QSAR seminar Report final

8/8/2019 QSAR seminar Report final

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QSAR STUDIES FOR PREDICTING THE TOXICITY OF

POLLUTANTS

Seminar Report submitted in partial fulfillment of the requirements

of the degree of Master of Technology

by

Srivastav Ranganathan

Roll No: 10318007

Seminar Guide/Supervisor:

Prof.Sumathi Suresh

Center For Environmental Science and Engineering (CESE)

INDIAN INSTITUTE OF TECHNOLOGY, BOMBAY

2010


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Declaration

I declare that this written submission represents my ideas in my own words and where

others' ideas or words have been included, I have adequately cited and referenced the

original sources. I also declare that I have adhered to all principles of academic honesty and

integrity and have not misrepresented or fabricated or falsified any idea/d ata/fact/source in

my submission. I understand that any violation of the above will be cause for disciplinary

action by the Institute and can also evoke penal action from the sources which have thus notbeen properly cited or from whom proper permission ha s not been taken when needed.

Signature:

Name of Student:

Srivastav Ranganathan

Roll No : 10318007

Date : 4nd

November 2010


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TABLEOFCONTENTS

Chapter Title Page

Number

1 Introduction 4

2 Principles for Developing

Environmental QSARs

10

3 QSAR Studies for non-typical cases

: Chronic toxicity and Active

Metabolite Activity

16

4 Classification of Chemicals into

classes for choice of training set

21

5 QSAR in Environmental

Toxicology Case Study on

`Estrogenic Activity of

Anthraquinones`

30


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CHAPTER 1:

AN INTRODUCTION TO ENVIRONMENTAL TOXICOLOGY AND

QSAR

Environmental toxicology and its importance

Human civilization has made rapid progress in the past century and the technological

advances made with each passing decade have been rapid and huge. These technological

advances, coupled with rapidly growing population has led to introduction of man-made

chemicals and materials in the environment, many of which have disrupted the functioning of

the ecosystem to an extent which might eventually be detrimental to the intricate balance ofthe ecosystem.

Although natural systems have a buffer to protect themselves against the human-introduced

toxic substances, the rate and amounts at which the toxic substances are released into the

environment do not allow the systems in nature to acclimatize and develop defence

mechanisms against these toxins. Hence, there exists a need to do an intensive assessment of

the potential toxic effects of the chemicals prior to their release into the surrounding

environment.

Environmental toxicology is that branch of science which deals with impact of pollutants

and chemicals on the structure and functioning of the ecosystems. Environmental toxicology

involves a multidisciplinary approach which requires the knowledge of molecular biology,

ecology, chemistry, genetics, biochemistry, mathematics, computational modelling and many

other fields to assess the eventual fate and toxic effects of a chemical or pollutant on the

ecosystem components (Table 1).

Table 1: List of subject areas which contribute to an assessment of the fate of toxic effects of

pollutants in environment

Area of Study Need

Molecular Biology , Pharmacokinetics To study pollutant-organism interaction at a

molecular level

Analytical Chemistry To understand the levels of pollutant in various


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environmental matrices and biotic componentsfollowing appropriate extraction methods.

Organic Chemistry To study structural and functional aspects of thepollutant molecules which forms a basis forQSAR studies

Biometrics, Mathematical andComputational Modeling

Data analysis, predictive models and hypothesistesting

Evolutionary Biology To study adaptive mechanisms of organisms tochanges in environment.

Microbiology and Molecular Genetics Effect of the compounds on microorganisms,their defence mechanisms and also the impact ofthe compound at the genetic level.

Ecology , Risk and Impact Assessment Basis to study the impact of a pollutants once itgains entry into the environment.

The seminar report attempts to provide an overview of QSAR (Quantitative Structure-

Activity relationship) approach and its role in evaluating/predicting the toxicity of a given

pollutant following its discharge into the surrounding environment.

