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.
For as long as I have known the regulatory tools market space, it has been much more fragmented that it’s peer business functions such as Clinical and Safety. If you take the Regulatory Affairs value chain from Strategy / Submission Planning all the way through Archival, many sponsors use different products / platforms / tools for different functions. Typically you would see a mix of Excel/Spreadsheets to established tools for regulatory document & content management as well as publishing, submissions and archival. This is in contrast with Clinical and Regulatory space where you would typically find 2, if not 3 platforms that can cater to a major portion of the value chain. More than Clinical I would say Safety is probably a better in example to highlight this situation where you’d most likely find 2 products ruling the roost in Oracle Argus and Aris Global’s ARISg (LifeSphere Safety).
This situation in Safety platforms space was not achieved overnight but rather over 5 – 7 years through market consolidation. Oracle essentially bought off all the other reputable and competing tools in the market leaving only ARISg to compete with. Off late there has been a similar trend in the regulatory market with some of the niche tool providers like Octagon, ISI etc. being acquired by larger players like PAREXEL, Accenture, and CSC. While this has resulted in consolidation of the tools/platforms under a smaller set of vendors, these tools still need lot of integration to make them work together. There are vendors like Veeva (Veeva Vault RIM) that have taken a different approach to this problem and have reached a point where they can claim more cohesive platform that integrates out-of-the-box, and as is the case with Safety platforms, caters to majority of the functions in Regulatory Affairs value chain. However the jury is still out whether some of these platforms deliver the value they are expected to since the adoption has just begun and long term use is still not proven.
With the advent of Cloud solutions and adoption becoming a reality in Life Sciences R&D, even though it is much slower as opposed to other industries and even departments within Life Sciences for regulatory reasons, platforms on the cloud with coverage across the Regulatory Affairs value chain can get us closer to RPaaS. The reality of budget challenges across many Life Sciences companies is going to force the issue. For Platform vendors like Veeva and Aris Global the key is interest and willingness to invest by their clients. It will not be a win-win proposition unless each party can look forward to cost reduction, productivity improvement, technology currency and regulatory compliance through these platforms.
What is Digital Clinical?
I noticed this term used quite often in my conversations with customers, colleagues and industry analysts in the last couple of years. As is the case with many things in the past, the building blocks of “Digital Clinical” have been in the works for a long time now. At its core, in my opinion, it is all about various technology advances coming together to progress and enhance the clinical research. To name a few, the core technologies/developments include Mobility, Analytics, Social, Cloud Computing, Big Data, Semantic technologies. As mentioned while this is not a comprehensive list, it is a good start to understand how these are being leveraged to improve clinical research and the impact they are having on Heathcare & Life Sciences Industry, Patients and society at-large.
Areas of Focus:
The following are some of the focus areas across the R&D Value Chain being considered as Digital Clinical initiatives by various life sciences companies in the market space:
- Mobile Data Collection in clinical trials, patient reported outcomes and general quality of life data
- Data aggregation from Payer, Provider, Rx, Clinical and other Health Economic & Outcomes Research Data
- Ontology based repositories, Master data management, meta data repositories and text analytics
- Bio-Sensor and other wearable data capture, aggregation & analysis by leveraging cloud computing
- Mining of data from EHR/EMR, Social Media and other data sources
- Leverage OMOP, Sentinel and other such industry initiative outcomes to kick-start Real World Evidence/Real Life Evidence strategies
Business Use Cases being considered:
Some of the use cases being considered are:
- Site selection and Patient Recruitment
- Use wearable Bio-sensors in Clinical Trials
- Adverse Events in Social Media & Safety Surveillance
- Protocol Validation : Inclusion/Exclusion criteria assessment
I hope this gives a high level overview of what Digital Clinical means, some of the technologies influencing and enabling it and practical use cases being considered by the industry to leverage these technologies and provide better quality of life to patients.
It is amazing to see how fast things change, when right technology comes along, picks up the willfully reluctant “legacy” way of doing things and takes them on a ride of their life time. Not too long ago paper based clinical trials was the norm, and still is in some countries. Then came the electronic data capture systems and technology. While that is, people used eCRFs same way as they did paper CRFs. Slowly that started to change with data validation, edit checks etc.
With the cloud revolution came the thought of having clinical data capture/management systems on the cloud and be managed by a third party while pharma companies controlled the protocol and trial design as well as data transformation, analysis and submission management. Off late we are seeing companies being more open to store and share clinical trial data on the cloud. I think the days of ‘Clinical Platforms on The Cloud’ as the norm will soon be a reality. These platforms will not be limited to Clinical Trial Data but will host systems that provide end-to-end clinical research process support capabilities. Not only that, they will also stretch the boundaries further, by accommodating social media, mobile, big data & analytics capabilities.
Future of technology companies that are pioneers in enabling this transformation is going to be interesting and bright with ample opportunities to take the lead and leap to the next orbit.