Time-Dependent Effects in Algae for Chemicals with Different

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Time-dependent effects in algae for chemicals with different adverse outcome pathways - A novel approach Carolina Vogs, and Rolf Altenburger Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00529 • Publication Date (Web): 05 May 2016 Downloaded from http://pubs.acs.org on May 7, 2016

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Environmental Science & Technology

Time-dependent effects in algae for chemicals with different adverse outcome pathways A novel approach Carolina Vogs∗,†,‡ and Rolf Altenburger† 1

†Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research, Leipzig, Germany ‡Current address: Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden E-mail: [email protected] Phone: +46 (0)8 524 874 43 Abstract

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Chemicals affect unicellular algae as a result of toxicokinetic and toxicodynamic

4

processes.

The internal concentration of chemicals in algae cells typically reaches

5

equilibrium within minutes, while damage cumulatively increases over hours. The

6

time-gap between steady-state of internal exposure and damage development is thus

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suspected to span up to hours mainly due to toxicodynamic processes. The quantification

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of rate-limited toxicodynamic processes, aggregated as progressed effect from an initiating

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molecular event through biological key events towards the adverse outcome on algae

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growth inhibition, might discriminate between different adverse outcome pathways

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(AOPs). To support our hypothesis, six chemicals were selected according to different

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physicochemical properties and three distinctly dissimilar AOPs. The time-courses

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of internal concentrations were linked to the observed affected Scenedesmus vacuolatus

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ACS Paragon Plus Environment


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growth using toxicokinetic-toxicodynamic modeling. Effects on cell growth were explained

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by effect progression and not by the time to reach internal equilibrium concentration

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were reached. Effect progression rates ranged over six orders of magnitude for all

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chemicals, but varied for less than one order of magnitude within similar AOP: photosystem

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II inhibitors > reactive chemicals > lipid biosynthesis inhibitors meaning that inhibitors

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of photosystem II progress an effect towards algae growth fastest compared to reactive

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chemicals and inhibitors of lipid biosynthesis.

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keywords: Toxicokinetic-toxicodynamic modeling, hazard assessment, phytotoxicity

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Introduction

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The occurrence of agricultural and industrial chemicals in the environment may potentially

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impact aquatic organisms 1 . For estimating and assessing the likelihood of effects on aquatic

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organisms under chemical exposure, mechanistic effect models are discussed to face two main

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challenges within current risk assessment requirements: 1) the estimation of combined effects

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on aquatic organisms exposed to transient concentrations of chemical mixtures and 2) the

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linkage of events in an effect chain which is initiated by a molecular-chemical interaction and

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triggers subsequent key events across different biological scales leading to an adverse outcome

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at the organism or population level 2–4 . Mechanistic effect models describe toxicokinetic and

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toxicodynamic processes over time which have been discussed for their ability to extrapolate

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the adverse outcomes between different chemicals, species, exposure conditions and exposure

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durations 5–8 .

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Toxicokinetic-toxicodynamic (TKTD) models thereby link the accumulated concentration

35

of a chemical in organism to the temporal dynamics of the adverse biological effects. Toxicokinetic

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processes are composed of the summed mass fluxes of chemical uptake, distribution, biotransformation,

37

and elimination that modify the time-course of the internal concentration 8–10 . The toxicodynamic

38

processes encompass the underlying effect mechanisms that lead to significant perturbations

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across different biological levels. In modeling, the temporal dynamics of toxicodynamics are 2


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typically described by the sum of the overall damage injury and damage recovery 8–10 . So far,

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TKTD studies have been performed mainly for fish and invertebrates, where the time-course

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of toxicity was examined by integrating the information of overlapping toxicokinetic and

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toxicodynamic processes 8,10–14 . In those studies, fish or invertebrate toxicity was interpreted

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to be dominated by either the rate-limiting processes of a chemical’s overall elimination or

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the chemical’s degree of binding reversibility at a biological target site as well as the ability

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to recover 8,9,15 . However, effect progression as the rate-limited toxicodynamic processes

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from the initiating molecular event across key events towards an adverse outcome at the

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individual level has yet not been studied quantitatively. This may be due to the overlap of

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the internal exposure changes over time and the emerging damage 16 . Figure 1 illustrates the

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toxicodynamic process of an effect progression across different biological effect levels towards

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an adverse outcome at the phenotype level after a certain amount of molecules reached the

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biological target site. Process parameters and system variables of the TKTD model have

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been included in Figure 1.

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The studies of Vogs et al. 17 and Vogs et al. 18 provided first evidence that the use of

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algae cells is suitable to quantify the time-limited toxicodynamic step as effect progression

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rates almost independent of the overlap of internal concentration changes. Toxicity in the

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unicellular green algae Scenedesmus vacuolatus (S. vacuolatus) has been shown to cumulatively

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increase over several hours for different chemicals 17,19,20 . By contrast, the internal concentration

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in S. vacuolatus is assumed to reach equilibrium within minutes simply due to hydrophobicity-dependent

60

partitioning processes 18,21,22 . Thus, we assumed that the internal concentration change over

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time overlap only for short time with the damage development in S. vacuolatus. It can

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therefore not explain the toxicity increase observable over hours. Second, the observed

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time-gap of several hours between the time points of reaching a steady-state internal exposure

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and the progression of effects in algae cells might be explainable by a rate-limiting toxicodynamic

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step. Thus, rates of effect progression may be quantifiable by using mechanistic indirect effect

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models as depicted in Figure 1 that describe the adverse outcome at the organism level as

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proposed in pharmacological research 17,23 . A conceptual scheme for different chemical classes

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on various adverse outcome pathways (AOP) was proposed by Ankley et al. 24 .

