*Pure Appl. Chem.,* Vol. 75, Nos. 11--12, pp. 2375--2388, 2003. © 2003 I
U PAC

> **Workshop 1.2**
>
> **Regulatory application of SAR/QSAR for priority setting of endocrine
> disruptors: A perspective\***
>
> Weida Tong^1,‡^, Hong Fang^2^, Huixiao Hong^2^, Qian Xie^2^, Roger
> Perkins^2^, Jeanne Anson^1^, and Daniel M. Sheehan^1^
>
> *1 FDA's National Center for Toxicological Research (NCTR), Jefferson,
> AR 72079, USA; ^2^Logicon ROW Sciences, Jefferson, AR 72079, USA*
>
> *Abstract:* Some seven years have passed since the U.S. legislature
> mandated the Environmental Protection Agency (EPA) to develop and
> implement a screening and testing program for chemicals that may
> disrupt the delicate endocrine system. The envisioned EPA program has
> evolved to incorporate a tiered scheme of in vitro and in vivo assays,
> and con­sidered QSAR as a viable method to set testing priorities. At
> the U.S. Food and Drug Administration\'s (FDA) National Center for
> Toxicological Research (NCTR), the Endocrine Disruptor Knowledge Base
> Project has developed models to predict estrogen and androgen receptor
> binding. Our approach rationally integrates various QSAR models into a
> sequential "Four-Phase" scheme according to the strength of each type
> of model. In four hierarchical phases, models predict the inactive
> chemicals that are then eliminated from the pool of chem­icals to which
> increasingly precise but more time-consuming models are subsequently
> ap­plied. Each phase employs different models selected to work
> complementarily in represent­ing key activity-determining structure
> features in order to absolutely minimize the rate of false negatives,
> an outcome we view as paramount for regulatory use. In this paper, the
> QSAR models developed at NCTR, and particularly how we integrated
> these models into the "Four-Phase" system will be discussed for a
> number of datasets, including 58 000 chemicals identified by the U.S.
> EPA.

**INTRODUCTION**

A large number of environmental chemicals are suspected of disrupting
endocrine function by mim­icking or antagonizing natural hormones in
experimental animals, wildlife, and humans. There is grow­ing concern
among the scientific community, government regulators, and the public
that these en­docrine-disrupting chemicals (EDCs) in the environment are
adversely affecting human and wildlife health \[1,2\]. Adverse outcomes
have been observed in experimental animals and wildlife; potential
ef­fects on humans include reproductive and developmental toxicity,
carcinogenesis, immunotoxicity, and neurotoxicity, among others \[3\].
EDCs may exert adverse effects through a variety of mechanisms, such as
estrogen receptor (ER)-mediated mechanisms of toxicity.

The scientific debate surrounding EDCs has grown contentiously, in part
owing to the fact that some suspected EDCs are produced in high volume,
and many chemicals are economically important. These public and
regulatory concerns led to government regulatory actions \[4\] and
expanded research across Europe, Japan, and North America. The U.S.
Congress in 1996 mandated that the Environmental

\*Report from a SCOPE/IUPAC project: Implication of Endocrine Active
Substances for Human and Wildlife (J. Miyamoto and J. Burger, editors).
Other reports are published in this issue, *Pure Appl. Chem.* **75,**
1617--2615 (2003).\
^‡^Corresponding author

2376 W. TO N G et al .

Protection Agency (EPA) should develop a strategy for screening and
testing a large number of chem­icals found in drinking water and food
additives \[4\] for their endocrine disruption potential. In response to
Congressional action, the EPA established the Endocrine Disruptor
Screening and Testing Advisory Committee (EDSTAC), which includes
scientific expertise from government, academia, and industry. EDSTAC
recommended a two-tier (Tier 1: screening and Tier 2: testing) strategy
to screen and test es­trogenic, androgenic, and thyroidal activities for
a large number of chemicals. To accomplish this, chemicals will be
screened (Tier 1) using a multiple endpoint strategy that includes more
than 20 dif­ferent in vitro and in vivo assays recommended by EDSTAC
\[5\]. Although more than \~87 000 chemi­cals were initially selected for
evaluation, many are polymers, leaving about \~58 000 chemicals for
eval­uation in Tier 1. The number that will progress to the testing step
(Tier 2) \[6\] is not known. Processing chemicals through both tiers
will require many years and extensive resources. Hence, the EPA has
adopted an approach requiring priority setting before Tier 1
(\<[[www.epa.gov/scipoly/oscpendo/]{.underline}](http://www.epa.gov/scipoly/oscpendo/)\>).