The seminar report has been organized into the following chapters :

yChapter 1 deals with an introduction to QSAR studies and its place in the broad

toxicological framework.

yChapter 2 discusses QSAR modeling and the various steps involved in developing a

QSAR model.

yChapter 3 discusses some special cases and modification to QSAR modeling with

respect to chronic toxicity and in cases where the metabolites are more toxic relative

to the parent compound.

yChapter 4 discusses how the pollutants are classed into various groups in order to be

used as a training set for QSAR, based upon their chemical structure or biological

activities.

yChapter 5 summarizes a case study for predicting the interaction of anthraquinone

model compounds with estrogen receptor for exertion of estrogenic activity.


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AN INTRODUCTION TO QSAR

Quantitative Structural Activity Relationship or QSAR is an mathematical/computational

modeling approach which is used to relate the chemical structure and structure related

properties of a compound to its biological activity.

The basic concept of QSAR is that chemicals which are similar in nature will behave

similarly in biological systems. However, the classification of chemicals as similar or

different and the choice of properties to decide the same are of key importance in QSAR.

Similarity must thus be described in relation to specific contexts and must take specific

attributes into consideration as well. For example, stereo-isomers may seem very similar in

structure but differ significantly in activity in biological systems.

1.1 ) Uses of QSAR

yOne of the most important uses of QSAR is in evaluating toxicity of a proposed

compound (novel compound) which has not been widely discharged into the

environment.

yQSAR is also of great potential in screening compounds with low toxicity but with

desired characteristics so that it could be used to replace those which are widely used

but more toxic.

yQSAR toxicity predictions may be used to screen untested compounds in order to

establish priorities for traditional bioassays, which are often expensive and time-

consuming.

QSAR uses the chemical and computational modeling approach to extrapolate the effects of

tested compounds to untested compounds which are similar in nature. Such models have been

successful in the estimation of toxicological endpoints like carcinogenicity, mutagenicity and

endocrine disrupting activity.

QSAR application in the domain of toxicological studies can be broadly divided into the

following categories:

yCarcinogenicity, mutagenicity, reproductive toxicity, acute toxicity and similar

systemic effects of toxicants. Also used to assess the ADME (Absorption,

distribution, metabolism and excretion) factors which influence the bioavailability of

a compound in an organism.


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yLocalized human health effects which include irritation to skin, eyes, respiratory tract

etc.

yEnvironmental effects of compounds using endpoints that are modeled to represent

the effects on different trophic levels of an ecosystem, as well as effects like

endocrine disruption.

yPrediction of the environmental fate of a chemical depending upon its persistence,

degradation, bioaccumulation, sorption etc

The European Union legislation has put forth the requirement to implement QSAR in order to

assess the toxicity of chemicals according to the Registration, Evaluation, Authorisation and

Restriction of Chemicals (REACH) program (EU 2006).

1.2) The need for Computational methods and QSAR for toxicological assessment

yAccording to USEPA and OECD reports, the current inventory of commercial and

industrial chemicals exceeds 160,000 and the number is growing at a rate of 3000 new

chemicals every year. The range of chemicals includes those from pharmaceuticals,

cosmetics and personal healthcare products, industrial chemicals and pesticides.

yIn addition to the fact that the number of chemicals is growing at an alarming rate, the

more worrisome fact is that the traditional toxicological testing assays only achieve

testing of about 500 chemicals every year. Thus, related data for environmental

effects and fate exists for only 20% of the chemicals.

yThe time lag between testing of chemicals and their widespread use could lead to

irreparable damage to the ecosystem by the time the harmful effects and toxicity

levels of certain chemicals are known and regulatory norms are established. This

poses a unique challenge in front of environmental toxicologists to look at non-testing

alternatives which would help us in prediction of toxicological endpoints of chemicals

at a faster rate and prioritization according to the toxicities predicted by these

methods.

yLaboratory testing of chemicals in animals are time consuming and incur a high

expense. Screening-level assessments can cost from $1-5M, while comprehensive risk

assessments can cost more than $60M in testing and analysis (USEPA).

yIn vivo experiments conducted in lab animals may also have the problem of relevance

to human beings due to species to species differences.


