Can Toxicokinetic and Toxicodynamic Modeling ... - ACS Publications

Sep 13, 2017 - to a data set with variable propiconazole or prochloraz concentrations, and the .... parameters are re-fitted to raw data (Tables S4 an...
1 downloads 8 Views 1MB Size
Subscriber access provided by Gothenburg University Library

Article

Can toxicokinetic and toxicodynamic modelling be used to understand and predict synergistic interactions between chemicals? Nina Cedergreen, Kristoffer Dalhoff, Dan Li, Michele Gottardi, and Andreas C. Kretschmann Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b02723 • Publication Date (Web): 13 Sep 2017 Downloaded from http://pubs.acs.org on September 13, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 22

Environmental Science & Technology

1

Can toxicokinetic and toxicodynamic modelling be used to understand

2

and predict synergistic interactions between chemicals?

3

Nina Cedergreena*, Kristoffer Dalhoffa, Dan Liab, Michele Gottardia and Andreas C. Kretschmannac

4 5

*Corresponding author, phone: +45 29611743, [email protected]

6

a

7

University of Copenhagen

8

Thorvaldsensvej 40

9

1871 Frederiksberg C

10

Department of Plant and Environmental Science

Denmark

11 12

b

13

Key Laboratory of Pollution Ecology and Environmental Engineering,

14

Institute of Applied Ecology,

15

Chinese Academy of Sciences,

16

110016, China

Present address:

17 18

c

19

University of Copenhagen

20

Universitetsparken 2

21

2100 Copenhagen Ø

22

Denmark

Toxicology Lab, Department of Pharmacy and Analytical Biosciences

1 ACS Paragon Plus Environment

Environmental Science & Technology

Page 2 of 22

23

ABSTRACT

24

Some chemicals are known to enhance the effect of

25

other chemicals beyond what can be predicted with

26

standard mixture models, such as concentration

27

addition and independent action. These chemicals are

28

called synergists. Up until now, no models exist that

29

can predict the joint effect of mixtures including

30

synergists. The aim of the present study is to develop a

31

mechanistic toxicokinetic (TK) and toxicodynamic

32

(TD) model for the synergistic mixture of the azole fungicide, propiconazole (the synergist), and the

33

insecticide, α-cypermethrin, on the mortality of the crustacean D. magna. The study tests the hypothesis that

34

the mechanism of synergy is the azole decreasing the biotransformation rate of α-cypermethrin and validates

35

the predictive ability of the model on another azole with a different potency: prochloraz. The study showed

36

that the synergistic potential of azoles could be explained by their effect on the biotransformation rate but

37

that this effect could only partly be explained by the effect of the two azoles on cytochrome P450 activity,

38

measured on D. magna in vivo. TKTD models of interacting mixtures seem to be a promising tool to test

39

mechanisms of interactions between chemicals. Their predictive ability is, however, still uncertain.

TOC art

40

2 ACS Paragon Plus Environment

Page 3 of 22

Environmental Science & Technology

41

INTRODUCTION

42

Some chemicals are known to enhance the effect of other chemicals beyond what can be predicted with

43

standard mixture models, such as concentration addition and independent action. These chemicals are called

44

synergists. Up till now no models exist that can predict the joint effect of mixtures including synergists.

45

Azole fungicides have been shown to act as synergists in a range of studies, enhancing the toxicity of

46

pyrethroid insecticides up to 10-50-fold in a range of organisms1-4. Also other pesticide or biocide

47

combinations have shown to induce repeatable synergy in a range of organisms 5. Synergy and antagony

48

between chemicals can occur through a range of mechanisms: First, one chemical can affect the availability

49

of another chemical outside an organism through either precipitation or change in speciation, as it has been

50

demonstrated for metals in mixtures6-8. Secondly, one chemical can affect the uptake rate of another chemical

51

e.g. by enhancing penetration9, or by facilitating availability by enhancing ventilation rates in aquatic

52

organisms10. Thirdly, one chemical can affect the transport of another chemical to its target, as it is often

53

observed in plants11, or a chemical can affect the biological action of other chemicals by either inhibiting or

54

promoting their transformation through interactions with biotransformation enzymes such as cytochrome

55

P450 monooxygenases and esterases12, 13. Finally, chemicals can compete for a common target site or affect

56

the excretion of one another.

57 58

In a review of synergistic interactions, 95 % of all documented pesticide synergies were caused by either

59

azole fungicides or carbamate and organophosphate insecticides, known to inhibit cytochrome P450

60

monooxygenases and/or esterases5. Hence, for pesticide mixtures it seems as if interactions involving

61

biotransformation of other pesticides are the main mechanism behind the observed synergies. Despite of this

62

hypothesis often being cited, very little direct evidence exists proving that inhibition or activation of

63

biotransformation of other chemicals is the single most important mechanism of synergy of azole fungicides,

64

carbamate and organophosphate insecticides3,10,14-16. For azole/pyrethroid interactions, for example,

65

ChalvetMonfrey et al (1996) found that the synergy of prochloraz on deltamethrin in bees could not be

66

explained by effects on biotransformation alone17, but that effects on uptake rates were also likely to take

67

place 16.

