Speeding Toward Innovation: How Intelligent Search Accelerates Drug Development

October 4, 2021

Contributed Commentary by Jeff Evernham

October 4, 2021 | Scottish rally driver Colin McRae once said, “Straight roads are for fast cars. Turns are for fast drivers.”  As the pandemic continues to bring new twists and turns in the road to recovery, biomedical research and pharmaceutical companies must take the driver’s seat and optimize for speed.

First-to-market leaders like Pfizer, AstraZeneca and Moderna are great examples of the proverbial “fast drivers” but catalysing the opportunity for speed relies heavily on data. Data is the fuel that powers innovation; driving better decision-making throughout research and drug development, whether that is during the selection of a target pathway, the confirmation of efficacy and safety through clinical trials, the delivery to market, or its ongoing monitoring.

However, researchers often struggle to find the data they need to perform both critical and routine tasks without unnecessary duplication of efforts. When the information isn’t readily available, even simple tasks can take hours and waste valuable time and resources.

In many enterprises, “relevant knowledge” is hidden in a deluge of content of all kinds, both structured data and unstructured text. In the biopharma industry, that content is extremely complex, and is growing exponentially. It comprises publicly available content from trade databases, scientific publications, and patents, as well as internal content from R&D, clinical trials, recorded patient interviews, lab reports, and even emails. Effectively utilizing all this material helps biopharmaceutical companies better identify new potential drug candidates and develop them into effective, approved medicines more quickly.

So, how can pharmaceuticals and life sciences teams get the most out of all this content to drive innovation, accelerate research, and shorten drug time-to-market, all while new content is added every day?

Cognitive search—and the intelligence and analytics that go with it—could well be the key element for optimizing innovation and improving the efficiency of research, clinical trials, and regulatory processes.

Why Cognitive Search Matters

There’s no avoiding the truth: getting pharmaceutical drugs to market is a long and expensive process with no guarantee of success. Deloitte estimates that the average cost of the R&D process is currently $2.2 billion per drug, and it’s only getting more expensive. The drug discovery phase, involving the discovery of novel and innovative compounds, consumes about a third of that investment, and takes 10 to 12 years.

Given this is one of the most important and competitive sectors on the planet, time is of the essence. M. Hall Gregg, Ph.D., Technology Leader and Advisor put it best: “Rapid delivery of safe, cost-effective medicines that cure disease or that offer life-changing benefit to individuals with disease is the driving force for pharmaceutical research and development. Speed delivers economic advantage but more importantly, it’s the right thing to do for people waiting for medicines that can change their lives. Access to the right content at the right time is the key to research and development moving quickly and safely. Whether it is internal or external, structured or unstructured, a web app or legacy system, comprehensive search and data management approaches are essential.”

For life sciences organizations, the ability to search easily and quickly is transformative. When researchers have the right information readily available, they can spend their valuable time on important tasks such as reviewing the right clinical research, accelerating drug discovery work, building on past results, accessing the right experts, and executing clinical trials. They can use an intuitive interface to search for content using common scientific terms and synonyms and surface the results they need when they need them. That means less duplicate work, more time for research, and faster innovation.

When search is used strategically, innovation and speed spark new opportunities. To put this in perspective, I’ll share an example from one of our life sciences customers. Starting with a relatively simple goal: enabling their research group to search across three existing repositories, the company designed and implemented a strategic insight application. The ability to get to this information easily and quickly created demand for more—so other sources like external libraries and electronic lab notebooks were included. Result views were refined. Information cards were added. A separate application was spawned from the first, with more focused content presented to a subset of scientists with a 360° view of a compound. Then they added an expert finder, to find knowledgeable people in addition to documents.

Next came other groups with more specialized information needs. For example, an application that allowed them—for the first time—to access doctor’s notes alongside clinical data. These notes had been recorded, but never used before – unstructured text lost in a system with no easy way to retrieve it. From there the demand snowballed. Now search is used strategically, creating value at every stage of research and drug development.

The Future of Search for Life Sciences

Leveraging information is key, but augmenting the user experience in how they interact with that information can further advance the capabilities of health and life sciences organizations to ensure a pipeline of successful, life-saving innovations. Moreover, with new advances in AI, machine learning and cloud computing, the future is bright. Here are a few of the ways intelligent search is evolving:

  1. Search-based applications: Like search apps we use every day, health and life sciences organisations have unique needs, many that can be met with applications powered by search. The potential for search-based applications in healthcare is unlimited, as organisations can use the capabilities of intelligent search to create personalised apps that can maximise the potential of any area of the business.
  2. Searching video and audio using transcription: In line with the growth of audio and video content—webinars, recorded Zoom meetings, keynote speeches—health and life sciences organisations need access to this information too. Intelligent search platforms can mine transcripts of this content so that knowledge is never lost.
  3. Conversational UI: Intelligent AI assistants like Alexa and Siri are becoming more and more popular every year. Such assistants may seem quaint for the needs of a researcher, but the ability to ask questions in natural language and get back answers on difficult and complex subjects is right around the corner. Question-answering abilities will soon be integrated into intelligent search so that healthcare professionals can answer complex queries, even with their hands full.

  4. Personalised experiences: Today’s intelligent search learns your behaviour to provide better search results for individuals while gaining proficiency over time. Intelligent search tools understand you as a user—your context, your work, and your previous actions—leading to richer insights and more precise information extraction.

Closing Thoughts

Health and life science companies depend on information to power innovation, time-to-market, competitiveness, and efficiency. But it’s difficult to harness textual data: there’s too much of it, it’s scattered, siloed, poorly organized, in many different formats, and always growing. Technology can help, but until recently it was impossible to bring a Google-like experience to the enterprise. The power of intelligent search to surface the right information at the right time and provide both a holistic view and the salient details can help you realize the full value of all your content and bring new levels of performance to the health and life sciences industry.

 

Jeff Evernham is Vice President of Product Strategy at Sinequa, the culmination of a 30-year career helping companies solve business problems with technology. He has led consulting and delivery practices in data, analytics, and visualization at multiple software and management consulting firms. He holds a Master of Engineering degree from MIT. He can be reached at https://www.linkedin.com/in/jeffevernham/