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“The dose makes the poison”. This principle assumes that once a chemical is cleared out of the organism (toxicokinetic recovery), it no longer has...
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Toxic mixtures in time – the sequence makes the poison Roman Ashauer, Isabel O'Connor, and Beate I. Escher Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b06163 • Publication Date (Web): 08 Feb 2017 Downloaded from http://pubs.acs.org on February 12, 2017

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Toxic mixtures in time – the sequence makes the poison

1 2

Roman Ashauer1,2*, Isabel O’Connor1, Beate I. Escher1,3,4

3 4 5 6 7 8 9 10 11 12

1

Department of Environmental Toxicology, Eawag - Swiss Federal Institute of Aquatic

Science and Technology, 8600 Dübendorf, Switzerland 2

Environment Department, University of York, Heslington, York YO10 5DD, United

Kingdom 3

Cell Toxicology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany

4

Environmental Toxicology, Center for Applied Geosciences, Eberhard Karls University

Tübingen, Germany

13 14 15 16

*Corresponding author:

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Phone: +44-(0)-1904-324052

18

Email: [email protected].

19 20 21

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Abstract: “The dose makes the poison.” This principle assumes that once a chemical is

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cleared out of the organism (toxicokinetic recovery), it no longer has any effect. However, it

24

overlooks the other process of re-establishing homeostasis, toxicodynamic recovery, which

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can be fast or slow depending on the chemical. Therefore, when organisms are exposed to

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two toxicants in sequence, the toxicity can differ if their order is reversed. We test this

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hypothesis with the freshwater crustacean Gammarus pulex and four toxicants that act on

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different targets (diazinon, propiconazole, 4,6-dinitro-o-cresol, 4-nitrobenzyl chloride). We

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found clearly different toxicity when the exposure order of two toxicants was reversed, while

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maintaining the same dose. Slow toxicodynamic recovery caused carry-over toxicity in

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subsequent exposures, thereby resulting in a sequence effect – but only when toxicodynamic

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recovery was slow relative to the interval between exposures. This suggests that carry-over

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toxicity is a useful proxy for organism fitness and that risk assessment methods should be

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revised as they currently could underestimate risk. We provide the first evidence that carry-

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over toxicity occurs amongst chemicals acting on different targets and when exposure is

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several days apart. It is therefore not only the dose that makes the poison but also the

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exposure sequence.

38 39 40

Keywords: Toxicology, dose, mixture toxicity, chemical safety assessment

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Introduction

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The paradigm “the dose makes the poison”, that is, that chemicals can be toxic or non-toxic

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depending on their dose, stems back to Paracelsus. Given that exposure to toxicants in the

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environment is often episodic and repeated 1, 2 and organisms are exposed to several toxicants

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at the same time or after each other, it is important to also take into consideration the

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temporal aspects of toxicity 3-5. The time required by an organism to recover from damage

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due to previous exposure events is defined as the organism recovery time 6, 7. Organism

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recovery results from toxicokinetic recovery, i.e. biotransformation, distribution and

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excretion of the toxicant, and toxicodynamic recovery, i.e. re-establishment of homeostasis.

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In previous studies 7-9, we developed toxicokinetic-toxicodynamic models using toxicants

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from different chemical classes and found that certain toxicants exhibit slow toxicodynamic

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recovery in Gammarus pulex. The modelling suggested that even after the substance was

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eliminated from the organism, more time was necessary to re-establish homeostasis. The

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conventional wisdom, as reflected by most current chemical risk assessment practices10, is

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that when a chemical is eliminated from an organism (toxicokinetic recovery), it no longer

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exerts an effect. This overlooks a subtler process of toxicodynamic recovery, which is

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determined by the mode of toxic action. Both aspects of recovery are related to the

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characteristics of the substance 3, 9, where toxicokinetic parameters are related to

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hydrophobicity 11 and toxicodynamic parameters to mechanism of toxicity 9 and hence

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molecular structure 12.

