> [www.elsevier.com/locate/theochem]{.underline}![](media/image1.png){width="0.7736111111111111in"
> height="0.8270833333333333in"}![](media/image2.png){width="0.85in"
> height="0.4701388888888889in"}

QSAR models in receptor-mediated effects: the nuclear\
receptor superfamily

Hong Fang^a^, Weida Tong^a,^\*, William J. Welsh^b^, Daniel M.
Sheehan^c,1^

^a^Logicon ROW Sciences, 3900 NCTR Road, MC 910, Jefferson, AR 72079,
USA\
^b^Department of Pharmacology, Robert Wood Johnson Medical School,
University of Medicine & Dentistry of New Jersey,\
675 Hoes Lane, Piscataway, NJ 08854, USA\
^c^Division of Genetic and Reproductive Toxicology, National Center for
Toxicological Research (NCTR), Jefferson, AR 72079, USA

**Abstract**

The nuclear receptor (NR) superfamily is ligand-dependent
transcriptional factors that mediate gene expression in humans and
wildlife. These receptor-mediated effects are stimulated and/or
inhibited by endogenous cognate ligands for each NR but also by
exogenous substances including natural products and synthetic chemicals.
The NRs and their ligands have thus attracted broad scientific interest,
particularly in the pharmaceutical industry for drug discovery and in
toxicology and environmental science for risk assessment as, for
example, pertaining to endocrine disrupting chemicals. Besides advancing
our fundamental knowledge of NR biology, these scientific efforts are
generating relevant biological data on NR ligands particularly with
respect to their binding affinities, receptor specificities, and agonist
versus antagonist activities. These data from diverse sources serve as
input for construction of quantitative structure--activity relationship
(QSAR) models and related approaches that employ statistical regression
techniques to correlate variations between the biological activities of
NR ligands and their calculated structural and physicochemical
properties. In this review, we attempt to summarize the substantial body
of work in the published literature related to QSAR models for NR
ligands, with special emphasis on different computational approaches and
specific applications. Special attention is placed on the estrogen
receptor, for which the greatest amount of relevant information is known
at present. We also describe efforts to create 'benchmark' sets of
high-quality biological data on NR ligands that may serve as resources
for building statistically robust and predictive QSAR models.

Published by Elsevier Science B.V.

Keywords: Receptor-mediated effects; Nuclear receptors; Quantitative
structure--activity relationships; Estrogen receptor; Endocrine
disrupting chemicals

> 1\. Introduction

\* Corresponding author. Tel.: þ1-870-543-7142; fax: þ1-870-543-7382.

> E-mail address:
> [[wtong@nctr.fda.gov]{.underline}](mailto:wtong@nctr.fda.gov) (W.
> Tong).

1 Present address: Daniel M. Sheehan and Associates, 1422 Scott Street,
Little Rock, AR 72202, USA.

0166-1280/03/\$ - see front matter Published by Elsevier Science B.V.
PII: S0166-1280(02)00623-1

Nuclear receptors (NRs) are a superfamily of ligand-dependent
transcription factors that mediate the effects of hormones and other
endogenous lig­ands to regulate the expression of specific genes. Members
of the NR superfamily, which may number

![](media/image3.png){width="3.6902777777777778in"
height="1.0166666666666666in"}

Fig. 1. Functional domains of NRs.

in the hundreds, include receptors for various steroid hormones
(estrogen, androgen, progesterone, and several corticosteroids),
retinoic acid (the retinoic acid receptor α, β, and γ isoforms, and the
retinoid X receptor α, β, and γ isoforms), thyroid hormones, vitamin D,
and dietary lipids (the peroxisome proliferator activated receptor
(PPAR) α, β, and γ isoforms). A large number of 'orphan' NRs have also
been identified whose cognate ligands are still unknown \[1\].
Diminished or excessive production of a particular hormone or
target-cell insensitivity to a hormone is among the major problems
related to human endocrine dysfunction diseases \[2\].

The NRs contain five functional domains from the N to C termini,
designated A/B, C, D, E, and F(Fig. 1). While the C domain containing
the DNA-binding domain is the region of highest sequence conservation in
the superfamily, there is considerable variability across receptors in
the A/B and D domain \[3\]. The E domain, known as the ligand-binding
domain (LBD), is the sequence specifying ligand binding. Despite the low
sequence homology (as low as 20%) between the LBDs of different NRs,
analysis of crystal structure data \[4--14\] has revealed a remarkable
similarity in their three-dimensional (3D) structures. By virtue of its
significance for ligand specificity and receptor­mediated effects among
the NRs, the LBD has attracted the greatest amount of scientific
curiosity and attention.