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The adverse outcome pathway is a theoretical framework that has been suggested to

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conceptualize a chain of biological events in an organism due to chemical exposure. The event

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chain is initiated by the molecular chemical-target interaction that triggers a cascade of key

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events on multiple biological levels progressing towards an adverse outcome 24 . Various AOPs

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have been devised based on comprehensive information of critical toxicological endpoints

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observable for multiple biological levels 24 . However, it remains challenging to fill the compartments

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with other than mainly qualitative data for critical toxicological effects. Apart from that,

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AOPs are theoretical constructs that do not automatically relate exposure concentration

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quantitatively to responses that are progressed through multiple biological levels over time.

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Complementary, a TKTD model may simplify an effect chain after chemical-target interaction

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and therefore enables the quantification of time-limiting steps by linking the internal concentration

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change over time with the effect progression towards an adverse outcome at the organism level

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(Figure 1). Simeoni et al. 23 , for instance, applied an indirect pharmacokinetic-pharmacodynamic

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response model that described a reduced tumor growth over time after the administration of

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various anticancer drugs. Similarly, TKTD modeling of concentration-dependent response

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changes over time might be able to characterize the rate-limiting toxicodynamic step in

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adverse outcome on algae growth and may thus help to discriminate different AOPs (Figure

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1).

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The objective of the present study was to test our proposed model for its capability to

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capture and differentiate between the observable adverse outcome on algae growth for a range

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of chemicals with different toxicokinetic and toxicodynamic properties. How toxicokinetic

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and toxicodynamic processes gear up to the overall time-course of toxicity of chemicals to

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S. vacuolatus growth has not been investigated yet. To this end, the experiments were

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set up for six model chemicals which represent (i) two hydrophobicity groups (log KOW < 3

93

and log KOW > 4) and (ii) three groups of different AOPs (inhibition of photosynthesis, lipid

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biosynthesis inhibition, oxidative stress). Dynamics of estimated internal concentrations were

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linked to the perturbed growth of S. vacuolatus through TKTD modeling for estimating and

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discriminating the different process parameters.

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Methods

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Concentration-time-response relationship determined for organic chemicals

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Algae growth assays were performed using synchronized cultures of S. vacuolatus (strain

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211-215 SAG, Göttingen, Germany). Algae cultivation was carried out according to Altenburger

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et al. 25 . A homogeneous algae size distribution of cultured autospore suspensions was used

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to measure effects on cell volume increase during the cell cycle according to a protocol

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published previously 17 . Cell volume of the synchronized algae suspension was measured

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for six concentrations per chemical every two hours by using an electronic particle analyzer

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(CASYII, Schärfe Systems, Reutlingen, Germany). Please note that the chemicals were

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added after six or eight hours of undisturbed algae growth at t6 or t8 to the algae suspension.

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The effects of six model chemicals (irgarol, isoproturon, triclosan, metazachlor, paraquat,

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and n-phenyl-2-naphthylamine (PNA)) on algae growth were then investigated over 16 h or

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18 h. Characteristics and relevant physicochemical properties of the chemicals are listed in

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the Table S1, Supporting Information. Concentration-effect relationships for the disturbed

111

algae growth were determined by fitting a four-parametric logistic model to the inhibited

112

cell volume for each time point (OriginLab, OriginPro 8.5.1 G). More detailed descriptions

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of algae cultivation, experiments for determining effects on algae growth over one-generation

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cycle and the established concentration- time-response relationships can be found in the

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Supporting Information, section Methods.

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Toxicokinetic and toxicodynamic modeling

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Toxicokinetic and toxicodynamic processes were formulated by a TKTD model in order

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to describe and simulate the chemical impacted S. vacuolatus growth 17 . The conceptual

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scheme of the toxicokinetic-toxicodynamic processes is depicted in Figure 1. The TKTD

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model consisted of a system of ordinary differential equations comprising a total of eleven

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parameters. The unperturbed dynamic of algae growth was mathematically expressed for

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three growth phases, namely an exponential growth phase (≈ 0 – 8 h), followed by a linear

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growth phase (≈ 8 – 16 h), which subsequently passes into a limited growth phase at the end

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of the 24 h generation cell cycle. According to Altenburger et al. 26 , the unperturbed growth

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pattern is mathematically expressed as: dVControl (t) = dt

µE × VControl (t) VControl (t) × 1 − µC × ψ ψ1 KCrit 1 + µµEL × VControl (t)

(1)

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where the parameter µE [h−1 ] represents the exponential growth rate, µL [fL h−1 ] is the

127

linear growth rate and µC [fL h−1 ] is the cell-clock rate. The parameter Ψ [-] forces the switch

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from exponential to linear growth and KCrit [fL] is the critical size for a commitment point

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for cell division. VO [fL] represents the initial cell volume of the autospore cells 26 .