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What if subsequent exposure events occur in intervals that are sufficient for

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toxicokinetic recovery but not for toxicodynamic recovery? We already know that

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toxicodynamic build-up of damage can lead to carry-over toxicity from subsequent pulses of

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the same toxicant 7 and from subsequent pulses of different toxicants that act on the same

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target 6. Therefore toxicokinetic-toxicodynamic models were developed recently, that extend

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the classical dose paradigm by complementing internal concentrations with scaled damage as

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the dose metric 9, 13. These early insights into toxicokinetics and toxicodynamics led to the

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sequence effect hypothesis: when organisms are exposed to two toxicants in sequence, the

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toxic effects may differ if that sequence is reversed 6.

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To date this hypothesis has only been tested once, with carbaryl and chlorpyrifos in

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G.pulex, two toxicants that both inhibit the same enzyme. The empirical evidence supported

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it but it could be explained by a build-up of inhibited enzyme 6 and therefore did not

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challenge the current paradigm. More recently we gained a much deeper understanding of

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toxicokinetics 14 and toxicodynamics 9 of a diverse range of toxicants in G. pulex, in ACS Paragon3Plus Environment

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particular how toxicokinetic and toxicodynamic recovery times differ among chemicals.

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Based on this new knowledge we now propose a significant broadening of the sequence

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effect hypothesis: it also applies to toxicants that act on different targets and via different

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mechanisms. Would carry-over toxicity also occur after subsequent pulses of different

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toxicants that act on different targets with different toxic modes of action? This is an

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important question because sequential exposure to toxicants is the norm, but the current risk

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assessment paradigm pays little attention to possible interactions of different toxicants over

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time. We think that substances acting on different targets can cause carry-over toxicity, which

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we view as a proxy for reduced organism fitness. If the sequence effect occurs for a wide

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range of toxicants with different mechanisms of toxicity, then we are potentially severely

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underestimating risk from chemicals.

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The sequence effect hypothesis can be tested experimentally because it predicts that

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the toxicity of two subsequent pulsed exposure events is more pronounced if we first expose

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organisms to the substance with a slower recovery time, followed by exposure to the

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substance with a faster recovery time. Conversely, exposure to the substance with a shorter

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recovery time followed by the substance with a longer recovery time will result in less carry-

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over toxicity and have a weaker impact on the organism.

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We tested the sequence effect hypothesis in the amphipod crustacean Gammarus

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pulex using four model toxicants that differ in their toxic mode of action. G.pulex are

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particularly suitable for this type of study because of their relatively long life-span and

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sufficiently large size to measure toxicant body residues. Diazinon [DIAZ] is an insecticide

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that inhibits acetylcholinesterase 15, propiconazole [PCZ] is a fungicide that likely acts as a

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baseline toxicant in G. pulex 16, 4,6-dinitro-o-cresol [DNOC] is an uncoupler of oxidative

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phosphorylation 17, and 4-nitrobenzyl chloride [NBCl] is a reactive toxicant that forms

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covalent bonds with proteins and other biomolecules 18. Therefore, each toxicant acts

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according to a different mode of action, and their mixture toxicity should be explained by the

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model of independent action 19, 20. Previously, we determined the times required for 95%

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elimination of the toxicologically relevant metabolites and for 95% recovery of

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toxicodynamic damage in G. pulex 7, 8, 14, 21 for each compound individually (Table 1). All

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four substances have toxicokinetic recovery times of less than three days, whereas the

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toxicodynamic recovery times are short for PCZ (< 3d) and DNOC (29000d). Based on this knowledge, we designed four experiments,

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each involving two toxic experimental groups and a control group. The two toxic

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experimental groups employed two subsequent pulsed exposures to two different toxicants ACS Paragon4Plus Environment

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(Fig. 1). The two toxic experimental groups in each experiment differed only in the sequence

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of the toxicants applied.