Adopting the generally accepted depiction of the ligand--receptor
interaction in NRs, the LBD can be envisaged as a molecular switch.
Depending on whether the bound ligand is an agonist or antagonist, the
C-terminal helix 12 (H12) is found in either one of two orientations. In
its agonist-bound conformation, H12 serves as a'lid' to close the
ligand-binding pocket \[15\]. Thus, the activation function-2 (AF-2,
located at the C-terminal of the LBD) \[16\] is brought into position to
recruit co-activators and/or

co-repressors which bind to facilitate transcriptional initiation
\[17\]. In its antagonist-bound conformation, however, H12 is positioned
in a different orientation such that the transcriptional machinery
remains de­activated. The preservation of the molecular switch, despite
the low sequence homology in the LBD across NR members, argues that
agonist/antagonist discrimi­nation is critically important for receptor
functioning.

There are a large number of ligands, diverse in both structure and
source, which act through the NRs to produce receptor-mediated effects.
Steroid hormones play a vital role in a wide variety of essential
physiological processes including cell growth, sexual development,
maintenance of salt balance, and sugar metabolism \[18\]. Estrogens
elicit many cellular responses in target tissues and can exert both
positive and negative effects on health and reproductive function. For
example, estrogens are used beneficially for fertility control (oral
contraception) and for relief of menopausal symptoms (estrogen
replacement therapy). The adverse developmental effects of
diethylstilbestrol (DES) demonstrate human fetal sensitivity to
estrogenic chemicals. Progesterone is a hormone that functions to help
regulate the menstrual cycle and plays a significant role in pregnancy.
Progesterone has also been reported to both stimulate and inhibit the
growth of experimental mammary tumors, dependent upon the dose and
experimental model \[19\]. Retinoids have received considerable
attention from their use in the therapeutic treatment of different
diseases (e.g. cancer) and from their critical role in embryonic and
fetal development \[20,21\].

It has also been recognized that numerous synthetic chemicals are
capable of interfering with the normal signaling pathway by interacting
with NRs \[22\]. Environmental chemicals, such as pesticides,
herbi­cides, and plasticizers are capable of activating the PPAR leading
to liver dysfunction and hepatoma. Numerous findings also show that
certain

environmental chemicals have the ability to disrupt the endocrine system
by mimicking the functions of natural hormones. These endocrine
disrupting chemi­cals (EDCs) may exert adverse effects on humans and
wildlife \[23\].

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
NRs. While their primary function in the pharmaceu­tical enterprise is
lead discovery and optimization, QSAR models have played an essential
role in toxicology as a priority setting tool for risk assess­ment. For
example, public heath concern about EDCs resulted in Federal legislation
mandating the environ­mental protection agency (EPA) to regulate
potential EDCs in drinking water and food additives \[24\]. Under this
requirement, up to 87,000 existing chemicals will be experimentally
evaluated for their potential to disrupt activities in the estrogen,
andro­gen, and thyroid hormone systems \[25\]. In an attempt to reduce
the time and expense in this prodigious task of screening and testing
such a large number of chemicals, QSAR models are being developed to
prioritize chemicals as to their endocrine disrupting potential for
further experimental evaluation \[26\].

QSAR models offer numerous additional benefits beyond prediction \[27\],
such as: (1) leveraging existing structure--activity data; (2) providing
insights into mechanisms of action (e.g. agonist versus antagonist) or
identifying alternative mechanisms (e.g. metabolism); (3) identifying
key structural features associated with high/low activity; (4)
suggesting new design strategies and synthetic targets; (5) narrowing
the dose range for a planned assay; (6) assisting in generation of new
hypotheses to guide further research; (7) revealing chemicals that
deviate from the QSAR model and, therefore, from the presumed biological
model.

Basically, QSAR models employ quantitative regression methods to
correlate, and rationalize, variations in the biological activity of a
structurally related series of chemicals with variations in their
molecular structures as encoded in pre-selected quantities commonly
known as molecular descriptors. The fundamental assumption inherent in
every QSAR model is that a chemical's physical and chemical

properties and its biological activities are predicated by its structure
\[28\].

The number of QSAR models derived from NR-ligand biological data is
already fairly sizable. The list is surely to expand in coming years as
our knowledge and appreciation of the critical role of NRs in biological
function continue to grow. Reflecting the availability of relevant
biological data, the largest number of QSAR models in the published
literature is associated with estrogen receptor (ER) binding. A few
papers have reported QSAR models for androgens, progesterone, retinoic
acids and thyroid hormone. In this review, we will first provide an
overview of various QSAR approaches applied for the NR super­family. We
will then proceed to survey the QSAR models available for ER with
emphasis on two separate but related applications: structural
character­ization and risk assessment. Section 4 will summarize QSAR
models for other members of the NRs, such as the androgen receptor (AR)
and the progesterone receptor (PR). Section 5 will discuss ongoing
efforts to assemble high-quality 'benchmark' sets of biological data
associated with ligands for ER and other NRs that can be used to develop
robust and predictive QSAR models for applications in risk assessment.
These efforts may also provide a rich source of data for use as
standards in the design, testing, and comparison of different QSAR
models and approaches.

