(160ad) Toxicokinetic Interactions of Industrial Chemical Mixtures As Internal Exposure Modifiers
AIChE Annual Meeting
Monday, November 8, 2021 - 3:30pm to 5:00pm
Based on Michaelis Menden kinetics, enzyme (E) induction increases the Vmax of its substrate, due to increased enzyme synthesis and/or decreased enzyme degradation. Enzyme inhibition may be irreversible or reversible. When an inhibitor binds irreversibly to the enzyme at the active site, it decreases the concentration of functional enzymes and thus Vmax. Among all the toxicokinetic (mixture) models published to date, reversible metabolic inhibition is by far the most frequently encountered type of interaction (Van Gestel et al., 2016). Hence the focus of this study was metabolic interactions. Three types of reversible enzyme inhibition, namely competitive, non-competitive, and uncompetitive were identified. Competitive inhibition occurs when chemicals compete for the enzyme active site, resulting in decreased apparent affinity (i.e., increased Km) and, therefore, reduced rate of metabolism at lower substrate concentration. Non-competitive inhibition occurs when a chemical bind to the enzyme (free or complexed with substrate) at a site that is away from the catalytic active site. This binding changes the conformation of the enzyme, resulting in a decreased catalytic activity (i.e., decreased Vmax). The less frequently encountered uncompetitive inhibition occurs when a chemical bind to the enzyme-substrate complex. The catalytic function is affected without interfering with substrate binding. The inhibitor causes a structural distortion of the active site and inactivates it. This has the effect of reducing the available enzyme for the reaction (i.e., lowering Vmax) and also driving the reaction (E+SâES) to the right (i.e., decreasing Km).
The parameters Vmax and Km of Michaelis-Menten equation dependent on each chemical and are unaffected by the presence of other compounds. The inhibitory effect of each chemical is described by a constant (Ki) in the binary level of metabolism in order to quantify the effect of interaction between chemicals (Krishnan et al., 2002). However, the number of binary interactions is increased with the addition of other chemicals, consequently the difficulty of the determination of the constants of each binary combination is increased. Accordingly, the assumption can be made that the affinity of an inhibitor can be replaced by its inhibition constant Km only when the mechanism of interaction is competitive inhibition (Bois F.Y, 2019; Price and Krishnan, 2011).
For this study thee relevant mixtures of chemicals were investigated. The first one is the bisphenolsâ mixtures consisting of bisphenol A, S and F and the second one is the phthalatesâ mixture consisting of DEHP, BBzP, DnBP and DiNP and the third In order to evaluate the inhibitory effect on the metabolism as a result of co-exposure, a broad range of mean daily intake levels (bodyweight normalised) have been tested for bisphenols and phthalates, starting from 0.01 Î¼g/kg bw/day up to 100,000 Î¼g/kg bw/day. The interaction effect due to concurrent exposure to a
chemical mixture can be better appreciated when the exposure levels are higher with respect to typical environmental exposures. This is the case for occupational exposure characterized by exposure levels of the same order of the Threshold Limit Value (TLV) for all the four substances
composing the BTEX mixture. TLV is defined as âthe concentration of a substance to which most workers can be exposed without adverse effectsâ. These limit values are defined taking into account exposure to only individual chemicals. Therefore, it is of great interest to verify if simultaneous exposure to a mixture containing multiple chemicals could vary the effective dose to the target organs. The TLVâs for the four chemicals considered here are (a) benzene 0.5 ppm (b) toluene 33.0 ppm (c) xylenes 50.0 ppm (d) ethylbenzene 50.0 ppm. To evaluate the effect of the interaction we compared the internal dose of benzene in the bone marrow when
workers are exposed to benzene alone at Â¼ the TLV vs. when they are co-exposed to the quaternary BTEX mixture, assuming for each chemical exposure levels at Â¼ the respective TLV.
From the results of the co-exposure interaction, it was clearly shown that under environmentally relevant exposure levels and even at the level of EFSAâs temporary tolerable daily intake (tTDI) of 4 Î¼g/kg bw/d for bisphenol A and 50 Î¼g/kg_bw/d for DEHP, the effect of interaction on the internal dose (expressed as increase of the Area Under Curve, AUC) is negligible (below 1%). However, as expected, the interaction effect is higher when the daily intake level increases. The interaction is significant for intake levels above 10,000 Î¼g/kg bw/d, which might be relevant only for occupational settings. The more abundant the compound in the work environment the more significant the higher the intake levels of the mixture. It has to be noted that the effect of the interaction is strongest for BPA when compared to the other two bisphenols (BPA and BPF). This is probably the result of the lower Km value compared to the other two bisphenols, rendering BPA more prone to changes in effective Km as a result of the interaction due to the co-exposure. The same observation is valid for DEHP and DINP that present stronger effect of interactions compared to the rest of the phthalates due to their widely usage, especially in products like teethers, rattles, and squeeze toys, which are in proximity to the consumer. Similarly, for BTEX (Sarigiannis and Gotti, 2008), the changes in internal dose upon inhalation exposure are dose dependent, and they become more evident as we move closer to the TLV. Benzene concentration in the bone marrow is higher under combined exposure to BTEX with respect to exposure to benzene alone (Figure 3). The increment can be estimated between 25 and 30% leading to an increased risk of neurotoxicity and analogously reduced risk of leukaemia to healthy individuals after lifelong exposure; the cancer risk associated with human exposure to BTEX mixtures is reduced with respect to exposure to benzene alone at the same concentration since the rate of formation of oxidative metabolites from benzene is reduced during concurrent exposure to TEX due to the competitive inhibition between the four chemicals that compete as substrates for the same isoenzyme (P450 2E1).
From all the case studies, it is evident that co-exposure to chemicals that are subjected to interaction at the level of metabolism, is crucial at high exposure levels, that are mostly met in occupational settings. Thus, although in environmentally relevant exposure levels, interactions at the level of metabolism regarding cumulative exposure might not be a major concern, they have to be taken into account when estimating or apply the exposure-response relationships. Hense, these results indicate, that a broader framework of interactions that affect bioavailability upon cumulative exposure might need to be investigated, as well as integration of toxicokinetic and toxicodynamic models (and more specifically biology based dose response models); in our view this has to be the next step in cumulative exposure risk assessment, and qAOPs (quantitative adverse outcome pathways) seem to be the ideal substrate towards this direction.
Bois F.Y, T. C., Brochot C., 2019. EuroMix PBPK model for combined exposures. .
Desalegn, A., Bopp, S., Asturiol, D., Lamon, L., Worth, A., Paini, A., 2019. Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures â A review. Computational Toxicology. 10, 158-168.
Krishnan, K., Haddad, S., BÃ©liveau, M., Tardif, R., 2002. Physiological modeling and extrapolation of pharmacokinetic interactions from binary to more complex chemical mixtures. Environmental Health Perspectives. 110, 989-994.
Price, K., Krishnan, K., 2011. An integrated QSARâPBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat. SAR and QSAR in Environmental Research. 22, 107-128.
Sarigiannis, D., Gotti, A., 2008. Biology-based dose-response models for health risk assessment of chemical mixtures.