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Publication Detail

Title: Grouping of UVCB substances with new approach methodologies (NAMs) data.

Authors: House, John S; Grimm, Fabian A; Klaren, William D; Dalzell, Abigail; Kuchi, Srikeerthana; Zhang, Shu-Dong; Lenz, Klaus; Boogaard, Peter J; Ketelslegers, Hans B; Gant, Timothy W; Wright, Fred A; Rusyn, Ivan

Published In ALTEX, (2021)

Abstract: One of the most challenging areas in regulatory science is assessment of the substances known as UVCB (unknown or variable composition, complex reaction products and biological materials). Because the inherent complexity and variability of UVCBs present considerable challenges for establishing sufficient substance similarity based on chemical characteristics or other data, we hypothesized that new approach methodologies (NAMs), including in vitro test-derived biological activity signatures to characterize substance similarity, could be used to support grouping of UVCBs. We tested 141 petroleum substances as representative UVCBs in a compendium of 15 human cell types representing a variety of tissues. Petroleum substances were assayed in dilution series to derive point of departure estimates for each cell type and phenotype. Extensive quality control measures were taken to ensure that only high-confidence in vitro data were used to determine whether current groupings of these petroleum substances, based largely on the manufacturing process and physico-chemical properties, are justifiable. We found that bioactivity data-based groupings of petroleum substances were generally consistent with the manufacturing class-based categories. We also showed that these data, especially bioactivity from human induced pluripotent stem cell (iPSC)-derived and primary cells, can be used to rank substances in a manner highly concordant with their expected in vivo hazard potential based on their chemical compositional profile. Overall, this study demonstrates that NAMs can be used to inform groupings of UVCBs, to assist in identification of repre­sentative substances in each group for testing when needed, and to fill data gaps by read-across.

PubMed ID: 33086383 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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