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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.

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.