in informatics are faced with an interesting paradox. They can continue to market their tools according to a paradigm where strict classes of software describe a given need, regardless of innovation they’ve made to move past those artificial constraints. Or, they can promote their solution as an unclassifiable ‘cure for all that ails’ and almost certainly sacrifice sales when customers begin their search for a particular type of tool. Similar challenges exist for customers on their search for solutions. These trends mirror the end


of traditional boundaries in the pharmaceutical market. The disruption introduced by the success of therapeutic biologics may now be disrupted by the availability of low-cost biosimilars. The increasing popularity of alternative medicine in Western markets is mirrored by the massive Chinese ‘Healthy China’ universal coverage initiative, and Japanese governments focus on increasing the use of generics to reduce treatment costs. Meanwhile, demands on informatics tools must keep up with new biotechnology including immunologic, antisense mRNAi, and cell-based therapies, to name a few. The research and


development driving all of these advances will generate a huge amount of data. R&D investment tracks with these advances. The 2018 annual survey conducted by the Pharmaceutical Research and Manufacturers of America, PhRMA, indicates member companies have spent more


than 20 per cent of global sales on R&D since 2016, about $70 billion annually (PhRMA, 2018). It is impossible to examine pharma R&D informatics without considering the impact of contract research, analysis, and manufacturing. From synthetic chemistry to biologics manufacturing, outsourcing has changed the landscape of how research is paid for, staffed, and conducted. The outsourced lab challenges every aspect of the connected ecosystem – how data is securely shared, controlled and contextualised. The extent of collaboration with these partners varies widely, from highly blinded relationships, to extensive cooperation. All of these features make specific demands of a scientific informatics platform. As vendors, service providers, and customers navigate this complex, growing environment, what attributes move a tool past the definition of LIMS, ELN, LES, SDMS and the like? They are scientist-focused: these next- gen tools stress data, method, workflow and test article. We pivot from the notion


of software-as-a-service to workflow-as-a-service. As vendors and consultants in the scientific informatics space, our product can only improve when we think like scientists and bring a full understanding of their process as we design and execute our work. This approach can also help our customers understand what technologies apply in the pharma space, in terms of fit and maturity, and which ones do not. This emphasis on the analysis of fit and maturity differentiates how we interact with our customer from commodity tools and services. Numerous industry leaders have embraced this


www.scientific-computing.com | @scwmagazine


approach. One senior R&D IT executive commented, that ‘scientific software customers take unnecessary risks when not fully understanding their processes from a people, data, technology, and services perspective. This, coupled with proper current and future requirements gathering, will ensure the right tool selection.’ The tools we provide must help our customer decouple data from the system of acquisition and the system of use. The more closely data becomes tied to a specific system or application, the less suitably it serves other purposes. Further, in an industry


“Customers take unnecessary risks when not fully understanding their processes from a people, data, technology, and services perspective”


where mergers, acquisitions, externalisation, and IP transfer are constants, the need to handle data with as few ‘attachments’ as possible is key. Beyond software, pre-


competitive industry collaborations are becoming a rallying point to address the need for harmonised data collection and disposition among partners, sponsors, regulatory authorities, and contract labs. Two leading efforts are the Pistoia Alliance and the Allotrope Foundation (Krol, 2014). Pistoia, now 10 years old, established the widely- adopted HELM vocabulary for


biomolecules, and funds nascent efforts to solve industry-wide challenges around making data FAIR (findable, accessible, interoperable, and reusable). Allotrope, founded in 2012, also embraces facile data sharing I recently joined a memorial


service for my longtime mentor, James E Davis. Jim was an extraordinary scientist and leader, with undergraduate work at Mississippi State, followed by post-graduate work in biochemistry at MIT and Cal Tech. A research assistant who worked with him in 1961 recalled the young post-doc telling him their growing understanding of biochemistry would revolutionise the treatment of disease. Jim understood – more than 50 years ago – an interdisciplinary barrier was at best contrived, and at worst an obstacle to innovation. I realised it probably works the same way for us in scientific informatics. IT departments frequently


wonder why the same platform can’t service exploratory research, preclinical pharma, and manufacturing. Why not? As we become more nimble in our ability to deliver workflows and more courageous in our willingness to market our solutions outside traditional software classes, we can lead software industry in this direction. Who will be the first?


References


PhRMA (2018). 2018 PhRMA Annual Membership Survey. [online] PhRMA. Available at: https://www. phrma.org/report/2018-phrma- annual-membership-survey [Accessed 4 February, 2019]. Krol, A. (2014). Bio-IT World. [online] Bio-itworld.com. Available at: http:// www.bio-itworld.com/2014/7/18/ universal-language-pistoia-alliance- takes-indescribable-biology.html [Accessed 4 February, 2019].


April/May 2019 Scientific Computing World


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