2\. QSAR models associated with nuclear receptors

The NR superfamily has been the subject of various QSAR models and
modeling approaches, of which comparative molecular field analysis
(CoMFA) is predominant \[29--34\]. To construct a CoMFA model \[35\], a
collection of chemicals with known activities (i.e. the training set)
are first aligned together usually employing structural similarity as
the basis for alignment. The aligned molecules are then embedded in a 3D
grid, after which the steric and electrostatic fields are computed for
each chemical at every grid point surrounding the molecules. The
variations in these steric/electrostatic fields are then correlated with
biological activity using partial least­squares (PLS) regression.

A major objective in applying CoMFA is to identify key structural and/or
steric--electrostatic

characteristics shared by all or most of the ligands under study. An
attractive feature of CoMFA is its capability to render a color-coded 3D
contour map depicting regions in space around molecules where
differences in steric and electrostatic fields are most strongly
correlated with differences in activity. These 3D contour maps provide
visual clues for modifying ligand structure so to either enhance or
diminish activity. (Whereas enhanced activity of a lead chemical is
sought in drug discovery, diminished activity of a toxicant is desired
in toxicology.) One can also use these maps to infer critical features
of the receptor-binding pocket based on arguments of ligand--receptor
complementarily. This information is essential when the crystal
structure of the receptor is unknown or not available. Even when the
receptor crystal structure is available, CoMFA provides an additional
source of information about the receptor from the perspective of the
ligands. It should be noted that the crystal structure of a
ligand--receptor complex provides but a single, albeit low-energy,
snapshot of the actual dynamic biological system. Structural information
derived from this crystal structure pertains only to the bound ligand in
the strictest sense and certainly not to the wide structural diversity
of ligands that bind to certain NRs, such as ER \[36\] and the orphan
pregnane xenobiotic receptor (PXR) \[37,38\].

Besides CoMFA and related 3D-QSAR approaches, other QSAR approaches have
been applied to the NRs and their ligands. So-called classical QSAR
models attempt to correlate the biological activity of a series of
ligands with their associated physicochemical properties or features
that are usually calculated but sometimes derived from experimental
measurements. A series of Hansch-type QSAR models have been developed
separately for several chemical classes that bind to the ER \[39,40\].
These models considered only a few descriptors and found that indicators
for the presence or absence of oxygen atoms, molar refraction (MR) and
the Hammett *σ* parameter correlated significantly with activity. In
fact, a comprehensive list of descriptors available to build classical
QSAR models would number in the hundreds and perhaps thou­sands.
Commercially available molecular modeling programs often include
statistical tools to help in choosing which descriptors best encode for

structure--activity variation. For example, a genetic function
approximation (GFA) approach developed by Rogers and Hopfinger \[41\]
and implemented in Cerius^2^
([[http://www.msi.com]{.underline}](http://www.msi.com/)) is a popular
genetic algorithm-based statistical approach that is now widely used in
QSAR model development \[42\].

Hologram QSAR (HQSAR), a novel fragment­based QSAR approach, was recently
introduced by Tripos, Inc.
([[http://www.tripos.com]{.underline}](http://www.tripos.com/)). In
HQSAR, each molecule in the dataset is divided into structural fragments
that are then counted in bins of a fixed length array to form a
molecular hologram. HQSAR has several attributes, including speed,
reproducibil­ity, and ease of use, that suggest its potential utility for
prioritizing large numbers of chemicals for sub­sequent testing \[34\].
Tong et al. compared the performance of HQSAR with CoMFA for several ER
datasets \[34,43\]. Although HQSAR and CoMFA achieved comparable results
for two smaller datasets, CoMFA produced better results for a diverse
dataset as judged by both internal and external validation.

Zheng and Tropsha \[44\] reported an automated variable selection QSAR
method that is based on the k-Nearest Neighbor (kNN) principle. In this
kNN­QSAR method, a chemical's activity is estimated as the mean activity
value of its k nearest neighbors based on Euclidean distance in a
multidimensional descriptor coordinate system. The method was tested on
58 ER ligands and demonstrated its effectiveness and generality.

