Pharmaceutical companies are increasingly recognizing that patients’ social media posts are a valuable source of insight into patient-reported outcomes, views, symptoms, use of competitive products and more. Analyzing social media posts has the potential to provide insights from a broad population of patients, healthcare professionals, and key opinion leaders.

The standard methodology to gather these insights is primary market research. Pharma companies work hard to establish and maintain focus groups for live interviews, questionnaires and other ways to gather insights from patients and providers. However, the research process is time-consuming and expensive, requiring pharma companies to invest a significant amount of resources; and the number of patients that can be targeted is small.

Analysis of social media has the potential to be more efficient, more cost-effective, and address much larger patient populations than primary market research. The problem in analyzing social media content, however, is that social platforms represent a constantly flowing firehose of noisy information, so separating the signal from the noise poses a significant challenge.

Text mining with Natural Language Processing (NLP) technology represents an alternative approach to unlocking the value of social media content. By enabling artificial intelligence-based NLP tools to gather and analyze social data, pharmaceutical companies can more swiftly and efficiently glean insights that will influence the drug development process.


We are pleased to announce that Linguamatics, an IQVIA company, and the leading Natural Language Processing (NLP) text analytics provider, has joined Accenture’s open partner ecosystem which is designed to help independent software vendors (ISVs) and life sciences companies team more effectively to accelerate drug discovery efforts and improve patient outcomes.

The INTIENT Network for Research is an integral part of Accenture’s cloud-based informatics research platform, which has been designed to help life sciences organizations improve productivity, efficiency and innovation in drug discovery. Accenture is currently working with a select number of ISVs and organizations—including Linguamatics— to integrate their technology and content into Accenture’s research platform. This will allow life sciences companies to give their researchers access to innovative capabilities, such as Linguamatics’ innovative Natural Language Processing-based AI for high-value knowledge discovery and decision support from text. The Linguamatics award-winning platform is proven across multiple real-world use cases to deliver actionable insights that address pressing bench-to-bedside challenges with quantifiable ROI.


Using Natural Language Processing (NLP), Agios Pharmaceuticals discovers new therapeutic candidate

As the search for novel anti-cancer agents continues apace, the biopharma industry struggles to make sense of the myriad studies that are published describing putative small molecule inhibitors, potential genetic targets, and possibly susceptible points of attack. One area of growing interest in oncology is cancer cell metabolism: companies are striving to develop compounds that can interrupt or inhibit the metabolic process, leading to tumor cell suppression or death. The dual challenges are to identify promising lead compounds, and to detect suitable genes implicated in the metabolic process and sensitive to chemical intervention.

Rather than starting from scratch with a blank structure-activity canvas and pursuing the traditional (and potentially lengthy and risky) lead identification/lead optimization route to a pre-clinical candidate, Agios Pharmaceuticals decided to short-circuit the process and build on previously published studies. They wanted to locate and source known inhibitors for use as tool compounds in their chemical genetics screens and to identify genes with “druggable Achilles’ heels” susceptible to chemical attack, and they chose to use NLP to quickly and effectively scour the literature.


We are proud to announce that the IQVIA Linguamatics Natural Language Processing (NLP) platform was recently awarded Questex’s 2019 Fierce Innovation Award – Life Sciences Edition in the Data Analytics/Business Intelligence category. In addition, the NLP healthcare platform was recognized with a Best in Show honor for ‘Best Technology Innovation’.

Sponsored by the publisher of FierceBiotech and FiercePharma, the Fierce Innovation Awards identify and showcase outstanding innovation that is driving improvements and transforming the healthcare industry. An expert panel of judges reviews all submissions to determine which companies demonstrate innovative solutions, technologies and services that have the potential to make the greatest impact for biotech and pharma companies.

The IQVIA Linguamatics NLP platform supports life sciences organizations seeking to speed up drug development and improve patient outcomes by breaking down data silos, boosting innovation, enhancing quality and reducing risk and complexity. NLP is an artificial intelligence technology that transforms unstructured and semi-structured text into normalized, structured data suitable for analysis or to drive machine learning algorithms.

The platform uses sophisticated algorithms to identify, extract and connect key concepts, facts and relationships buried in the text rather than just retrieve documents based on keyword search. Key solution areas include:


Natural Language Processing: Standing on the shoulders of giants

Many of us know the joys and sorrows of research. Weeks, months and years can pass, developing hypotheses, working in the lab or clinic, analyzing results, sometimes going back to square one, but then writing the paper, and finally, seeing the final version published and in print. The intent is that your research is shared, discussed, re-used, so that others can build on it, “standing on the shoulders of giants,” as Isaac Newton famously said.

Traditionally, getting information out of written papers for re-use has been manual; individuals reading, reviewing and extracting the key facts from tens or hundreds of papers by hand, in order to summarize the most up to date research in a field, or understand the landscape of information around a particular research topic. Over the past few decades Artificial Intelligence (AI) tools, such as Natural Language Processing (NLP), have evolved that can hugely speed up and improve this data extraction. NLP solutions can enable researchers to access information from huge volumes of scientific abstracts and literature; developing strategies and rules that drill deep into literature for hidden nuggets, or more broadly, ploughing the landscape for the nuggets of desired information.

To give you a couple of examples, I’ll share two use cases, both published recently, that use Linguamatics NLP platform across published literature, enabling researches to benefit from years of previous research.