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"We strive to help improve outcomes and create a healthier, more sustainable world for people everywhere.

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Harness the power to transform clinical development

Reimagine clinical development by intelligently connecting data, technology, and analytics to optimize your trials. The result? Faster decision making and reduced risk so you can deliver life-changing therapies faster.

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Real World Evidence. Real Confidence. Real Results.

Generate and disseminate evidence that answers crucial clinical, regulatory and commercial questions, enabling you to drive smarter decisions and meet your stakeholder needs with confidence.

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See markets more clearly. Opportunities more often.

Elevate commercial models with precision and speed using AI-driven analytics and technology that illuminate hidden insights in data.

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Orchestrate your success across the complete compliance lifecycle with best-in-class services and solutions for safety, regulatory, quality and medical information.

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"Digital Health Trends 2024 - Implications for Research and Patient Care

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"DCTs Deliver Big ROI

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Advancing Drug Safety With Natural​ Language Processing (NLP)

Advancing Drug Safety With Natural Language Processing (NLP)

Pharmaceutical organizations are facing a safety data problem. The volume of safety information is growing, leading to unsustainable increases in the costs of traditional safety operations.

IQVIA NLP transforms safety-relevant unstructured text (from case reports, literature, call center feeds, drug labels, regulatory approvals and more) into actionable structured data that can be rapidly analyzed for safety vigilance at every stage through the safety lifecycle of a drug. 

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Solutions Real World Evidence Natural Language Processing Advancing Drug Safety With Natural​ Language Processing (NLP)

Harness your internal and external safety data

Across the whole span of drug development, safety-relevant data is being both generated and sought from unstructured text. Applying innovative technologies, such as AI, across these data can speed up safety processes and decision support

Natural language processing (NLP) text mining can optimize safety platforms and lower development costs. NLP can be applied in many areas across the safety life cycle of a drug with key areas such as:

  1. Better access to information for safety assessment, providing a rich landscape of safety-relevant data sources
  2. To reduce manual effort in safety case processing with tagging and medical coding (e.g. to the Medical Dictionary for Regulatory Activities - MedDRA) of adverse events.
By 2023, 60% of the top 100 life science companies will use AI augmentation in one or more safety vigilance solutions
GARTNER REPORT: Jeff Smith, “Life Science CIOs Reduce Runaway Costs With Innovative Safety Vigilance Technology”. March 2021

Illuminating drug safety insights with NLP

Explore each drop down to the right to learn more about the key capabilities of NLP for drug safety.

Auto-coding Adverse Events to MedDRA

Adverse event reports are usually provided by a reporter in natural language which needs to be transferred (coded) into a standardized format to allow database processing. Most of this coding is manual and time consuming. 

NLP speeds up coding of adverse events to MedDRA, improving coding consistency, in a reliable and repeatable process that decreases manual workload.

NLP to Support Adverse Event Detection and MedDRA Coding in Post-Market Safety Case Processing

Pharma companies need to screen huge volumes of reports for potential AEs for drugs on the market, from patients in the real world. Within pharmacovigilance workflows, reports are received from many sources—call center feeds, emails, regulatory AE reports and more. These are often in everyday language, and need to be coded to a standardized form to allow database processing. For adverse events, MedDRA must be used. Most MedDRA coding is manual and time consuming. Only when the verbatim exactly matches a MedDRA term is coding automatic. For example, a verbatim might read: “I really got ill the other day, had a horrible headache and couldn’t sleep for two days.” “Headache” has an exact match in MedDRA, so can be auto-encoded, but “couldn’t sleep” is not a MedDRA term and so has to be manually coded to “sleeplessness.” IQVIA worked with CSL Behring to develop an NLP workflow that doubled the level of auto-coding (from only 30% of AEs to over 60%). Use of NLP improved coding consistency, and can reduce risk for case processing and medical evaluation.
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NLP to Support Contextualization of Safety Signals

AstraZeneca wanted to understand the risk potential of a particular safety signal, neutropenia, to translate from preclinical to clinical. Building an understanding of the landscape around drugs causing neutropenia would enable them to build predictive models. However, data on drugs reported to cause neutropenia in humans and model animal species are buried in scientific literature and other textual sources. They used NLP to mine scientific abstracts and clinical trial reports to find evidence of drugs causing the condition, specifically searching for drug and condition in phrases with a suitable verb (looking for causality).

IQVIA NLP enabled them to build queries that looked for modifiers (e.g. drug-induced, transitory, severe, prolonged) and also species information (e.g. rat, mouse, dog, human). This rich landscape of structured data was used to feed predictive models, and enabled the understanding of risk at the preclinical stage, before clinical trials began

NLP to Enhance the Searchability of Internal Preclinical Toxicology Safety Reports

There is a wealth of valuable information in legacy preclinical safety reports. Locked away in these historical data are answers to questions such as: Has this organ toxicity been seen before? In what species, and with what chemistry? Many pharma organizations use document repositories to store their preclinical tox studies, but the search functionality of many of these document management systems is limited, hindering access. At Lilly, they use IQVIA NLP to enable rapid effective searches over the toxicology reports. The intuitive NLP web portal allows scientists who aren’t experts in NLP to run effective searches and pull back the data and documents of interest, for downstream review and analysis.
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NLP for Clinical Safety at Agios Pharmaceuticals

Agios Pharmaceuticals says that it “uses [IQVIA] NLP to get decision support as fast and as comprehensively as possible.” They applied NLP in clinical safety workflows to mine AE reports and assist with coding of reported events and WHO drugs. In a specific case, Agios explored the risk of a rare, yet potentially life-threatening AE, Differentiation Syndrome, in patients on a clinical trial for Agios’ IDH-1-inhibitor AG120. NLP was used to extract key information from Serious Adverse Event Report Forms, enabling a 10-fold decrease in time taken to process the forms. The resulting data visualizations enabled clinicians to explore the patterns of symptoms between patients and, critically, to identify those at risk of the potentially fatal AE.
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Fact Sheet
NLP text analytics for Regulatory Affairs

Reduce costs and speed compliance across labeling, regulatory changes and master data management
Fact Sheet
Generating Clinical Trial Design Insights using Natural Language Processing

Improve efficiency 10-100x and enhance key decision making support
Case Study
How a Top-10 Pharma Creates Clinical Trial Benchmarks with NLP
Case Study
Fast-Tracking Clinical Trials

The IQVIA NLP platform enabled Eli Lilly and Company to assist in the design of new clinical trials and increase speed to business insight
Blog
Drug Safety Landscapes – Painting the Picture with Natural Language Processing
By: Jane Reed
Fact Sheet
Advancing Drug Safety With Natural Language Processing (NLP)
Case Study
How CSL Behring uses Natural Language Processing to Improve MedDRA Coding
Webinar
Natural Language Processing innovations for drug safety in Pharma
September 13, 2022
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