Effect-based trigger values for in vitro bioassays: Reading across from existing water quality guideline values
Graphical abstract
Introduction
Health-based water quality guideline values (GV) are defined as concentrations of chemicals in drinking water that do not pose an appreciable risk to health over a lifetime of exposure. GV are based on acceptable daily intake (ADI, also called reference dose RfD, or tolerable daily intake TDI). The ADI is typically extrapolated from ‘No Observed Adverse Effect Levels’ derived from a set of animal toxicity tests with extrapolation factors, accounting for uncertainties related to extrapolation from a model system to a human population (Ritter et al., 2007).
Guideline values for individual chemicals were defined, for example, by the WHO for drinking water (WHO, 2011). Health-based guidelines are also available for recycled water for indirect potable reuse and for various other water types and environmental protection goals (Escher and Leusch, 2012).
Snyder et al. (2008) proposed to derive drinking water equivalent levels for pharmaceuticals using the minimum therapeutic dose. This matches the approach proposed by Schriks et al. (2010) who developed a pragmatic approach to derive provisional drinking water guideline values from ADIs for unregulated chemicals immediately after their detection in drinking water.
Given the overwhelming number of chemicals present in water (Schwarzenbach et al., 2006, Villanueva et al., 2014), chemical analysis has been complemented for many years by in vitro bioassays which not only give information on the level of effect but also on the type of effect, i.e., the mode of action. Further, bioanalytical tools also integrate the mixture effects of chemicals that act according to the same mode of action in a concentration-additive manner (Escher and Leusch, 2012). In vitro bioanalytical tools have also been recommended for the assessment of drinking water (van Wezel et al., 2010).
Since 2008, the high-throughput in vitro toxicity program Tox21 of the National Institute of Health (NIH) and the US Environmental Protection Agency (EPA) has been and continues to provide quantitative information on the toxicity pathways of almost 10,000 organic compounds (Attene-Ramos et al., 2013b, Shukla et al., 2010). In vitro assays included in Tox21 are phenotypic assays (e.g., targeting mitochondrial toxicity (Attene-Ramos et al., 2013a)), pathway assays (e.g., targeting cell viability in specific cell lines) and nuclear receptor assays (e.g., targeting hormone receptors and metabolic pathways (Huang et al., 2011)) and various other endpoints (Tice et al., 2013). The chemicals and pathways screened for Tox21 are also relevant for water quality and some of the assays have been applied successfully to water quality testing together with more established in vitro assays (Escher et al., 2014).
Modern reporter gene assays and other cellular bioassays are so sensitive that even very clean waters often induce detectable responses in vitro. High sensitivity is an advantage from the point of view of achieving the highest possible sensitivity and in the assessment of treatment efficacy (i.e., water treatment plants). In terms of water quality, however, a cellular response does not translate directly into a toxicological effect. A ‘simple’ yes or no answer will be defined by the sensitivity of the test and is not sufficient to evaluate if a water is “good” or “bad”. Therefore, trigger values are required that differentiate an acceptable response from an unacceptable response.
Recent work that compared the bioassay response of chemicals detected in recycled water with the bioassay response of the entire water sample has shed light on knowledge gaps. For bioassays indicative of integrative effects (i.e., non-specific effects, reactive effects and adaptive stress responses) typically only a very small fraction (often less than 1% of the effect) can be explained by known and identified chemicals (Escher et al., 2013, Reungoat et al., 2012, Tang et al., 2013). Thus unidentified and unknown chemicals cause the majority of the biological response indicating the need to account for mixture effects in the derivation of EBTs. Also for these types of responses there is no clear-cut reference chemical available because many different chemicals can induce the effect.