68 69

Synergists acting on biotransformation pathways could potentially be screened by using in vitro or in vivo

70

assays for the effect of chemicals on different metabolic enzymes12,18,19. But even if a chemical is known to

71

inhibit certain enzymes, the size of the potential synergistic interactions and its development over time,

72

cannot be quantified with any existing model approach20. A possible tool to test mechanisms of synergy and

73

ultimately to predict the size of synergy over time are toxicokinetic (TK) and toxicodynamic (TD) models21.

74

TK models predict uptake and elimination of chemicals over time and TD models predict the development of

75

effects over time as a function of the modelled or measured internal chemical concentrations21,22. Mixture

3 ACS Paragon Plus Environment

Environmental Science & Technology

Page 4 of 22

76

toxicity calculations using TKTD models have been proposed, but only for non-interacting chemicals with

77

similar molecular target sites23. TKTD models for interactive chemicals could be a tool to test hypotheses on

78

the mechanism of interaction. If they are successful, they may also be used to predict the size of synergistic

79

interactions under different exposure scenarios.

80 81

The aim of the present study is therefore three-fold: First, we wish to build and parameterise a full TKTD

82

model for the synergistic interactions between the azole fungicide propiconazole (the synergist) and the

83

pyrethroid insecticide α-cypermethrin on the mortality of the crustacean D. magna (Figure 1), when

84

propiconazole is present at one constant concentration. Secondly, we wish to test the different hypotheses

85

concerning the mechanism of synergy (effects on uptake versus the effect on biotransformation rate), and

86

thirdly, we will validate the model assumptions in terms of synergistic interactions with a synergist with a

87

similar mode of action but different potency, the azole fungicide prochloraz. To confirm the hypothesis that

88

the azoles induce synergy through interference with biotransformation, the model is parameterised to a

89

dataset with variable propiconazole or prochloraz concentrations, and the modelled effect on

90

biotransformation rate is compared with cytochrome P450 activity inhibition measured in vivo.

91

92 93

Figure 1. The figure shows a conceptual model of the toxicokinetic (left side) and toxicodynamic (right side) processes

94

of a pyrethroid insecticide and an azole fungicide and their interactions in Daphnia magna, symbolised by the large

95

square. Inside the daphnid, there are two targets for the pesticides: the sodium channel (grey half-circle) which is the

96

target site of the pyrethroid, and P450 enzymes (grey circle), the main target site for the azoles. The pyrethroids act as

4 ACS Paragon Plus Environment

Page 5 of 22

Environmental Science & Technology

97

substrates for the P450 enzymes when they are biotransformed (green circle). The state variables, describing how the

98

amounts of pesticide in the different locations change over time, are given in blue circles for the pyrethroids and by red

99

squares for the azoles, and are all described by differential equations given in the text. The rate constants, describing TK

100

processes, are given next to the solid arrows denoting the specific processes (values are given in Table 1), while

101

parameters relating damage to mortality, assuming GUTS-SD (Equation 8 and 9), are given next to the grey dashed

102

arrows. The synergistic interaction is proposed to occur when azoles bind to P450 enzymes, making them unavailable

103

for pyrethroid biotransformation, thereby decreasing the rate by which the P450 enzymes can biotransform the

104

pyrethroid (red dashed arrow). The alternative hypothesis for synergy, where azoles affect pyrethroid uptake rates, is

105

denoted by a red dotted arrow. Mechanisms that are neglected in the first versions of the model, but which might be of

106

importance, such as direct effects of the azoles on daphnia mortality, or effects on uptake rates due to the α-

107

cypermethrin damage done to daphnia mobility, are denoted by dotted grey arrows.

108

METHODS

109

Theory The conceptual model is shown in Figure 1. It is initially assumed that the uptake of the pyrethroid

110

will follow a first order kinetic uptake and elimination model including a biotransformation rate constant,

111

km_pyr, describing the rate by which the pyrethroid is biotransformed in the organism.

112 113

_ ( ) 

= _ ∗ _ () −  _ ∗ _ () − _ ∗ _ ()

(1)

114 115

In this equation the change in internal pyrethroid concentrations, Cin_pyr, in the daphnids over time is

116

described as a function of the uptake rate, kin_pyr, the excretion rate, kout_pyr, the biotransformation rate, km_pyr,

117

and the external pyrethroid concentration in the water, Cw_pyr. Phase I biotransformation is, in the case of

118

pyrethroids, believed mainly to be governed by P450 monooxygenases 24, though esterases may also play a

119

substantial role 24-26. As pyrethroids are very hydrophobic (log Kow = 6.94 at pH 7 for α-cypermethrin),

120

sorption to the daphnid exoskeleton could also be a process of quantitative significance. It is given in Figure

121

1 as Csorp_pyr, and the change over time of Csorp_pyr is proposed to be described with first order kinetics using a

122

sorption specific uptake and elimination rate constant, ksorp_pyr and kdesorp_pyr, respectively:

123 124

_ ( ) 

= _ ∗ _ () − _ ∗ _ ()

(2)

125 126

The toxicokinetics of the azoles are described with a simplification of Equation 1, only including an uptake

