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From molecule to market: Today’s NLP optimizes every step of drug development


In the pharma pipeline from drug discovery through development and into delivery, insight is needed at every stage to respond to challenges, clear decision gates, or achieve milestones. Not so long ago, the industry challenge was harnessing enough content to make good decisions. Now, with a constant stream of new data flowing into siloed information systems, we face the opposite challenge: How do we sift through the tsunami of data to extract the right insights to make good decisions? Given that about 80 percent of available information exists in unstructured text that is difficult to extract and use, it’s a real and pressing challenge.

To overcome the limitations of time-consuming, manual searches through constant streams of data, pharma and life sciences companies are looking to artificial-intelligence-powered tools such as natural language processing (NLP). A key benefit of NLP is that, unlike a standard keyword search that retrieves documents for users to read, NLP does the “reading” for the users, rapidly identifying relevant facts and relationships. Those can then be structured for fast analysis and actionable insights.

NLP has always held massive promise for life sciences organizations, but recent breakthroughs have made the technology increasingly accurate and user friendly for easy application across the enterprise. That means insights and evidence that were once generated in siloed departments can now be shared, and data sources that were once disparate can be linked and leveraged at scale. Here, we’ll discuss how that breakthrough is transforming drug development from molecule to market.

Illuminating insights with NLP: A pharma lifecycle case study

Savvy life sciences companies are seeing the merit of using NLP not as a point solution, but holistically as workflows through the development process. Let’s explore how one top 10 pharmaceutical company is elevating its entire development process by unlocking textual data at scale.

Research & development

Early Discovery: The identification process to determine unmet need, find promising drug leads, and identify biological markers is ripe for NLP. This Linguamatics client uses our NLP to access literature sources for landscapes of gene-disease associations, a process that reduces time spent on manual curation. The tool is easy to use and presents cluster visualizations of potential diseases for any chosen gene to enable rapid indication targeting.

Adverse Drug Reaction (ADR) Prediction: The client’s R&D teams also use NLP to develop an integrated systems pharmacology approach that allows reliable prediction of potential “on-target” and “off-target” ADRs for new drugs in development. As a result, the client can develop potential therapies with more speed and precision than was previously possible.

Trial Analytics & Intelligence: Using Linguamatics NLP, the client can rapidly run clinical trial analytics to optimize trial design and capture valuable clinical competitive intelligence. They achieve this executing queries over the detailed unstructured textual record fields in databases such as or Citeline's TrialTrove to rapidly identify, extract, synthesize and analyze relevant information such as clinical trial site, eligibility criteria, study characteristics, patient numbers and characteristics that would not be possible using other approaches.

Drug safety

Risk Analysis: Safety is paramount. That’s why our customer uses our NLP to perform fast, comprehensive assessments of potential toxicity across multiple organs, searching preclinical toxicology safety reports in mere seconds to glean a much more rapid and accurate risk analysis than they ever could with manual curation. 

Real world evidence

Epidemiology Metrics: Real world evidence teams are tasked with building models to understand the patient journey, treatment patterns and comparative effectiveness of a product to successfully demonstrate value. Literature, claims, and patient-reported data are replete with insights that can feed these models, but they are incredibly cumbersome to parse. This client uses Linguamatics’ NLP to scan the available evidence from literature and seamlessly extract epidemiology metrics to provide evidence for value and positioning of products.

Medical affairs

Data Monitoring: Medical Affairs teams need to capture internal and external intelligence streams around product brands, which often come from a diverse range of unstructured sources. Our top 10 pharma client is using NLP to pull those insights into a single integrated environment for visual analytics that support brand team decisions.

Post-Market: The client also uses NLP to look across scientific literature and prescription databases to enable a better understanding of drug-drug interactions and co-prescription trends.

Creating value across the enterprise

The explosion in healthcare data is set to continue unabated. Fortunately, NLP now has the capacity to relieve life sciences companies of the need to spend hours scouring documents and data streams as they try fruitlessly to keep up, missing potential opportunities in the process. Point NLP is great, but the true value of the technology will come to life as organizations shift their thinking of NLP from a reactive “Band-Aid” solution to a proactive insights accelerator that can improve quality and speed from bench to bedside.

For more information on how NLP can be used to support the life sciences product lifecycle, watch our webinar: Step Change: Unlocking the Hidden Value of Your Enterprise’s Dark Data or reach out to us directly for a demo.

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