Among the types of hormonal activities, estrogenic activities have been
most widely studied. Estrogenic endocrine disruption can result from a
variety of biological mechanisms. We found a strong linear correlation
among a diverse group of chemicals between binding affinities with the
ER from the rat uterine cytosol and those with the human ER of the
α-subtype \[7\]. Furthermore, the rat ER binding data also correlates
strongly with the results from assays measuring estrogenicity using
downstream events, i.e., the yeast-based reporter gene and the MCF-7
cell proliferation assays. Importantly, chemi­cals positive in
uterotrophic responses (in vivo estrogenic activity) are also positive
in the ER binding assay, indicating that binding affinity is a good
predictor of in vivo activity with few false negatives ob­served \[8\].
These findings demonstrate that ER binding is the major determinant for
estrogenic EDCs, and therefore the prediction of the rat ER binding
affinity provides an important piece of information for priority
setting.

Structure--activity relationship (SAR) and/or quantitative
structure--activity relationship (QSAR) models have proven their
utility, from both the pharmaceutical and toxicological perspectives,
for iden­tification of chemicals that might interact with ER. While QSAR
models (QSAR is used hereafter to encompass models that predict activity
on either an ordinal or categorical scale rather than only on a
quantitative scale) in pharmaceuticals identify high-affinity ligands,
they are particularly effective in toxicology in separating active and
inactive chemicals, and in rank-ordering chemicals according to po­tency.
Developing a useful QSAR model heavily depends on many factors,
particularly, including the quality of biological data, the descriptor
selection, and the choice of statistical approaches. Since any QSAR
approach has strengths and weaknesses, the careful selection of a
specific model or a combina­tion of models needs to be done in accordance
with the intended application.

This review first summarizes our motives and efforts to set up a robust
training set (the NCTR dataset) for developing ER QSAR models. The
development and validation of the QSAR models to predict ER binding is
then described, as well as the rationale for integrating models into a
hierarchical scheme for use in priority setting of potential estrogenic
EDCs. The review will conclude with a dis­cussion of some key issues for
applying QSAR models for regulatory purposes.

**NCTR ER DATASET: A ROBUST TRAINING SET FOR QSAR DEVELOPMENT**

Although an effective QSAR model depends on a number of factors, the
most critical is a training set with high-quality biological data. It is
desirable that the training set has the biological data coming from the
same assay protocol, and contains a sufficiently large number of
chemicals with diverse structure. Most importantly, both the biological
activity/potency data and structural characteristics in terms of var­ious
descriptors are evenly distributed as far as possible in a range
reflecting the domain in which chem­icals to be predicted are located.

A number of QSAR models for ER binding developed several years ago
\[9--13\], including some of our early works \[14--17\], were based on
datasets available in the literature. These datasets were too small
and/or lacked structural diversity \[11,12,14\]. Although these models
yield good statistical results

in the training and cross-validation steps and explain some structural
determinants for ER binding, they had limited applicability in
predicting the ER-ligand binding affinity of chemicals that, in fact,
cover a wide range of structural diversity.

In order to obtain an adequate training set to develop more robust QSAR
models, we developed and validated a rat ER binding assay and assayed
232 chemicals \[18,19\]. The ER binding activity is rep­resented by
relative binding affinity (RBA), where the RBA value for the endogenous
ER ligand, 17β-estradiol (E2), was set to 100. This NCTR dataset
contains chemicals that were selected to cover the structural diversity
that bind to ER with an activity distribution ranging over six orders of
magni­tude. Figure 1 compares the chemistry space/domain of the NCTR
dataset with other literature datasets used for early ER QSAR models.

![](media/image1.png){width="3.6131944444444444in"
height="3.2527777777777778in"}

**Fig. 1** Comparison of structural diversity of NCTR, Waller, and
Kuiper datasets in a chemistry space defined by three principal
components (PC) of structural descriptors.

**DEVELOPMENT OF QSAR MODELS FOR ER BINDING**

We evaluated a number of QSAR approaches useful for predicting ER
binding affinity ranging from simple rejection filters often used for
drug-like chemical identification to more sophisticated QSAR models
often used in the lead optimization. The methods selected for the
four-phase approach are dis­cussed below.