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yIn vitro studies consist of administration of the pollutant to cells cultured in vitro.

However, these results may not be reliable due to various other factors which come

into play when the entire biological system is taken into picture.

1.3) QSAR and the broader Toxicological Framework

As observed from figure 1, QSAR studies feed on the data from previously performed

toxicological studies using traditional assays as well as provide data to prioritize in-vito

assays and animal studies. The data from the available studies have to be coherently

maintained and organized along with various data related to biochemical and biological

information. The predictive results from QSAR models then contribute to setting new, safer

regulatory norms from regulatory agencies.

An insight from QSAR models on toxicological profiles of the chemicals enables the focus of

industries to be channelized on identifying and developing newer alternatives to the

compounds with more toxic profiles. Thus, its a mutual, inter-connected relationship where

the industry, academia (academic research) and regulatory agencies have to work in tandem

to set new acceptable norms and thereby try and minimize the impact of chemicals on the

environment.


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Fig 1. Valerio Jr, 2009 The path of work flow for the use of drug and chemical toxicity

databases and models; starting from the source of data to the goal of predicting environmental

health effects


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CHAP

P CIP S F D PING ENVIRONMENTA QSARS

2.1) STEPS INVOLVED IN QSARMODELLING

Fi 2. St i l i QSARmodelli

Note: It must be noted t t alt ough described in a sequential order above, there is a

considerable amount oftrial and error method involved while selecting the molecular

descri tor and endpoint Thus, based upon the nature ofthe correlation, the procedure would

involve going back and forth in the above schematic in orderto optimi e the choice of

dataset, endpoint or descriptor.

ySelection of a training set or dataset of chemicals goes hand in hand with the choice of

molecular descriptor. The datasetto be chosen depends on the biological endpointto

be modeled. The dataset stongl depends upon grouping the chemicals with known

biological endpoints and molecular descriptors on the basis of different parameters.

i ne such approach is grouping the chemicals on the basis oftheir biological

targets, for e.g protein targets like enzymes, hormones etc.

Explore the Statistical Variances between the molecular descriptors and the numericalvalues ofthe endpoint , forthe training set.

Correl

te the E

oi ts with the Molecul

r Descri tor and obtain a regression equationto find out the endpoint values for the compound in question .

Compile the Values ofBiological Endpoints ofthe target set from databases and literature .

Select a dataset of similar compounds. ( raining SetBased on the chemical structure (ligand

based approach )Based on the biological targets (protein,

DNA etc) known as target based approach

Choose a property to be linked to the chemical structure and likely to be related to endpoint .

Molcular Descriptors: e.g LogP values, boiling points, pKa values etc

Select a Biological Endpointto be Predicted (LC , LD50 etc)


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ii.) The other approach towards grouping the chemicals is on the basis of their

structures, for e.g, classification of chemicals into chemical classes like

carbamates, polyaromatic hydrocarbons, polychlorinated biphenyls etc.

As mentioned earlier, development of a QSAR model involves three primary elements.

Firstly, the choice of an endpoint for the training set with known measured values must be

compiled. The second, equally important aspect is the choice of chemical/physicochemical

properties which are linked to the chemical structures of the molecules (molecular

descriptors). The third step involves linking of these two elements with the help of statistical

techniques to derive a QSAR model. Choice of biological endpoint and molecular descriptors

is a tedious process which involves use of trial and error to arrive upon a proper choice.

