摘要：Background:
Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data.
Objectives:
We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools.
Methods:
We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC)
Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., “benzene activates DNA damage”). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach.
Results:
We found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation.
Discussion:
Our method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an
a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability.
https://doi.org/10.1289/EHP9112