Pharmacophore-based screening has become a common tool in the field of
computer-aided drug design. A new method of rapid pharmacophore
fingerprinting has been developed \[45\]; these finger­prints are used as
descriptors to construct a QSAR model using PLS regression. Examples are
given using the datasets reported by Kuiper et al. for both ER-a and
ER-b \[46\]. The results are compared with previously published QSAR
models for the same data to demonstrate the superiority of a full 3D,
confor­mationally flexible approach. The QSAR model can be readily
interpreted in structural/chemical terms.

Information extracted from molecular spectra has recently been applied
to develop QSAR models associated with NR ligands \[47\]. The binding
affinities of 45 progestagens (progesterone mimics) have been
quantitatively modeled using the compara­tive spectra analysis (CoSA)
approach, in which

experimental ^1^H-NMR, mass, simulated ^13^C NMR and IR spectra were
used separately or in combination to predict PR binding. The results are
comparable with CoMFA, strongly supporting the use of spectro­scopic
fields in QSAR studies \[48,49\].

QSAR methods that incorporate information on ligand--receptor
interactions have been investigated by a number of groups. The receptor
coordinates are required either from crystal structure data or from
homology modeling analysis, as is energy minimiz­ation of the bound
ligand--receptor complex. Methods, such as VALIDATE \[50\], COMBINE
\[51\] and free energy perturbation (FEP) \[52\] are the examples to use
ligand--receptor interaction for QSAR models. As demonstrated in a
recent study by Jayatilleke et al. \[53\], ligand-based and
receptor­based approaches are highly compatible and yield more highly
predictive QSAR models when employed in tandem. The utmost goal, whether
in drug discovery or in computational toxicology, is to develop and
apply QSAR models that provide the highest statistical quality and
predictive ability. This can be achieved by using the full extent of
information provided for the biological system under study. For some
NRs, this information may include knowledge of the crystal structure of
the receptor and/or receptor--ligand binary complex. Recently,
Oosten­brink et al. \[54\] reported a single-step perturbation method
allowing the calculation in a single simulation of relative free energy
for a large number of polyaromatic hydrocarbons (PAHs) binding to the
ER-α subtype. Agreement between the calculated and experimental results
had a maximum deviation of only 3.3 kJ/mol. Moreover, this method is
between four and six times less computer-time intensive as the
thermodynamic integration method.

Predicting the receptor-binding affinity of bio­molecules is one of the
major challenges in computational approaches to drug design. Basically,
two strategies are used: indirect ligand-based approaches (e.g. CoMFA,
CoMSIA, CoMMA) and the direct receptor-based approach. Sippl \[55,56\]
combined these two approaches and tested them on a set of ER ligands.
The binding conformation was determined using an automated docking
program \[57\], which was further verified through comparison with the
crystal structures. The ligand alignments obtained from the docking
simulations

were subsequently taken as the basis for a comparative field analysis
using the GRID/GOLPE program \[58\]. The model constructed on the basis
of the receptor structure supplies a better explanation of the binding
activity.

3\. QSAR models for estrogen receptor

Estrogens are widely prescribed in menopausal women for hormone
replacement therapy to main­tain bone mineral density and preserve
cardiovas­cular health. Anti-estrogens, such as raloxifene \[59,60\] and
tamoxifen, are being studied as agents to prevent breast cancer in woman
at high risk.

Estrogens regulate the expression of specific genes and the secretion of
certain hormones, and coordinate diverse processes, such as cell
prolifer­ation, cell differentiation and tissue organization through
pleiotropic actions. Once estrogens reach the bloodstream, they may
remain free or bind to serum estrogen-binding proteins like
α-fetoprotein (AFP) in rodents \[61,62\] or sex hormone-binding globulin
(SHBG) in humans \[62\]. Only the free (unbound) hormone is able to
diffuse into the target cells, where it binds to the ER to form a
hormone-- receptor complex. The prevailing model suggests that this
complex then interacts with an estrogen response element (ERE) of target
genes and activate the transcriptional machinery \[63,64\].

Fang et al. \[65\] found a strong linear correlation for ER-binding
affinities among a diverse group of chemicals assayed with ER from rat
uterine cytosol and human ER-α. Furthermore, the ER-binding data also
correlated strongly with the results from assays measuring estrogenicity
using a downstream event, i.e. a yeast-based reporter gene assay and
MCF-7 cell proliferation assay. These findings demonstrate that ER
binding is the major determinant for ER-mediated effects. Therefore,
modeling ER binding is essential for understanding the structural
requirements for potential drug candidates and for identifying potential
EDCs. Thus far, most QSAR models for ER binding have focused on (1)
structural characterization of ligands to identify those features
required for binding; and (2) identification of estrogenic EDCs for the
purpose of priority setting.