Therefore, we have previously proposed EBT-effect concentrations (EBT-EC) that account for mixture effects of these types of chemicals in non-specific bioassays (Escher et al., 2013, Tang et al., 2013). For example, for the derivation of the EBT for the bioluminescence inhibition test we assumed 1000 chemicals were present at 5% of their guideline value and applied a concentration-additive mixture toxicity model. The EBT-EC50 was expressed in relative enrichment factor units (REF), which describe how much the water sample was enriched by, e.g., solid phase extraction prior to the bioassay. The provisional EBT-EC50 for drinking and recycled water then came to 2.8 REF (Tang et al., 2013). In other words, if a sample that is roughly three times enriched (by solid phase extraction for example) shows 50% or more effect in this bioassay, then the EBT is exceeded; if on the other hand it induces less than 50% effect, then the water quality is acceptable. A similar approach for the oxidative stress response quantified with the AREc32 bioassay resulted in a provisional EBT-ECIR1.5 of 3 REF (Escher et al., 2013). Both provisional EBT-EC were able to suitably differentiate between water quality before and after advanced water treatment in recycling plants.
In contrast to integrative assays, the majority of receptor-mediated effects (e.g., inhibition of enzymes or activation of hormone receptors) can typically be explained by known chemicals (Escher et al., 2011, Leusch et al., 2005, Leusch et al., 2014b, Rutishauser et al., 2004, Tang and Escher, 2014). Effects can be expressed as so-called bioanalytical equivalent concentrations (BEQ), which represent the concentrations of a reference chemical that would elicit the same effect as the water sample of unknown composition. It has been suggested to apply concentration addition as the realistic worst-case scenario for mixture effects in risk assessment (Backhaus and Faust, 2012) and the BEQ concept is based on this mixture toxicity concept, which is valid for chemicals that have a common mode of action.
As straightforward as it may seem, however, it would be wrong to simply compare the BEQ to the GV of the reference compound. While the BEQ of the water sample includes information about the mixture effect, the derivation of a single chemical GV is exclusively based on the effects of that single compound.
EBT are not meant to replace other monitoring tools but can be applied to complement chemical analysis and to decrease uncertainty in water quality assessment. A possible tiered approach to water quality assessment is shown in Fig. 1. In a first screening step, a limited number of indicator chemicals would be monitored and in parallel a limited number of indicator bioassays would be applied. Only if the measured chemical concentrations exceed one or more GV or the effects in the indicator bioassays are above the EBT, then a much larger list of chemicals with defined GV would need to be monitored to determine overall water quality. This is just one of many possible ways to implement EBTs in a water quality monitoring strategy.
The aim of this study was to derive an algorithm for the derivation of EBT for receptor-mediated effects and to apply it as an illustrative example to Australian recycled water that was previously screened with 103 different bioassays (Escher et al., 2014). Eighteen of the 103 bioassays were selected for this exercise. The omitted bioassays were either not relevant for water quality (i.e., they had not shown any effects with water samples in the mentioned benchmarking study (Escher et al., 2014)), had no available data on reference chemicals or other regulated chemicals, or the BEQ concept could not be applied to these bioassays. We included bioassays without any direct connection to human health endpoints to test whether non-target bioassays could serve as purely (bio)analytical tools for the quantification of the presence of certain groups of chemicals. For example, the mixture effect of the herbicides included in a guideline can be evaluated using their effects on algae because this is a sensitive and quantitative indicator for this chemical group despite its lack of direct human health relevance.
We applied the proposed EBT derivation method to GV from the Australian Guidelines for Water Recycling: Augmentation of Drinking Water Supplies (AGWR) (NRMMC & EPHC & NHMRC, 2008), which was adopted in the Public Health Regulation, Schedule 3B Standards for quality of recycled water supplied to augment a supply of drinking water in Queensland, Australia (Queensland Government, 2005). The water samples tested were all sampled in Queensland (Escher et al., 2014). The GV in the Australian Drinking Water Guidelines (ADWG) (NHMRC, 2011) are a subset of the GV in the AGWR thus the proposed EBTs should be applicable to both recycled and drinking water in Queensland and other States of the Commonwealth of Australia. Without much effort, the approach can be adapted to guidelines from other countries and regions, including the WHO Guidelines for Drinking-Water Quality (WHO, 2011).
