What Combining Big Data And AI Makes Possible

For Rene Van Den Bersselaar, CIO of pharmaceutical company Debiopharm, using the tools in tandem is ushering in a new era.

Data analytics powered by artificial intelligence can prove invaluable across a multitude of purposes—including the development of critical medications.

Rene Van Den Bersselaar, global head IT and CIO at Debiopharm, is working to leverage AI in the Swiss pharmaceutical company’s drug research and development efforts. He spoke with StrategicCIO360 about what combining AI and big data makes possible, the importance of involving all stakeholders and the value of virtual patients.

Can you tell us how you’re integrating big data and AI into drug development for Debiopharm?

AI and big data have the potential to improve and make our drug optimization process faster in research and development, as well as in clinical trials. Both AI and big data will make it more cost effective, with the hope of reducing the time it takes a new drug to reach the patient and, of course, decrease the cost for developing a new drug. Integrating biobank data with our own study results will give us richer insights and help us make more informed, intelligent decisions during our drug development process.

We have built a cloud-based data collection and storage platform that provides the functionality to run analytics for greater insights and data intelligence to advance its clinical outcomes and investigations. This approach gives us the power to apply AI and machine learning capabilities. We’re also able to enrich our data with external findings from real-world, genomic and clinical databases that are based on the same type of studies, allowing us to supplement the data from our own research.

How do you feel technology will impact the evolution of drug development, oncology specifically and generally, in the next five to 10 years?

It’s obvious that the traditional ways of drug development in R&D are no longer sustainable; the amounts of data—clinical and patient—and types of sources that are becoming available is enormous and growing exponentially. These data sources will become the driver and differentiator for our competitive position in the market. Only with a technological uplift of infrastructure and application architecture can a successful digitization program be executed to support the use and integration of these data sources for the benefit of drug development.

We are working on the next step in drug development by simulating clinical trials with virtual patients and having those patients engage with apps on their smart devices combined with the use of wearables for direct data collection. This will also allow us to offer feedback and engage them more directly in our study. In turn, this provides faster, more accurate reporting of how they feel, the effects of medicines and how their treatment is progressing.

Ultimately, this leads to a higher quality clinical trial with more precise outcomes for product development in the future and puts more emphasis on the quality of life of the patients taking the drug under investigation.

What is your advice for other CIOs in their quest to integrate AI and big data in big-picture initiatives?

“Think big and start small,” is what I practice at Debiopharm. Don’t start on your own; there is so much knowledge and expertise out there. Research the market on what’s being offered by different types of vendors and find yourself a reliable, knowledgeable partner that fits your vision and strategy. From there, you can lay out the ideal design for building a data lake and select the right AI tools for developing your digital and data strategy roadmap.

Make sure you can deliver relatively small proof of concepts that show your internal stakeholders the business value it brings and how it can reduce complexity in day-to-day research operations. Work with different tools to obtain insights into each technology’s capabilities.

Remember, the key to success is simply getting clarity that applying AI and big data is not just one thing, but rather many different things. It’s important to repeat your brand mission and story so people know where you are coming from and what the future looks like together. Innovation leadership must listen to all stakeholders and build trusted relationships by delivering what they promise, while ensuring practical solutions for complex business problems.

When implementing AI technologies, it’s important to align on the right leader, resources, insights and measures of success with your stakeholders—this will contribute greatly to your success.

What are the automation best practices that other CIOs should keep in mind?

Automation takes time, and is a series of evolutionary, and occasionally disruptive, steps. Like in any journey, you need to decide where to go first. Set clear, tangible goals and agree on your priorities with the key stakeholders.

Typically, companies begin with IT automation and digitizing operations, followed by digital R&D and new business building. However, the order may differ and can even come in parallel. Moving to the cloud and implementing SaaS-based solutions is one of the key strategic decisions we have made at Debiopharm to ensure an open, secure and reliable data collection and storage platform.

In addition, be sure to establish a high-quality cybersecurity tool to safeguard your most valuable asset: data. We have implemented a sophisticated cybersecurity monitoring software engine based on AI algorithms that is becoming the core element of our cybersecurity monitoring.

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