> **Impact on Public
> Health**![](media/image1.png){width="0.32013888888888886in"
> height="7.98611111111111e-2in"}![](media/image2.png){width="0.5333333333333333in"
> height="7.98611111111111e-2in"}![](media/image3.png){width="2.5104166666666665in"
> height="8.680555555555555e-2in"}

**Development of Quantitative Structure-Activity Relationships
(QSARs)**![](media/image4.png){width="0.48333333333333334in"
height="0.6368055555555555in"}![](media/image5.png){width="0.48055555555555557in"
height="1.3069444444444445in"}![](media/image6.png){width="0.5333333333333333in"
height="5.0e-2in"}![](media/image7.png){width="0.30625in"
height="0.2902777777777778in"}![](media/image8.png){width="0.2465277777777778in"
height="0.2298611111111111in"}

**and Their Use for Priority Setting in the**

**Testing Strategy of Endocrine Disruptors**

**Weida Tong ^1^, Roger Perkins ^1^, Hong Fang ^1^, Huixiao Hong ^1^,
Qian Xie 1~,~\
William Branham ^2^, Daniel M. Sheehan ^2^\* , Jeanne Farr Anson 2**

**1 Logicon ROW Sciences, Jefferson, Arkansas 72079\
2 FDA's National Center for Toxicological Research (NCTR), Jefferson,
Arkansas 72079,\
\* Current address: Daniel M. Sheehan and Associates, Little Rock,
Arkansas 72202**

> **Abstract:** Considerable scientific, regulatory and popular press
> attention has been devoted to the Endo­crine Disrupting Chemicals
> (EDCs), of which estrogenic chemicals figure prominently. A large
> number of potential estrogenic EDCs are associated with products
> regulated by the Food and Drug Administration (FDA), including
> plastics used in food packaging, phytoestrogens, food additives,
> pharmaceuticals, cos­metics, etc. Recent legislation mandates the U.S.
> Environmental Protection Agency (EPA), a sister regu­latory agency, to
> develop a screening and testing program for potential EDCs in drinking
> water and food additives. Under the legislation, a large number of
> chemicals will undergo various *in vitro* and *in vivo* assays for
> their potential estrogenicity, as well as other hormonal activities.
> There is a crucial need to set priority for these chemicals to reduce
> the cost and speed the screening and testing process. At the FDA
> National Center for Toxicological Research (NCTR), quantitative
> structure-activity relationships (QSARs) is a major component of the
> Endocrine Disruptor Knowledge Base (EDKB) project -- a prototype
> Toxicological Knowl­edge Base. By integrating experimentation and
> modeling, a series of QSAR models have been developed and validated in
> the project. These models are integrated into a \"Four-Phase\" scheme,
> with each succes­sive phase eliminating unlikely estrogen receptor (ER)
> binders, resulting in a priority listing of chemicals for regulatory
> application. The system performance has been validated using several
> data sets with known es­trogenic activity and, subsequently, applied to
> three environmental data sets, identified by the EPA. It has also been
> used to assess estrogenic activity of chemicals of concern at other
> Centers within the FDA, namely the Center for Food Safety and Applied
> Nutrition (CFSAN) and the Center for Drug Evaluation and Research
> (CDER). The rigorous validation of the integrated system is ongoing
> via the interagency agree­ment (IAG) between EPA and NCTR. The approach
> presented here for estrogen is anticipated to be equally applicable
> for other receptor-mediated, endocrine disrupting mechanisms, e.g.,
> androgen receptor binding, and other toxicity endpoints.
>
> **Introduction** instrumentation, and in the general understanding of
> biological mecha‑

While the toxicological sciences nisms, the overall paradigm is

have advanced steadily over past largely unchanged. Specifically,

decades owing to constant improve- models for risk assessment are pre‑

ment in experimental techniques and dominantly dependent on slow and

expensive testing in laboratory ani­mals, since epidemiological data are
insufficient and testing in humans is not possible (except for drugs
during the advanced stages of develop‑

> *(Continued on page 2)*

*(Continued from page 1)*

ment). A fundamental tenet of this paradigm is that effects in
laboratory animals can be extrapolated to hu­mans (or other animals).
Intrinsic vari­ability in the response of the test spe­cies, and the
number of animals tested, limits the resolving power of the affordable
protocols to determine a No Observed Adverse Effect Level (NOAEL), the
dose which provides no statistically significant increase in adverse
effects above the control value. This, together with the need to
extrapolate to humans, results in the use of safety factors, generally
in the range of two or more orders of magni­tude to determine an
allowable dose for non-genotoxic chemicals.

In 1995, the National Center for Toxicological Research (NCTR)
reori­ented its strategic goals to begin to alter the paradigm of
toxicological research, taking direct aim at in­creasing regulatory
efficiency by re­ducing the time, expense and animal use in the
regulation process. One primary strategic goal was the devel­opment of
knowledge bases, that were defined to be computer-based systems that
unify applicable litera­ture and data and provide computa­tional models to
predict a chemical's toxic potential, predict experimental needs and
improve regulatory risk assessment capability. The inspira­tion for the
Estrogen Knowledge Base (EKB) program came from a center wide challenge
issued by then NCTR Director, Dr. Bernard Schwetz, to develop a
knowledge base with the capability to extend the predictability of
existing data. Scientists within the Division of Re­productive and
Developmental Toxi­cology suggested that estrogens might provide an
appropriate area to develop a prototype. Knowing that NCTR scientists
had been engaged in estrogen related research for more than two decades,
Dr. Schwetz posed the questions whether they could recognize an estrogen
recep­tor ligand, solely based on chemical structure, and whether models

based on existing data could be used to develop models to make such a
prediction. The answers were \"no\" and, \"let's try,\" respectively.

The "let us try" subsequently devel­oped into a concept to develop a
pro­totype knowledge base to predict estrogenicity, and a grant from
FDA\'s Office of Women's Health enabled the acquisition of the required
com­puter hardware and software.

The earliest models, based on 13 chemicals tested at NCTR, proved very
inadequate. Despite the clear statement in endocrinology text­books that
a single nuclear receptor protein that exhibited high ligand specificity
mediated most estrogenic effects, it became quickly apparent that the
estrogen receptor binds chemical structures of surprisingly broad
diversity. Next, we used the extensive data published in the litera­ture
to build models based on 50-chemical training sets. While these models
were much improved, we learned that better models were pos­sible given a
proper database.