**Rejection filters**

Rejection filters are useful to quickly exclude chemicals from further
evaluation. We investigated vari­ous physicochemical parameters to use as
rejection filters. The ideal filter to be used in the earliest stage of
modeling should (1) not generate any false negatives and (2) be able to
significantly reduce the num­ber of chemicals for further evaluation. Two
rejection filters, molecular weight range and lack of a ring motif, were
found to satisfy the two criteria. Chemicals matching any one of these
two filters were ex­cluded from subsequent models. The first rejection
filter is a molecular weight range, set to \<94 and

© 2003 I U PAC, *Pure and Applied Chemistry* 75, 2375--2388

2378 W. TO N G et al .

\>1000. The molecular weight of phenol, 94, was considered as the lowest
limit for a chemical to bind to ER, whereas a molecular weight of 1000
was considered as the upper limit. The second rejection fil­ter requires
that an ER ligand contains at least one ring structure of any size based
on the finding from a large literature survey that no known estrogens
were found to lack a ring \[20\].

**Structural alerts**

Structural alerts are key 2D structural fragments associated with ER
binding. Figure 2 depicts the three structural alerts, i.e., the
steroid, DES, and phenolic skeletons, selected to identify potential ER
binders. Each alert independently characterizes a unique structural
feature important for the ER binding. Any chemicals containing any one
of these structural alerts are considered to be a potential ER binder.

![](media/image2.png){width="3.6034722222222224in"
height="1.2770833333333333in"}

**Fig. 2** Three structural alerts defined by 2D substructural features
commonly observed for ER ligands.

These three substructures were selected as structural alerts because the
length and width of both steroid and DES skeletons conform well to the
dimension of the ER binding pocket. In addition, while most endogenous
hormones contain the steroid skeleton, most strong estrogens have two
benzene rings separated by two carbon atoms \[20\]. It has been long
understood that the phenolic ring is often associ­ated with estrogenic
activity \[21\]. In fact, the contribution of the phenolic ring in ER
binding is much more significant than any other structural feature
\[20\]. By overlaying the crystal structure of ER com­plexes of E2,
4-hydroxytamoxifen, raloxifene, and DES based on their common protein
residues at the binding site, we found that the phenolic rings of all
four of these ligands are closely positioned at the same location as
shown in Fig. 3, allowing hydrogen bond interactions with Glu 353 and
Arg 394 of the receptor and a water molecule \[22\].

**Pharmacophore queries**

The pharmacophore is represented as a 3D arrangement, in which such
molecular features as H-donor, H-acceptor, and hydrophobic center are
positioned and combined with a certain geometry, that is sup­posedly
needed for a molecule to exhibit a certain type of biological activity
\[23\]. The pharmacophoric arrangements are used as the queries. A
query-matched chemical is considered positive and segregated for further
evaluation. One of the advantages of the 3D pharmacophore searching is
that it can identify chemicals whose 3D structures are similar to the
template structure (normally, a highly active chemi­cal) that may not be
discernable by chemists in 2D.

The bound ligand-ER crystal structures \[24,25\] guided our selection of
pharmacophore queries. Using 3D structures of E2, raloxifene,
4-hydroxytamoxifen, and DES in the ER-bound conformation as templates,
all possible molecular features as well as molecular shapes were
delineated. For each tem­plate, any of three to six features were
combined to form pharmacophore queries. These queries were sorted
according to their discriminatory power to separate active from inactive
chemicals in the NCTR dataset. A chemical with any of multiple 3D
conformations (up to 100) \[26--28\] matching the query was considered
to be active. Queries with high discriminatory power were further
evaluated for their bio‑

![](media/image3.png){width="3.073611111111111in"
height="2.1930555555555555in"}

**Fig. 3** Relative positions of estradiol (E2), DES, raloxifene, and
4-hydroxytamoxifen (OHT) in the complex with ER.

![](media/image4.png){width="3.973611111111111in"
height="1.9465277777777779in"}

**Fig. 4** Seven pharmacophore queries where green mesh balls represent
H-bond acceptor sites, blue mesh balls represent hydrophobic centers and
yellow mesh balls represent aromatic centers. Solid white surfaces
represent shape constraint.

logical relevance based on careful SAR examinations of a large number of
chemicals for their binding affinities to ER \[20\] in conjunction with
analysis of the ligand-ER crystal structures \[24,25\]. Finally, the
Tanimoto similarity score was used to determine the uniqueness of each
query. Through this process, we identified seven queries that provide
unique pharmacophoric signatures for the ER binding (Fig. 4). A chemical
could match none, a few, or many of the seven separate queries. We
generally found that the number of matches increased in direct
proportion to measured activity among the training-set chem­icals. Thus,
the number of pharmacophore matches could be used to rank-order
chemicals in accordance with the potential activity.

