For decades, regulatory publishing has been dominated by manual tasks. Publishers spend hours formatting documents, generating agency-compliant PDF documents, performing quality checks, compiling documents for submissions and troubleshooting issues to ensure that all submission requirements have been resolved and deadlines met.
These tasks are laborious and exacting, requiring the efforts of professionals with advanced science and medical knowledge to ensure accuracy and integrity of the components and compilation of the submission. For example, on a 200-page summary document in a New Drug Application dossier, a team may spend hundreds of hours to author, review, create summary document links to pages in other documents, and corroborate every statement. This translates to an expensive and time-consuming set of activities that require laser-sharp
focus from the people ensuring basic data searching and verification tasks.
But imagine if many of those tasks were automated. In the not-too-distant future, authors’ and publishers’ jobs will be transformed by artificial intelligence (AI) and machine learning (ML), with algorithms custom-built to manage all the routine work, freeing up skilled professionals to focus on the high-value tasks that leverage their scientific expertise. We aren’t there yet, but innovations in this space are moving rapidly in that direction.