During the course of our work on the early models, a major scientific
and regulatory issue developed sur­rounding environmental chemicals,
termed endocrine disrupting chemi­cals (EDCs), suspected of disrupting
endocrine function by mimicking natural hormones in experimental
animals, wildlife and humans. There was a constantly growing concern
among the scientific community, gov­ernment regulators and the public
that EDCs in the environment were adversely affecting human and wild­life
health *(1, 2).* Adverse outcomes had been observed in experimental
animals and wildlife; potential effects in humans included reproductive
and developmental toxicity, carcinogene­sis, immunotoxicity, and
neurotoxic­ity, among others *(3).* The scientific debate surrounding
EDCs grew con­tentious, in part owing to the fact that some suspected
EDCs are high pro­duction volume, economically impor­tant chemicals. The
public and regu­latory concerns led to government

regulatory actions and expanded re­search across Europe, Japan and North
America *(4, 5).* In 1996, the U.S. Congress mandated that the
Environmental Protection Agency (EPA) develop a strategy for screen­ing
and testing a large number of chemicals found in drinking water, food
additives and other sources for their endocrine disruption potential

*(4).*

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 for estrogenic, androgenic and thy­roid
endpoints 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 *(6).* Although more than \~87,000 chemicals were
initially selected for evaluation, many were polymers or otherwise
unlikely to bind to steroid receptors, leaving about \~58,000 chemicals
for evalua­tion in Tier 1. The number that will progress to the testing
step (Tier 2) *(7)* is not known. Processing all chemicals through both
tiers, if re­quired, 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/),)
and where QSAR predictions are likely to prove of particular utility.

The EKB multidisciplinary team of researchers developed a plan to meet
the newly important challenge for precise and validated predictive
models that was reviewed and en­dorsed by the NCTR Scientific Advi­sory
Board (SAB) in 1997. The most important finding of the early model
building (*8-12)* effort was that, de­spite decades of testing for estro‑

> *(Continued on page 3)*

*(Continued from page 2)*

genicity, the existing data were in­adequate to construct robust QSAR
models (SETAC chapter) *(12).* Ac­cordingly, the EKB plan called for
developing a model training data set by conducting ER binding
measure­ments in a validated assay. The chemicals to be tested would be
chosen by the computational chem­ists to obtain a training set spanning
the broad range of chemical struc­tures of ER ligands, both agonists and
antagonists. Furthermore, there needed to be several types of models
ranging from fast and easy classifica­tion methods to rule out inactive
chemicals, to highly accurate but time-intensive, three-dimensional QSAR
models to quantify binding affinity for active chemicals. As the scope
of the EKB project expanded in the late 1990s, to include all Endo­crine
Disrupting Compounds, we re­named the prototype knowledge base the
Endocrine Disruptor Knowl­edge Base (EDKB). A follow-up sec­ond SAB review
occurred in 2000, which supported placing the EDKB database on the WEB
([[http://edkb. fda.gov]{.underline}](http://edkb.fda.gov/)). As the
program developed, additional funding was provided by the OWH, in 1998
and 2000, by a Cooperative Research and Develop­ment Agreement (CRADA)
with the American Chemistry Council (ACC), formerly the Chemical
Manufacturer\'s Association (CMA), and again in 2001, with continued
support to 2005, by EPA.

The objective of the EDC priority setting is to rank order a large
num­ber of chemicals for more resource­intensive and costly Tier 1
evaluations from most important to least impor­tant. There are a number
of criteria that can be used for this purpose, such as production
volume, persis­tence and fate in the environment, human exposure levels,
etc. Most of the 58,000 chemicals required for as­say have no biological
data. Both QSARs and transcription-based high throughput pre-screening
(HTPS)

were recommended by the EDSTAC as the primary source of biological
effect information for priority setting. However, in a pilot study
undertaken by EPA, the HTPS system did not perform well *(13),* such
that the EDKB models could prove critical in EDC screening and testing
program.

The compounds assayed at NCTR were selected based on providing uni­form
coverage of the diverse chemical structure space of chemicals that bind
the receptors, as well as cover­age of an activity range extending down
to a million-fold below that of the endogenous hormones. The model
training set designed for chemical structure diversity com­prises 130 ER
binders and 100 non­binders *(14, 15).* The large number of inactive
chemicals, included in the training sets, enables models to be trained
to distinguish active from inac­tive compounds. Many SAR, QSAR and
chemometric predictive models were developed using the many pow­erful
commercial software packages that are now routinely applied in drug
discovery and development. In the end, the rigorous, three-dimensional,
QSAR method of Comparative Mo­lecular Field Analysis (CoMFA) was selected
for quantitative prediction of receptor binding affinity *(16).* Three
different types of models (structural alerts, pharmacophores and
classifi­cation methods) were combined ( *17)* to make qualitative
prediction (i.e., active or inactive) of ER binding activ­ity.

In the sections that follow, we pre­sent results of integrating the
qualita­tive and quantitative predictive models into a sequential
\"Four-Phase\" scheme *(18, 19)* according to the strength of each type
of model. Hier­archical sequencing of the models allows faster models to
be used to eliminate the majority of inactive chemicals with an
extremely low rate of false negatives. The more time­consuming but more
precise models can be used to refine predictions for an increasingly
smaller number of r e-

maining chemicals. The application of the more refined models further
elimi­nates true negatives, as well as false positives, from earlier
models. Re­sults are presented suggesting that the use of this scheme
could elimi­nate from testing about 90% of the chemicals of potential
concern in the national screening and testing pro­gram. Should the
ER-binding models be used for priority setting, the pro­gram begun as a
prototype effort will have matured to one of the most sig­nificant uses
of QSAR in the regula­tory application.

Currently, the EDKB team is com­pleting models for prediction of bind­ing
to the androgen receptor (AR). A validation program is also now under­way
via an Interagency Agreement between FDA/NCTR and EPA. A large number of
chemicals will be tested blind by the models, for both ER and AR
binding, and the predic­tions then compared to assay results from an
outside contract laboratory. The validation results will define whether
and how model predictions are used in priority setting in the EPA\'s
Endocrine Disruptor Screening and Testing Program.

