1 Appendix G: Section 7 Supplemental, ERES TrSet Decision Criteria for Binder/Nonbinder and Data Collection and Processing Details

G. 1 Data Interpretation: Decision Criteria using Binding, Gene Activation and Associated

Information to Determine Binders and Nonbinders.

The following text and figures describe the types of information collected and how the

information is used to classif y test chemicals as Binders or Nonbinders in the ERES TrSet. This is meant to be a quick summary of the approach described more fully in the 2009 SAP to illustrate the decision process and the multiple lines of evidence collected and used in interpreting t he data. The series of figures (Figures G

- 1 to G

- 8) illustrate various steps in the process and how the information is used together to provide a mechanistic understanding of the chemical’s activity in the assay as well as any effects assay components may have on the measured response.

The empirical mechanistic evidence from rtER cytosolic binding assays and trout liver Vtg gene expression (GA) assay s run in agonist and antagonist mode , along with recombinant rtERa binding assay data

, and binding assay Ki determinations are all u tilized together, as applicable (further described below) to assign a Binder or Nonbinder ‘hitcall’ for a parent chemical structure tested in the assays. These decisions are fu r ther supported by including measures of
assay paramete rs such media pH stability over the incubation period , cytotoxicity in the Vtg (GA) assay , and solubility in binding and GA assays in the interpretation. Additionally, analytical chemistry is used when applicable to verify sustained presence of parent chem ical in binding assays or investigate hypothesized bioactive metabolites. I nformation on compound chemistry is also used to assess the likelihood of c harged chemical species presence under the assay conditions. Decision pathways also include a consideratio n of the activity of the other

TrSet chemi cals found within an ERES chemical group. Figure 1 provides an overview of the process, showing how rtER binding and GA assays are used in concert and iteratively in assigning Binder and NonBinder determinations. ( Details for interpretations of binding curves and how the information is used to trigger GA assays are provided in Figures G

- 2 to G

- 6.)

The decision process overview is as follows and shown in Figure G

- 1. It begins by asking if there is a reproducible ER binding result. If Yes, the type of result detected leads to further questions. Where there is no indication of binding in 2 reproducible replicate runs, each using different trout liver cytosol preparations (a flat line means no indication of binding

dis placement), and the chemical is in a ERES TrSet class where all members tested so far are Nonbinders (verified with GA confirmation for representative chemical(s) within the class), the

chemical is added to the TrSet database as a Nonbinder . If there is re producible binding displacement, an additional binding assay is run to obtain a third replicate for calculation of an RBA. The degree of displacement triggers a more detailed decision path (Figure G

- 2). If displacement is >80%, and the chemical is in a pr evious ERES class, and within the LogKow range of previously determined ER Binders (confirmed by Vtg GA), the chemical is added to the TrSet as an ER Binder. Where significant displacement is <80%, or the chemical is in a class new to ERES, the chemical pr oceeds to GA testing. (Again, more details are provided in additional figures and text below.) If GA is confirmed the chemical is added to TrSet as Binder,2 and if not confirmed it’s added as a NonBinder. More detailed information on additional questions a sked (information gathered) and decision within the boxes is provided in Figure G

- 2

to G

- 8. If NO is the answer to the first question in Figure G

- 1, because replicate binding runs are outside of the typical tight assay variance, further investigation is d one. Historically this occurs when the parent chemical is converted to a metabolite with higher affinity for the receptor, and because it occurs to different degrees in the 3 replicate cytosols the result is a higher CV. Thus, investigations to confirm or refute the possibility of metabolic transformation are conducted, including identification of the metabolite responsible for the binding. Figures G

- 7 to G

- 8 illustrate how metabolism hypotheses are investigated in conjunction with binding, and GA data,

res pectively.

Figure G

- 1. Overview of Decision Paths used to make final ER Binder or Nonbinder activity call for ERES TrSet chemicals. The figure illustrates the questions asked and resulting actions taken to gain sufficient confidence to assign chemical activity. Details of portions of the pathways are provided in Figures G

- 2 to G

- 8.3 Figure G

- 2 shows 5 categories or ‘t ypes’ of binding displacement curves that are found. Depending on whether a chemical yields a curve of type A through E determines whether a GA assay or other tests are done. Figures G

- 3 to G

- 6 show detailed flow charts for each binding Type. Type A & B are covered in Figure G

- 3; Type C in Figure G

- 4; Type D in Figure G

- 5, and Type E in Figure G

- 6. One common question is whether the chemical belongs to a “known inactive” class, for instance one that can yield assay results of ‘apparent’ but not real bindi ng displacement (e.g., refer to detailed discussion on charged compound in SAP 2009). Thus, the questions asked and final interpretation build on previous knowledge and detailed investigations done to gain confidence in data interpretation.

