Prioritizing cancer hazard assessments for IARC Monographs using an integrated approach of database fusion and text mining

Using a data science approach, the researchers used advanced chemoinformatics, text mining, and database fusion to integrate searches of the global literature on cancer epidemiology and mechanistic evidence about carcinogenicity (based on the key characteristics of carcinogens) to group and rank the volume and type of literature pertaining to agents prioritized for evaluation by the IARC Monographs during 2020-24.

This novel database fusion and chemoinformatics approach provides new insights into evidence on human cancer and key characteristics of carcinogens that may influence future cancer hazard evaluations. Ultimately, the strategy provides a logical framework for ranking and grouping prioritized agents for evaluations in future IARC Monographs. The approach also will be useful to identify new potentially carcinogenic agents for consideration by the IARC Monographs and other cancer hazard identification programmes, and for laboratory research and molecular epidemiology investigations, based on the volume and types of evidence in the global research literature.