Combined Actions and Interactions of Chemicals in Mixtures

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Where Ql and Ca refer to liver blood flow rate and arterial blood concentration, respectively. The use of this equation (3) permits the simulation of the theoretical limits of the impact of metabolic interactions, since the value of E can only range between 0 and 1. Enzyme induction can increase the value of E but only to a maximum value of 1 due to blood flow limitations, whereas inhibition of metabolism decreases the value of E but the value cannot be lower than 0 (Haddad et al. 2000 a, 2000b). In principle, the in vivo human metabolism can be predicted by using in vitro enzyme kinetic data and can thus be compared with the in vitro and in vivo data from experimental animals. For example, experiments using microsomes or hepatocytes may predict the in vivo velocity of metabolism for a single metabolic pathway. Such data may be incorporated in physiologically based toxicokinetic (PBTK) modelling (Andersen et al. 1995, Leung and Paustenbach 1995, Yang et al. 1995). As a rule, the description of the rate constants such as Vmax and Km for the individual (iso)enzymes follows Michaelis-Menten kinetics. Therefore interindividual differences in expression levels of enzymes, and genetic polymorphism can also be modelled. Ploemen et al. (1997) have presented a strategy to combine PBPK modelling with human in vitro metabolic data to explore the relative and overall contribution of critical metabolic pathways in man. In order to use PBTK modelling in the assessment of mixtures, Cassee et al. (1998) suggest that one of the components is first modelled and regarded as the prime toxicant being modified by the other components. Based on in vitro data on the other components, effects of for example inhibition or induction of specific biotransformation isoenzymes can be incorporated in the model. Effects of competition between chemicals in a mixture for the same biotransformation enzymes may also be incorporated by translating the effects into effects on the Michael-Menten parameters that are then incorporated into the model. PBTK models can be extended to include the toxicodynamic phase (PBTK/TD model) if a direct relationship exists between the concentration of the active metabolite (or parent compound) and the toxic effect (Yang et al. 1995). As an example, both glutathione depletion and covalent binding to cellular macromolecules was easily predicted in the human situation for 1,2dichlorobenzene based on comparison of in vitro data from rats and humans (Hissink et al. 1996). 2.4.3 Isobole methods An isobole is a contour line representing equi-effective quantities of two agents or their mixtures (Loewe and Muischnek 1926). The theoretical line of additivity is the straight line that connects the individual doses of each of the single agents that produce a predetermined, fixed effect alone, for instance an ED50 of a given toxicity or biochemical effect. The isobole method is widely used to evaluate the effects of binary mixtures. However, a large number of different mixtures of the two compounds have to be tested in order to identify combinations that produce the fixed effect. If the graphical representation of the combinations that produce the fixed effect shows a straight line the two compounds behave in a dose-additive manner and subsequently can be regarded as chemicals that have a similar mode of action. In case of an antagonistic interaction all the equi-effect concentrations in the mixtures represent a downward concave line, whereas a synergistic interaction would produce an upward curve. Examples of isoboles produced from theoretical mixtures of two agents assumed to act either additively, antagonistic or synergistic in combinations are gives in Figures 1, 2 and 3, respectively. 26
Figure 2.4.3.1. Isobologram of two agents A and B that act additively Dose Agent B (mg/kg bw) Figure 2.4.3.2. Isobologram of two agents that act synergistically Dose agent B (mg/kg bw) 9 8 7 6 5 4 3 2 4,0 3,5 3,0 2,5 2,0 1,5 1,0 0,5 Isobologram of two agents A and B that act additively 1 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 Dose agent A (mg/kg bw) Isobole ED50 of agent A is 5 mg/kg bw ED50 of agent B is 10 mg/kg bw Experiments showed that: 4 mg/kg bw A + 2 mg/kg bw B produce ED50 3 mg/kg bw A + 4 mg/kg bw B produce ED50 2 mg/kg bw A + 6 mg/kg bw B produce ED50 1 mg/kg bw A + 8 mg/kg bw B produce ED50 Isobologram of two agent A and B that act synergistically 0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 Dose agent A (mg/kg bw) Isobole ED50 of agent A is 5 mg/kg bw ED50 of agent B is 10 mg/kg bw Experiments showed that: 4 mg/kg bw A + 0.18 mg/kg bw B produces ED50 3 mg/kg bw A + 0.50 mg/kg bw B produces ED50 2 mg/kg bw A + 1.35 mg/kg bw B produces ED50 1 mg/kg bw A + 3.70 mg/kg bw B produces ED50 Figure 2.4.3. 3. Isobologram of two agents that act antagonistic 27
Page 1 and 2: Combined Actions and Interactions o
Page 3 and 4: Contents CONTENTS 3 PREFACE 6 SAMME
Page 5 and 6: 7.2.4 Test systems 85 7.2.5 In vitr
Page 7 and 8: Sammenfatning og konklusioner Indle
Page 9 and 10: er ingen viden om, hvorledes den ko
Page 11 and 12: estemt biologisk respons (ED10, ED2