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Furthermore, a simple one-compartment toxicokinetic model with a first-order kinetic

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was applied to simulate the internal effect concentration in algae cells for the respective

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exposure concentration used. The time-course of the internal concentration in the whole

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body Cint (t) [µmol LBiovolume −1 ] is written as: dCint (t) = kin × C(t) − kout × Cint (t) dt

(2)

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where C (t) [µmol L−1 ] is the ambient concentration over time. The parameters kin [h−1 ]

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and kout [h−1 ] represent the uptake rate constant and the overall elimination rate constant

136

of the chemical, respectively. Kinetic rate constants for the six chemicals in S. vacuolatus

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have been determined by Vogs et al. 18 and were implemented in the TKTD model. Vogs

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et al. 18 estimated uptake and overall elimination rates for all six chemicals by fitting a

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toxicokinetic model (according to Equation 2, parameter values given in Table 1) to the

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observed concentration depletions in the ambient exposure medium as a consequence of the

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accumulated amount by a sufficient high algae biomass.

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Finally, a pharmacodynamic model, developed for analyzing the drug effect on cancer

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cell growth, was adapted and modified to describe the affected algae cell growth 17,23 . To this

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end, the simulated internal effect concentration Cint (t) was linked to a three compartment

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model simplifying the progressive degrees of damage over time. The system variable damage

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describes the fraction of how much the cell volume is reduced as a consequence of the

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progressing effect between the different effect compartments 23 . Damage was initiated by

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conceptualizing a molecular chemical-target interaction in a first damage stage D1 (t) [fL],

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if the exposure concentration exceeded a certain no-effect concentration (NEC [µmol L−1 ])

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(Equation 3, lower part). Otherwise, the growth of algae remained unperturbed (C (t) < NEC )

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and can be mathematically expressed according to Equation 1 (symbolised by VControl in

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Equation 3, upper part):    VControl (t)

for C(t) ≤ N EC D1 (t) =  dt  VControl (t) − (kI × Cint (t) × D1 (t) − kR × D1 (t)) for C(t) > N EC

(3)

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where kI [LBiovolume µmol−1 h−1 ] is the chemical injury rate and kR [h−1 ] represents the

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repair/recovery rate. The lower part of Equation 3 represents the index of chemicalÂťs

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efficacy that describes the chemical interaction with the biological target as a second-order

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kinetic proportional to CInt (t)*D1 (t) 23 . Furthermore, including repair/recovery mechanism

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in Equation 3 allows for variability in the degree of binding reversibility at a molecular

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target depending on the chemical that becomes especially crucial for sequential exposure 9 .

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Please notice that the no-effect concentration is here related to the external concentrations.

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However, the threshold value can be calculated to represent an internal concentration according

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to Vogs et al. 18 which is a relevant dose metric that considers variability in toxicokinetics.

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Damage was further assumed to be progressed across different levels of biological organization

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over time abstracted by an effect progression rate constant τ [h−1 ]. The second and third

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compartments represented further progressive degrees of damage on higher effect response

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levels such as the physiological and phenotypical response level, respectively (Figure 1). This

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degree of damage in the second and third compartments were quantified as:

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D2 (t) = (kI × Cint (t) × D1 (t) − kR × D1 (t)) − D2 (t) × τ dt

(4)

D3 (t) = τ × (D2 (t) − D3 (t)) dt

(5)

The final cell volume V (t) is the sum of the damage fractions

V (t) = V0 + D1 (t) + D2 (t) + D3 (t)

(6)

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The toxicodynamic model used here describes the apical effect development by simplifying

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the adverse outcome pathway. However, toxicodynamic processes are neglected so far which

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present for instance unequal time-limited progression steps between the effect scales, the

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nonlinear link between two different effect scales or feedback mechanisms. Model calibration

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and parameter estimations for toxicodynamic processes were conducted by a global numerical

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optimization technique. See supplemental information for details.

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Results

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Unperturbed algae growth

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The objective of this work was to study the effect of different chemicals on algae growth

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compared with an unperturbed algae growth. In a first step, we analyzed the pattern

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of unperturbed algae growth for two negative controls and two DMSO treated controls

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per experiment. 82% of 51 grouped measured cell volumes of negative controls did not

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significantly differ from the co-solvent DMSO treated controls per time point (see details in

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Supplemental Information). Therefore, all data were pooled into one control group per

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experiment consistent of four to 16 cell volume measurements per time point, depicted

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in Figure 2. The growth model for unperturbed algae growth (Equation 1) fitted the

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pooled control data very well as indicated by a mean absolute error (MAE ) < 21.63 fL and

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R2 > 99.23 %. The estimated growth rates for each experiment are listed in Table 1. Inverse

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modeling led to the average rate constants for exponential growth µE of 0.235 ± 0.016 h−1 , for

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the linear growth µL of 78.24 ± 36.22 fL h−1 and for the limited growth µC of 0.015 ± 0.005 fL h−1 for

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all independent six experiments, while at the same time the parameters KCrit and Ψ were

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fixed to 80 fL and 20, respectively.