**CLASSIFICATION MODELS**

Classification is a supervised learning technique that provides
categorical prediction (e.g., active/inac­tive). A number of
classification methods were evaluated to categorize chemicals as ER
binders or non­binders. While the methods differ in a number of ways,
they generally produce similar results \[29\]. We found that the nature
of the descriptors used, and more specifically the effectiveness in
which descrip­tors encode the structural features of the molecules
related to the ER binding activity, is far more im­portant than the
specific method employed. The selection of biologically relevant
descriptors is the crit‑

2380 W. TO N G et al .

ical step to develop a robust model. We found the Genetic Algorithm to
be the preferred method to iden­tify the most biologically relevant
descriptors from a large set of descriptors.

For example, using the best 10 descriptors selected by the Genetic
Function Approximation ap­proach \[30,31\] from among 153 descriptors, we
were able to construct a Decision Tree model for the classification. The
model displays a series of YES/NO rules to classify chemicals into
active and inac­tive categories based on five meaningful descriptors: the
Phenolic Ring Index (0 or 1), log *P,* two types of parameters related
to the charged partial molecular surface area, and a geometric
descriptor con­nected with the breadth of the molecule. The model
identified the Phenolic Ring Index as the most im­portant descriptor for
ER binding. If chemicals contained a phenolic moiety but also had log
*P* values larger than 1.49, they were more likely to be ER binders. In
contrast, chemicals without a phenolic moi­ety were less likely to be ER
binders unless they had relatively large hydrophobicity and charged
sur­face area, and breadth of the structure.

**3D-QSAR/CoMFA model**

We evaluated three different techniques for the QSAR modeling---CoMFA,
CODESSA (COmprehensive DEscriptors for Structural and Statistical
Analysis), and HQSAR (Hologram QSAR)---for their utility (predictivity,
speed, accuracy, and reproducibility) to quantitatively predict the ER
binding activity \[16,22\]. For three relatively small datasets under
investigation, the CoMFA and HQSAR procedures were of a comparable high
performance \[16\]. These two procedures were further investigated and
compared, particularly for their predictivity, by using the NCTR dataset
and two other test sets \[22\]. We found that CoMFA performed better for
the training set as well as for predicting two different test sets.

To develop a CoMFA model, the molecules of interest must first be
aligned to maximize super­position of their steric and electrostatic
fields in 3D. The critical and difficult aspect of the CoMFA pro­cedure
is choosing the most appropriate set of alignment rules for a
structurally diverse training set. Fortunately, crystal structures of
the ER complex of four ligands were published \[24,25\] that aided the
derivation of rational CoMFA alignment rules. The CoMFA model based on
the crystal structure-guided alignment is statistically robust. With the
conventional *r*^2^ = 0.91 *(r:* the correlation coefficient) and the
cross-validated *q*^2^ (leave-one-out) = 0.66, *(q:* the predictive
correlation coefficient) indicating that it is both internally
consistent and highly predictive.

**Model validation**

Concordance, specificity, and sensitivity \[32\] are commonly used to
assess the quality of a classifica­tion model, while a quantitative
regression model is assessed using *r*^2^ *\[14\].* The current
challenge in QSARs is no longer in constructing a model that is
statistically sound to predict the activity within the training set, but
in developing a model with the capability to accurately predict the
activity of untested chemicals. Most experts in the QSAR field, as well
as the present authors, assert that a model's predic­tive capability at
least needs to be demonstrated using some sort of cross-validation
procedure. All mod­els developed in our laboratory are validated using
the leave-one-out technique for quantitative models and leave-10-out for
qualitative models. It is worthwhile to point out that cross-validation
methods only assess the interpolation within the training set, and have
a limited ability to validate the prediction of untested chemicals that
are structurally different from the training-set chemicals.