Fig 3. Aspects of QSAR Modelling

2.1.1)Biological Endpoint

A biological endpoint which is to be predicted is first chosen for the target compound to be

studied. A biological endpoint is a biological effect such as biotransformation,

bioaccumulation, bioconcentration or toxicity values like LC50, EC50 etc. For example, one

of the first and simplest relationships of bioconcentration factor and octanol-water (Kow)

coefficient, using QSAR was described as follows (Mackay, 1982) :

QSAR

Models

Biological

Endpoint for

training set

StatisticalRelationship

Molecular

Descriptor


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log BF = log Kow 1.32

This relationship was developed from variety of aquatic organisms and its dataset included 51

organic compounds. The choice of a well defined biological endpoint is of immense

significance in developing a reliable QSAR model. The source of data must be those obtained

from standardized test results. The most commonly used examples of endpoints used in

environmental toxicology are LC50, LD50 or EC50 values which indicate the dosages which

are lethal to 50% of the population (organisms). These values of biological endpoints are

generally expressed in molar concentrations. The data are then converted to a logarithmic

scale in order to avoid issues associated with regression analysis. While choosing the training

set and using biological data for endpoints, it must be ensured that:

i.) The data was obtained by using standardized protocols.

ii.) All the values for the endpoints representing each member of the training set was

obtained using the same experimental procedures, so as to negate the issue of

variability.

iii.) The source of data and the age of the test organisms must be roughly uniform in

order to avoid variability issues.

Another cause for variability is the presence of impurities in the tested chemical. If the

presence of impurities which cause a synergistic or toxicity lowering effect on the compound

under question, there would be major variance in the data for these endpoints and hence mustbe avoided.

2.1.2)Molecular Descriptors

The second step involves chosing of a particular property which is linked to chemical

structure of the compounds. In order to choose a molecular descriptor on which the model

would be based, one would have to know how the descriptors are linked to the biological

endpoint which is chosen or in the chemical behaviour of the compound. Hence, it is the

structural representational part of QSAR.

The values of the molecular descriptors are arranged in a particular order and the relationship

to the numerical value of the biological endpoint is observed. If there is no trend observed, it

means that the molecular descriptor is not related to the biological endpoint and thus cannot

be used to model that particular biological endpoint. Molecular descriptors are generally

measured behaviours of the compounds which are expressed numerically. QSAR models in


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environmental toxicology generally use molecular descriptors that are linked to the

physicochemical properties of the chemicals which are experimentally measurable. Studies

have suggested that biological responses to various chemicals are linked to their

hydrophobicity, electronic properties, steric effects etc. As an example, hydrophobicity is the

concentration of the compound in octanol compared to its concentration in water after it has

been partitioned into two phases. It gives an indication of the tendency of the compound to

accumulate in lipid deposits of the body or cross lipid membranes in order to exert a

biological effect. In general, an increase in hydrophobicity would manifest itself as an

increase in the ability of that compound to cross cell membrane, thereby exerting greater

biological response. The fate of a pollutant in a biological system would thus depend on the

hydrophobicity because :

i.) Compounds that are too hydrophobic will not show any solubility in aqueous

phase.

ii.) If the hydrophobicity is extremely high, the compund would get trapped in fat

deposits and never reach the target site.

Many algorithms are available in the literature for calculating these interesting molecular

descriptors, which can be easily computed for all the existing, new, and in development

chemicals for a multivariate description of the molecules when they are judiciously

combined.

2.1.3)Statistical Methods

QSAR uses data from a variety of sources, all of which may not be acquired using a uniform

protocol and hence might cause problems in statistical assessment. Also, the difficulty in

measuring certain toxicological (biological) endpoints accurately might cause additional

statistical difficulty. Hence external validation methods like the use of an external test set are

routinely used to validate the model. Other statistical methods applied to assess the model

reliability are RMSE (Root mean square error), squared correlation coefficient (R2

) which areinternal validation methods.

Choice of Training Datasets or Test-set:

The choice of training set is considered to be the most important factor for deciding the

accuracy of a QSAR model. The choice of a dataset depends on the type of predictive model


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which is desired. The primary means are to secure representativeness ofthe chemicals based

on biological activity or chemical structure.

Fig 4:Choice oftraining set and a flowsheet governing the choice.