**Quantitative Structure-Activity\
Relationships (QSARs)**

QSAR modeling employs statisti­cal approaches to correlate and
ra­tionalize variations in the biological activity of a series of
chemicals with variations in their molecular struc­tures. The molecular
structure is often represented by a set of quantities commonly known as
structural de­scriptors. QSARs have been applied extensively in a wide
range of scien­tific disciplines including chemistry, biology and
toxicology *(20, 21).* In both drug discovery and environ­mental
toxicology *(22),* QSAR models are now regarded as a scientifically
credible tool for predicting and classi­fying the biological activities
of un­tested chemicals. As we enter the

> *(Continued on page 4)*

*(Continued from page 3)*

informatics era, QSAR has become essential in the drug discovery proc­ess
as a screening and enrichment tool to eliminate, from further
develop­ment, those chemicals lacking drug­like properties *(23-25)* or
those chemi­cals predicted to elicit a toxic re­sponse. This developing
scenario por­tends the spread of QSAR, beyond the pharmaceutical
industry, to human and environmental regulatory authori­ties for use in
toxicology *(16, 19, 26-30).* The EDSTAC considers QSAR as an important
part of its priority set­ting process *(31).* QSARs can be used to
evaluate untested chemicals to provide biological data for use in
priority setting *(32-36).*

The basic assumption in every QSAR model is that a chemical's physical
and chemical properties and its biological activities are predicted by
its structure *(37).* Since structural descriptors of a chemical can be
de­termined by computational means more efficiently than its biological
ac­tivity using *in vitro* or *in vivo* ap­proaches, a statistically
validated QSAR model is capable of predicting the biological activity of
a new chemi­cal in lieu of the time-consuming and labor-intensive
processes of chemical synthesis and biological evaluation. Applied
judiciously, QSAR can save substantial amounts of time, money and human
resources. A major advan­tage of QSARs, to priority setting, is the
efficiency of scale when applied to a large number of chemicals. When
several endpoints are analyzed simul­taneously, the efficiency of scale
of computation is even more pro­nounced.

The first step in developing a QSAR model is acquisition of a train­ing
set of chemicals that have known activities. Second, descriptors
repre­senting molecular structure of individ­ual chemicals (i.e.,
hydrophobicity, structural fragments, charged surface area, the number
of hydrogen bonds, solubility, and etc.) are calculated. Then, a
correlation between descrip­tors and activity for the training set is

evaluated by employing various statis‑

tical approaches to determine the

most statistically significant relation‑

ship (the QSAR model). A proper vali‑

dation is required to ensure the

model's predictive value for the chemi‑

cals not used in the training set. With

adequately validated performance,

such models can be used to predict

activities of untested potential EDCs. Obtaining a good quality QSAR

model heavily depends on many fac‑

tors, in the approach, particularly on

quality of biological data, descriptor

selection and statistical methods:

\(1\) Quality of biological data -- It is desirable that data come from
the same assay protocol, and care is taken to avoid inter­laboratory
variability. Any bad data points may corrupt the proper correlation of
structure and activity. Rules of thumb for a good QSAR data set are: 1)
the dose-response relationship is smooth; 2) the potency (or affin­ity)
is reproducible; 3) the activity range spans two or more orders of
magnitude from the least ac­tive to the most active chemical in the
series; 4) the number of chemicals used to build the QSAR model is
sufficiently large to ensure statistical stability; 5) activities of the
chemicals are

> evenly distributed across the range of activity; and 6) the chemicals
> selected for the train­ing set possess enough struc­tural diversity to
> span the range of "chemistry space" associated with the biological
> activity under study. It is important to note that most toxicity data
> do not meet all these criteria because of the nature of toxicological
> research, in which care should be taken in interpreting QSAR results.

\(2\) Descriptor selection - There are many types of chemical struc­ture
descriptors available from commercial software. Obtaining a
statistically robust model is very much dependent on how well the
selected descriptors can

encode the variation of activity with structure. The more that is known
at the molecular level about the biological mechanism of action of the
chemicals, the better the chemist is able to s e­lect among the wide
variety and types of specific structural de­scriptors. Commercially
available molecular modeling programs often include statistical tools to
help in evaluating which descrip­tors best encode structure­activity
variation. Some of these tools include the genetic algo­rithm (GA) in its
various incarna­tions, which employs the evolu­tionary rules of natural
selection to select the optimal (i.e., fittest) subset of descriptors
amongst its wide set for a particular prob­lem.

Statistical methods -- It is also critical that the QSAR method selected
to develop the structure­activity correlation be suitable. Although the
relationship be­tween a structural descriptor and biological activity may
be linear or non-linear, it is still common practice today to employ
linear approaches such as multiple (or multivariate) linear regression
(MLR) or partial least-squares (PLS) regression to construct the QSAR
model. For non-linear modeling, the Polynomial Neural Network (PNN)
offers an alterna­tive that combines the best fea­tures of Artificial
Neural Networks (ANNs) and MLR/PLS by provid­ing the inherent
non-linearity of the ANN with the desired analyti­cal regression equation
furnished by MLR and PLS *(38).* The most common scenario encountered in
practice is for the number of pos­sible descriptors to exceed the number
of chemicals, a situation that can lead to chance correla­tion.
Fortunately, soft modeling methods, such as PLS, reduce the risk of
encountering chance correlation by transforming the

> *(Continued on page 5)*
>
> **Figure 1.** Depiction of the recursive process used by NCTR to
> develop QSAR models for predicting estrogen receptor binding. The
> proc­ess starts with an initial set of chemicals from literatures for
> QSAR modeling. Next, the preliminary QSAR models are used
> pro­spectively to define a set of chemicals that will further improve
> model's robustness and predictive capability. The new chemicals are
> assayed, and these data are then used to challenge and refine the QSAR
> models. The process output is the models for use in toxicological
> regulation. Validation of the model is critical, particularly with
> respect to confirming minimal false negative
> predic­tions.![](media/image9.png){width="3.79375in"
> height="2.7631944444444443in"}

*(Continued from page 4)*

> dimensionality of the regression problem from chemical-descriptor
> space to so-called chemical­principal components (PCs) space.

QSAR models are useful in re­search for purposes beyond predic­tion
*(39).* Additional benefits that may accrue include: (1) leveraging
existing structure-activity data; (2) providing insights into mechanism
or identifying an alternative mechanism (e.g., me­tabolism) of action;
(3) identifying im­portant chemical structure character­istics; (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; and (7) revealing chemicals that
deviate from the QSAR model.

**The NCTR\
Model Development Process**

In the past few years, a number of QSAR models have been developed for
ligand binding to the ER *(8-11, 40-*

*44).* Most of these QSAR models were constructed using the Compara­tive
Molecular Field Analysis (CoMFA). Although a predictive QSAR model is
dependent on a num­ber of factors, a training set with a broad
representation over the chemis­try space is critical to ensure its
pre­dictive capability for a large number of diverse chemicals.
Unfortunately, most QSAR models for ER binding, developed previously,
were based on data sets available in the literature, which to date had
been small data sets with limited structural diversity (*8, 42, 43).*
Although these models yield good statistical results and ex­plain some
structural determinants for ER binding, they have 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 a more robust
QSAR model for regulatory purpose, a recursive process (Figure 1) has
been adopted at NCTR by integrating assay and QSAR modeling to deter­mine
chemicals for the training set *(12, 45)* along with the model con-

struction. The process is highly inter­disciplinary, involving
computational chemists, biologists and experimental toxicologists. As
depicted in Figure 1, the process starts with an initial set of
chemicals from literatures for QSAR modeling. Next, the prelimi­nary QSAR
models are used pro­spectively to define a set of chemi­cals that will
further improve model's robustness and predictive capability. The new
chemicals are assayed, and these data are then used to challenge and
refine the QSAR models. Through this process, we identified and as­sayed
\~230 chemicals for final model construction. This NCTR data set
contains chemicals that were se­lected to cover the structural diversity
of chemicals (Figure 2a) that bind to ER with an activity distribution
rang­ing over six orders of magnitude (Figure 2b), which is an essential
re­quirement for a robust predictive model for structurally diverse
estro­gens. The NCTR data set is a highly consistent data set for use in
develop­ing models for estrogens.