When additional data are available, the models are validated by
predicting chemicals, not used in the training set but with the known
activity data (the test set). The major difference between the
cross­validation and external validation is that the chemicals selected
in the latter case are in a sense of ran­domness, providing a more
rigorous evaluation of the model's predictive capability for untested
chem­icals. We strongly believe that the confidence in a model's
predictive capability can be tested and validated when predictability
has been demonstrated on an external test set.

It is usual that the QSAR training set barely contains enough chemicals
to develop a statistically robust model such that setting aside
chemicals for use in external validation (10\~20 % of the dataset is
recommended) is not possible. One approach for selecting a test set is
to identify datasets in the litera­ture with the same type of activity.
In such cases, care must be taken to avoid interlaboratory and assay
variability among different data sources. It is desirable that the
potential test set has activity data meas­ured in an assay protocol as
similar as possible to that for the training set to keep the variability
at min­imum. Another important consideration in selecting the test set is
to ensure that chemicals in the dataset relate to the real problem in
question. Based on these considerations, the datasets reported by Kuiper
et al. \[33\], Waller et al. \[10\], and Nishihara et al. \[34\] were
selected as test sets. In Kuiper's study, the pure human ERα was used,
whereas the mouse uterine cytosol that primarily contains ERα was used
in Waller's data. The Nishihara dataset contains 517 chemicals tested
with the yeast two-hybrid assay, of which over 86 % are pesticides and
industrial chemicals.

**NCTR "FOUR-PHASE" SYSTEM AS A PRIORITY SETTING TOOL FOR REGULATORY
APPLICATION**

The objective of priority setting is to rank order, from most to least
importance, a large number of chem­icals to undergo more
resource-intensive experimental evaluations. The strategy of QSAR is
highly de­pendent on the application domain and goals. The QSAR used in
drug discovery is to increase the chance of finding active chemicals or
"hits" that may become "lead" chemicals, and false positives are of
great concern. In contrast, a good priority setting method for the
regulatory application should have minimal false negatives. False
negatives constitute a crucial error, because they will be assigned a
lower priority for subsequent evaluation. In addition, the methods
should provide reasonable quantitative ac­curacy for true positives, as
those with higher affinities will generally be of higher priority.

The QSAR procedures described in the previous section have strengths as
well as weaknesses, and they all have a degree of prediction error. All
procedures and particularly those that only provide active/inactive
predictions can be optimized to minimize either the overall prediction
error or the false negative or positive rate. Decreasing false negatives
is achieved at a cost of increasing false positives and vice versa.
Because selecting a single procedure is problematic, we adopted an
approach of ration­ally combining different QSAR procedures into a
sequential "Four-Phase" scheme according to the strength of each type of
procedures. A progressive phase paradigm is used to screen out chemicals
and thus reduce the number of chemicals to be considered in each
subsequent phase. The four phases work in a hierarchical manner,
incrementally reducing the size of the dataset while increasing
precision of the prediction during each phase. Within each phase,
different models are selected to work complementar­ily in representing
key activity-determining structure features in order to minimize the
rate of false neg­atives. The overall architecture of the NCTR
"Four-Phase" system for identification of ER ligands is il­lustrated
below and in Fig. 5.

2382 W. TO N G et al .

![](media/image5.png){width="3.359722222222222in"
height="2.1902777777777778in"}

**Fig. 5** NCTR "Four-Phase" system as a priority-setting tool.

***Phase I: Filtering --*** Two filters, the molecular weight range and
ring-structure indicator described above, were selected to efficiently
eliminate chemicals very unlikely to have ER binding activity. As shown
in Table 1, the two rejection filters correctly eliminated 6 and 98
inactive chemicals from the NCTR and Nishihara datasets, respectively.
No false negative was introduced with these two rejection filters. For
the Nishihara dataset, the data size was reduced by some 21 %. This
sug­gests that, for real-world applications, the rejection filters
significantly reduce the number of chemicals for further evaluation with
minimum risk of introducing false negatives.