127

and elimination rate constant, kin_az and kout_az20. The elimination rate constant in this equation therefore

128

describes the sum of all biotransformation processes and efflux of the parent azole compound:

129 130

_ ( ) 

= _!" ∗  () −   ∗ _!" ()

(3)

131 5 ACS Paragon Plus Environment

Environmental Science & Technology

Page 6 of 22

132

To test the hypothesis that the synergy is caused by the effect of the azole on the biotransformation rate, it

133

was initially assumed that both pyrethroid uptake, kin_pyr, and excretion, kout_pyr, were independent of the

134

presence of the azole, and that the only effect of the azole was on km_pyr. As azoles bind to the catalytic site of

135

the P450 enzymes, thereby prohibiting binding of the pyrethroid for biotransformation, we assume

136

competitive inhibition of P450 enzymes by the azoles27. This means that the presence of azoles will decrease

137

the amount of active P450 enzymes with a fraction depending on the internal azole concentration. This

138

fraction is given by the parameter s. The parameter s can be defined by the ratio of the biotransformation rate

139

constant km_pyr with and without co-exposure to the azole under steady state conditions.

140 141

$%_ (&')

#=$

(4)

% ( &')

142 143

For a variable internal concentration of azoles, we expect s to vary according to the internal concentration of

144

the azole, cin_az, following a sigmoidal function. We here describe the relationship with a log-logistic two-

145

parameter model, where IC50 is the internal azole concentration inhibiting the biotransformation rate of the

146

pyrethroid by 50% and b is the slope parameter of the curve:

147

# = )*(&

) _

(5)

/,-. )/

148

We choose to use internal azole concentrations rather than scaled damage (Equation 7) to describe s, as it can

149

be measured experimentally. We recognise that the presence of the pyrethroid may also affect the activity of

150

P450 monooxygenases. However, as the pyrethroid acts as a substrate for the P450 enzymes rather than as an

151

inhibitor28, and in addition is expected to occur at much lower internal concentrations compared to the

152

azoles, we assume the quick catalytic biotransformation action will not significantly affect the total pool of

153

P450 catalytic sites available. Hence, we chose not to include the pyrethroid’s effect on P450 activity in the

154

presented model

155

The toxicodynamic part of the model describes the relation between internal pyrethroid concentrations and

156

observed mortality. All azole concentrations included in the studies of synergy are chosen not to affect

157

daphnid mortality (< EC10 2). Hence, mortality was assumed to depend on internal pyrethroid concentrations

158

alone. Toxicodynamic parameters for the azoles are, however, inserted in Figure 1, and given in Table 1, as

159

they have been determined in a previous publication20, and will be used in the combined TKTD-model

160

(Figure 1). Internal pyrethroid concentrations were related to mortality by including a damage stage,

161

assuming that the pyrethroid insecticide induces some undefined damage with a rate, kdam_pyr, proportional to

162

the internal pyrethroid concentration, and that the damage can be repaired by a rate, kdr_pyr, proportional to

163

the size of the damage.

6 ACS Paragon Plus Environment

Page 7 of 22

164

Environmental Science & Technology

0 ( ) 

= !_ ∗ _ () − _ ∗ 1 ())

(6)

165

This is analogue to how internal chemical concentrations depend on external concentrations over time. In

166

this case, however, we cannot measure damage directly. Pyrethroids inhibit the sodium channels of the

167

nerves 28, which will lead to a range of biochemical disruptions in the organism, which ultimately leads to

168

immobilisation and death. Because damage can rarely be measured directly, the authors of Jager et al. (2011)

169

introduced the concept of scaled damage, D*, which is proportional to the actual (but undefined) damage

170

level, and has the unit of an internal concentration21. This is done by dividing Equation 6 with the ratio of

171

damage accrual and damage repair, kdam_pyr/kdr_pyr, thereby getting:

172

0 ∗ ( ) 

= _ ∗ (_ () − 1 ∗ ())

(7)

173

The parameter kdr_pyr can be determined from the time course of survival of the test organisms. How damage

174

relates to survival can be determined in two ways, representing extreme cases: One is assuming stochastic

175

death above a certain damage threshold (the GUTS-SD model), the other is assuming that the organisms in

176

the trial die, when they have exceeded an individual threshold for damage (the GUTS-IT model). For

177

derivation, discussion and testing of the two assumptions we refer to: Jager et al. (2011) and Ashauer et al.

178

(2011, 2015 and 2016) 21,29-31. Here we present the equations used to link scaled damage to survival under the

179

assumption of stochastic death. For the individual threshold implementation and test, see SI B. For GUTS-

180

SD, hazard to the organism, Hpyr, takes place when the damage increases above a certain threshold defined

181

by zpyr. Above zpyr, hazard increases proportionally with the damage with a rate defined by the killing rate

182

kk_pyr:

183 184

2 ( ) 

= $_ ∗ 345(0, (1 ∗ () − 8 )

(8)

185 186

The survival probability as a function of time Spyr(t) is calculated from the hazard, adding the background

187

hazard (hb), derived from observations of control mortality:

188 189

9 () = : ;(2 ( )*