The assay used in the process to provide ER binding data is a rat uter‑

> *(Continued on page 6)*

![](media/image10.png){width="4.060416666666667in"
height="1.5763888888888888in"}![](media/image11.png){width="9.305555555555556e-2in"
height="0.1361111111111111in"}![](media/image12.png){width="0.10972222222222222in"
height="0.8930555555555556in"}![](media/image13.png){width="4.389583333333333in"
height="3.220138888888889in"}

ine cytosol ER competitive binding assay that is considered the gold
standard for *in vitro* ER assays *(14, 15).* We found a strong linear
correla­tion for ER binding affinities among a diverse group of chemicals
assayed with ER derived from our rat uterine cytosol and human ERa
*(46).* Further­more, the rat 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. 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 *(47).* These
findings demon­strate that ER binding is the major determinant for
estrogenic EDCs, and the prediction of the rat ER binding affinity
provides an important piece of information for priority setting.

In this process, a model validation step is specifically emphasized to
ensure the model's predictive value for priority setting purposes
*(12).* Internal and external validation were included in the process;
each provides a differ­ent level of confidence for the model's
predictivity. Generally, the model is first validated using
leave-one-out cross validation. In this method, each chemical in the
training set is sys­tematically excluded once from the data set, after
which its activity is pre­dicted by a model derived from the remaining
chemicals. This internal validation assesses the model's ex­trapolation
within the training set. Sometimes, we employ leave-*N*-out
cross-validation to achieve more ro­bust internal validation; a procedure
similar to leave-one-out, but in this one, we systematically exclude one
group of chemicals after randomly dividing the training set into *N*
groups. When additional data are available, the model is used to predict
other chemicals not used in the training set, but which have known
activities

(the testing set). This external valida­tion assesses the model's
predictive capability for untested chemicals.

Several benefits accrue from the integration of the experimental and

modeling efforts. Immediate feedback can be given to the
experimentalists so that suspected problems can be rapidly investigated.
Also, as the pre‑

> *(Continued on page 7)*
>
> *(Continued from page
> 6)*![](media/image14.png){width="0.12638888888888888in"
> height="0.3701388888888889in"}![](media/image15.png){width="4.513194444444444in"
> height="4.190277777777778in"}![](media/image16.png){width="0.3763888888888889in"
> height="3.00625in"}![](media/image17.png){width="0.12986111111111112in"
> height="0.3263888888888889in"}![](media/image18.png){width="0.12638888888888888in"
> height="0.22708333333333333in"}
>
> dictive and diversity models evolve, the modelers can select the
> chemi­cals for subsequent testing, based on considerations of
> structural diversity and the activity range. While cross validation
> using the training set re­mains an important part of the model
> validation, each new data point di­rectly from the lab become a
> chal­lenge to the evolving model, the re­sults of which can then be
> assessed by the joint team of the modeler and experimentalist.
>
> Each time the model is chal­lenged, the result is either further
> con­firmation of its validity, identification of a limitation or an
> outlier prediction. Failure of the model will, in turn, pro­vide
> important information. This may include identification of the need for
> new data based on a rational under­standing of the dependence of
> activity on structure. Alternatively, it may help elucidate which of
> many mechanisms may play an important role in a spe­cific chemical's
> response; for exam­ple, delineating agonists from antago­nists, or
> defining where metabolism may be important. Regardless of the

cause of model failure, in essence, a research hypothesis is spawned
that should lead to new data and/or an improved training set, and an
improve­ment to what is a living model.

**The NCTR\
"Four-Phase"\
System**

Priority setting using QSAR is widely applied in the process of drug
discovery. The objective of priority setting in pharmaceutical industry
is to increase the chance of finding ac­tive chemicals or \"hits\" that
are more likely to be developed into \"leads\". Hence, false positives
are of great concern. In contrast, a good priority setting method for
regulatory applica­tion should generate a small fraction of false
negatives (chemicals pre­dicted to fail to bind to their receptor, but
which actually bind). False nega‑

> *(Continued on page 8)*

*(Continued from page
7)*![](media/image19.png){width="4.906944444444444in"
height="4.096527777777778in"}

tives constitute a crucial error be­cause they will receive a relatively
lower priority for evaluation, in Tier 1, and may remain in use for many
years. Furthermore, the methods should provide reasonable quantitative
accuracy for true positives, as those with higher affinities will
generally be of higher priority. Based on these considerations, we have
developed an integrated computational system that rationally combines
different QSAR models into a sequential \"Four-Phase\" scheme according
to the strength of each type of model (Figure 3). A progressive Phase
paradigm is used as a screen to reduce the num­ber of chemicals to be
considered in each subsequent Phase. Therefore, these four phases work
in a hierarchi­cal way to incrementally reduce the size of a data set
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
these four phases were:

-   ***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 considered unlikely to
>
> bind ER *(17).* These two filters
>
> were validated on \~2000 chemi‑
>
> cals whose ER activities were
>
> available from the literature.

-   ***Phase I: 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 sub-structure 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.
To ensure a lower false negative rate in this Phase, a chemical
predicted to be active, by any of these models, is subsequently
evaluated in the Phase III, while only those pre­dicted inactive, by all
11 models, will be eliminated for further evaluation. 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 *(17).*

-   ***PhaseI: 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
    *(16).* A plot of the CoMFA-predicted ver­sus experimental RBAs (as
    logs), computed for the training-set compounds, is given in
    Figure 4. The conventional *r^2^* and cross­validated *q^2^* are 0.91
    and 0.76, respectively, indicating that the CoMFA model is both
    internally consistent and highly predictive. Figure 5 shows two
    different dis­tributions: 1) the range of fold­differences for
    individual experi­mental data points and 2) the range of
    fold-differences for CoMFA predicted and experimen­tal means. The
    predictions fall in a similar range to the experimen­tal data points.