> **Table 1** Results of two rejection filters for the NCTR, Nishihara
> and Walker datasets\*.
>
> [Eliminated]{.underline} [by]{.underline}
>
> [Datasets]{.underline} [MW range]{.underline} Number (%)
>
> MW Ring of eliminated
>
> Sets Size Active Inactive Active Inactive chemicals
>
> NCTR 232 0 0 0 6 6 (2.6 %)
>
> Nishihara 463 0 28 0 89 98 (21.2 %)
>
> Walker 558 230 16 048 1495 16 689 (28.7 %)
>
> \*Table lists the number of chemicals eliminated by either MW range or
> lack of ring criteria as well as their combination. No active chemical
> was rejected by these two filters.

***Phase II: Active/Inactive Assignment --*** This phase categorizes
chemicals from Phase I as either active or inactive. The three
structural alerts, seven pharmacophore queries, and the Decision Tree
clas­sification model (11 models in total) discussed above were used in
parallel to discriminate be­tween active and inactive chemicals. To
ensure the lowest possible false negative rate in Phase II, a chemical
predicted to be active by any of the 11 models is presumed active and
subsequently evaluated in Phase III, while only those predicted to be
inactive by all these models are deemed inactive and eliminated from
further evaluation. Since structural alert, pharmacophore, and Decision
Tree methods incorporate and weigh differently the various structural
features that endow a chemical with the ability to bind the ER, the
combined outputs derived from the three approaches are complementary in
minimizing false negatives.

> All active chemicals in the NCTR, Waller \[10\], Kuiper \[33\], and
> Nishihara \[35\] datasets were identified by combining the 11 models.

***Phase III: Quantitative Predictions --*** In Phase III, the CoMFA
model described above \[22\] was used to make a quantitative activity
prediction for chemicals categorized as active in Phase II.

> Chemicals with higher predicted binding affinity are ranked at higher
> priority for further evalua­tion in Phase IV.

***Phase IV: Rule-Based Decision-Making --*** In this final stage of the
integrated system, we believe that a set of rules needs to be developed
as a knowledge-based or expert system to make a priority set­ting
decision. The system is useful only after incorporating accumulated
human knowledge and expertise (i.e., rules). This system can make
decisions on individual chemicals based on the rules in its knowledge
base. Computational chemists, toxicologists, and regulatory reviewers
should jointly develop and define the rules. The following are
suggestions for such rules:

> 1\. Special attention needs to be placed on the following chemicals,
> which may need to be reeval­uated by assaying or modeling according to
> the "IF-THEN" scheme depicted in Fig. 6\[20\]:

1)  The chemical is predicted to be inactive, but its structure
    information has been mod­ified during structural preprocessing, e.g.,
    by "correcting" such chemicals as mixtures and organic salts into
    separate entities.

2)  The chemical whose structure is dissimilar to all those that have
    been used to train and test the models.

3)  The chemical is active in Phase II, but inactive in Phase III.

![](media/image6.png){width="3.4166666666666665in" height="3.9in"}

**Fig. 6** Identification of ER ligands using a set of "IF-THEN" rules:
(a) IF non-cyclic structure, THEN it is unlikely to be an ER ligand; (b)
IF a chemical has non-aromatic ring structure, THEN unlikely to be an ER
ligand if does not contain an O, S, N or other heteroatoms for
H-bonding. Otherwise, binding potential is dependent on the occurrence
of key structural features; (c) IF chemical has a non-OH aromatic
structure, THEN binding potential is dependent on the occurrence of the
key structural features; and (d) IF chemical contains a phenolic ring,
THEN it tends to be an ER ligand if it contains any additional key
structural features. For the chemicals containing a phenolic ring
separated from another benzene ring with the bridge atoms ranging from
none to three, it will most likely be an ER ligand.

© 2003 I U PAC, *Pure and Applied Chemistry* 75, 2375--2388

2384 W. TO N G et al .

> 2\. Information on the level of human exposure and production,
> environmental fate, and other public health related parameters can be
> used independently or jointly incorporated for pri­ority setting.

The NCTR "Four-Phase" system has been validated using a number of
existing datasets, includ­ing the E-SCREEN assay data \[36\], the yeast
two-hybrid reporter-gene assay data \[35\], and other datasets
\[10,37--41\]. The system has produced no false negative for these test
datasets. The system was recently applied to two environmental datasets
recognized by EPA as important chemical subsets of po­tential EDCs:

1.  ***HPV-Inerts dataset,*** containing 623 high-production-volume
    inerts (HPV-Inerts), which is a subset of the Toxic Substances
    Control Act (TSCA) Inventory. The EPA is including HPV-Inerts in
    ver­sion 2 of the Endocrine Disruption Priority Setting Database
    (EDPSD2), and there was a need to prior­itize HPV-Inerts for further
    experimental evaluation \[42\]. Of 623 chemicals, 166 chemicals were
    either mixtures or their structures were not available, leaving 457
    chemicals for prediction.