The flowsheet shown in figure 4 represents how the choice of datasetinfluences the model

accuracy. Once a datasetis established on which the modelis to be built and the

relationship/correlation between the chemical and biological aspects is first established, the

modelis furthertested using a test set of chemicals whose toxicological endpoints are known

and the quality of prediction is then derivable from this prediction. Ifthe accuracy ofthe

prediction is not upto desired levels, it would indicate that some ofthe data used was

inaccurate orthat a revision of molecular descriptor choice is necessary. Elimination ofthe

chemical acting as a chemical outlieris performed in cases when it has been clearly identified

thatthe outlier has a different biological activity compared to the training set altogether.

Assesment of Outliers

In the course of developing most QSARmodels, itis always observed that a major hurdle is

thatthe prediction of some chemicals ofthe training set are poor and inaccurate. Such a

situation is known as the problem of statistical outliers. Understanding the behaviour of such

outliers is of great help in gaining a betterinsightinto the mechanisms oftoxicity at a more

fundamentallevellike the biochemicallevel and also help reassess any errors in data choice

made in the previous steps.

Training Se

Training Se

ataset of already tested c

emicals with known endpoints and modes of activity.

S Model

Correlation between thechemical structure and biological endpoint.

trend is looked for between the two, if there are outliers, themodel is revisitedor molecular descriptor choice is reassessed.

Testing Set

test set ofchemicals ischosen and the quality and accuracy of predictions forthe test set chemicals are assessed.

FinalAssessmen

t

Have the MODELLING OBJECTIVES been met? If YES, then the model is ready for use as a predictive tool.

Revi i and

Reassessmen

IF NO


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The major causes of outliers are as follows :

yThe choice of training set is poor or needs to be revisited.

ySome of the values of endpoints of the training set used is inaccurate and/or due to

non-standardization of the protocol used to acquire these endpoints.

yThe chemical which is a statistical outlier exhibits a biochemical mode of action

which is different to the training set.

yThe possibility of metabolic products of the chemical might be responsible for the

toxicity and not the parent molecule itself.

yChoice of descriptor was not suitable enough or not sufficiently related to the

biological endpoint.

Hence, outliers help to revisit the choice of parameters in the initial steps and improve the

overall qunatity of the QSAR model chosen.


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CHAPTER 3:

QSARSTUDIESFOR NON-TYPICAL CASES:CHRONIC TOXICITY

ANDACTIVE METABOLITE ACTIVITY

3.1) Prediction ofChronic Toxic Effects

QSARmodels generally work reliably when predicting short term effects and acute toxicity

related to the chemical and its structure. However, there exist certain long term effects which

are caused due to prolonged exposure of a biological system to a chemical and also

influenced by multiple biological factors as well, in addition to the properties ofthe chemical

itself. Also, at a particular dose of a toxicant, multiple modes of carcinogenecity or

mutagenecity might be active and such effects cannot be taken into account reliably by

QSARmodels.

However, one approach which offers promise in overcoming this limitation is the use of a

technique called as the Adverse Outcome Pathway (AOP). This is an approach which

utilizes the information available from recent advances in bioinformatics, toxicogenomics,

systems biology to predict adverse effects with greater reliablility.

ParentChemical

ActiveMetabolic

Productsof the

Chemical

MolecularInitiating

Mechanism

s (Binding toDNA,

SurfaceReceptors,

Effects likeGene

Activation,Signal

Alteration

etc

In VivoEffects :

Organ leveleffects,tissue

damage,developmental effects

Organism

and

Ecosystem

Level

Effects

QSAR and ChemicalStructural Bi l icalDatabases (Pr tein

Databases, Metabolic andBiochemical

Pathway information)


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Fig 5. Prediction of chronic toxicity and multiple modes of action using QSAR

Adverse Outcome Pathway of (AOPs) study the effects at two levels :

i.) the chemical aspects and chemical interactions

ii.) Biological responses

In comparision to the classical approach of QSAR modeling where the toxicity is predicted

based on relationships between a molecular descriptor and a biological endpoint in the chosen

chemicals of the training set, the AOP approach uses all available information from both the

biological and chemical domains of knowledge to predict the toxic outcome of a compound

with greater confidence.