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We chose a pulse interval that was long enough to allow toxicokinetic recovery (such

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that no residual chemical remained in the body at the onset of the second pulse (Supporting

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Information Fig. S1–S4)), but too short to allow complete toxicodynamic recovery after

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DIAZ and NBCl exposure. Hence, we expected carry-over toxicity for those experimental

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groups in which the first applied pulse was a toxicant with slow toxicodynamic recovery

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(DIAZ or NBCl) but not in the groups in which the first applied pulse was a toxicant with fast

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toxicodynamic recovery (PCZ and DNOC). Consequently, we expected to observe a

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sequence effect, i.e. differences in survival at the end of the experiment, in the first, second

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and third experiments (Fig. 1). In the fourth experiment, we expected both experimental

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groups to exhibit carry-over toxicity due to slow toxicodynamic recovery of both toxicants,

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and therefore did not expect the sequence effect.

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We also carried out toxicokinetic-toxicodynamic mixture toxicity modelling. This

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dynamic modelling expands the applicability domain of mixture toxicity models to pulsed or

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time-variable exposures. The model structure was derived by assuming that the toxicants act

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on different targets and therefore independently. The model parameters were taken from

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previous studies. This enables us to compare model forecasts with new experimental data

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from this study to assess how good the model predictions are.

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Materials and methods

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Experiments

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Experimental procedures closely followed established protocols 6, 7, 9. Adult G. pulex were

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collected in a headwater stream in the Itziker Ried, Switzerland near Zurich (E 702150, N

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2360850) and acclimatized to 13°C (Table S2). G. pulex were fed horse-chestnut leaf discs

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conditioned with Cladiosporum herbarum22 fungi. Experiments were performed in pyrex

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beakers filled with 500 mL pre-aerated artificial pond water22 and at 13°C under a 12:12 h

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light:dark cycle. Each beaker initially contained 10 G. pulex and the total number of test

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organisms in each toxic experimental group is given in Tables S6, S 8, S10 and S12. Test

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organisms were a natural mixture ratio of adult males and females and the numbers used in

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each experiment varied depending on the availability of organisms at the Itziker Ried. Leaf

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discs were added for ad libitum feeding, ranging from three to five leaf discs per beaker

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depending on the number of alive G.pulex. Live organisms were counted by gently prodding

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with a spatula and observing their resulting movement (Tables S6, S8, S10, and S12). Each ACS Paragon5Plus Environment

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experiment included also a control group which was not treated with toxicants. No blinding

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was employed. Any neonates that were released by females during the experiments were not

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counted. Dead individuals were removed.

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The test concentrations were chosen based on previous toxicity studies7-9 to achieve partial

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mortality after the first pulse. The test chemicals were dosed as a mixture of 14C-labelled and

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unlabelled substances to allow quantification of exposure concentrations. Radiolabelled

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DIAZ and PCZ were supplied by the Institute of Isotopes (Budapest, Hungary). Radiolabelled

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DNOC and NBCl were sourced from American Radiolabeled Chemicals (St. Louis, MO,

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USA). Unlabelled chemicals were of analytical grade and supplied by Sigma-Aldrich (Buchs,

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Switzerland). Dosing stocks of labelled and unlabelled test compounds were prepared in

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acetone as solvent. Each beaker was dosed by pipetting the required volume of stock into the

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medium, followed by gentle stirring with a glass rod. Concentrations of the solvent acetone in

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the test medium were 0.1% or lower (Table S4), i.e. three orders of magnitude below

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concentrations toxic to crustaceans23. Actual exposure concentrations were measured by

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liquid scintillation counting (Tri-Carb 2200CA, Packard, USA) using 1 mL aliquots of the

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medium (Tables S5, S7, S9, and S11), correcting for efficiency using internal standards,

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subtracting background activity using control medium samples and converting radioactivity

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to molar concentrations by using the known mixture ratio of labelled and unlabelled

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substance. Immediately before and at the end of an exposure pulse, organisms were separated

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from the medium by using a tea strainer, the test medium was discarded, and the organisms

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were rinsed with clean water and then placed in beakers with fresh artificial pond water and

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fresh leaf discs. The pH, conductivity and dissolved oxygen concentration of the medium

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were monitored (Table S4).