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The mathematical simplification of cellular algae growth mechanisms provided an interpretation

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tool, which was used to set-up the exposure regime. Algae growth was slower in the first phase

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of exponential growth than in the second phase of linear growth according to the estimated

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kinetic rate constants as previously shown by Altenburger et al. 26 and Vogs et al. 17 . In

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order to gain a good time resolution for effect observations of cell volume changes, chemical

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exposure started after unperturbed algae growth of six (t6 ) or eight hours (t8 just for PNA).

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Around that time point, the exponential growth switches into the linear growth without

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having already exceeded the critical cell size for cell division commitment. By using this

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exposure design for the first time, we aimed to detect earliest and most sensitive responses

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of the chemical exposure on algae growth within the linear growth phase. 9



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Perturbed algae growth pattern in dependence of exposure concentration

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and time

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Algae growth assays were performed for studying the chemical concentration-dependent

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responses over time. For that purpose, six concentrations per chemical were chosen based on

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range-finding experiments by using the modified exposure regime in a first step. We observed

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effects on algae growth in a concentration-dependent relationship at t14 . Concentration-response

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curves as a result of the range-finding experiments are depicted in the Supplemental Information

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(Figure S1, Table S2). Metazachlor did not impact growth by more than 50% in any

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experiment independent of whether the exposure started at t0 (data not shown) or at t6 .

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Furthermore, the range-finding experiments demonstrated that the effect concentrations of

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chemicals of lower hydrophobicity (isoproturon log KOW = 2.87, metazachlor log KOW = 2.13,

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paraquat log KOW = -2.71) were one order of magnitude higher compared with the effect

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concentrations of the chemicals of moderate hydrophobicity (irgarol log KOW = 4.07, triclosan

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log KOW = 4.76, PNA log KOW = 4.47) (Table S2 and Table S9). Exposure concentrations

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that elicit similar inhibition levels of < 5%, 20%, 40%, 60%, 80% and > 95% on algae cell

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volume at t14 for irgarol, isoproturon, triclosan, paraquat, PNA and on cell number at t24 for

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metazachlor (Figure S1) were used in the subsequent algae growth assay for testing damage

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development in algae growth.

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We observed slightly different patterns of perturbed algae growth for the chemicals used

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(raw data Table S3 - S8, Figure 2) compared to the range-findings that might result from

220

several sources of variance. First, variance between the cell volume measurements per

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observation time point were shown to be low and unperturbed algae growth patterns from

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the six independent experiments were even similar (Figure S2). Secondly, the cell volume

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measurements of the second algae culture followed the time-course of the cell volumes of

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the first culture due to synchronized algae cultivation (Figure S2) which has been already

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shown by Altenburger et al. 26 . Consequently, we supposed that the algae growth pattern

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was differently inhibited by the selected chemicals due to variances in chemical bioavailability 10


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(and/or modes of action). An observable effect on growth is understood here as the first

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time point of the experiment where the median cell volume of the algal culture was clearly

229

different from the control. We have about 10000 cells sized for the mean volume in each

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sample with two technical replicates. In general, our observations indicated that algae

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growth was affected by all six concentrations of irgarol and PNA, the five highest isoproturon

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and paraquat concentrations, the three highest triclosan concentrations and the highest

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metazachlor concentration. Moreover, higher exposure concentrations showed faster responses

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on algae growth than lower concentrations (Figure 2). First time point of algae growth

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inhibition appeared at different time points for the chemicals used (Figure 2, Table S3 - S8).

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The adverse outcome on individual algae growth further led to subsequent effects on

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cell division (i.e. population growth) as indicated by reduced cell numbers (Figure S4).

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We observed effects on reproduction in a concentration-dependent relationship at t24 for all

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chemicals used (Figure S1). Median effect concentrations inhibiting 50% reproduction (EC50 )

240

at t24 ranged between 0.015 µmol L−1 for triclosan and 7.24 µmol L−1 for paraquat (Table S12).

241

Compared with the cell division of the control group starting at t20 , we observed a time delay

242

in cell division and a reduced number of autospore cells for all chemical-treated algae (Figure

243

S4). Thus, algae cells divided later or did not divide at all during the experiment as observed

244

for the three highest concentrations of paraquat, the two highest concentrations of PNA and

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the highest concentrations of irgarol, metazachlor, and triclosan.

246

Time dependence of toxicity

247

Toxicity development was analyzed as the temporal change of the median effect concentration

248

(EC50 ) for the disturbed algae growth. To this end, we aggregated the data of the observed

249

growth pattern under cumulative exposure starting from t6 or t8 (PNA) in order to predict

250

parameters of the concentration-response relationships for different exposure durations (Table

251

S9). In general, EC50 values for each of the analyzed chemicals decreased over exposure time

252

except for metazachlor for which we could not determine an EC50 value on growth (Figure 3, 11



253

Table S9). The time-courses of EC50 values for algae growth inhibition differed considerably

254

for the six chemicals used. The decrease of EC50 values over exposure time was smallest

255

for isoproturon (1.6-fold) and largest for paraquat (8.5-fold) (Figure 3, see Table S9 for

256

more details). Regarding the damage development over time, algae growth inhibition was

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firstly detected approximately after two hours of isoproturon, irgarol and PNA exposure

258

and after four hours of triclosan and paraquat exposure. Steady state effect concentrations

259

were reached between four hours and twelve hours in the following order: isoproturon
4 (Table 1). The estimated NEC parameter of the algae TKTD model represents

320

a threshold concentration below which no effect on algae growth is expected.