> *(Continued on page 9)*

Volume 1, Issue 3

> *(Continued from page 8)*
>
> · ***Phase IV: Rule-Based Decision-Making System -*** In this final
> stage of the integrated priority setting approach, we propose to use a
> knowledge-based system, or expert system, to make a prior­ity setting
> decision. This is a mul­tidisciplinary effort that includes
> computational chemists, toxicolo­gists and environmental and regulatory
> scientists from different agencies. The system is useful only after
> incorporating accumu­lated human knowledge and e x­pertise (i.e.,
> rules). This system can make decisions on individual chemicals based
> on the rules in its knowledge base, which at this juncture include but
> are not lim­ited to:

1.  Information gained at each phase of the integrated com­putational
    approach.

2.  Information on human expo­sure, environmental fate and other effects,
    and chemical production level.

3.  Chemical structure novelty, that is, when a structure is encountered
    that is dissimilar to all those that have been used to train and
    test the models.

> The NCTR "Four-Phase" system has been validated by a number of
> existing data sets, including the NCTR ER binding data set *(14),* the
> E­SCREEN assay data *(48),* the yeast two-hybrid reporter gene assay
> data *(49),* and other data sets *(41, 50-54).* To date, the system
> has produced no
>
> **Regulatory Research Perspectives**

false negatives, which is critical in priority setting for regulatory
pur­poses. The same integrated scheme is being extended to include
end­points of other endocrine disrupting mechanisms (e.g., AR binding) at
NCTR.

**Regulatory Application**

> The NCTR "Four-Phase" system is

a primary candidate priority setting

tool for chemical entry to Tier 1

screening. The system was recently

applied to three environmental data

sets, recognized by EPA, as represen‑

tative subsets of potential EDCs:

· HPV-Inerts data set -- It contains 623 High Production Volume in­erts
(HPV-Inerts), which is a por­tion 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. Of
623 chemicals, 166 chemicals were either mixtures or their structures
were not available, thus excluding them from prediction. Therefore, 457
chemicals were predicted by this system.

· Walker data set - Walker *et al.* developed a database that con­tains a
large and diverse collec­tion of known pesticides and in­dustrial
chemicals, as well as some food additives and drugs *(55).* The database
contains 92,964 Chemical Abstract Serv‑

> ice (CAS) Registry numbers of chemicals that will probably have to be
> evaluated for their potential endocrine disruption. A final data set
> of 58,391 chemicals was processed by our system after eliminating
> those chemicals for which structures were not avail­able *(55)* and/or
> 3D structures could not be generated *(17).*

· Validation data set -- To validate the NCTR \"Four-Phase\" system, the
EPA provided a list of 6,645 chemicals. The EPA randomly selected 200
chemicals from the list and 50 chemicals from those predicted to be
active by the sys­tem for the list. These 250 chemi­cals are going to be
assayed. By comparing the assay results with the prediction, we will be
able to estimate (assess) the degree of false negatives, false positives
and quantitative accuracy associ­ated with the system. With this
validation, we hope to establish QSARs as a priority setting tool for
regulatory application.

Table 1 summarizes the priority setting results for these data sets
us­ing the NCTR "Four-Phase" system. When only the Phase I and II
proto­cols are used, the system dramati­cally reduced the number of
potential estrogens by some 80-85%, demon­strating effectiveness in
eliminating these 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

> *(Continued on page 10)*

+-----------------+----------------+----------------+----------------+
| > **Table 1.    |                |                |                |
| > Size          |                |                |                |
| > reduction of  |                |                |                |
| > three         |                |                |                |
| > environmental |                |                |                |
| > data sets     |                |                |                |
| > processed     |                |                |                |
| > by**          |                |                |                |
| >               |                |                |                |
| > **the NCTR    |                |                |                |
| > "Four-Phase"  |                |                |                |
| > system**      |                |                |                |
+-----------------+----------------+----------------+----------------+
|                 | HPV-Inerts     | Walker         | Validation     |
+-----------------+----------------+----------------+----------------+
| **Original Data | 457 (100%)     | 58,391 (100%)  | 6,645 (100%)   |
| Size**          |                |                |                |
+-----------------+----------------+----------------+----------------+
| After Phase I   | 15.7%          | 12.0%          | > 11.0%        |
| and II          |                |                |                |
+-----------------+----------------+----------------+----------------+
| After Phase III | 9.8%           | -----          | > 4.8%         |
+-----------------+----------------+----------------+----------------+

*(Continued from page 9)*

binding affinity prediction from Phase III provides an important
rank-order value for priority setting.

**Concluding Remarks**

EDCs have potential adverse ef­fect on human beings and wildlife. The
potential of chemicals to interfere with estrogen functions may be
re­lated to their ability to mimic estrogen and bind to ER. The potential
to mimic estrogen and bind to ER can be quantitatively predicted using
com­putational methods such as QSARs. QSARs are an important part of
prior­ity setting to determine which chemi­cals should be experimentally
evalu­ated first in Tier 1 screening recom­mended by EDSTAC.

In conjunction with EPA, the EDKB project team at the FDA/NCTR has
developed a number of QSAR models for prediction of chemical binding to
the ER. These models are integrated into a "Four-Phase" scheme, which we
have shown to demonstrate efficiency and accuracy for priority setting
of potential estro­genic EDCs for use, by regulators, at EPA. We
anticipate that the same scheme will be equivalently applicable

> **Regulatory Research Perspectives**

to other mechanisms (e.g., androgen receptor binding) involved in
endocrine disruption and other toxicity end­points. The stringent
requirement for developing models for toxicity mecha­nisms is
appropriately designed train­ing data set similar to the one e m­ployed
here for the ER-binding mod­els. Properly validated data allow the
structural rules that govern activity to be determined and used to
develop robust predictive models.

While the results presented here clearly show both the feasibility and
utility of using QSARs for priority set­ting, it is important to note
that pre­dictions from any model are intrinsi­cally no better than the
experimental data employed for modeling. Any limi­tations of the assay
used to generate the training data apply equally to the model's
predictions. Moreover, false negatives and false positives depend on the
defined cut-off value to distin­guish active from inactive for the mod­els
only providing YES/NO prediction. As the cut-off value is lowered, it is
likely that error will increase even for a well designed and executed
assay, and false positives and false nega­tives will both increase.
Similarly, more false prediction might be intro­duced for chemicals with
activity

> Volume 1, Issue 3

close to the cut-off value. The issue for a large number of chemicals,
of the rate of false positives and false negatives in predicted values,
must be dealt with experimentally by run­ning assays on a sufficiently
large number of chemicals to characterize the rates. Therefore, our
current model validation process with EPA is an important step to ensure
the model's quality for regulatory applica­tion.