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Our experimental design aimed to maximise the number of test organisms that we could

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handle so that we would maximise experimental power. However no formal power analysis

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was carried out and the experimental design was constrained by the practicalities of the

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experiment.

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Statistical Analysis

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Statistical testing was performed using GraphPad Prism (v6.03, GraphPad Software Inc., La

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Jolla, CA, USA). To detect the sequence effect, we compared the numbers of dead and live

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individuals at the end of the experimental groups using contingency table analysis, more

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specifically, Fisher’s exact test (Fig. 1B, D, F, H, J, L, N, P). For evaluation of carry-over

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toxicity, we compared the survival curves after the first and second pulses for each

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experimental group using the Gehan-Breslow-Wilcoxon test (Fig. 2). These survival curves

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were constructed by recording the absolute number of alive G.pulex at the start of a pulse

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(this could be the first or the second pulse in an experimental group) as the initial number of

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organisms and then following each group until either the end of the experiment or the onset

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of the second pulse. In other words: we compared survival curves during and following the

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pulsed exposure to the same toxicant in different groups of the same experiment, once as the

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first pulse and again as the second pulse. If there was a difference, then we concluded that

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carry-over toxicity had occurred because we used the same dose for a single toxicant in both

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of its exposure pulses within each experiment. The only difference was that the order was

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reversed in the second experimental group. All four experiments were analysed following the

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same method.

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Toxicokinetic-toxicodynamic modelling

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Toxicokinetics: modelling the internal dose

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The toxicokinetic model was calibrated previously using time series data of measured internal

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concentrations in pulsed exposure experiments7-9,

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concentrations in G. pulex was simulated using the previously established toxicokinetic

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models and parameters from those studies:

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195

 , ( )   ,$ ( ) 

14

. Here, the time-course of internal

=  () × , −  , () ×  , − ∑"  , () × ! ," #

(1)

=  , () × ! ," −  ," () ×  ,"

(2),

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where Cinternal,p(t) is the concentration of the parent compound in the organism [nmol/kgw.w.],

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Cw(t) is the concentration of the parent compound in water [nmol/L], Cinternal,j(t) is the

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concentration of biotransformation product j in the organism [nmol/kgw.w.], kin,p is the uptake

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clearance coefficient [L/(kgw.w.×d)], kout,p is the elimination rate constant of the parent

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compound [1/d], kmet,j is the first-order biotransformation rate constant for the formation of

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metabolite j [1/d], and kout,j is the elimination rate constant of the biotransformation product j.

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Internal concentrations of propiconazole (PCZ) and 4,6-dinitro-o-cresol (DNOC) were

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directly used as inputs in the toxicodynamic model; by contrast, for diazinon (Diaz) and 4-

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nitrobenzyl chloride (NBCl), biotransformation products were the driving variables for the

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toxicodynamic model (see Tables 1 & S1, also for model parameter values). In the case of 4-

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nitrobenzyl chloride, the metabolites are unidentified and considered to act as baseline

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toxicants9. For diazinon, metabolite 1 is diazoxon, which is the toxicologically relevant

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molecule (Table 1). For propiconazole and 4,6-dinitro-o-cresol, we included only the parent

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compounds in the model, because biotransformation in G.pulex is negligible8, 14.

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Toxicodynamics: modelling survival

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We used the two limit cases stochastic death (SD) and individual tolerance (IT) of the

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General Unified Threshold model of Survival (GUTS)

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concentrations and scaled damage

9, 13

13

with explicitly modelled internal

as the dose metric. These models have been described

9, 13

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previously

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parameters are given in Table S2. Scaled damage is a proxy for the toxicodynamic state of

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the organism, is generally inferred by model fitting 9, 13 and it is calculated as follows:

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dDscaled (t ) = k r × (Csum tox, organism (t ) − Dscaled (t ) ) dt

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where Dscaled(t) is the time course of the scaled damage [nmol/kgw.w.], t is time [d], Csum tox,

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organism(t)

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active chemicals in the organism [nmol/kgw.w.], and kr is the damage recovery rate constant

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[1/d]. The damage recovery rate constant captures the time course of toxicodynamics, and we

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use two different parameters, kr SD and kr IT, for the two limit cases of GUTS.