Repair/recovery rate constants spanned between

321

Estimated NEC values ranged from 0 µmol L−1 to 3.62 µmol L−1 in the following order:

322

isoproturon < irgarol ≈ triclosan < PNA < metazachlor < paraquat. This result indicates

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that in order to produce an observable adverse outcome on growth a lower number of

324

molecules of photosystem II inhibitors and triclosan need to be present in the exposure

325

media than PNA, metazachlor and paraquat molecules. The NEC value is related here to the

326

exposure concentration in the ambient medium, but it can be also transformed to an internal

327

threshold concentration by accounting for the differences in the toxicokinetic processes.

328

Table 1 additionally provides NEC values as internal concentration thresholds which were

329

calculated according to Vogs et al. 18 . Moreover, estimated NEC values were higher than

330

zero for all chemicals analyzed except for isoproturon. According to the respective NEC

331

values, the lowest exposure concentration used for irgarol and paraquat, the three lowest

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exposure concentrations used for triclosan and the four lowest exposure concentrations

333

used for metazachlor did not affect algae growth at any exposure duration monitored.

334

All exposure concentrations of isoproturon and PNA exceeded the estimated NEC values.

335

Effect progression rate constants spanned over six orders of magnitude in the following

336

order τirgarol ≈ τisoproturon > τparaquat ≈ τPNA > τtriclosan > τmetazachlor and ranged from 2.50 h−1 to

337

5.45 × 10-6 h−1 . Furthermore, the estimated τ values for the chemicals affecting algae growth

338

through similar AOPs have been found to be in the same order of magnitude and independent

339

of the chemical’s hydrophobicity: inhibitors of photosystem II > chemical reactivity > inhibitors

340

of lipid biosynthesis (Table 1).

341

Discussion

342

Data quality assessment

343

In the present study, we investigated the impact of specifically acting and reactive chemicals

344

on algae growth by examining toxicokinetic and toxicodynamic processes over time. Chemicals

345

were added to the algae cell suspension at the transition from exponential to linear growth

346

and prior to the commitment for cell division (t6 or t8 of the cell cycle) for a maximal response

347

resolution on algae growth. The exposure duration was therefore significantly shorter in

348

comparison with literature-reported effect concentrations based on traditionally determined

349

growth and reproduction responses on S. vacuolatus that span entire generation cycles.

350

Thus, EC50 values for reproduction at t24 determined in this study were between two- to

351

almost ten-fold higher than literature-reported EC50 (t24 ) values (Table S12). Moreover, the

352

determined EC50 values for growth at t14 and EC50 values for reproduction at t24 based on the

353

algae growth assay corresponded to EC50 values at t14 and at t24 based on the range-finding

354

experiments, respectively, except for triclosan (Table S12). In particular, effect estimations

355

derived from range-finding experiments differentiated between nearly zero for photosystem

356

II inhibitors and 18–fold for triclosan to effect estimations from the algae growth assay. 15



357

The time-course of EC50 values for growth integrates information on the kinetics of

358

bioconcentration as well as on the intrinsic toxicity for chemicals with different AOPs 27 .

359

An increase in toxicity over exposure duration of triclosan, PNA and norflurazon have been

360

reported by various studies 17,19,20 . This is in agreement with our study demonstrating

361

the decrease of EC50 values for algae growth over exposure duration until steady state

362

of effect were reached for all chemicals analyzed, except for metazachlor (Figure 3). Böger

363

et al. 28 investigated that the elongation of very-long-chain fatty acids (C20, C22, and C24) is

364

specifically affected by metazachlor which are required for division processes in algae cells 29 .

365

Thus, it seems in line with our understanding that cell division at t24 was inhibited by

366

metazachlor in a concentration-dependent relationship but not algae growth. In conclusion,

367

we believe that the measured data are sufficiently robust and reliable for the modeling

368

purpose.

369

Modeling quality assessment

370

The TKTD model fitted the pattern of disturbed algae growth well (R2 ≥ 98%) for all

371

simulations (Figure 2). Nevertheless, the absolute deviations between measurements and

372

simulation became slightly larger with higher cell volumes. In some cases, the measured cell

373

volume was still increasing at the end of one generation cycle, while at the same time the

374

simulation showed a decrease (Figure 2). This occurs because the fraction of damage increase

375

is higher than algae growth in the limited phase and therefore simulated growth slightly

376

decreases. A mean absolute error (MAE ) value of 37.8 fL signified the lowest accuracy which

377

was found in the case of triclosan compared with the simulations for the other chemicals

378

(Table S10). The highest variance in the individual errors of fit was also denoted for triclosan

379

indicated by a 35.1% lower MAE value compared with its root mean squared error (RMSE )

380

value (Table S10). By contrast, lowest variance in the individual errors was indicated for

381

isoproturon compared with the other compounds. The fit for isoproturon affected growth

382

was characterized by the MAE value of 15.4 fL, which was 17.7% lower than its RMSE value 16


Page 16 of 33

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383

(Table S10). The degrees of freedom ranged between 94 and 136 (Table S10). The TKTD

384

model was hence considered suitable to describe the perturbed growth of the unicellular

385

organism. To conclude, we conceive the process-based parameter estimations as sufficiently

386

reliable for discussing the differentiation of adverse outcome pathways.

