# Presentation: 350407

## Component-Based Methods for Investigation of Interactions

-  J.E. Simmons1, L.K. Teuschler2, R.C. Hertzberg2, G. Rice2, V.C. Moser1, K.M. Crofton1, M.J. DeVito1, D. Herr1, C. Gennings3
- 1U.S. Environmental Protection Agency, Office of Research and Development, NHEERL, RTP, NC, 2U.S. Environmental Protection Agency, Office of Research and Development, NCEA, Cincinnati, OH, 3VCU, Richmond, VA

- Can efficient experimental designs and associated analysis methodologies be developed, and used to examine priority mixtures, that a) use single chemical data to predict the health effect of mixtures containing many chemicals, b) include estimates of variability and c) have sufficient power to detect nonadditivity in the low-dose region?  These questions are important because the majority of risk assessments for chemical mixtures use component-based methods.  However, the default assumption of additivity underlying component-based methods relies on an inadequate data base due to the lack of efficient experimental designs and approaches to evaluate higher order mixtures.

**Research Goals**

**Methods/Approach**

**Impact and Outcomes**

**Results/Conclusions**

**Future Directions**

**Science Question**

- Our methods and approaches are consistent with the  recommendations of the SOT Mixtures Task Force. Important environmental mixtures were targeted. Environmentally-relevant mixing ratios were used and data points at the lower end of the dose-response curve were included.   Efficient experimental designs and predictive models were used and experiments planned by multi-disciplinary teams (toxicologists, risk assessors, statisticians).  The statistical methods developed are consistent with Berenbaum's definition of additivity: d1/DX1 + d2/DX2 + ...+ dn/DXn = 1, where d1, d2 and dn are the concentrations of the chemicals in the mixture and DX1, DX2 and DXn are equally effective (equally toxic) doses of each chemical.

- • For the THMs, serum results to date provided consistent evidence that the hepatotoxic interactions between the THMs were either dose additive or antagonistic (less than additive) with the nature of the interaction (additive, antagonism) dependent on the chemicals in the mixture, their dose levels and the chemical mixing ratios.
- • For the OP mixtures, greater than additive effects (synergistic) were detected for all endpoints examined (blood and brain cholinesterase, motor activity, gait score) except tail-pinch which was dose additive.  Greater than additive responses were observed at the lower tested doses; however, the ratio of predicted to observed response never exceeded 2.1.  The influence of malathion varied with endpoint.
- • For the TDC mixture, the effects of the mixture on serum T4 were dose-dependent.   Responses consistent with dose additivity  were observed in the lower portion of the mixture dose-response curve and responses consistent with nonadditivity (greater than additive, synergistic) occurred in the higher portion of the mixture dose-response curve.   Deviations from additivity were in the range of 2- to 3-fold.

- • This effort fulfills the regulatory requirement of the Safe Drinking Water Act Amendments of 1996 charging EPA to develop new approaches to the study of mixtures found in drinking water.
- •  This research is directly applicable to the Food Protection Act Amendments of 1996 as it provides useful research tools for investigation of dose additivity for chemicals with a common mode of action.
- •  The methods developed are useful for detecting a several-fold shift in dose-response, and for testing mode-of-action hypotheses.
- • These methods are being used in other projects, including research on carbamate mixtures and pyrethroid mixtures see Crofton et al. poster).
- •  In the absence of mixtures data, these methods provide, under an assumption of dose additivity, a valuable predictive modeling tool for use in risk assessment as the mixture response can be predicted from single chemical data.
- •  When sufficient mixtures data are available, but single-chemical data are not, the assumption of additivity can be evaluated by a ‘mixtures-only’ technique.

- •  These experimental efforts are largely completed. The methods developed and the mixture specific  results are being communicated to the scientific community.
- • Effort has begun to focus, and will increase in the future, on development of techniques to power adequately mixtures experiments. This becomes increasingly important as researchers increasingly focus on lower-dose regions.
- • Future efforts will also focus on understanding the health impact of the unidentified fraction of highly complex, incompletely characterized environmental mixtures.