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Effects of scaled damage on survival in GUTS-SD

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The hazard rate is the probability of an organism dying at a given point in time and is

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calculated as follows8, 13, 24:

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%( ) 

, but we repeat their description here for clarity. The toxicodynamic model

(3),

is the time course of the sums of the internal concentrations of the toxicologically

= & × '() (*+, () − -, 0) + ℎ_2345367

(4),

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where dH(t)/dt is the hazard rate [1/d], kk is the killing rate constant [kgw.w./(nmol×d)],

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Dscaled(t) is the time course of the scaled damage [nmol/kgw.w.], t is time [d], z is the threshold

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[nmol/kgw.w.], and h_controls is the background hazard rate (control mortality rate, assumed

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to be constant during the experiment, see Table S3) [1/d]. The survival probability, i.e. the

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probability of an individual surviving until time t, is given as follows:

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S (t ) = e − H ( t )

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where S(t) is the survival probability [unitless].

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Effects of scaled damage on survival in GUTS-IT

(5),

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We assume a log-logistic distribution of the threshold in the study population (individual

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tolerance)8, 13. Then, the cumulative log-logistic distribution of the tolerance threshold in the

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study population, which changes over time as individuals die, is calculated as follows:

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F (t ) =

240

where F(t) is the cumulative log-logistic distribution of the tolerance threshold over time

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[unitless], Dscaled(t) is the time course of the scaled damage [nmol/kgw.w.], t is time [d], τ is

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time [d], α is the median of the distribution [nmol/kgw.w.], and β is the shape parameter of the

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distribution [unitless]. Under the assumption of individual tolerance, the survival probability

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is then given as follows 8, 13:

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S (t ) = (1 − F (t ) ) × e − h _ controls×t

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where S(t) is the survival probability [unitless], and h_controls is the background hazard rate,

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i.e. the control mortality rate, which is assumed to be constant over time [1/d].

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Mixture toxicity model

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Mixture toxicity model assumptions

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Mixture toxicity models generally assume either that the toxicants act on the same target or

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on different targets 25, 26. Independent action is the model for the second assumption, which

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applies to our combinations of toxicants. Traditional mixture toxicity models are designed for

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exposure to multiple toxicants at the same time and for constant concentrations. We expand

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the applicability domain of the mixture toxicity modelling to fluctuating, pulsed or sequential

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exposures using a dynamic model, GUTS 9, 13. As we assume independent action for

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compounds with different modes of action, we sum the hazards from the different toxicants 9,

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1  max Dscaled (τ )   1 +  0< τ < t   α  

(6),

−β

(7),

27

, which is equivalent to multiplying the survival probabilities.

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Mixture toxicity model equation

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The same mixture toxicity model is used for GUTS-SD and GUTS-IT:

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8!9 () = : ; ∑ %

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which is equivalent to

263

8!9 () = ∏ 8 ()

(8),

(9)

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Smix (t) is the survival probability under toxic stress from i toxicants. In equation (8), we sum

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the hazards, which is equivalent to multiplying the survival probabilities (eq. 9) and

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corresponds to the assumption that toxicants act on different target sites or via different

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toxicity pathways.

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Treatment of NBCl metabolites in the toxicity model

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In the case of NBCl, the internal concentration of the parent compound was modelled as a

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reactive toxicant, whereas the sum of the internal concentrations of the metabolites was

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modelled as a baseline toxicant, as in a previous study 9. We used the toxicodynamic

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parameters of 1,2,3-trichlorobenzene, a well-known prototype baseline toxicant, to simulate

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the effects of the metabolites assumed to act via baseline toxicity 9. As we assumed the two

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modes of toxic action are independent, we modelled separate scaled damages for each using

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equations (3) to (7) and then multiplied the resulting survival probabilities:

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8,!= () = 8=+ 9, ? () × 8+,@, 9, ? () × : ;A_, +×