Cyto rtER binding displacement results

Type A Displacement :
80% or greater Decision Path Step 1: Analyze Binding Displacement Curve to Trigger Next Step

Type B Displacement:

50 to 80% Type C Displacement:

20 to 50% Type D Displacement:

5 to 20% Type E: Displacement:

Flatline / skijump

Figure G

- 2. TrSet Decision Path Step 1: Characterize the Type of Reproducible Binding Displacement Curve Produced for the Chemical Under Study. The figure illustrates categories of binding assays results that are analyzed to determine if a confirmatory slice Vtg GA assay is needed. See Figures 3 to 6 for detailed decision paths for each binding type.4 Figure G

- 3 shows the decision path when a chemical yields a Type A and B binding displacement curve. If there is a lot of previous information for chemicals in this ERES chemical group then the confidence is high in assigning a Binder determination to the newly tested chemical in the group yielding at least a 50% displacement curve (Type B) or > 80% displacement (Type A), if its within a known active class. Or if it’s in a class known to give false positives with a good mechanistic understanding, then the chemical is classes as a Nonbinder. There is the option of using the in

- depth determination of nature of the binding displacement referred to as Ki investigations where needed to gain more mechanistic understanding. When previously done these experiments h ave confirmed the GA results, that is, that the apparent binding was not due to ER

- ligand interaction but other effects (e.g., charged species denaturing proteins resulting in labeled ligand release but not true competitive binding).

Figure G

- 3. Decis ion Path Step 2 to determine if a Vtg gene activation (GA) assay needs to be run, and the final activity assignment given to the chemical based on results of the GA assay are shown. This decision path is used for: A) chemicals yielding a Type A ER Binding displacement curve of > 80% and also in a previously untested ERES chemical class; and B) chemicals with 50% to 80% binding displacement. In each case the assay result must be reproducible.5 A similar analysis is done in Figure G

- 4 when Type C binding cu rves are noted. Notice the additional use of recombinant (rec) rtER assays also appears in this figure. The higher total protein concentration in cyto rtER preparations can result in lowering chemical bioavailability so only partial curves are obtained, wh ile the same chemical tested in the rec rtER preparation (with lower total protein) may yield a more complete curve. Thus, this assay can help to confirm parent chemical ER binding is responsible for the GA result and not a metabolite. However, if

no dis placement is seen in this assay, but GA was seen, the chemical is put in a ‘hold for further investigation’ box which can include looking for a possible metabolite to which GA might be

attributable.

Figure G

- 4. Decision Path Step 2 for Type C chemicals yielding 20 to 50% displacement in cyto rtER binding assays. The questions asked to determine if a Vtg gene activation (GA) assay needs to be run and the final activity assignment given to the chemical to add it to the

TrSet are shown.6 In Figure G

- 5 the information is used to gain confidence in the Binder or NonBinder decision when Type D curves are noted, where there may be some slight indication of binding and thus more uncertainty exists than for members of “known inactive” classes. The additiona l information may include some evidence of binding from literature references, and/or that the ERES class the chemical is in raises the likelihood that it may be a binder, or there is little evidence either way for a chemical from a new ERES chemical group

.

Figure G

- 5. Decision Path Step 2 for Type D chemicals yielding 5 to 20% displacement in cyto rtER binding assays. The questions asked in determining if Vtg gene activation (GA) assays need to be run, and the final activity assignment given to the chem ical to add it to

the TrSet are shown.

The next figure (Figure G

- 6) largely pertains to investigations into a new ERES chemical group, or for a chemical with evidence in the literature that it may be an ER Binder. In instances with some TrSet knowledge w ithin the class and no TrSet evidence of binding, but uncertainty due to literature reports, the metabolism hypothesis warrants investigation. Further investigations to determine if the activity noted is due to a metabolite and not the parent chemical are shown in Figures G

- 7 (utilizing binding assays along with analytical chemistry) and in Figure G

- 8 (for investigating metabolism hypotheses in GA assays with chemical analyses).