2.  ***Walker dataset \[43\],*** containing a large and diverse
    collection of known pesticides and indus­trial chemicals as well as
    some food additives and drugs. The database contains 92 964
    chemicals, of which the Chemical Abstract Service (CAS) Registry
    number is available, that will probably have to be evaluated for
    their potential endocrine disruption. After eliminating chemicals
    for which structures were not available \[43\] and/or 3D structures
    could not be generated \[32\], a final dataset of 58 391 chemicals
    were predicted.

Table 2 summarizes the priority setting results for these two datasets
using the NCTR "Four-Phase" system. Even when only the Phase I and II
protocols are used, the system dramatically reduces the number of
potential estrogens by some 80\~85 %, demonstrating effectiveness in
eliminating the most unlikely ER binders from further expensive
experimentation. The Phase III CoMFA model further reduces the data size
by about 5\~10 %. Importantly, the quantitative binding affinity
prediction from Phase III also provides an important rank-ordering value
for priority setting.

> **Table 2** Size reduction of two environmental datasets processed by
> the NCTR "Four-Phase" system.

+----------------------------------+----------------------+-----------+
|                                  | HPV-Inerts           | > Walker  |
+----------------------------------+----------------------+-----------+
| Original data size               | 457                  | 58 391    |
+----------------------------------+----------------------+-----------+
| After Phases I and II            | 15.7 %               | 12.0 %    |
+----------------------------------+----------------------+-----------+
| After Phases III                 | 9.8 %                | --        |
+----------------------------------+----------------------+-----------+

**QSAR APPLICATION IN PERSPECTIVE**

The QSAR procedures are applied extensively to a wide range of
scientific disciplines including chem­istry, biology, and toxicology
\[44,45\]. In both drug discovery and environmental toxicology \[46\],
QSAR models are now regarded as a scientifically credible tool for
predicting and classifying the biological activities of untested
chemicals. However, analogous to misinterpretation of experimental
results, QSAR results can also be misleading when limitations of the
procedures are not well understood. Presented below are our
experientially based suggestions of the salient aspects in applying
QSARs for toxicology and regulation.

**1. Limitation in the fundamental principle of the QSAR --** Any QSAR
model will produce some degree of error. This is partially due to the
inherent limitation to predict a biological activity solely based on the
chemical structure. One can argue from the principles of chemistry that
the molec­ular structure of a chemical is key to understanding its
physicochemical properties and ultimately its biological activity and
the influence on organisms. Since both molecular structure and physico‑

> chemical properties are associated with the chemical itself, the
> relationship between structure and physicochemical properties should
> be apparent and, therefore, more accessible using the QSARs. In
> contrast, the biological activity of a chemical is an induced response
> that is influenced by nu­merous factors dictated by the levels of
> biological complexity of the system under investigation. The
> relationship between structure and activity is thus more implicit and
> thereby poses a more challenging problem in QSAR applications.

2.  **Limitation in extrapolation of QSAR models --** A chemical can be
    represented in three distinct, but also related, structural
    representations such as 2D substructures, 3D pharmacophores, and
    physicochemical properties. If a biological mechanism is mainly
    related to the chemical structure (probably in the case for receptor
    binding), QSARs become meaningful using the aforementioned
    structural features. However, we often find that, even for a simple
    mechanism such as ER-bind­ing, some features may well represent
    binding dependencies for one structural class, whereas other
    features will better represent binding dependencies for a different
    structural class \[20\]. In such cases, caution is warranted in
    interpreting QSAR results for the chemical classes that are not well
    represented in the training set. In other words, no matter how
    rigorous is the validation pro­cedure used, the model may give
    incorrect predictions for some chemicals since the entire chem­istry
    space of active chemicals is unknown. This realization is especially
    important when a QSAR model is used for regulatory application since
    it could lead to false negatives.

