Application of an in Vitro New Approach Methodology to Determine Relative Cancer Potency Factors of Air Pollutants Based on Whole Mixtures

Air pollution is an example of a complex environmental mixture with different biological activities, making risk assessment challenging. Current cancer risk assessment strategies that focus on individual pollutants may overlook interactions among them, potentially underestimating health risks. Therefore, a shift towards the evaluation of whole mixtures is essential for accurate risk assessment. This study presents the application of an in vitro New Approach Methodology (NAM) to estimate relative cancer potency factors of whole mixtures, with a focus on organic pollutants associated with air particulate matter (PM). Using concentration-dependent activation of the DNA damage-signaling protein checkpoint kinase 1 (pChk1) as a readout, we compared two modeling approaches, the Hill equation and the benchmark dose (BMD) method, to derive Mixture Potency Factors (MPFs). MPFs were determined for five PM2.5 samples covering sites with different land uses and our historical pChk1 data for PM10 samples and Standard Reference Materials. Our results showed a concentration-dependent increase in pChk1 by all samples and a higher potency compared to the reference compound benzo[a]pyrene. The MPFs derived from the Hill equation ranged from 128 to 9793, while those from BMD modeling ranged from 70 to 303. Despite the differences in magnitude, a consistency in the relative order of potencies was observed. Notably, PM2.5 samples from sites strongly impacted by biomass burning had the highest MPFs. Although discrepancies were observed between the two modeling approaches for whole mixture samples, relative potency factors for individual PAHs were more consistent. We conclude that differences in the shape of the concentration–response curves and how MPFs are derived explain the observed differences in model agreement for complex mixtures and individual PAHs. This research contributes to the advancement of predictive toxicology and highlights the feasibility of transitioning from assessing individual agents to whole mixture assessment for accurate cancer risk assessment and public health protection.