Page 13 and 14: vivo. The COMET-assay in vivo and g
Page 15 and 16: and antagonistic effects may be see
Page 17 and 18: 1 Introduction Prepared by John Chr
Page 19 and 20: Table 1.3.1: Classification of comb
Page 21 and 22: Physicochemical interactions will t
Page 23 and 24: US EPA (1986) applied the concept o
Page 25: 2.4 Test strategies to assess combi
Page 29 and 30: CI = d1 /D1 = 1 In this case the is
Page 31 and 32: 2.5 Toxicological test methods When
Page 33 and 34: 3 General concepts in the risk asse
Page 35 and 36: NOAEL, this indicates that the subs
Page 37 and 38: of uncertainty i.e. differences in
Page 39 and 40: describe toxicities that appears to
Page 41 and 42: should be estimated for all endpoin
Page 43 and 44: 1,2,3,6,7,8-HxCDF 0.1 1,2,3,7,8,9-H
Page 45 and 46: shows the pronounced influence of t
Page 47 and 48: Table 4.3.1 Estimates of carcinogen
Page 49 and 50: Figure 4.4.1.1. Scheme for safety e
Page 51 and 52: obvious ranking of individual const
Page 53 and 54: 4.5 Approach for assessment of join
Page 55 and 56: 4.6 Approaches currently used by re
Page 57 and 58: no internationally accepted procedu
Page 59: Figure 4.6.3.1 Flow chart of the ri
Page 62 and 63: octyltin dichloride). A number of a
Page 64 and 65: stannous chloride and cadmium chlor
Page 66 and 67: seen in the animals from the 1000x
Page 68 and 69: c TCTFP = 1,1,2-trichloro-3,3,3-tri
Page 70 and 71: 6 Interactions in toxicokinetics Pr
Page 72 and 73: once more exposed and the kinetics
Page 74 and 75: Dermis The dermis consists of a sup
Page 76 and 77:
7.1.3 Ocular irritancy 7.1.3.1 Comp
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Another in vitro test for ocular ir
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lipid peroxidation was observed, wh
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mixture. These interactions may dev
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0.6% of live births in humans. In s
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DNA, and that the cell was capable
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(Sorsa et al. 1994, Myslak & Kosmid
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Four inhibitors of DNA topoisomeras
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cyclophosphamide-metabolising enzym
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Nickel compounds are carcinogenic t
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7.2.8 Conclusion As shown in this r
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Since no compounds are known to cau
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In a more mechanistic sense, promot
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Several inhibitors of tumour promot
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Table 7.4.1.1 Overview of in vivo t
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analysis was used to predict the hi
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interaction of TCDD and retinoic ac
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and now includes compounds as diver
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Furthermore, the estrogenic effects
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Dose of A (arbitrary units) 10 8 6
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chance or experimental error. One w
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7.6.2.3 Receptors A receptor is a b
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7.6.5.2 Kindling Kindling is the ph
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Science Institute (RSI) have conven
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eaction with sulfhydryl groups). Th
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7.7.1.3 Mechanisms of immunotoxicit
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tested separately. This is a phenom
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In practical life an allergen may b
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8.3 Conference proceedings Proceedi
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with combinations of environmental
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Boyd MR, Statham CM and Longo NS (1
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De Wall EJ, Schuurman HJ and Van Lo
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theoretical considerations and a su
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Groten JP, Sinkeldam EJ, Muys T, Lu
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Howes D, Guy R, Hadgraft J, Heyling
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cytochrome P450 1A2 expressed in Am
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Lison D, Carbonnelle P, Mollo L, La
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Narotsky MG, Weller EA, Chinchilli
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Ramsey JC and Andersen ME (1984). A
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Silva E, Rajapakse N and Kortenkamp
Page 156 and 157:
Leeuwen FXR, Liem AKD, Nolt C, Pete
Page 158:
Wlodarczyk B, Biernacki B, Minta M,
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