**References**

-      Gennings, C., Schwartz, P., Carter, H.,  Simmons, J. E., An Efficient Approach for Detecting Departure from Additivity in Mixtures of Many Chemicals with a Threshold Additivity Model, *J. Agricultural, Biological and Environmental Statistics*, 1997, 2:198-211. Erratum. 2000, 5:257-259.
-      Teuschler, L. K., Gennings, C., Hartley, W. R., Stitler, W. M., Colman, J. T., Hertzberg, R. C., Thiyagarajah, A. Lipscomb, J. C.,  Simmons, J. E., A Multiple-Purpose Design Approach to the Evaluation of Risks from Mixtures of Disinfection By-Products, *Drug and Chemical Toxicology*, 2000, 23(1):307-321.
-      Teuschler, L. K.,  Simmons, J. E. Approaching the Toxicity of Disinfection By-Products in Drinking Water as a Mixtures  Problem. *J.  the American Water Works Association.* 2003, 95(6): 13-138.
-      Simmons, J. E., Teuschler, L. K., Gennings, C., Speth, T. F., Richardson, S. D., Miltner, R. J.,  Narotsky, M. G., Schenck, K. D., Hunter, E. S. III, Hertzberg, R. C.,  Rice G., Component-Based and Whole-Mixture Techniques for Addressing Toxicity of Drinking-Water Disinfection-ByProduct Mixtures. *J. Toxicology and Environmental Health*, 2004, 67:741-754.
-      Casey, M., Gennings, C., Carter, W. H., Jr.,  Moser, V. C. and Simmons, J. E., Detecting Interactions and Assessing the Impact of Component Subsets in a Chemical Mixture Using Fixed-Ratio Mixture Rays Designs.  *J. Agricultural, Biological and Environmental Statistics*, 2004, 9(3):339-361.
-      Gennings, C., Carter, W. H., Jr., Casey, M., Moser, V., Carchman, R., Simmons, J. E., Analysis of Functional Effects of a Mixture of Five Pesticides Using a Ray Design. *Environmental Pharmacology and Toxicology*, 2004, 18(2):115-125.
-      Teuschler, L. K., Hertzberg, R. C., Rice G. E.,  Simmons, J. E., EPA Project-Level Research Strategies for Chemical Mixtures: Targeted Research for Meaningful Results. *Environmental Toxicology and Pharmacology*, 2004, 18(3):193-199.
-      Meadows, S. L., Gennings, C., Carter, W. H., Jr.,  Simmons, J. E. Analysis of Mixtures of Drugs/Chemicals Along a Fixed Ratio Ray Without Single Chemical Data to Support an Additivity Model. *J. Agricultural, Biological and Environmental Statistics*, 2004, 9(4): 500-514.
-      Casey, M., Gennings, C.,  Carter, W. H., Jr., , Moser, V. C.,  Simmons, J. E. , Ds-Optimal Designs for Studying Combination of Chemicals Using Multiple Fixed-Ratio Ray Experiments. *Environmetrics* (In Press, 2005).
-      Gennings, C., Teuschler, L. K., Hartley, W., Thiyagarajah, A.,  Simmons, J. E., Analysis of Ordinal Histopathology Scores in an Additivity Model for Four Trihalomethanes,  *Environmetrics *(Accepted Subject to Revision).
-      Casey, M., Gennings, C., Carter, W. H., Jr., Moser, V. C., Simmons, J. E., Power and Sample Size Calculations for Linear Hypotheses Associated with Mixtures of Many Components Using Fixed-Ratio Ray Designs.* J. Environmental and Ecological Statistics* (Submitted).
-      Moser, V. C., Casey, M., Hamm, A., Carter, W.H. Jr., Simmons, J. E.,  Gennings, C.,  Neurotoxicological and Statistical Analyses of a Mixture of Five Organophosphorus Pesticides Using a Ray Design. *Toxicological Sciences* (Submitted).
-      Crofton, K. M., Craft, E. S., Hedge, J. M., Gennings, C., Simmons, J. E., Carchman, R. A., Carter, W. H. Jr., DeVito, M. J., Short-Term Exposure to an Environmental Mixture of PHAHS: Effects on Serum Thyroxine. (In NHEERL Review*).*

** ****Methodology  Development and Evaluation of  Trihalomethane (THM) Mixtures**
- ▪ THMs are formed during chemical disinfection of water resulting in ubiquitous exposure. Four THMs  (chloroform, CHCl3; bromoform, CHBr3 bromodichloromethane, BDCM; chlorodibromomethane, CDBM; bromoform, CHBr3) are regulated as a mixture.
- ▪ Hepatotoxicity was selected as it is the health endpoint used to calculate 3 of the (CHCl3, CDBM, CHBR3) of the 4 THM oral Reference Doses.
- ▪ Female CD-1 mice were exposed by gavage in an aqueous vehicle daily for 14 days.  Hepatotoxicity was assessed by SDH, a serum marker of hepatic damage.
- ▪ Each  experiment had 12 dose groups.  These are:  1 vehicle control group
-                  3 dose levels, THM ‘A’ alone
-                  3 dose levels, THM ‘B’ alone
-                  3 dose levels of  A+B at a 1:1 mixing ratio
-                  2 dose levels of A+B at a mixing ratio based on chlorinated drinking water.
-  ▪The environmentally relevant mixing ratios were different for each binary combination:
- 	CHCl3:BDCM – 2.7:1		CHCl3:CDBM – 6.5:1
- 	CHCl3:CHBr3 – 65:1			BDCM:CDBM – 2.4:1
- 	BDCM:CHBr3 – 24:1			CDBM:CHBr3 – 10:1
- ▪ The data were analyzed by the threshold dose-additivity model developed  in this project. Single-chemical dose-response curves are used to predict mixture response. Biological variability is incorporated by calculation of prediction intervals.  If the experimental response falls within the 95% prediction interval, the null hypothesis of dose additivity is not rejected**.**

