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An Evaluation of Models to Predict the Activity of Environmental Estrogens Candice M. Johnson and Rominder Suri, Ph.D.,P.E. NSF Water and Environmental.

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Presentation on theme: "An Evaluation of Models to Predict the Activity of Environmental Estrogens Candice M. Johnson and Rominder Suri, Ph.D.,P.E. NSF Water and Environmental."— Presentation transcript:

1 An Evaluation of Models to Predict the Activity of Environmental Estrogens Candice M. Johnson and Rominder Suri, Ph.D.,P.E. NSF Water and Environmental Technology (WET) Center, Department of Civil and Environmental Engineering, Temple University, Philadelphia Pennsylvania 19122 1

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3 Endocrine Disrupting effects observed in the environment http://www.asnailsodyssey.com/LEARNABOUT/WHELK/whelImpo.php Imposex condition in snails Increased risk of cancer in humans? Masculanization/Feminization of fish Altered sex ratio Normal female Normal male Masculanized female 3

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5 Routes of entry for endocrine disruptors in the environment 5 Pretreatment Water treatment plant Surface Water Manufacturing Human Use Biota Agriculture products Effluent

6 Endocrine disrupting activity is related to wastewater treatment Relative distance from water treatment plant 6

7 Removal of EDCs End-of-Pipe Technologies Ozonation, Ultrasound, Adsorbents Source Control Strategies Risk assessment & hazard characterization Development of policy and laws Research and development of safer products Replacement of endocrine active ingredients 7

8 Methods for detecting potential endocrine disruptors Chemical Analysis - target analysis - low limits of detection - rapid analysis methods - high throughput - no indication of biological activity Biological Analysis (Bioassays) - detects the activity of mixtures and unknowns -detects interactions, measures net biological activity -does not indicate the identity or concentrations of specific contaminants 8

9 Approaches to testing EDCs 1.Chemical-by- chemical approach May be too simplistic and may underestimate the risks of chemicals 2.Test mixture toxicity on a case by case basis Chemical mixtures vary with respect to constituents and to concentrations of those constituents, Provides site specific data Band-Aid but not a cure to the characterization of chemical mixtures (LeBlanc & Olmstead, 2004) 3.Component-based approach (estimating the total toxicity from information on identified components) A step towards a generalized understanding and assessment of mixture toxicity

10 Effect Directed Analysis (EDA) scheme extraction and pre-concentration Unknown Environmental Samples Bioassay Screening LC-MS or GC-MS analysis of target compounds Correlation and quantification of casual factors (confirmation) -- Antagonistic activity? Mathematical models are used to estimate the biological effects from the concentration of target compounds 10

11 Concentration addition (CA) model Independent action (IA) model (probabilistic model) Additive models RP = Relative Potential C n = Concentration of Component n in the mixture IEQ = Induction equivalents in terms of a standard E mix = Predicted effect of the mixture E max = Maximum effect F i, (c i ) = activating effects determined from the regression of the concentration response relationships 11

12 CA versus IA Concentration Addition (CA) Applied to chemicals with a similar mode of action EC 50 of a mixture can be predicted based on the EC 50 values of the individual components Independent action (IA) Applied to chemicals with diverse modes of action Mixture effects predicted from precise effects of each individual component and at the concentration found in the mixture. This information is not readily available Assumes strictly independent events, may not be relevant in biological systems due to converging signalling pathways and inter-linked subsystems

13 Approach 1.Extract hormones from wastewater influent and effluent samples 2.Measure the estrogenic activity of the extracts using the Yeast Estrogen Screen (YES) Assay 3.Quantify the concentrations of suspected estrogens using LC-MS/MS 4.Estimate the estrogenicity of the extracts using additive models Objective: To assess the ability of additive models to predict estrogenic activity 13

14 Estriol17β-estradiolEstrone 17α- dihydroequilin Influent (ngL -1 )8.665.070.15679.18 Effluent (ngL -1 )6.55 * { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/3500302/12/slides/slide_13.jpg", "name": "Estriol17β-estradiolEstrone 17α- dihydroequilin Influent (ngL -1 )8.665.070.15679.18 Effluent (ngL -1 )6.55 *

15 Assessment of additive models Predicted and observed concentration response curves in the YES Antagonistic- like activity is evident in both the wastewater influent and effluent samples 15

