As we enter 2022, Covid-19 and digital transformation will continue to impact healthcare and the life science industry, writes Anca Ciobanu.
Throughout 2021, we saw an increased acceleration of digital transformation while the pandemic catalysed the pharmaceutical industry to further embrace collaboration. Both of these trends will underpin success in emerging areas in the next 12 months. Many of the big breakthroughs will be made through cross-company and cross-discipline collaboration, and through sharing pre-competitive data.
Driven by macro geopolitical trends and Big Tech, emerging technologies are being developed increasingly rapidly. Reflecting this, we are set to see a rise in deal-making and activity – particularly in quantum computing, AI and machine learning, and autonomous laboratories.
Major tech players, to thrive in 2022, will increase their focus on the life sciences and will play an important role in developing new products and initiatives that will, ultimately, benefit patients. Efficiency in R&D is on an exponential growth path as more pharma and biotech organisations partner with AI and robotics companies, enabling a more automated drug-discovery process.
In my role as strategic theme lead for improving efficiency and effectiveness of R&D at the Pistoia Alliance, one of my key initiatives is driving forward our Lab of the Future community. We have gathered a good mix of industry experts from Pharma, CROs, tech suppliers, academia and other types of organisations, with a wide diversity of roles working in the ecosystem of R&D labs. The discussions are focused on project ideas that can provide a practical approach in supporting the transformations from a traditional lab into a lab of the future, no matter how far the organisations are in the process. The pre-competitive approach fostered by the Pistoia Alliance encourages open discussions and helps participating organisations learn from each other, brainstorm and work together for the same cause - a faster drug-discovery process.
During these discussions we identified one important challenge that is being faced by the majority of laboratories in their digitalisation path towards a 'lab of the future: building an integrated ecosystem that takes into account the IT complexity and the data governance framework.
Consequently, laboratories have started changing their application-centric approach towards data centricity, which means that before implementing new systems, workflows and automations, organisations are now firstly considering the FAIR data collection to ensure that the vast amount of experimental data can be findable, accessible, interoperable and reusable.
This means having the integrated systems in place to track the data from its origin and provide reliability and transparency of data through the different datasets. If the stored data is 'clean', it can power AI and ML (machine learning) algorithms.
Without robust data management strategies for the entire life cycle of data, AI and automation efforts will not deliver. The lack of FAIR research data and associated metadata is costing the European economy alone at least €10.2bn every year[1] and the downstream inefficiencies arising from not implementing FAIR are likely to account for substantial further losses every year.
FAIR implementation can increase productivity in biopharma by unlocking the long term potential of data for future research and secondary purposes and by improving collaboration and enabling scientific queries to be answered more quickly. These productivity gains are vital as the time and cost of creating new therapies continues to skyrocket.
Consequently, before implementing automations, an organisation needs to have a clear strategy for gathering and storing good quality data and to ensure that the process is properly described. If the workflow used before implementing automation is tight to legacy paper-based way of working, the workflows need to be re-designed to fit the purpose.
The research labs of many large bio-pharma organisations passed the very early AI adoption phase either by increasing their collaborations with AI companies, or by building up their own AI capabilities.
Despite having many successful AI pilots targeting benefits in different areas across the R&D value chain, many organisations are still struggling to develop a coherent AI implementation strategy for their labs.
Most organisations today are using some form of automation across their R&D processes but help is required to see the value and enable advanced technologies to be adopted effectively. The Alliance is committed to helping organisations embrace AI trends in lab automation and to help organisations find the answers to pressing questions such as: What are the pre-requisites that need to be considered for building up a successful AI implementation strategy in the labs?
There is no doubt, despite the global challenges posed by the pandemic, our thriving life sciences community will continue to move forward. Automation will get a boost from the increasing willingness among our industry to collaborate to innovate.
To find out more about how to get involved in the Pistoia Alliance and its Lab of the Future Community, contact anca.ciobanu@pistoiaalliance.org.
Anca Ciobanu is strategic theme lead for improving efficiency and effectiveness of R&D at the Pistoia Alliance
References:
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Cost-benefit analysis for FAIR research data: cost of not having FAIR research data. [Internet]. Op.europa.eu. 2019 [cited 27 May 2021]. Available from: https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1/language-en/format-PDF/source-search