Taken together, the decision pathways in Figures G

- 1 to G

- 8 illustrate that m any different lines of information are used to gain mechanistic insights into whether a chemical is interacting at the7 ER ligand domain and capable of producing GA apart from possible artifacts, and to gain confidence that the binding is due to the parent chemical form and not a metabolite. The figures also demonstrate the process of combining this information to derive a final “hit call” for a chemical that reduces the level of uncertainty and providing more confidence in both Binder and

NonBinder TrSet de terminations.

Figure G

- 6. Decision Path Step 2 for chemicals yielding no indication of binding (Type E) but there is greater uncertainty, e.g., the chemical class has not been previously investigated, or there is literature evidence of apparent ER Bind ing. The questions asked to determine if Vtg gene activation (GA) assays need to be run, and the final activity assignment given to the chemical to add it to the TrSet are shown.8 Figure G

- 7. Decision Path used when an ER active metabolite is suspected. The questions asked using a combination of analytical chemistry and metabolite confirmation in binding assays is shown.

Figure G

- 8. Decision Path to investigate metabolic activation hypot heses in slice Vtg GA assays. The questions asked using a combination of analytical chemistry and metabolite confirmation in slice assays is shown.9 G. 2. Data Collection and Processing

Cytosolic rt ER Binding Assay
Trout liver c ytosol containing endogenous ERs was prepared from individual fish using a

method modified from Laws et al. (1996) as reported in Schmieder et al. (2004). Competitive binding a ssays modified from Laws et al. (1996) were performed to measure rtER affinity for

test chemicals as describ ed in Schmieder et al., (2004)

. The competitive binding assays used displacement of [3H]

- E2 from the rtER as the binding endpoint. Data were analyzed to determine relative binding affinity (RBA) for Binders with respect to the IC50 for E2 conducted

in tha t experiment. L igand binding characterization s (Ki binding assay) were performed when necessary on a case by case basis to assess whether binding was true competitive binding assays or not (Schmieder et al. 2004).

Rainbow Trout Liver Slice Vitellogenin mRNA Assay

Slices were prepared from the livers of immature male rainbow trout and incubated at one slice per well in a multi

- well plate system as previously described Schmieder et al. (2000). Slices were incubated with ethanol (0.2%) controls, E2 (10 - 10 to 10 - 5 M, in duplicate) as positive controls, and six concentrations of test chemical. After 48h incubation slices were analyzed for VTG mRNA by real time reverse transcriptase polymerase chain reaction (Q

- PCR) as described

in Schmieder et al. 2004. Slice viability was assessed three ways: 1) through visual observation, 2) observed decrease in total extractable slice RNA as a crude indicator of viability, and 3) by monitoring lactate dehydrogenase (LDH) leakage into slice media as previously describe d (Schmieder et al. 2000). Each chemical was tested in at least two agonist tests and if no agonist activity was apparent , was further tested in at least one antagonist test.

Chemical Analyses
A subset of the chemicals tested in the slice and binding a ssays assay underwent chemical analysis for behavior over time in the assay system . Chemicals were chosen for chemical analysis based on their activity in the slice assay and analytical knowledge of other chemicals in

the same chemical class. A variety of analytical chemistry techniques were employed to measure test chemical concentration in binding and gene assays where necessary (Schmieder et al. 2004). Techniques of analysis included HPLC, GC

- MS and LC

- MS.

Analysis of Binding and Vtg mRNA Data: Graph Pad Prism (GraphPad Software, San Diego, CA; www.graphpad.com ) was used for curve generation and statistical analysis. Competitive binding curves and IC50s for the full E2 response curve and test chemicals were generat ed as described in Schmieder et al. 2004. The Vtg mRNA data for test chemical was analyzed using the nonparametric Kruskal

- Wallis Rank Sum test with a Dunn’s post hoc test comparing test chemical results to negative control results (p

- value 0.05) to i dentify test chemical concentrations which significantly induced Vtg mRNA expression above the level in the control slices in that same run. The Vtg mRNA induction curve for the concentration response of E2 run in pa rallel were not compared to the test ch emical in an assay. The response of these positive controls was used as a QC verification that the assay was responding as expected. For an acceptable slice assay the maximum E2 response needed to be greater than 1E+06 Vtg mRNA copies/400 ng10 total RNA an d the LOEC for E2 should be equal to or less than

- 8 log M. If this response level and potency was not achieved the slice data was not used.

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