16 Assessment of additive models in ‘clean’ water Comparison of predicted and observed mixture responses for 17β- estradiol, estriol, estrone, and 17α-dihydroequilin in simulated sample Predictions based on simulated samples do not suggest that the mixture should be interactive Clear contribution from the wastewater matrix 16

17 Assessment of additive models for estimating estrogenicity and androgenicity 58% Biological Interaction with unknown mixture components 26% Successful use of CA 11% No Confirmation 5% Cytotoxicity 17

18 Conclusions and Recommendations Incomplete degradation of estrogen hormones during wastewater treatment - 24 - > 99% removal of steroid hormones from this wastewater treatment plant. Similar results were reported by Chimchirian et al., 2007 Residual estrogenicity after water treatment may lead to endocrine disrupting effects in fish -Suggested no effect concentration for 17β-estradiol is 2ngL -1 (Caldwell et al., 2012) - Estrogenicity of effluent in our study is 7ngL -1 EEQ No synergism or antagonism between estrogen hormones in “clean” water 18

19 Other unknown components in the wastewater matrix may cause antagonistic responses Additive models are applicable to “clean” water but may be limited in their use with complex mixtures More advanced models that can capture interactions or antagonistic effects are needed 19 Conclusions and Recommendations

20 Testosterone a R BPA b R DBP aRP BPA aRP DBP TEQ(μg/L) (μg/L)CA ModelInteractionObserved (% Error)Model (% Error) 200-2.61E-04-5.37E-052 (0.1)2.000 (0.1)1.999 2405000-2.58E-04-5.37E-052 (39)1.437 (0.4)1.443 2805000-2.55E-04-5.37E-052 (35)1.411(5.1)1.487 21605000-2.50E-04-5.37E-052 (46)1.362 (0.3)1.367 23205000-2.40E-04-5.37E-05*2 (53)1.270 (3.0)1.31 26405000-2.22E-04-5.37E-05*2 (50)1.109 (17)1.333 212805000-1.88E-04-5.37E-05*2 (53)0.869 (33)1.305 2187510000-1.62E-04-5.37E-05*2 (433)*0.186 (50)0.375 225605000-1.36E-04-5.37E-05*2 (96)0.625 (39)1.024 2375010000-1.01E-04-5.37E-05*2 (748)*0.039 (83)0.236 251205000-7.13E-05-5.37E-05*2 (133)0.630 (27)0.858 2750010000-3.91E-05-5.37E-05*2 (534)0.289 (8)0.417 * p<0.01 (These predictions are significantly different from the observed values) a – concentration ratio of BPA to testosterone b – concentration ratio of DBP to testosterone TEQ – Testosterone equivalents 20 C n - concentration of nth mixture component γ - interaction index RP - relative potential IEQ - Induction equivalent concentrations Johnson, C.M., et al., Environmental Science and Technology. 2013

21 21 17β-E2 (µgL - 1 ) E317α- EQN a DBPEEQ (μg/L) (µgL -1 ) CA ModelInteractionObserved (% Error)Model (% Error) 0.06256.250.51200*0.1219 (123)0.0473 (13)0.0546 0.06256.250.5600*0.1219 (59)0.0878 (14)0.0765 0.06256.250.53000.1219 (34)0.1080 (18)0.0913 0.06256.250.51500.1219 (9)0.1181 (6)0.1118 0.06256.250.5750.1219 (6)0.1231 (5)0.1292 06.250.5600*0.0594 (77)0.0253 (25)0.0335 06.250.5300*0.0594 (50)0.0455 (15)0.0396 06.250300*0.0362 (68)0.0200 (7)0.0216 00412000.1858 (38)0.1229 (9)0.1349 0046000.1858 (27)0.1633 (12)0.1459 0.03136.250.56000.0907 (50)0.0565 (6.8)0.0607 0.03133.1250.5600*0.0726 (51)0.0364 (24)0.0481 0.03131.5630.5600*0.0635 (44)*0.0263 (40)0.0441 0.03130.7810.5600*0.0590 (42)*0.0213 (49)0.0416 0.03130.3910.5600*0.0567 (54)*0.0188 (49)0.0368 0.03136.250.51200 *0.0907 (136)*0.0161 (58)0.0384 0.03136.250.25600*0.0791 (92)0.0438 (6)0.0413 0.03136.250.125300*0.0733 (67)*0.0576 (31)0.0439 0.03136.250.0625150*0.0703 (64)*0.0645 (50)0.043 0.03136.250.0313750.0689 (48)0.0680 (46)0.0467

22 Thank you! Questions?


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