Back

Toxicogenomic analysis of the carcinogenic potential of plastic additives

Vincoff, S.; Schleupner, B.; Santos, J.; Morrison, M.; Zhang, N.; Dunphy-Daly, M. M.; Eward, W. C.; Armstrong, A. J.; Diana, Z.; Somarelli, J.

2023-07-02 pharmacology and toxicology
10.1101/2023.06.30.547246 bioRxiv
Show abstract

Plastics are the most prevalent human-made substance in the world and are ubiquitous throughout all ecosystems. Microscopic plastic particles are routinely ingested and inhaled by humans and other organisms. Despite the frequency of plastic exposures, little is known about their health consequences. Of particular concern are plastic additives -chemical compounds that are mixed into plastics to improve functionality or are unintentionally acquired during plastic production and use. Additives are loosely bound to the plastic polymer and may be released during plastic exposures. These compounds may pose health concerns, such as cancer, but little is known about the potential impact of these chemicals on health. To better understand the health effects of plastic additives, we performed an integrated toxicogenomic analysis on 2,712 additives, focusing on cancer as a well-studied toxicological endpoint. Screening these substances across three chemical databases revealed two key observations: 1) over 150 plastic additives have known carcinogenicity and 2) the majority ([~]90%) of plastic additives lack data on carcinogenic endpoints. Analyses of additive usage patterns pinpointed specific polymers, functions, and products in which carcinogenic additives reside. Based on published chemical-gene interactions, both carcinogenic additives and additives with unknown carcinogenicity impacted similar biological pathways. The predominant pathways involved DNA damage, apoptosis, immune response, viral diseases, and cancer. This study underscores the urgent need for systematic and comprehensive carcinogenicity assessment of plastic additives and regulatory responses to mitigate the potential health risks of plastic exposure.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.