> **Acknowledgement**

The EDKB team gratefully ac­knowledges funding from the FDA's Office of
Women's Health that en­abled the EDKB Program to begin and make early
rapid progress. The American Chemical Council, through a Cooperative
Research and Develop­ment Agreement, supported the es­trogen and androgen
receptor com­petitive binding assays. Finally, through an Interagency
Agreement, the Environmental Protection Agency provides support for
predictive model development and validation. The EDKB team acknowledges
the con­tinued support of Dr. Bern Schwetz, who initiated the concept of
knowl­edge bases at the NCTR.

+---+--------------------------------+---+------------------------------+
|   | *mental Health Perspectives*   |   | R., Collantes, E. R.,        |
|   | 104                            |   | Keenan, S.,                  |
|   |                                |   |                              |
|   | Suppl 4:715-40.                |   | Welsh, W. J., Branham, W.    |
|   |                                |   | S.,                          |
+---+--------------------------------+---+------------------------------+
| 4 | U.S. Congress. 1996. The Food  |   | Sheehan, D. M. 1997.         |
| \ |                                |   | Quantita‑                    |
| . |                                |   |                              |
+---+--------------------------------+---+------------------------------+
| 5 | Quality Protection Act (FQPA)  |   | tive structure-activity      |
| \ |                                |   | relationships                |
| . | and the Safe Drinking Water    |   |                              |
|   | Act                            |   | (QSARs) for estrogen binding |
|   |                                |   | to                           |
|   | (S DWA).                       |   |                              |
|   |                                |   | the estrogen receptor:       |
|   | 104^th^ U.S. Congress. 1996.   |   | predictions                  |
|   | Food                           |   |                              |
|   |                                |   | across species.              |
|   |                                |   | *Environmental*              |
+---+--------------------------------+---+------------------------------+
|   | Quality Protection Act 21      | 9 | *Health Perspectives*        |
|   | U.S.C.                         | \ | 105:1116-24.                 |
|   |                                | . |                              |
|   | 346a(p). The Safe Drinking     |   | Tong, W., Perkins, R., Xing, |
|   | Water                          |   | L.,                          |
+---+--------------------------------+---+------------------------------+
|   | Act (42 U.S.C. (300j-17).      |   | Welsh, W. J., Sheehan, D. M. |
|   | Public                         |   |                              |
|   |                                |   | 1997\. QSAR models for       |
|   | Law 104-182.                   |   | binding                      |
+---+--------------------------------+---+------------------------------+
| 6 | Gray, L. E., Jr. 1998. Tiered  |   | of estrogenic compounds to   |
| \ |                                |   | es‑                          |
| . |                                |   |                              |
+---+--------------------------------+---+------------------------------+
|   | screening and testing strategy |   | trogen receptor alpha and    |
|   | for                            |   | beta                         |
|   |                                |   |                              |
|   | xenoestrogens and antiandro‑   |   | subtypes. *Endocrinology*    |
|   |                                |   |                              |
|   | gens. *Toxicol Lett*           |   |                              |
|   | 102-103:677‑                   |   |                              |
+---+--------------------------------+---+------------------------------+
|   |                                | > |                              |
|   |                                |   |                              |
|   |                                | 1 |                              |
|   |                                | 3 |                              |
|   |                                | 8 |                              |
|   |                                | : |                              |
|   |                                | 4 |                              |
|   |                                | 0 |                              |
|   |                                | 2 |                              |
|   |                                | 2 |                              |
|   |                                | - |                              |
|   |                                | 5 |                              |
|   |                                | . |                              |
+---+--------------------------------+---+------------------------------+
|   | 80\.                           | 1 |                              |
|   |                                | 0 |                              |
|   |                                | \ |                              |
|   |                                | . |                              |
|   |                                | T |                              |
|   |                                | o |                              |
|   |                                | n |                              |
|   |                                | g |                              |
|   |                                | , |                              |
|   |                                | W |                              |
|   |                                | . |                              |
|   |                                | , |                              |
|   |                                | L |                              |
|   |                                | o |                              |
|   |                                | w |                              |
|   |                                | i |                              |
|   |                                | s |                              |
|   |                                | , |                              |
|   |                                | D |                              |
|   |                                | . |                              |
|   |                                | R |                              |
|   |                                | . |                              |
|   |                                | , |                              |
|   |                                | P |                              |
|   |                                | e |                              |
|   |                                | r |                              |
|   |                                | k |                              |
|   |                                | i |                              |
|   |                                | n |                              |
|   |                                | s |                              |
|   |                                | , |                              |
+---+--------------------------------+---+------------------------------+
| 7 | Patlak, M. 1996. A testing     | R |                              |
| \ | dead‑                          | . |                              |
| . |                                | , |                              |
|   |                                | C |                              |
|   |                                | h |                              |
|   |                                | e |                              |
|   |                                | n |                              |
|   |                                | , |                              |
|   |                                | Y |                              |
|   |                                | . |                              |
|   |                                | , |                              |
|   |                                | W |                              |
|   |                                | e |                              |
|   |                                | l |                              |
|   |                                | s |                              |
|   |                                | h |                              |
|   |                                | , |                              |
|   |                                | W |                              |
|   |                                | . |                              |
|   |                                | J |                              |
|   |                                | . |                              |
|   |                                | , |                              |
|   |                                | G |                              |
|   |                                | o |                              |
|   |                                | d |                              |
|   |                                | ‑ |                              |
+---+--------------------------------+---+------------------------------+
|   | line for endocrine disrupters. |   | dette, D. W., Heritage, T.   |
|   | *En‑*                          |   | W.,                          |
|   |                                |   |                              |
|   | *vironmental Science &         |   | *(Continued on page 11)*     |
|   | Technol‑*                      |   |                              |
|   |                                |   |                              |
|   | *ogy* 30:540A-544A.            |   |                              |
+---+--------------------------------+---+------------------------------+
| 8 | Tong, W., Perkins, R.,         |   |                              |
| \ | Strelitz,                      |   |                              |
| . |                                |   |                              |
+---+--------------------------------+---+------------------------------+

*(Continued from page 10)*

> Sheehan, D. M. 1998. Evaluation of quantitative structure-activity
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11. Xing, L., Welsh, W. J., Tong, W., Perkins, R., Sheehan, D. M. 1999.
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12. Perkins, R., Anson, J., Blair, R., Branham, W. S., Dial, S., Fang,
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16. Shi, L. M., Fang, H., Tong, W., Wu, J., Perkins, R., Blair, R.,

> **Regulatory Research Perspectives**
>
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> Volume 1, Issue 3
>
> genic activity. *Environmental Health Perspectives* 108:723-729.