The initial steps involve the classical QSAR approach to study the chemical structural aspects

of the compound and then also predict its metabolic products which might be biologically

active. The succeding steps then take into account the available biological macromolecular

and biochemical information databases in order to then model the toxic effects based upon

the biological target, effects of the biological target interaction and eventual biological

endpoints.

An example of such an Adverse Outcome Pathway is described below for a toxicity study

done on the reactive toxicity effect of moderate electrophiles:

The various interactions and known reactions between the moderate electrophile (e.g an

ester) and the biological systems are taken into account. The first step here is to first predict

the fate of the parent compund. One such fate is the formation of a Schiff`s base which is an

enzyme catalyzed reaction leading to formation of Imines.

A few of the possible interactions are binding of the active metabolic product of the

electrophile to DNA leading to formation of DNA adducts.


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Fig 6. Adverse outcome pathway for moderate electrophiles

Another commonly known effectis the interaction with glutathione (GSH) which in a

protective molecule in the cells, preventing them from the effect of free radicals. Each of

these pathways would lead to a different endpoint and would thus necessiate the development

of models to predictthe differenttoxicity endpoints. Thus, AOP helps to seamlessly integrate

the information from the biological pathways and macromolecularinformation to the

chemical structuralinformation to give a more accurate account oftoxicity as well as give a

picture ofthe toxicity endpoints to be modeled.

Eventually, assessing the various biological endpoints is finally of greatimportance to a

QSARmodelerto predict various outcomes of a toxic compound and the range of dosage at

which each ofthese outcomes would take effect.

3.2) Prediction ofToxicity of Metabolic Products/Bio-transformation Products

The basic QSARapproach is quite reliable when it comes to predicting the effects of

chemicals whose parent compound is responsible forthe effect. Howeverin many cases the

parent compound might be almost non-toxic and is metabolized by transformation

mechanisms in the biological systems. The products ofthese reactions called active

ParentChemica

l

ichael`sAdditi

n,

Schiffs

Basef

rmati n

,Acylati

n

Reacti n

IrreversibleC

valent

Binding,

DNAAdducts,

Glutathi

neDepleti n,

Glutathi

neOxidati

n ,

SurfaceIrritati

n,

OxidativeStress,

SystemicImmune

Resp

nses

Necr sis

SystemEffects

P ssi l Bi l ic l End oints


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metabolites are responsible for the toxic effects.Predicting metabolic activation of

compounds is a limitation of QSAR. However, this limitation can be overcome by

maintaining an ordered biochemical database of reactions and transformation pathways which

would help us predict the metabolic products. Once the metabolic products of a given

compound are predicted for their toxic effects, QSAR can then be applied to identify the

most toxic form of the chemical.

Figure 7 represents an example of such a case where the different active metabolites formed

as a result of 2-acetyminofluorene metabolism are predicted for their toxic effects using

QSAR. This approach first makes use of the information about the various metabolic and

biochemical pathways in the biological systems into which the compound in question might

enter and thereby undergo transformation reactions. The active and inactive products formed

in the reaction pathway are predicted from the knowledge of these pathways from existing

literature and biological pathway databases like NCBI`s Bio Systems.


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EXAMPLE OF ACT E METABOLITE SELECTION

Fig 7. Prediction of metabolites to be chosen for modelling studies (International QSAR

Foundation , OECD QSARToolbox)

Selection of metabolites which are to be chosen while modeling

interactions


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CHAPTER 4.)

CLASSIFICATION OFCHEMICALS INTO CLASSESFOR CHOICE OF

TRAINING SET

4.1) Congeneric QSAR (Training set pollutants chosen according to chemical structure)

During the early phase of QSARmodelling techniques were at an early phase, it was noticed

that xenobiotics belonging to the same class chemically acted through a similar biological

mechanism to produce toxic effects. Subsequently, many QSARmodels were developed

which were based on specific group of chemicals like chlorobenzenes, chlorophenols etc.

