In vitro to in vivo extrapolation to derive a metabolism factor for estimating the aggregate exposure to salicylic acid after dermal exposure of its esters.

As part of the safety assessment of salicylate esters in cosmetics, we developed a metabolism factor based on in vitro to in vivo extrapolation (IVIVE) to provide a better estimation of the aggregate internal exposure to the common metabolite, salicylic acid. Optimal incubation conditions using human liver S9 were identified before measuring salicylic acid formation from 31 substances. Four control substances, not defined as salicylic esters but which could be mistaken as such due to their nomenclature, did not form salicylic acid. For the remaining substances, higher in vitro intrinsic clearance (CLint, in vitro) values generally correlated with lower LogP values. A "High-Throughput Pharmacokinetic" (HTPK) model was used to extrapolate CLint, in vitro values to human in vivo clearance and half-lives. The latter were used to calculate the percentage of substance metabolised to salicylic acid in 24 h in vivo following human exposure to the ester, i.e. the "metabolism factor". The IVIVE model correctly reproduced the observed elimination rate of 3 substances using in silico or in vitro input parameters. For other substances, in silico only-based predictions generally resulted in lower metabolism factors than when in vitro values for plasma binding and liver S9 CLint, in vitro were used. Therefore, in vitro data input provides the more conservative metabolism factors compared to those derived using on in silico input. In conclusion, these results indicate that not all substances contribute equally (or at all) to the systemic exposure to salicylic acid. Therefore, we propose a realistic metabolism correction factor by which the potential contribution of salicylate esters to the aggregate consumer exposure to salicylic acid from cosmetic use can be estimated.

A Cautionary tale for using read-across for cancer hazard classification: Case study of isoeugenol and methyl eugenol.

Chemical grouping and read-across are frequently used non-animal alternatives for filling toxicological data gaps. When grouping chemicals, it is critical to define the applicability domain because minor differences in chemical structure can lead to significant differences in toxicity. Here, we present a case study on isoeugenol and methyl eugenol, which are scheduled for review by IARC in June 2023, to illustrate that structural similarity alone may not be sufficient to group chemicals for hazard classification. Isoeugenol and methyl eugenol are plant-derived phenylpropenes that share similar physicochemical properties. The major metabolic pathway for isoeugenol includes conjugation of the phenolic hydroxyl group with sulfate and glucuronic acid as an efficient detoxification process, whereas the major metabolic pathway for methyl eugenol involves benzylic hydroxylation and formation of the 1'-sulfoxymethyleugenol which leads to carbocation formation. The carbocation can form DNA adducts and induce genotoxicity and carcinogenicity. Consistently, genotoxicity and carcinogenicity alerts are identified from in silico prediction tools for methyl eugenol but not isoeugenol. Moreover, the available toxicogenomic, genotoxicity, and carcinogenicity studies confirm that these chemicals have significantly different bioactivities. Data on other structurally similar chemicals further supports our conclusion that it is not appropriate to group these two chemicals for cancer hazard classification.

Extreme Competence: Keystone Hosts of Infections.

Individual hosts differ extensively in their competence for parasites, but traditional research has discounted this variation, partly because modeling such heterogeneity is difficult. This discounting has diminished as tools have improved and recognition has grown that some hosts, the extremely competent, can have exceptional impacts on disease dynamics. Most prominent among these hosts are the superspreaders, but other forms of extreme competence (EC) exist and others await discovery; each with potentially strong but distinct implications for disease emergence and spread. Here, we propose a framework for the study and discovery of EC, suitable for different host-parasite systems, which we hope enhances our understanding of how parasites circulate and evolve in host communities.