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49. Nishirara, T., Nishikawa, J., Kanayama, T., Dakeyama, F., Saito, K.,
    Imagawa, S., Takatori, S., Kitagawa, Y., Hori, S., Utsumi, H. 2000.
    Estrogenic A c­tivities of 517 Chemicals by Yeast Two-Hybrid Assay.
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50. Gaido, K. W., Leonard, L. S., Lovell, S., Gould, J. C., Babai, D.,
    Portier, C. J., McDonnell, D. P. 1997. Evaluation of chemicals with
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    receptor gene transcription as­say. *Toxicology & Applied
    Phar­macology* 143:205-12.

51. Coldham, N. G., Dave, M., Si­vapathasundaram, S., McDonnell, D. P.,
    Connor, C., Sauer, M. J.1997. Evaluation of a recombi­nant yeast cell
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    105:734-42.

52. Routledge, E. J., Sumpter, J. P. 1997. Structural features of
    alkyl­phenolic chemicals associated with estrogenic activity.
    *Journal of Biological Chemistry* 272:3280-8.

53. Routledge, E. J., Parker, J., Odum, J., Ashby, J.,
    Sumpter, J. P. 1998. Some alkyl hydroxy benzoate preservatives
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    153:12-9.

54. Harris, C. A., Henttu, P., Parker, M. G., Sumpter, J. P. 1997. The

*(Continued from page 12)*

> estrogenic activity of phthalate esters *in vitro. Environmental
> Health Perspectives* 105:802-11.

55\. Walker, J. D., Waller, C. W., Kane, S. 2001. The Endocrine

**Regulatory Research Perspectives**

Disruption Priority Setting Data­base (EDPSD): A Tool to Rapidly Sort and
Prioritize Chemicals for Endocrine Disruption Screening and Testing. In:
*Handbook on Quantitative Structure Activity*

> Volume 1, Issue 3

*Relationships (QSARs) for Pre­dicting Chemical Endocrine Dis­ruption
Potentials* (Walker, J. D., ed). Pensacola, FL:SETAC press.

**Glossary**![](media/image20.png){width="0.32013888888888886in"
height="6.944444444444445e-2in"}![](media/image21.png){width="2.479861111111111in"
height="9.652777777777778e-2in"}

**Quantitative structure-activity r e­lationships (QSARs):** Technique to
quantitatively correlate for a set of chemicals **structural
descriptors** encoding chemical structures or prop­erties with a
dependent variable rep­resenting biological activity using, for example,
**regression** methods.

**Structural Descriptors:** Parame­ters that are used to characterize
chemical structure. Categories of de­scriptors commonly used in **QSAR**
include, but are not limited to, spatial, electronic, topological,
information­-content, thermodynamic, conforma­tional, quantum mechanical,
and shape descriptors.

**Regression:** Mathematical ap­proaches to generate equations that
correlate independent variables (e.g., **descriptors)** with dependent
vari­ables (e.g., biological activity). The equations can be used to
predict val­ues of one variable (e.g., biological activity) when given
values of the oth­ers (e.g., **descriptors).** Many types of statistical
regression techniques are used to develop **QSAR** equations.

**Training set:** The set of chemicals used to develop the **QSAR**
equation for which the biological activity data are known.

**Testing set:** The set of chemicals for which biological activity are
known that is used to challenge the QSAR models developed based on the
**training set.**

**Cross-validation (or internal vali­dation):** Statistical approaches
that are often used to determine predictive effectiveness of a QSAR
model de­velop based on a particular **training set.** For example, in
the leave-one-out **cross-validation** method, each com­pound is
systematically excluded once from the **training set,** after which its
activity is predicted by a model de­rived from the remaining compounds.
Therefore, the summary of differences between the actual and predicted
ac­tivity data for each compound can be used to assess the predictive
effec­tiveness. This process only assesses interpolation of the model
within the **training set.** Thus, sometimes it is called **internal
validation. Internal validation** is less rigorous than **exter­nal
validation.**

**External validation:** A more con­vincing process to evaluate how well

the QSAR equation generalizes for an external **testing set.** One
common practice is to divide the original data into two groups, the
**training set** and the **testing set.** The **training set** is used
to derive a model, and the model is then used to predict the a c­tivities
of the **testing set.** The sum­mary of differences between the a c­tual
and predicted activity data for the **testing set** can be used to
assess the predictive effectiveness of the QSAR model for those
chemicals not in­cluded in the **training set.**

**Endocrine Disrupting Compounds (EDCs):** An exogenous agent that i
n­terferes with the production, release, transport, metabolism, binding,
action, or elimination of natural hormones in the body responsible for
the mainte­nance of homeostasis and the regula­tion of developmental
processes.

**High Throughput Screening (HTPS):** A general term referring to the
automation, often employing robotics, of biological assays to achieve a
high volume of separate tests.

**Weida Tong** is Manager of Bioinfor­matics/Computational Science Group
of Northrop Grumman Health Solu­tions located at the U.S. FDA\'s Na­tional
Center for Toxicological Re­search (NCTR). He is also an adjunct
assistant professor in the Department of Pharmaceutical Sciences at the
University of Arkansas for Medical Sciences. Dr. Tong received his
Ph.D., in 1990, and was a research associate in computational chemistry
for six years at the University of
Mis‑![](media/image22.png){width="1.8402777777777777in" height="2.0in"}

souri-St. Louis. In 1996, he joined a multidisciplinary research team at
NCTR that has been focusing on de­velopment of computational toxicol­ogy
tools for risk assessment and bioinformatics approaches for ge­nomics and
proteomics. Dr. Tong\'s primary research interests encom­pass
computational chemistry, bioin­formatics, chemoinformatics, and
sci­entific visualization applied to toxicol­ogy. His specialty areas
included

> *(Continued on page 14)*

data mining, QSARs, 3D database searching, programming, molecular
modeling, and chemometrics. Cur­rently, he manages the
bioinformat­ics/computational science group that supports toxicological
and regulatory research at NCTR through the provi­sion of modern
computing
technolo­gies.![](media/image23.png){width="2.776388888888889in"
height="0.34305555555555556in"}

![](media/image24.png){width="2.09375in" height="1.9597222222222221in"}

Roger Perkins

**Roger Perkins** received his B.S. and M.S. in Nuclear Engineering
Sci­ences at the University of Florida and has since had a diverse career
cen­tered in computational science. He currently is Director of the
Scientific Computing Division of Northrop Grumman Health Solutions. He
also is Program Manager of a diverse in­formation technology contract at
the FDA/NCTR located in Jefferson, Ar­kansas. This contract provides
sys­tems, network, software engineers, statistics, and computational
sci­ence support. Mr. Perkins spent 10 years as a reactor physicist in
the U.S. Fast Breeder Reactor Program. He was part of the management
team at the inception of the National Science Foundation's San Diego
Supercomputer Center, in 1985, prior to becoming Director of the Navy's
Advanced Scientific Computational Center in 1990. He became manager of
the information technology con­tract at NCTR, in 1994, where he has been
directly involved in a range of computational science efforts.