** ****Methodology  Extension and Evaluation of Organophosphorus (OP) Pesticide Mixtures**
- ▪ OP pesticides have waning household usage but are still widely used on a variety of food and commercial crops, with a resulting high potential for cumulative exposure.   OP pesticides were the first class of chemicals  to undergo a cumulative risk assessment under the 1996 FQPA.
- ▪ Male Long-Evans rats were exposed by oral gavage to either the individual OPs, a mixture of either five OPs (the full ray) or four OPs (the reduced ray). Toxicity was assessed between 3.5 and 4.5 hr after dosing.
- ▪ Both neurochemical (blood cholinesterase (ChE), brain ChE) and behavioral endpoints (motor activity, gait score, tail-pinch response) were evaluated.
- ▪ The mixing ratios  were based on the US. EPA Dietary Exposure Model.  The full ray contained chlorpyrifos, 0.031; acephate 0.040; diazinon 0.002; dimethoate 0.102; and malathion 0.825   The reduced ray excluded  malathion but contained the other 4 OPs at the same relative proportion as the full ray.
- ▪ The data were analyzed by methods developed for this project in which the single chemical data were used to construct an additivity model.   The additivity model was compared statistically to the mixture model, which was constructed using the mixture data.   The full and reduced mixture rays were compared using novel methodology developed for this project by comparing the dose-response curves for the two rays within the same dose range

- The goals of this project are development, refinement and utilization of efficient component-based experimental designs and analysis techniques for evaluation of departure(s) from additivity and associated methods for prediction of the effects of chemical mixtures.

**Evaluation of the Interactive Effects of  a Mixture of 18 Thyroid Disrupting Chemicals (TDCs)**
- ▪ Endocrine disruption from exposure to environmental chemicals has been linked to a variety of adverse health outcomes.   There is wide-spread exposure to multiple EDCs and concern with regard to the potential for additive or greater-than additive effects.
- ▪ Female Long-Evans were exposed by daily oral gavage for 4 days to either one of 18 polyhalogenated aromatic hydrocarbons  (PHAHs) or to a mixture of the 18 PHAHs.
- ▪ On the 5th day,  blood was collected for measurement of serum thyroxine (T4) concentration.
- ▪ The mixing ratio was based on the ratios of PHAHs found in breast milk, fish and other sources of human exposure.
- ▪ The data were analyzed by an extension of methodology developed for this project in which the single chemical dose-response curves used to construct an additivity model were not required to have the same maximum effect.   The additivity model was compared statistically to the mixture model.    Further comparisons were made to determine for which mixture dose groups the experimentally observed serum T4 response was significantly different from the response predicted under additivity.

- For both  the full and the reduced rays, the mixture data fell  significantly below the additivity model, indicating greater than additive effects (synergism) in the lower dose range. Blood ChE showed a similar pattern (data/figures not shown) with greater than additive effects observed in the lower dose range.  However, brain ChE and blood ChE differed in the influence of malathion.  For brain ChE inhibition, the full and reduced rays were not significantly different, evidence that malathion was not responsible for the nonadditive interaction.  In contrast, the full and reduced rays for blood ChE were significantly different, indicating that malathion participates in  the interactive effect of the OPs on blood ChE.

|  | 
Statistic | P value |
| --- | --- | --- |
| Overall F test  | 5.055 | <0.001 |
| Individual F tests  |  |  |
| Total Dose group (g/kg) |  |  |
| 20.03 | 1.197 | 0.274 |
| 66.74 | 0.0006 | 0.980 |
| 200.26 | 0.038 | 0.846 |
| 667.54 | 9.75 | 0.002* |
| 1335.08 | 15.6 | <0.001* |
| 2002.62 | 11.5 | <0.001* |