Objective: To align pharmacologic classes in ATC and MeSH with lexical and instance-based techniques.

    Methods: Lexical alignment: we map the names of ATC classes to MeSH through the UMLS, leveraging normalization and additional synonymy. Instance-based alignment: we associate ATC and MeSH classes through the drugs they share, using the Jaccard coefficient to measure class-class similarity. We use a metric to distinguish between equivalence and inclusion mappings.

    Results: We found 221 lexical mappings, as well as 343 instance-based mappings, with a limited overlap (61). From the 343 instance-based mappings we classify 113 as equivalence mappings and 230 as inclusion mappings. A limited failure analysis is presented.

    Conclusion: Our instance-based approach to aligning pharmacologic classes has the prospect of effectively supporting the creation of a mapping of pharmacologic classes between ATC and MeSH. This exploratory investigation needs to be evaluated in order to adapt the thresholds for similarity.