3.1. Structural characterization

In the past few years, a number of QSAR models have been developed for
ligand binding to the ER \[29--34,43,44,66--68\]. Most of these ER
models were constructed using CoMFA \[35\].

The ER subfamily is comprised of ER-a and ER-b, which share \~55%
sequence identity in their LBDs and \~72% identity (26 of 36 residues)
within the ligand-binding pocket. The expression patterns of these two
ER isoforms across tissues as well as their ligand-binding patterns were
found to exhibit notable differences \[46\]. Tong et al. \[33\] employed
CoMFA to identify and differentiate the structural features of estrogens
responsible for ligand binding to ER-α and ER-β. In the CoMFA contour
maps (Fig. 2), the green/yellow polyhedra denote regions around the
ligands where an increase in steric bulk is favorable/ unfavorable for
binding, and the red/blue polyhedra denote regions in which
negative/positive electro­static potential is preferred for enhanced
binding.

![](media/image4.png){width="2.176388888888889in"
height="3.283333333333333in"}

Fig. 2. CoMFA steric and electrostatic contour plots for estrogen
binding to ER-α and ER-β. The 17β-estradiol molecule has been inserted
inside the fields as a 3D geometrical reference. Enhanced RBA (i.e.
higher-binding affinity) is associated with adding/sub­tracting steric
bulk from green/yellow regions and with adding/sub­tracting positive
electrostatic charge in the blue/red regions.

Comparison of the respective CoMFA contour plots between ER-α and ER-β
revealed only subtle differences at the 7α position (Fig. 3) that
nevertheless may be biologically relevant.

Several contour maps are reported in the literature from CoMFA studies
on a number of datasets, including 58 structurally diverse ER ligands by
Waller et al. \[30\], 42 steroidal congeners by Wiese et al. \[31\], 30
DES derivatives by Sadler et al. \[68\], 71 halogenated estradiol
derivatives by Gantchev \[69\], and 130 structurally diverse ligands by
Shi et al. \[43\]. These maps consistently indicated that negative
charge around the 3-OH and 17β-OH of estradiol favored binding. These
findings are consistent with historical observations as well as studies
on the ER crystal structure \[12\] showing that the 3 and 17β-OH
positions of estradiol are critical for high-affinity ER binding. The
CoMFA contour provided by Gantchev \[69\] also showed that some small
sterically bulky groups (e.g. CH3) at the 17α, 7α and 11 β positions are
tolerated, in agreement with SAR results \[70\]. In several CoMFA models
\[30,31,33\], steric intolerance in the vicinity of the steroid A-ring
was interpreted to indicate that this region of the receptor's binding
pocket exhibits a preference for planar aromatic rings. This explanation
is consistent with both biological activity data \[70,71\] and
structural information \[11, 12\] on ER.

QSAR models developed using CoMFA are sensitive to molecular alignment,
a process which can be somewhat arbitrary and subjective. Conse­quently,
other QSAR approaches have been evaluated for ER binding. Tong et al.
\[32\] investigated the utility of structural descriptors to construct
classical QSAR models for a set of ER ligands. They found that obtaining
a statistically robust model is highly dependent on the ability of the
selected descriptors

![](media/image5.png){width="1.820138888888889in"
height="1.1902777777777778in"}

Fig. 3. Structure of 17β-estradiol. The same atom numbering scheme
applies to other steroids mentioned in this paper.

> ![](media/image6.png){width="1.0166666666666666in"
> height="0.8201388888888889in"}

Fig. 4. Depiction of structural indicators deemed important for ER
binding: (A) phenolic ring; (B) DES
skeleton.![](media/image7.png){width="0.27291666666666664in"
height="0.5965277777777778in"}

to encode the variation in activity with chemical structure. It is clear
that more one knows at the molecular level about the estrogenic activity
of the chemicals, the easier the task in selecting among the wide
variety and types of specific molecular descriptors that correlate with
binding \[72\].

For example, the crystal structure of the estradiol-- ER complex reveals
that the hydroxyl group at the three position of the phenolic A ring
forms hydrogen bonds with Glu 353, Arg 394 and a conserved water
molecule in the receptor-binding site \[12\], thus stabilizing the
binding conformation. Moreover, the crystal structures of ER complexed
with DES, 4-OH-tamoxifen and raloxifene reveal this same binding pattern
\[11\]. These observations suggest that a phenolic ring is likely a
common structural feature associated with tight binding to the receptor.
By including an indicator for the phenolic ring (Fig. 4(A)), Gao et al.
\[73\] was able to develop a statistically valid classification model
using a binary QSAR approach. Shi et al. \[43\] recently combined the
same phenolic ring indicator with the standard steric-- electrostatic
CoMFA descriptors for model develop­ment. The inclusion of this indicator
enhanced the predictive ability of the model as measured by the increase
in the cross-validated r^2^ (q^2^) from 0.65 to 0.71. Although the
steric/electrostatic field contrib­uted predominately to the ER binding,
the 6.8% contribution from the structural indicator confirms the
appropriateness in separately including the contri­bution of the phenolic
A ring.