Depending on the structural similarity ofthe compound for which the prediction is to be

made, a dataset of similar chemicals is chosen for modelling purposes. The similarity in this

case is strictly defined by the chemical structural parameters.The list of chemical classes is

exhaustive. However, a few examples of chemicals grouped according to their structures is as

follows

4.1.1) Organochlorine Pesticides (OCPs):

Fig.8 Structure of DDT, an organochlorine

They are generally used in agriculture as a potentinsecticide. Oflate, some ofthe compounds

belonging to OCPs like aldrin, dieldrin, heptachlor, DDT, HCH, etc. which have been listed

underthe group known as persistent organic pollutants (POPs) by the USEPA. The use of

these compounds have been restricted ortotally banned. When absorbed into the body,

chlorinated hydrocarbons are not metabolized rapidly and are stored in the fatty tissues. OCPs

are persistentin the environment owing to their high stability and lipophilic nature which in

turn leads to their accumulation in the food chain components (Watanabe,2005). The

concentration ofthese compounds declines at a very slow rate even when the source of

contamination has been eliminated. These compounds are biomagnified at highertropic


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levels and hence elevated contamination is detected in the human body.Mild cases of

poisoning are characterized by headache, dizziness, gastrointestinal disturbances, numbness

and general weakness, apprehension and hyperirritability.

4.1.2) Organophosphates

Fig 9.Chemical Structure ofMalathion, an Organophosphate

These are another class of compounds which are used as insecticides or chemical warfare

agents. They are persistentto an extentless than organochlorines. The toxicity of compounds

ofthis group can be linked to its structure, the double bonded O or S (see figure) in addition

to the groups surrounding the phosphate in the compound. The mosttoxic compounds ofthis

group have been observed to have a short phosphonate groups with fluoride or cyano group

surrounding the phosphate. Metabolic activity leads to replacement of sulphur by oxygen or

other modes oftransformation leads to its conversion to a more toxic species. These

compounds have been observed to bind to amino acid serine, thus reducing the catalytic

activity of enzymes by blocking the active site of enzymes. Another mode oftoxicity ofthese

compounds is its tendency to bind to acetyl cholinesterase, a key enzyme ofthe Central

Nervous System (CNS). Cholinesterase is an enzyme in the human body thatis essential for

the normal functioning ofthe nervous system. Inhibition ofthe activity ofthe cholinesterase

enzyme prevents neural signals from being transmitted from the brain to various parts ofthe

body. Symptoms ofthis inhibition include excess salivation, difficulty in breathing, blurred

vision, cramps, nausea and vomiting, rapid or slow heart rate, headache, weakness and

giddiness. They are also known to cause reproductive and endocrinal damages also.Typically,

acetylcholine is released in orderto excite the receiving neurons to receive the signal during

the transmission of a nerve impulse. Acetylcholine is rapidly broken down by

acetylcholinesterase afterthe initial binding ofthis substrate to serine residue in the active

site ofthe enzyme. However, when an organophosphate binds to the serine, itleads to


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irreversible blockage ofthe active site. A covalent bond between serine and phosphate is

formed with a loss of fluoride or other groups. The next step is the irreversble binding at

glutamyl residue ofthe enzyme called as ageing ofthe protein which is accompanied by loss

of activity.

4.1.3) Polycyclic Polyaromatic Hydrocarbons (PAHs)

PAHs are be referred to as polycyclic, or polynuclear, aromatic hydrocarbons (PAHs). The

chemical structure is characterized by three or more aromatic (e.g., benzene) rings, usually

fused together such that each pair of fused rings shares atleasttwo carbon atoms. The PAH

structure can contain five-membered nonaromatic hydrocarbon rings fused to the six-

membered rings, e.g., benzo(j)fluoranthene. (EPA Gui!

" li# " $ ).