387

A tool for analysing adverse outcome pathways by using toxicokinetic-

388

toxicodynamic modeling

389

Time lags of several hours between maximum internal effect concentrations and the time

390

course of cumulative damages in S. vacuolatus have not been described and quantified before

391

to the best of our knowledge 17,19,20 . The algae system is hypothesized to provide an unique

392

tool for studying the effect progression on growth through key events at multiple levels of

393

biological organization leading to an adverse outcome, independent from a change in internal

394

concentration. To this end, the TKTD model was calibrated to the perturbed growth pattern

395

of algae cells exposed to six chemicals for analyzing if toxicodynamic processes in algae cells

396

may be rate-specific for different adverse outcome pathways. For that purpose, the process

397

parameters were considered to possibly aggregate an effect progression specific for a given

398

AOP towards an apical adverse outcome at the organism level.

399

Differentiating adverse outcome pathways using effect progression rate constants

400

Chemical-target interaction may provoke a reaction which blocks or triggers an array of

401

molecular and biochemical events progressing towards an adverse outcome at the physiological

402

level 30,31 . In the present study, effect progression has been generalized by implementing three

403

compartments into the TKTD model as proposed by Simeoni et al. 23 (Figure 1).

404

The observed time lags of hours between steady state internal concentrations in algae

405

cells and the observable effect on algae growth were quantified by the effect progression

406

rates spanning six orders of magnitude for all analyzed chemicals (Table 1). In general,

407

the progress of an effect which operate on the order of hours to days being suggestive 17



408

of a time-dependent transduction function where toxicodynamic processes determine the

409

time-course of effect 30,32 . Then, effect progression in vivo is slow and the affected turnover

410

processes in physiology are not rate-limiting like in the case of our observations presented

411

here 32,33 . In comparison with Vogs et al. 17 , we estimated a 40.72-fold higher τ value for

412

triclosan and a 5.44-fold lower τ value for PNA in the present study. First, these differences

413

in estimated effect progression values may be related to divergent estimations of the internal

414

concentration time-course. Here, we incorporated measured uptake rates from Vogs et al. 18

415

as we thought to increase the precision and interpretation power for the toxicodynamic

416

parameters 17 . Second, the modification of the exposure time frame for maximising the

417

time resolution in effect measurement might be a further reason for the ability to better

418

discriminate τ values between chemicals with different adverse outcome pathways in this

419

study.

420

In pharmacological studies, an average effect progression rate of 0.025 ± 0.016 h−1 was

421

reported for the effect of drug administration on tumor growth dynamics such as paclitaxel

422

(cytoskeletal drugs that target tubulin), 5-fluorouracil (inhibitor of thymidylate synthase),

423

camptothecin-11 (inhibitor of the DNA enzyme topoisomerase I) and three more undefined

424

tested drugs 23,34 . In comparison, the reported value is one to two orders of magnitude

425

smaller than the estimated τ values for photosystem II inhibitors and reactive chemicals,

426

but three to four orders of magnitude larger than the τ values for lipid synthesis inhibitors.

427

Further, the deviation of effect progression rate constants estimated for different drugs was

428

relatively constant (as indicated by a standard deviation of 0.016 h−1 ) compared with the

429

data variability of six orders of magnitude for the estimated τ values in this study. A reason

430

might be that the turnover process of cancer growth measured as tumor weight over days in

431

the studies of Simeoni et al. 23 and Magni et al. 34 is the rate-limiting process that overlaps

432

with the progression of effect.

433

Effect progression rates have been estimated to be in the same order of magnitude

434

within the three groups of AOPs and independent between the two hydrophobicity groups:

18


Page 18 of 33

Page 19 of 33


435

inhibitors of photosystem II > chemical reactivity > inhibitors of lipid biosynthesis. Photosystem

436

II inhibitors specifically alter the central algae metabolism resulting in a fast response on

437

affected algae growth compared with the reactive chemicals and lipid biosynthesis inhibitors.

438

By contrast, reactive chemicals unspecifically affect various biomolecules and the slower

439

propagation on perturbed algae growth in comparison with the photosystem II inhibitors

440

can be made plausible through a slower accumulation of damage resulting from this process

441

compared to receptor-specific binding. Finally, the lipid biosynthesis inhibition led to the

442

slowest response on algae growth, because the perturbation of cell wall and lipid metabolisms

443

becomes relevant for the organism mainly when the cell prepares for division, that is at later

444

stages in the cell cycle. Our results denoted that the progression of an effect from the

445

molecular initiating event over changes of biochemical responses and physiological dynamics

446

towards the adverse outcome on growth was discriminated between the three AOPs and

447

could be diagnosed based on the concentration-depended observations on growth. Further,

448

this study quantitatively linked the time to progress an effect for various chemicals for

449

the first time and thus offers the scope for the discrimination between AOPs by biological

450

toxicodynamic parameters.