Gao et al. \[73\] also introduced a hexestrol indicator by recognizing
that this structural common feature was associated with the
high-estrogenic activity of DES analogues. The hexestrol indicator was
used for a binary QSAR model. In an integrated system devel­oped by Tong
and co-workers \[43,74--76\] for use as a priority setting tool for
EDCs, a similar structural indicator, DES skeleton (Fig. 4(B)) was used
in one of the beginning Phases of the 'four-phase' model (vide infra) as
a structural alert to identify potential ER binders \[74--76\].

Commercial molecular modeling programs often feature a wide array of
chemical structure descriptors available for developing QSAR models.
However, the aforementioned examples demonstrate that inclusion of
biologically relevant descriptors (which are not necessarily included in
the commercial software) through benefit of expert knowledge can improve
substantially the performance of the QSAR model, at least for ER
binding.

3.2. Hazard identification

This particular application has focused most recently on development of
QSAR models to predict ER-binding affinity for prioritizing potential
estro­genic EDCs. Two QSAR-based systems, the common reactivity pattern
(COREPA) approach \[77,78\] and the four-phase approach \[74--76\], are
being evaluated by the EPA to determine their appropriateness for
priority setting of potential EDCs.

The endocrine disruptor knowledge base (EDKB) project team \[79\] at the
FDA's National Center for Toxicological Research (NCTR) has developed
integrated systems for priority setting of EDCs \[74--76\]. The system
is focused on minimizing possible false negatives since chemicals
labeled as 'inactive' in the process are dropped into a lower priority
category for experimental testing. For this purpose, different
computational models have been rationally integrated into a four-phase
scheme accord­ing to the nature of each model. A progressive phase
paradigm is used as a screen to reduce the number of chemicals to be
considered in the subsequent phase. Therefore, these four phases work in
a hierarchical way to incrementally reduce the size of a dataset with
increasing precision of prediction. Within each phase, different models
have been selected to work com­plementarily in representing key
activity-determining structure features to minimize the rate of false
negatives. For predicting ER-binding affinity, the models comprised of
the four phases following:

† *Phase I: Filtering*---Two rejection filters, molecu­lar weight \<94 or
\>1000 and no-ring structure, were used to significantly and with high
confidence

> eliminate those chemicals extremely unlikely to bind ER \[76\]. These
> two filters were validated on
>
> \~2000 chemicals whose ER activities were available from the
> literature.

-   *Phase II: Active/inactive assignment* ---The chemi­cals passing
    through Phase I were assigned as YES/ NO for ER binding using three
    different methods, i.e. structural alerts, pharmacophore searching,
    and classification models. While structural alerts ident­ify key 2D
    structural features associated with ER binding, pharmacophore search
    identifies 3D sub­structure important for ER binding. Classification
    models use pattern recognition to qualitatively categorize chemicals
    into active and inactive subsets on the basis of their similarity in
    physico­chemical properties. In its current form, this Phase employs
    in parallel 11 models, three structural alerts, seven pharmacophores
    and one classifi­cation model to discriminate active from inactive
    chemicals. A chemical predicted to be active by any of these models
    is subsequently evaluated in the Phase III, while others are
    eliminated. Since each method incorporates and weighs 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.
    Moreover, combining the outputs of these 11 models provides a
    rational means to rank order the chemicals in decreasing order of
    potential activity \[76\].

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-   *Phase III: Quantitative predictions*---In this phase, a CoMFA model
    is used to make a more accurate quantitative activity prediction for
    chemicals from Phase II. Chemicals with higher predicted binding
    affinity are given higher priority for further evaluation in
    Phase IV. The CoMFA model demonstrated good statistical reliability
    using both internal and external validation \[43\].

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-   *Phase IV: Rule-based decision-making system*---In this final stage
    of the integrated priority setting approach, a rule-based (or
    knowledge-based) decision-making system is employed to foster
    definitive decision making. The system would be useful only after
    incorporating accumulated human knowledge and expertise (i.e.
    rules). Nonetheless, combining information from Phases II and III
    with other sources, such as production volume,

> environmental fate and so forth should provide sufficient information
> to make a final decision on priority setting.

