Natural language processing-based assessment of consistency in summaries of product characteristics of generic antimicrobials.

To investigate consistency in summaries of product characteristics (SmPCs) of generic antimicrobials, we used natural language processing (NLP) to analyze and compare large amounts of text quantifying consistency between original and generic SmPCs. We manually compared each section of generic and original SmPCs for antimicrobials listed in the electronic Medicines Compendium in the United Kingdom, focusing on omissions and additions of clinically significant information (CSI). Independently, we quantified differences between the original and generic SmPCs using Kachako, a fully automatic NLP platform. Among the 137 antimicrobials listed in the electronic Medicines Compendium, we identified 193 pairs of original and generic antimicrobial SmPCs for the 48 antimicrobials for which generic SmPCs existed. Of these 193 pairs, 157 (81%) were consistent and 36 were inconsistent with the original SmPC. When the cut-off value of RATE (the index of similarity between two SmPCs) was set at 0.860, our NLP system effectively discriminated consistent generic SmPCs with a specificity of 100% and a sensitivity of 61%. We observed CSI omissions but not additions in the SmPC subsection related to pharmacokinetic properties. CSI additions but not omissions were found in the subsections dealing with therapeutic indications and fertility, pregnancy and lactation. Despite regulatory guidance, we observed substantial inconsistencies in the information in the United Kingdom SmPCs for antimicrobials. NLP technology proved to be a useful tool for checking large numbers of SmPCs for consistency.