451

Estimated no-effect concentrations

452

We compared the estimated NEC concentrations to literature values of statistically determined

453

no-observed effect concentration based on concentration-dependent responses (NOEC ) as

454

further discussed for metazachlor, paraquat and PNA. First, the NEC value for metazachlor

455

has been estimated to be 19-fold higher than the NOEC value of 0.0551 µmol L−1 35 . Furthermore,

456

Jamers and De Coen 36 reported a NOEC value for paraquat of 0.1 µmol L−1 based on affected

457

C. reinhardtii growth at t72 which is 36-fold lower than the estimated NEC value in this

458

study.

459

approaches, are known to depend on the exposure duration and tested concentrations,

460

the endpoint, and the estimation method. By contrast, NEC value of TKTD models are

Statistically determined threshold values, which are relevant in risk assessment

19



461

mechanistically derived and estimated from concentration and time-dependent changes of

462

the endpoint observations. NEC values may thus be assumed to be a more robust indicator

463

of threshold values. However, threshold values can be misjudged, if the exposure regime

464

misses sensitive early developmental stages.

Page 20 of 33

465

Sans-Piché et al. 37 suggested to distinguish pharmacological effects at the metabolism

466

level (0.00713 µmol L−1 - 0.228 µmol L−1 ) from toxic effects at the phenotypic level (0.45 µmol L−1 -

467

1.82 µmol L−1 ) by anchoring concentration- changes of metabolites to effects on photosynthesis

468

and growth of S. vacuolatus under PNA exposure. We found a critical threshold value

469

estimated for PNA in this study that was 1.75-fold lower than the reported threshold value

470

of 0.228 µmol L−1 PNA exposure that caused a response on the algae metabolome level 37 .

471

Our results thus, by contrast, suggest that the effect might alternatively be interpreted as a

472

gradual change in an adverse outcome pathway.

473

Estimated injury rate constant and repair/recovery rate constant

474

Specifically acting and reactive chemicals interact with biological targets such as membranes,

475

proteins, transporters or macromolecules by different mechanisms of toxic action. The injury

476

rate constant kI characterizes the intrinsic activity to produce an effect caused by a certain

477

amount of molecules at the target site. The repair/recovery rate constant characterizes the

478

degree of binding reversibility at the target site by taking into account repair mechanisms,

479

de novo synthesis of receptors and detoxification mechanisms 15,27 .

480

In this study, the average injury rate constants and the standard deviation were within

481

the same range of magnitude. Our estimations were two-fold lower than the average injury

482

rate constant of 0.40-3 ± 0.51 × 10-3 mL ng−1 d−1 determined for cancer growth kinetics after

483

drug administration in tumor-bearing mice 23,34 . Furthermore, chemical injury rate constants

484

increased in the following order: reactive chemicals < lipid biosynthesis inhibitors < photosystem

485

II inhibitors. A lower chemical injury rate constant might signify higher number of target

486

sites that need to be hit for elucidating damage or an unspecific mechanism with lower

20


Page 21 of 33


487

intrinsic affinity causing an effect as it would be reasonable in the cases for paraquat and

488

PNA 19,38 . By contrast, the high injury rate constants of irgarol and isoproturon potentially

489

reflect the specific mechanism of the photosystem II inhibitors at the QB binding site of the

490

D1 protein 39 . In the present study, an average kR value of 0.36 ± 0.64 h−1 was determined for

491

all chemicals analyzed. However, a two-fold higher standard deviation compared with the

492

average kR value indicates a somewhat less reliable estimate.

493

To conclude, the estimated parameters kI and kR need to be interpreted carefully, because

494

the experimental data do not explicitly reveal the more complicated mechanism of chemical-target

495

interaction involving dynamics of receptor binding, aging processes and different types of

496

interaction. For an adequate estimation of kI and kR , either in vitro assays representing

497

the specific target sites could be used in addition to in vivo bioassays 40 or pulsed exposure

498

experiments could alternatively provide better indication of the intrinsic affinity as well as

499

recovery/repair mechanism 41,42 . Furthermore, information on target sites, target densities

500

and types of interaction would improve the understanding of the mechanisms of action 40 .

501

Doing so would improve our understanding of further-time limiting steps and how those

502

might affect the effect progression towards an adverse outcome.

503

Implementation into future research

504

The time-course of cumulative damage on S. vacuolatus growth was not explainable by

505

the change of the internal concentrations solely, but was rather dominated by rate-limiting

506

toxicodynamic processes. Therefore, we suggest that the simple unicellular system used

507

here provides a useful tool for investigating key events on different biological levels which

508

are independent from the overlap of internal exposure changes. Dynamics of toxicogenomic

509

responses like changes in gene expression, protein expression, or metabolic responses may

510

be characterized for impacts of chemicals only 16 . To study temporal changes in effects

511

only would facilitate the basis for a mechanistic link between molecular responses and the

512

adverse outcome on different biological levels such as physiological or phenotypical changes 43 . 21



Page 22 of 33

513

Adapting more advanced model structures like the fifth-generation pharmacokinetic-pharmacodynamic

514

model 44 for simulating toxicogenomics could quantitatively anchore phenotypic effects in

515

(eco)toxicology. Additionally, modeling dose-dependent responses progression across molecular,

516

cellular, and phenotypical levels would improve the capability to identify and estimate

517

inaccessible system variables 31 as well as to assess and predict likely impacts on organism

518

and population level for multiple exposures as is asked by current risk assessment asks for 2 .

