R&D digital transformation is essential to remain competitive in the near future. There will come the time when more patients than the systems can handle. The COVID19 dilemma will become a daily struggle. Digitisation – and digitalisation – are not something companies can debate on. It isn’t about building a competitive advantage or a core differentiato; it is rather about staying relevant in the very near future.
Developing drugs is a tedious and costly journey, averaging 12-15 years and costing 1-2 billion dollars. Leveraging digital and data to ease this pressure could be successful if starting by focusing on the following five areas as PWC’s article suggests
In silico discovery and optimisation of target identification
While investing in AI and artificial neural networks to improve in silico discovery might never yield the expected results in the short run, developing the capabilities and underlying thought processes will support the transition to the quantum paradigm shift.
Using the past to predict the future – maximise the value of your data
There is a lot of internal data that companies can use to inform their strategies, portfolios and decisions. In this instance, the main challenge is around data interoperability
The reason is that efficient data interoperability implies a clear data strategy, data governance and data management. Once these pillars are implemented, the use cases will be abundant.
3. Making protocols easy
Using ‘intelligent’ algorithms can minimise protocol amendments, which cost between 100 and 500.000 dollars each, two thirds of which are thought to be avoidable. The trial design and clinical operations processes must become more data-driven, fluid and circular, with one feeding into the other.
4. Maximising patient recruitment through avatars
With 80% of trials failing to start or end on time, and 30% of trial time spent on it, patient recruitment is being scrutinised more than ever. Despite the increase in competition due to more trials run simultaneously, there is room for improvement when it comes to in silico modelling.
Defining the ideal trial avatar can inform on the feasibility of the study and help better plan the duration of recruitment as well as the trial length.
Making patients partners
Digital maturity and pervasiveness provides an unprecedented opportunity to become more patient-centric.
A good strategy is to see Patient Reported Outcomes (PROs) as a source of knowledge that can be fed back into the R&D process. In order to succeed in this approach, process automation and contextual language processing are key.