There surely can’t be anyone in the pharma industry who hasn’t heard the story of thalidomide. The disaster that followed the release onto the market of thalidomide in 1959 triggered a wave of regulatory changes to ensure reliable evidence of drug safety, efficacy and chemical purity, before a new drug is released onto the market.
While failure of clinical efficacy is the major cause of drug attrition, a poor safety profile is also a major factor in failure of drugs in development, at all stages from initial lead candidate through preclinical and clinical development to post-marketing surveillance. In order to ensure the safety of drugs on the market, rigorous testing is carried out throughout the pipeline, and can be categorised into preclinical safety/toxicology in animal models, clinical safety in human subjects, and then post-market pharmacovigilance, to look for safety signals across a wide patient population (see schematic below).
At every stage, critical data is being both generated and sought from unstructured text – from internal safety reports, scientific literature, individual case safety reports, clinical investigator brochures, patient forum, social media, conference abstracts. Intelligent search across these hundreds of thousands of pages can provide the information for key decision support. Many of our customers are using the power of Linguamatics Natural Language Processing (NLP) solution to transform the unstructured text into actionable structured data that can be rapidly visualized and analyzed, at every stage through the safety lifecycle of a drug.