One previous study has derived bioassay trigger values for bioassays indicative of receptor-mediated effects in drinking water. Brand et al. (2013) proposed to derive effect-based trigger for the CALUX bioassays from the ADI of the reference compound using bioanalytical equivalents (BEQ) that account for the concentration-additive effect of chemicals that act similarly but may have different toxicokinetics (Punt et al., 2013). The toxicokinetic differences between in vivo and in vitro were considered by accounting for oral bioavailability and binding to plasma proteins but not for metabolism (Brand et al., 2013). The derived toxicokinetic factors were used to estimate the acceptable target concentrations of the reference compound, which in turn were translated to acceptable target concentrations of another compound than the reference compound in the associated CALUX assay by the relative effect potency in vitro. Finally, the water consumption and an allocation factor were used to derive the trigger value in the various CALUX bioassays.
An alternative approach would be to apply bioassays purely as an analytical tool and as a means to capture the mixture effects of chemicals in water. In this case one does not need to invoke any in vitro to in vivo extrapolation, which avoids the associated uncertainties. The algorithm we propose starts with existing sets of chemical GV and directly read across from the GV to the EBT. The only input data required are the GV and published effect concentrations (EC) of the regulated chemicals.
Thus the proposed method is simple and can be easily adapted if water quality guidelines change or bioassays are applied in another region of the world because they can be easily transferred from one regulatory framework to another. Here we develop this read-across approach and illustrate it with examples for chemicals included in the AGWR.
The approach is inspired by the work of Jarosova et al. (2014), who predicted safe concentrations of estrogenic activity in wastewater using similar in vitro bioassays as applied in the current study by reading directly across from the environmental predicted no effect concentrations that are derived from in vivo data.
Section snippets
Collection of literature data
The effect concentrations of chemicals listed in the AGWR for the selected 18 bioassays were collected from the literature. All bioassays included are described in detail in a previous paper (Escher et al., 2014, Table 1). In a few cases, review papers were used but mostly original publications were selected. If more than one EC value was available, the median was used. All median EC values are listed in the Supplementary Data, Table S1 and S2. Additional experiments were performed according to
General approach to derive EBT-BEQs
For each bioassay j, a separate EBT-BEQ must be derived (Fig. 2). As a first step, effect concentrations were collated from literature for each bioassay and for as many chemicals as possible for which GV are defined (Fig. 2, “Input data”). The GV are not associated with specific modes of action but generally protective of human health. In vitro bioassays quantify one step in the toxicity pathway but not necessarily the one that is most toxicologically relevant for a given chemical. The effects
Development of EBT-BEQs
Of the 103 bioassays applied to the ten water samples, only 65 bioassays targeted receptor-mediated effects. The other 38 bioassays targeted reactive MOAs, adaptive stress response pathways and cytotoxicity, for which no EBT-BEQs can be derived, although EBT-ECs have been derived (Escher et al., 2013, Tang et al., 2013). For 18 out of the 65 receptor-mediated bioassays, we found experimental data for chemicals included in the AGWR (Table 1 and individual EC-values in Supplementary Data,
Conclusions
While this novel approach was illustrated with chemicals included in the Australian Guidelines for Water Recycling, it is versatile and can be adapted to any set of guideline values and protection targets - although of course it is important that the bioassays selected are relevant to the protection target. Relevance can be quite broad as the bioassays serve as analytical tools rather than as hazard indicators. Therefore inclusion of an algae bioassay for assessing human health-relevant GV is
Funding sources
This research was partially funded by the Australian Water Recycling Centre of Excellence (set up under the Commonwealth Government's Water for the Future Program), the WateReuse Research Foundation (WRF 10-07), and the Australian Research Council (FT100100694).
Acknowledgments
We thank Klara Hilscherova, Armelle Hebert, Sander van der Linden, Anita Poulsen and Janet Tang for helpful discussions.
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