3.  **Limitation in processing chemical structure --** Most
    computational chemistry programs accept only discrete organic
    chemicals for the descriptor calculation and QSAR modeling.
    Unfortunately, most toxicological databases contain chemicals that
    are not necessarily discrete or­ganic chemicals, but sometimes are
    mixtures. Thus, it is necessary to process molecular structures of a
    toxicological dataset by "correcting" chemicals with separate
    entities (e.g., mixtures, organic salts, the presence of H~2~O and
    HCl, etc.), and eliminating those chemicals whose descriptors can­not
    be calculated. Such procedures might lead to a prediction that might
    not reflect the real ac­tivity of the preprocessed chemical.

4.  **Limitation in quality and transformation of biological data --**
    Predictions from any model are intrinsically no better than the
    experimental data employed to calibrate the model. Any limitations
    of the assay used to generate the training data apply equally to the
    model's predictions. Furthermore, additional limitations could be
    introduced by transforming the biological data for use in the QSAR
    modeling. When developing quantitative models, inactive chemicals
    are usually either not used because their exact values cannot be
    determined or included by assigning an arbi­trary value. Either case
    will tend to decrease the model sensitivity in prediction of
    chemicals with low activity. For qualitative models, false negatives
    and false positives depend on the defined cut­off value to
    distinguish active from inactive. As the cut-off value is lowered,
    it is likely that the error will increase even for a well-designed
    and executed assay. The increased experimental error in close
    proximity to the cut-off value will be transferred to the QSAR
    model, which in turn will increase false prediction for chemicals
    with activity in this region. For example, as defined by EPA, the
    cut-off log RBA value to distinguish ER binders from non-ER binders
    is set to --3.0. This cut-off was used to develop the models in the
    NCTR "Four-Phase" system. There are 31 chemi­cals exhibiting the
    binding affinity in our assay that were assigned to be inactive
    because their RBA is below this cut-off value. Among these 31, 14
    chemicals have the RBA within 0.3 log units of the cut-off value
    ("within 0.5 log units" is expected for a good assay). Since the
    real activity classification for these chemicals are unknown,
    assigning them arbitrarily as inactive to train QSAR models
    introduces errors in prediction for chemicals with similar
    structures. Thus, caution must be taken in interpreting QSAR
    prediction results for these chemicals. Apparently, a high
    confidence in the prediction can only be expected for chemicals with
    the log RBA more positive than --2.5.

2386 W. TO N G et al .

While the results presented in this article clearly show both the
feasibility and utility of using QSARs for various applications, it is
important to realize that any QSAR model has to be considered as a
*living* model that will be improved whenever new data is available. In
other words, the model devel­opment should be a recursive process that
alternates between incorporating new data in the model and using the
model to choose new chemicals for assay \[47,48\]. As depicted in Fig.
7, the process starts with an initial set of chemicals from the
literature for QSAR modeling \[14--16\]. Next, the preliminary QSAR
models are used prospectively to define and rationalize a set of
chemicals that may further improve the model's robustness and predictive
capability. These new chemicals are assayed, and the data are then used
to challenge and refine the QSAR model. Thus, the process results in the
iterative increase of the chemistry space of the training set.

![](media/image7.png){width="0.6263888888888889in"
height="0.4534722222222222in"}![](media/image8.png){width="0.6604166666666667in"
height="0.18680555555555556in"}![](media/image9.png){width="1.6097222222222223in"
height="1.1833333333333333in"}

**Fig. 7** The recursive process to develop QSAR models.

Several benefits accrue from the integration of the experimental and
modeling efforts. Immediate feedback can be given to the
experimentalists so that suspected assay problems can be rapidly
investi­gated. Also, as the models evolve, the modelers can select the
chemicals for subsequent testing, based on considerations of structural
diversity and activity range. Each new assay data point coming from the
lab becomes a challenge to the evolving model; the result is either
further confirmation of its validity, identification of a limitation, or
an outlier prediction. Failure of the model also provides important
in­formation, such as identification of the need for new data based on a
rational understanding of the de­pendence of activity on structure.
Regardless of the cause of model failure, a research hypothesis is
spawned during each iteration process that should lead to new data
and/or an improved training set, and, ultimately, an improvement to the
living model.

**ACKNOWLEDGMENTS**

The authors gratefully acknowledge the U.S. Environmental Protection
Agency (EPA) and the American Chemistry Council for financial support.

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