This approach has been validated by a number of existing datasets,
including the NCTR ER-binding dataset \[80\], the E-SCREEN assay data
\[81\], the yeast two-hybrid reporter gene assay data \[82\], and other
datasets \[30,83--87\]. Thus far, the system has produced no false
negatives as would be critical in priority setting for regulatory
purpose. When the Phase I and II protocols were applied to 58,000
chemicals recognized by EPA as a representative subset of the 80,000
chemicals, some 9100 chemicals were identified as potential estrogens of
which some 3600 have activity no less than 10^5^-fold below
17b­estradiol. Therefore, the method dramatically reduced the number of
potential estrogens by some 83% and with a low rate of false negatives
as required. The same integrated scheme is being extended to include
endpoints of other endocrine disrupting mechanisms (e.g. AR binding).

COREPA is a unique SAR approach designed to analyze 'reactivity
patterns' of structurally diverse but biologically similar chemicals
\[88\]. COREPAs rep­resent a set of the specific ranges of structural
descriptors determined with the biological activity of concern.
Constructing a SAR model using COREPA consisted of the following steps:
(1) multiple conformers within 20 kcal/mol of the lowest energy
structure are generated for each chemicals in a dataset; (2) a set of
descriptors are calculated for each conformer of each chemical, thus the
discrete distribution of the descriptor across the conformers is
obtained for each chemical; (3) specify a cutoff to separate a dataset
into two groups, active and inactive; (4) a set of parameters are
determined by evaluating the degree of overlap between the distribution
associated with active and inactive groups, which provided the maximum
measure of similarity within groups and least overlap between groups;
(5) the cutoff-dependent common reactive patterns are obtained as
products of the probabilistic distributions for specific parameters
associated with active or inactive chemicals; (6) a decision tree based
on these parameters is established to construct an SAR model; (7) a
cutoff is re-specified to separate the dataset, and steps 4--7 are
repeated.

The COREPA models based on 26 steroids and 19 non-steroids \[89,90\]
were recently reported for ER relative-binding affinity (RBA) by
selecting cutoff of \>150, 100--10, 10--1, and 1--0.1% \[78,91\].
How­ever, no internal or external validation was performed in the study.

4\. Models for other nuclear receptor superfamily

Second to ER in the number of published QSAR studies are the PR
\[42,92--94\] and the AR \[29,93\]. Not many QSAR models have been
developed for corticosteroids \[95--97\], retinoid acid \[98\] and
thyroids \[99\]. This is mainly due to the limited amount of biological
data for these receptors.

An AR CoMFA model reported by Waller et al. \[29\] was based on 28
structurally diverse natural, synthetic, and environmental chemicals, of
which 21 were used as a training set and seven as a test set. The
ability of the model to accurately predict-binding affinity of the
testset molecules was demonstrated in the study.

A comparison of PR and AR binding using the CoMFA technique was reported
by Loughney \[93\]. The contour map indicates that sensitivity to steric
bulk in the region of the steroid A ring is greater for PR than for AR.
This is consistent with the knowledge that a large class of the
anti-androgens \[100\] have an electron withdrawing substituent in the A
ring. Furthermore, the steric contour for the PR model indicates
tolerance for steric bulk in the region of the 17α position whereas, in
contrast, binding affinity is decreased in the AR model. This is
consistent with the rational design of orally active analogues of
pro­gesterone by adding substituents to the 17α position. Chemicals, such
as norethindrone (norethisterone) and (levo)norgetrel have lower
AR-binding affinity but higher PR-binding activity.

In an early study, Ojasoo et al. \[101\] compared 3D structures of AR
with PR where both receptor structures were derived from homology
modeling. They found that empty space is present in both receptors
around the 3-keto, 11 β, 7α positions, and C21 positions for PR. The
crystal structures for both AR \[15\] and PR \[10\] are now available.
Comparison of the LBDs between AR and PR should allow us to

better understand structural requirements for both receptors.

So et al. reported \[42\] QSAR studies on PR binding by using several
variable selection approaches, including forwarding stepping regression
(FSR), GFA, generalized simulated annealing (GSA), and genetic neural
network (GNN). A comparison of the predictive qualities for both
training and test chemi­cals demonstrated that the GNN protocol achieved
the best results.

An excellent review paper on PR QSAR and SAR models is reported by Bursi
\[92\]. Thus, this topic will not be discussed further.

