Discovering the Knowledge in Unstructured Early Drug Development Data Using NLP and Advanced Analytics

Abstract

Discovering the knowledge in unstructured archived data is an area of active interest in academia and industry, as it offers opportunities to learn, confirm and eventually, address R&D productivity challenges. Our interest in this area prompted us to investigate an Natural Language Processing based approach to extract unstructured Pharmacokinetics (PK) and Pharmacodynamics (PD) data from PK-PD study reports, and perform analytics using in-house developed compartmental and non-compartmental analytics engines. For this purpose, we have developed a dictionary of two thousand twenty-one (2321) PK-PD keywords based on published study reports. Details of our approach and its applications in discovering the knowledge in the unstructured archived early drug development reports, is the subject of this paper.