519

Acknowledgement

520

This research was funded by the German Federal Ministry of Education and Research within

521

the project ProDarT (FKZ 0315399). The Helmholtz Research Program "Chemicals in

522

the Environment" and the Helmholtz Interdisciplinary Graduate School for Environmental

523

Research-"HiGrade" supported this study. Further support through the SOLUTIONS project

524

(European Union, grant agreement no. 603437) is acknowledged. The authors are grateful

525

to J. Krüger and K. Herold for excellent technical support. The authors declare that they

526

have no competing interests.

527

Supporting Information Available

528

The raw data reported here are available in the Supporting Information (Table S3 - Table

529

S8). Information is provided in the supplemental Material and Method section about algae

530

cultivation, determination of the concentration-response relationship and chemical specific

531

properties (Table S1).

532

estimated EC50 values (Table S2 and S12) and the goodness-of-fit parameters for the TKTD

533

model (Table S10 and S11) as well as explanatory figures.

534

Moreover, Supplemental Information provides results about the

This material is available free of charge via the Internet at http://pubs.acs.org/.

22


Page 23 of 33

535


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Figure 3: Median effect concentrations EC50 (± standard error) derived from perturbed algae growth over cumulative exposure duration (t6 –t20 , circle symbol) and from compromised reproduction at t24 (square symbol) in comparison with the time-course of the estimated internal concentration IEC50 (line). IEC50 simulations were calculated by using experimental uptake data from Vogs et al.18 and EC50 (t14 ) values for the six model chemicals. Grey boxes represent the exposure duration.

3 ACS Paragon Plus Environment

Page 32 of 33

4


toxicodynamic process

toxicokinetic process kI kR τ NEC

injury rate repair/recovery rate effect progression rate no-effect concentration

0.07 0.02 0.18 0

0.73 0.03 2.50 0.01

[1×103 LBiov µmol-1 h-1 ] [h-1 ] [h-1 ] [µmol L-1 ]

± ± ± ±

1.83 ± 0.01 0.36 ± 0.05

[1×103 h-1 ] [h-1 ]

no-effect concentration* N ECInt [mmol kgwetweight -1 ] 0.05 no-effect concentration* N ECInt [1×103 molecules per cell] 614.87

kin kout

uptake rate overall elimination rate

0.25 ± 0.006 44.54 ± 5.15 0.010 ± 0.003

[fL h-1 ] [fL h-1 ]

irgarol

0.70 8487.52

0.02 0.15 0.05 0 6.47 77933.52

± ± ± ±

415.69 ± 91.29 8.36 ± 2.34

0.21 ±0.01 75.35 ± 79.01 0.018 ± 0.010

PNA

0.26 3151.30

0.15 ± 0.04 8.88×10-5 ± 0.03 0.52 ± 0.09 3.62 ± 0

9.67 ± 4.36×105 133.81 ± 4.36 ×105

0.235 ± 0.001 62.95 ± 48.57 0.013 ± 0.001

paraquat

0.68 ± 0.13 0.18 0.38 ± 0.08 1.77 2.50×10-5 ± 0.08 0.32 0.01 ± 0 0.13

62.75 ± 2.32 0.92 ± 0.08

0.22 ± 0.01 84.93 ± 168.38 0.022 ± 0.004

triclosan

[h-1 ]

common name

0.33 ± 011 0 ± 0.01 5.46×10-6 ± 0.18 0.19 ± 0

2.97 ± 0.22 0 ± 0.01 2.22 ± 0.25 0 ± 0

[1×103 LBiov µmol-1 h-1 ] [h-1 ] [h-1 ] [µmol L-1 ]

0.18 614.87

kI kR τ NEC

injury rate repair/recovery rate effect progression rate no-effect concentration

639.7 ± 1644×105 0.21 ± 0.01 1852.70 ± 4.76×108 0.22 ± 0.04

[1×103 h-1 ] [h-1 ]

0.246 ± 0.002 146.20 ± 162.67 0.020 ± 0.001

metazachlor

0.246 ± 0.003 55.47 ± 4.74 0.009 ± 0.002

isoproturon [fL h-1 ] [fL h-1 ]

[h-1 ]

no-effect concentration* N ECInt [mmol kgwetweight -1 ] 0 no-effect concentration* N ECInt [1×103 molecules per cell] 0

kin kout

uptake rate overall elimination rate

exponential growth rate µE unperturbed algae growth linear growth µL cell-clock rate µC

toxicodynamic process

toxicokinetic process

exponential growth rate µE unperturbed algae growth linear growth rate µL cell-clock rate µC

common name

Table 1: Estimated parameters and their 95% confidence interval for unperturbed algae growth, toxicokinetic and toxicodynamic processes. Parameters of unperturbed algae growth rates were estimated according to the unperturbed algae growth (Figure S2) and toxicokinetic parameters for all six chemicals have been fixed to literature reported values18 . N ECInt values were calculated by considering variabilities in toxicokinetics according to Vogs et al.18 .

log KOW < 3

log KOW > 4

Page 33 of 33 Environmental Science & Technology

Time-Dependent Effects in Algae for Chemicals with Different

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