5\. Compilation of standard biological datasets on NRs for developing
statistically valid QSAR models

Many QSAR approaches have been developed in the field of drug discovery.
The methods of choice are dependent on a number of factors. Comparison
of these approaches by use of the same dataset permits evaluation of the
quality of a method relative to other methods. The developers of the
CoMFA technique, first introduced in 1988 \[35\], selected a dataset
composed of 31 steroids with binding affinity for corticosteroid-binding
globulins to introduce and validate the method. This dataset was divided
into a training and test sets of 21 and 10 steroids, respectively. A
variety of new QSAR methods were evaluated using this dataset to compare
their per­formance with CoMFA, including SOMFA \[102\], CoMMA
\[103,104\], CoMSIA \[105,106\], COMPASS \[107\], molecular similarity
matrices \[102\], MS­WHIM \[108\], MEDV-13 \[109\], etc. \[110--119\].
Consequently, and inadvertently, this steroid dataset has become the de
facto benchmark for assessing a QSARs acceptability compared with CoMFA
as reviewed by Coats \[120\].

Evaluation of new methods based on this particular dataset has certain
limitations, specifically for appli­cation in the NRs. One of the major
concerns is that the dataset contains only 21 steroidal congeners with
an activity range of less than three orders of magnitude. Such QSAR
models, which explore only a small portion of chemical-structure and
biological­activity space, fail to predict activity even for

![](media/image8.png){width="5.033333333333333in"
height="2.0305555555555554in"}

Fig. 5. The distribution of (A) binding activity and (B) chemical
classes for the NCTR dataset. The activity is represented as RBA. The
RBA for the endogenous ligand, 17β-estradiol, was set to 100.

chemicals that differ only slightly in structure from the training set.
Additionally, the structures of eight chemicals were drawn incorrectly
in the original publication \[115,120\], and the activities for seven
inactive chemicals (K~i~ \< 1 x 10^5^ M^-1^) were arbitra­rily assigned a
specific value (pKi \< -5.0). These assumptions, uncertainties, and
errors would argue against using this steroid dataset as a standard for
developing, validating, or comparing various QSAR models.

Recently, the EDKB team at the US FDA's NCTR has reported a more
adequate dataset to validate QSAR methods, particularly for use in the
NRs \[70,80\]. They validated a rat ER-binding assay and measured the
binding affinity for over 230 chemicals for use in QSAR model
development. This 'NCTR dataset' contains chemicals that were selected
to cover the structural diversity of chemicals that bind to ER with an
activity distribution ranging over six orders of magnitude, which is an
essential require­ment for a robust predictive model applicable to
structurally diverse estrogens (Fig. 5). The chemical selection process
was highly interdisciplinary, invol­ving computational chemists,
biologists and exper­imental toxicologists, and has resulted in steady
improvement in performance of the QSAR models \[79\]. In terms of
experimental quality, number of chemicals, activity range and structural
diversity, the NCTR dataset represents the most reliable and
self­consistent dataset currently available on estrogens to build QSAR
models. The NCTR dataset has been used recently to compare the
performance of HQSAR to

CoMFA \[43,70\]. Along with the NCTR dataset, the Waller \[121\] and
Kuiper \[89\] datasets were used as the testsets for external
validation. This study demonstrated that these three datasets are
superior to the earlier small datasets \[34\] for evaluating the
relative strengths and weaknesses of CoMFA versus HQSAR.

6\. Concluding remarks

It is clear that significant progress has been made in developing and
validating QSAR models for the prediction of binding affinity for the
NRs. The NRs have become the subject of numerous studies, not only for
medicinal chemists and toxicologists but also for computational
chemists, crystallographers, and statisticians. These models help us to
understand the mechanisms in receptor binding, to predict binding
affinity for environmental and other chemicals as tools for government
regulation control, and to identify leads in drug discovery. The recent
growth of lab-on­chip (i.e. microarray and protein array) technology
\[122--124\] and advanced recombinant DNA technol­ogies (cDNA cloning,
Southern blotting, PCR, etc.) have in the last decade enabled rapid
identification of macromolecules as well as their expression at
sufficient purity and quantities adequate for structure determination.
This dramatic increase in the avail­ability of 3D structures for many
receptors has greatly expanded the list of potential drug targets, and
also provides a rich source of data for QSAR and related

computational techniques \[125,126\]. By combining ligand- and
structure-based approaches in QSAR, accurate and biologically meaningful
models should be possible for the receptor-mediated effects.

Acknowledgments

The authors wish to thank Roger Perkins (Logicon ROW Sciences,
Jefferson, AR), Gary Timm (EPA, Washington, DC) and Jim Kariya (EPA,
Washington, DC) for providing guidance and support to the EDKB project.
The authors also thank Dr Willie Owens (The Procter & Gamble Company,
Cincinnati, OH) for providing valuable input on the EDKB project. WJW
wishes to acknowledge the financial support provided by a grant from the
US Environmental Protection Agency's Science to Achieve Success (STAR)
program.

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