Identifying human toxicodynamic variability: a systematic evidence map of the current knowledge

Current chemical risk assessment uses a default uncertainty factor (UF) of 3.16 for toxicodynamic (TD) variability in humans. The objective was to create a systematic evidence map (SEM) of the human variability in TD by identifying and organizing the available empirical data to assess if a further refinement of the default UF of 3.16 for TD can be achieved. PubMed and Web of Science™ were searched from 2004 to 2023. Studies were screened according to the eligibility criteria. Inclusion criteria included studies, where TD could be separated from toxicokinetics (TK) to exclude an impact of TK on TD variability. The literature search retrieved 2408 studies. Manual screening identified 23 in vitro studies assessing human TD variability quantitively, of which only seven in vitro studies provided quantitative estimates of a TD variability factor. No in vivo study met the inclusion criteria. Several studies found TD UF of 3.16 not covering human variability; others did. However, the data were heterogeneous, and variability in Points of Departure (PODs) and methods used to estimate TD variability complicated comparisons across studies. A standardized approach for TDVFs determination is identified. This SEM underscores the scarcity of data assessing human variability in TD, while omitting the influence of TK.Toxicolog

Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro

Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA), independent action (IA) and generalized concentration addition (GCA) models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot always be accounted for by single chemicals