**Hong Fang** received her Ph.D. in Chemistry from the University of
Mis­souri-St. Louis, in 1995, and contin­ued her post-doctoral work at the

![](media/image25.png){width="1.9729166666666667in"
height="2.079861111111111in"}

Hong Fang, Ph.D.

same university. She next worked for two years as a forensic scientist
with the St. Louis Police Department. In 1997, she began a two-year
post­doctoral assignment at the NCTR, in the Division of Reproductive and
De­velopmental Toxicology working for Dr. Daniel Sheehan, in the
Endocrine Disruptor Knowledge Base (EDKB) program. Dr. Fang's EDKB work
ranged from design and conduct of assays to data analysis, and then
using the experimental data to de­velop computer-based predictive models.
In 1999, she joined the Northrop Grumman Health Solution's contract at
NCTR as a Senior Com­putational Scientist, where she con­tinued EDKB work
developing mod­els to predict estrogen and androgen activity. She is now
developing mod­els to predict chemically-induced carcinogenicity and has
participated in development of the NCTR bioinfor­matics capability.

**Huixiao Hong** received his Ph.D. in computational chemistry at
Nan­jing University, in 1990, in China. From 1990 to 1992, he worked on
computer-assisted organic synthesis at the Maxwell Institute for
Molecular Sciences at Leeds University in Eng-

land as a postdoctoral research fel­low. Dr. Hong held an Associate
Pro­fessor position at Nanjing University, from 1992 to 1995, where he
investi­gated computer-assisted molecular design and developed a suite of
soft­ware for analytical instrumentation simulations. From 1995 to 1998,
he worked at the National Cancer Insti­tute (NCI) at National Institutes
of Health (NIH) as a Visiting Scientist where he discovered and designed
more than 60 potent HIV Integrase inhibitors and Cyclooxygenase (COX)
inhibitors through molecular modeling and pharmacophore identification
and

![](media/image26.png){width="1.8666666666666667in"
height="2.1465277777777776in"}

Huixiao Hong, Ph.D.

searching on NCI database. He also identified a Mononucleotide binding
site in HIV Integrase, and elucidated the stereoselective binding mode
of ibuprofen in Cyclooxygenase-1 (COX1) using docking and molecular
dynamics simulation. He held a Re­search Scientist position at Sumi­tomo
Chemical Co., Ltd. in Japan, from 1998 to 2000, where his re­search was
mining two-dimensional NMR data and developing software for structure
elucidation from two-­dimensional NMR spectra. D r. Hong joined the
computational science group of Northrop Grumman at NCTR, in 2000, and
has been en­gaged in both analysis and methods development for
chemoinformatics

> *(Continued on page 15)*

*(Continued from page
14)*![](media/image23.png){width="2.776388888888889in"
height="0.34305555555555556in"}

and bioinformatics.

**Qian Xie** received his Ph.D. in Chemistry from Beijing Normal
Univer­sity, Beijing, China in 1991. He worked as an assistant and then
as­sociate professor in the laboratory of

![](media/image27.png){width="1.9736111111111112in"
height="1.8166666666666667in"}

Qian Xie, Ph.D.

computer chemistry of the Institute of Chemical Metallurgy, Chinese
Acad­emy of Science, Beijing, China, from 1991 to 1995, where his
research fo­cused on computer-aided drug design for agricultural
chemicals. He was a research associate in the biochemis­try department of
Hong Kong Univer­sity of Science and Technology, Hong Kong from 1996 to
1997, where he worked on protein recognition and geometric complements.
From 1998 to 1999, he worked as a research as­sociate in the department
of biochem­istry and biophysics at Washington State University, Pullman,
Washing­ton, where his research involved com­puter simulations of electron
transfer proteins. In 2000, he joined the bioin­formatics/computational
sciences group of Northrop Grumman at NCTR and is currently working on
bioinfor­matics and structure-activity relation­ship models for estrogens
and andro­gens of environmental chemicals.

**William (Bill) Branham** received a Master of Science degree in 1972
from West Virginia University. Follow­ing a four-year commitment to the
Air Force, he came to the NCTR to work with Dr. Daniel Sheehan as part
of the Hormone Research Program. During

the 25 years of their working relation­ship, Mr. Branham published a
series of papers describing the toxic effects of estrogens and
antiestrogens on the development of the reproductive tract in the female
rat. This work was, in part, responsible for increased pre­cautionary FDA
warnings to deter the prescription of antiestrogenic drugs

![](media/image28.png){width="1.9729166666666667in"
height="2.2930555555555556in"}

William (Bill) Branham

during pregnancy. Mr. Branham is currently working under the direction
of Dr. Jim Fuscoe, in the Center for Functional Genomics, on a project
to bring microarray technology to the NCTR.

**Daniel Sheehan** received his Ph.D. in Biochemistry from the
Uni­versity of Tennessee\'s Biomedical

![](media/image29.png){width="2.1333333333333333in"
height="1.8395833333333333in"}

Daniel Sheehan, Ph.D.

School located at the Oak Ridge N a­tional Laboratories. He did
postdoc­toral studies at Baylor University, in the laboratory of Bert
O\'Malley, inves‑

tigating receptor-mediated regulation of cellular physiology. He joined
the NCTR approximately 25 years ago and has spent the majority of that
time investigating estrogen agonist and antagonist effects on uterine
mor­pholgy and physiology. Several years ago, he initiated a
collaboration to build knowledge bases at the NCTR beginning with the
Estrogen Disruptor Knowledge Base. He retired from the FDA in the summer
of 2001.

**Jeanne Farr Anson** is the NCTR Associate Director for the Office of
Planning, Finance and Information Technology. In 1974, Ms. Anson be­gan
her career in FDA as a microbi­ologist studying unscheduled DNA

![](media/image30.png){width="2.027083333333333in" height="2.0in"}

Jeanne Farr Anson

synthesis using tissue culture. She went on to study nuclear proteins to
identify biomarkers of toxic stress using flow cytometric and
autora­diographic techniques. At this junc­ture, she returned to school at
night and obtained her Master's in Health Services Administration. She
moved into management at NCTR, in 1990, and is active in promoting the
compu­tational biosciences for use in the de­velopment of knowledge bases
(including development of a data warehouse) and predictive models. She
serves as project officer for an IAG with EPA to validate androgen
receptor binding as a screen for endo­crine disrupting activity.

> Page 16 **Regulatory Research Perspectives** Volume 1, Issue
> 3![](media/image31.png){width="7.3069444444444445in"
> height="9.333333333333334in"}