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Results and discussion

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Mixture toxicity model prediction

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(10)

We compared the survival prediction determined by the mixture toxicity models with

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the observed survival (Fig. S1 to S4). The agreement or disagreement between the predicted

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and observed survival curves after the first pulses is a measure of the inter-experimental

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variability. In half of the cases, the predicted survival after the first pulse did not match the

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observed survival very well (Fig. S1G, S3G, S4G, H). Only in experiment two was survival

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after the first pulses predicted accurately in both groups (Fig. S2G, H); in this experiment,

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survival after the second pulses was also predicted accurately (Fig. S2G, H). The

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disagreement between the predicted and observed survival after the second pulse in

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experiment one (Diaz, Fig. S1H) or three (NBCl, Fig. S3H) is consistent with the

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disagreement seen after the first pulse and is not necessarily an indicator that the mixture

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toxicity models fail to predict full recovery after PCZ (experiment one) or DNOC

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(experiment three). In experiment four the model predicted that all organisms would die

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almost immediately following exposure to NBCl, thus assessment of the predicted mortality

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after DIAZ in this experiment is difficult.

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We also compared the mixture toxicity model prediction with the observed survival curve in the five experimental halves where we found evidence of carry-over toxicity

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(survival after PCZ, DNOC, DNOC, DIAZ and NBCl in experiments one, two, three, four

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and four, respectively; Fig. 2 A, C, E, G, H) and observed in four out of five cases that the

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observed mortality following the respective second pulses exceeded the predicted mortality

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(Fig. S6 to S10). From that observation we conclude that the toxicants interact, even if

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exposure is days apart, and that the observed mortality following the second pulses is more

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than just continued mortality from the other toxicant of the respective first pulses in all the

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‘sequence’ treatments and the ‘inverse sequence’ treatment of experiment four (i.e. those with

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carry-over toxicity).

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We need more studies of this type to better understand how the model performs for

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different substances and classes of chemicals and where the limits of its predictive

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capabilities are28. Uncertainty arising from calibration experiments themselves can be

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included in the model predictions28 to better understand inter-experimental variability.

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Interesting questions arise also from the temporal mismatch in the onset of predicted and

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observed mortality (particularly NBCl, see Fig. S3 & S4) as well as our choice of toxic

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molecules (Table 1).

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In summary, we found no evidence that the mixture models fail to predict carry-over

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toxicity or the sequence effect. However, we did find large inter-experimental variability

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evidenced by a disagreement between the predicted and observed mortalities. Since the

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models were calibrated based on previously published experiments, this disagreement only

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indicates that the variability in survival from one pulsed exposure experiment to the next is

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too large to assess the performance of the model prediction. However, the experimental

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evidence depicted in Figures S1 to S4 fully supports the sequence effect hypothesis.

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Conclusions drawn from comparisons within an experiment, such as in this study, are not

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subject to the limitations of inter-experimental variability. Variability between experiments

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could possibly be reduced by using laboratory-cultured organisms.

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Furthermore we identified a knowledge gap in the suite of toxicokinetic-

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toxicodynamic mixture toxicity models available. We provide equations for summing up

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hazard rates for toxicants which are assumed to act independently. However it is also

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conceivable to model the combined effects of toxicants that act on different targets by

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summing up the state variable scaled damage. However there are two challenges. First,

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summing up damage has been previously used to model combined effects of toxicants that act

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on the same target6, 29. This raises a question: How can we reconcile that previous work with

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any future use of the same approach for toxicants acting on different targets? The task of

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reconciling this previous work with newer studies is further complicated by subtle issues and

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inconsistencies in the models used in the older studies13. Second, the model equations for

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summing up scaled damage in GUTS-SD are straightforward to derive, however the model

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equations for summing up scaled damage in GUTS-IT are unknown and not trivial to derive.

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The sequence effect

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We observed the sequence effect in experiments one, two and three (Fig. 1). Contingency

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table analysis of the number of dead and alive individuals at the end of each experimental

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group indicated a sequence effect for the combinations of DIAZ and PCZ (p