One of the cool things about being part of technology industry is living through certain hype cycles. I have experienced, in past 20 years of my professional life, ups and downs that tend to get everybody riled up about ‘this is going to end the world’ dooms day scenarios like Y2K to ‘this can do everything’ conversations about things like Artificial Intelligence, Virtual Reality etc. I still remember back in the day when my friends in the Computer Science branch in college had subjects like Virtual Reality, Neural Networks and Artificial Intelligence. I am talking about 1994 – 1998, which is almost 2 decades+ to the day. Not many of them pursued their careers in those areas, but it is amazing to think that it took as long as it did to get to current developments in this space that is revolutionizing the consumer and enterprise software industry.
There have been many developments along the way that got us here from Internet to client / server technologies to cloud to big data to NLP, to name a few. Today we can genuinely claim there are things that machines can do, faster and cheaper and more than anything else, SMARTER than humans with very little to no intervention from us. As BigData hype reaches a plateau, Deep Learning is picking up steam and more and more companies are investing in this area to genuinely unleash the power of data through smarter analysis with help from neural networks, NLP, Deep Learning and the likes.
Having spent considerable amount of time dealing with Life Sciences industry for the past 14 years, I can speak to the utmost conservative approach these companies take when it comes to technology adoption. It is a heavily regulated industry and rightfully so since it deals with human lives. Life Sciences companies can release life saving ‘Elixirs’ but can also unleash ‘Drug from the Devil’. In my experience Life Sciences companies typically are 2 to 3 years behind in terms of technology adoption. This may change depending on the department within the value chain but this tends to be the average duration before they use the latest version of Windows or IE or Office.
I hope to highlight some of the use cases where newer technology developments can be leveraged in Drug/Device/Vaccine development, specifically in the areas of Regulatory and Safety in a series of posts starting with this one. I will try to prioritize areas where there is a lot of manual intervention (Compliance) as well as areas that could leverage technology to deliver faster ROI (increase Revenue) and improve Operational Excellence (Reduce Cost).
One of the first areas that I thought could benefit from these technological advances is Regulatory Intelligence. The EU Regulatory Intelligence Network Group (RING Europe) defines it as “Regulatory intelligence is the act of processing targeted information and data from multiple sources, analysing the data in its relevant context and generating a meaningful output – e.g. outlining risks and opportunities – to the regulatory strategy. The process is driven by business needs and linked to decisions and actions.” RI is a key part of Life Sciences industry primarily for three reasons:
- It is a heavily regulated industry
- If companies operate globally they ought to comply with ever changing regulations and
- Influence policy and advocacy of future development
- Please refer to this presentation from Carol Hynes of GSK for more details on “Regulatory Intelligence: Implications for product development“.
Many organizations have built Regulatory Intelligence Repositories by collating information from various sources. The diagram below represents various sources of RI data (courtesy : Regulatory Intelligence 101 By Meredith Brown-Tuttle).
These repositories cannot be built overnight. They have to be collated piece-by-piece over a period of time. The sources could go beyond the ones identified in the above diagram. Also, the repository may contain structured as well as unstructured content and data. Extracting information from such repositories is typically not a straight forward process. It definitely will not be as easy as asking a colleague who would then manually conduct the research needed and collate the information that can then be circulated to one or more individuals in the Reg Affairs organization for consumption and decision making . Therefore leveraging Automation, Machine Learning and Natural Language Processing in order to glean into the information in such repositories will make the life of Regulatory Intelligence colleagues lot easier. They can easily query the repository in their language of preference (for regular users with NLP capabilities) or write No-SQL and Semantic queries (experienced/super users) to extract the relevant information.
Information thus obtained can be leveraged to put together documents, newsletters and other communication vehicles which in turn could be stored back in the repository thus continually enriching and expanding the wealth of information available. This idea can be extended to create a federation of such repositories (internal, external, partners, vendors etc.) that can be scoured for the necessary information. Also leveraging even more advanced technology advances like Deep Learning might enhance the effectiveness and Return on Investment even more.
As always, your feedback and comments are most welcome. Thank you.
With thanks to Venugopal Mallarapu : https://vmallarapu.wordpress.com/2017/10/11/hello-regulatory-intelligence-meet-artificial-intelligence/