Real-time decision making in life sciences using regulatory intelligence
Jared Kimble, life sciences product specialist consultant at DXC Technology, writes about how regulatory intelligence can be utilised by life science companies.
Today, data both structured and unstructured is being generated and collected faster than ever before. In life sciences, structured data includes that from clinical trials, regulatory filings, manufacturing and marketing, drug interactions and real-world evidence. Unstructured data comes from the internet of things (IoT), such as social media forums, blogs and so on. Massive quantities of data are both overwhelming and useless without regulatory intelligence to make sense of it.
In this context, regulatory intelligence refers to taking multiple data sources and putting those into a system that can look at the data, analyse it, find trends, collect information from it, and then distribute that information it to relevant stakeholders and authorities. This could be to regulatory agencies requesting updates or information about the drug portfolio to satisfy compliance mandates, to partners that you’re working with, such as trading partners, or it might be consumed internally.
While it is referred to as regulatory intelligence, it goes beyond that and encompasses many other areas of the product life cycle, from early stage clinical research, to development for detailed analysis and safety, to pharmacovigilance for signal detection. Life sciences companies can use these types of data for real-time decision making to protect public safety, respond to supply shortages, and protect or advance the brand — for example, into new indications or new markets.
The benefits of using data
As data is consumed across life sciences in different ways and by different stakeholders, being able to provide intelligence requires clear targets and objectives. For example, real-world data may show adverse events that weren’t detected in clinical trials, by having that intelligence early on it allows companies to act accordingly — both to protect public safety and to safeguard brand reputation. The specific course of action will be dictated by what the data shows, and primarily by what the regulatory agencies require. It might simply be to reinforce a message about drug-drug interactions or foods to avoid while taking specific medications, or it might require a broader response.
Data can also be leveraged through real-time strategic decision making to support the brand. For example, IoT data or data held by the authorities might show a weakness in a competitor’s product or in the market — perhaps a gap in a region the company has begun targeting. Using the intelligence from the data, companies can take advantage of those gaps or competitor weaknesses and promote their brand as a better alternative or prepare a new market launch.
Regulatory intelligence from IoT sources, physicians’ blogs or positive side effects in clinical trials can also highlight other potential indications for a product. The most famous example is Viagra, which was initially studied as a drug to lower blood pressure. As was the case here, not all side effects are negative, and during clinical studies an unexpected side effect led to the drug being studied and ultimately approved for erectile dysfunction. By having this information available it enabled a case to be made for expanding the clinical study extending therapeutic use.
Extracting intelligence from data
Now that we have explored the definition of and some purposes for regulatory intelligence, we should also look at how you get from that point of data to intelligence. The initial step is to find and utilise the most suitable and effective analytical tool to sift through that data and pull out relevant information. In order to make use of that data, the end goal must be known, then the scope of the data search can be narrowed to eliminate extraneous data.
With the ever-increasing amount of data available, automated collection and analysis is required to avoid wasting time and resources. Automated robotic processes make it possible to keep up to date with the latest findings and pull relevant data into your regulatory operational environment. Without regulatory intelligence, real-time strategic decisions across research and development can’t be made, as such, its importance to the organisation can’t be overstated.