Fig 10. Chemical Structures of 16 priority PAHs


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Polycyclic polyaromatic hydrocarbons are formed as by-products of combustion and present

almost across the entire world. In addition to the by-products, they have also been known to

existin industrial waste like coaltar, petroleum refinery sludge, waste oils and fuels etc. They

have also been classified as carcinogenic chemicals by agencies like WHO and USEPA.

PAHs are being monitored worldwide in environmental matrices like ground water. PAHs are

highly hydrophobic and hence accumulates in organic matter ofthe soilleading to its

persistence .

The EPA and WHO have identified and classified 16 PAHs as priority pollutants (see figure

10). The European Community Directive 98/83/CE states a maximum value of 0.1 g L1

for

PAHs in drinking waters expressed as the sum of benzo(b)fluoranthene,

benzo(k)fluoranthene, benzo(ghi)perylene and indeno(% ,&

,3-'(

)pyrene. As far as Italian

legislation is concerned, a limit of 0.01 g L1

has also been set for benzo() )pyrene

(European Communit0 Directi 1 e 98/83/CE).

4.1.4) Synthetic Pyrethroids

Fig 11. Structure of Allethrin, a synthetic Pyrethroid

Synthetic Pyrethroids are synthetic derivatives and analogues of a plant extract pyrethrin

which is obtained from the chrysanthemum flower. The design of pyrethroidsis to make it

more toxic to targetinsects with longer breakdown times. Thus, pyrethroids are very

persistent with adverse biological effects. These chemicals are designed to rapidly penetrate

insects and paralyze their nervous system. The synthetic pyrethroids are generally

ketoalcoholic esters of pyrethroic acids. Traditionaltoxicity assays have shown that

pyrethroids have mild irritant activity although not very easily absorbed through skin. Studies

have shown that pyrethroids are highly neurotoxic following oral administration. Pyrethroids

act by interfering with the ionic conductance of nerve membranes. World Health

Organization reports suggestthat pyrethroids act by acting on axons incentral and peripheral

nervous system by interacting with sodium ion channels (Soderlund, 2002).


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Pyrethroids have been found to be extremely toxic to aquatic organisms like laketrout,

bluegill etc (Go et al, 1999). Their LC50 values have found to be as low as 1 parts per billion

(ppb) which is very similarto the toxic levels to target organisms like mosquitoes, blackfly

etc. Adverse impacts in lobsters, zooplanktons include damage to gills and behavioural

changes. Indirect effects on birds has been in relation to the threatto their food supply.

Insectivorous bird populations are the most badly affected. (Fishel, 2005)

Pyrethroids are formulated in combination with chemicals called as synergists which increase

their stability and persistence and hence theirtoxic potency. Synergists are often oils or

petroleum distillates. One such synergistis PBO (piperonyl butoxide) which inhibits liver

enzymes like hepatic microsomal oxidase and thus interfere with detoxification mechanism

ofthe liver. Hence the toxic effects in combination with synergists is marked in mammals as

compared to pyrethroids alone.

4.1.5) Polychlorinated Biphenyls (PCBs)

Fig 12.Chemical Structure of Arochlor, a PCB

PCBs are mixtures of upto 209 chemicals which are ofindustrial origin. The use of PCBs as

industrial coolants, lubricants in transformers, capacitors, in plastics and as paint plasticizers

are the common sources of PCB pollution. 130 such PCBs are commonly used in industry.

PCBs have also been classified as Persistent Organic Pollutants by the USEPA.

According to a WHO reportin the year 2003 (Geneva, 2003) around 2 x 108 kg of PCBs

existed in environmental matrices atthattime. Adsorption and subsequent sedimentation

immobilize PCBs in the aquatic systems for a long time. Biodegradability is related to the

amount of chlorination of a specific PCB. Higher chlorination leads to higher persistence and

lower biodegradability rates. Thus, PCBs accumulate in the environment and cause

environmental problems. Bioconcentration factor of PCBs which is the ratio of concentration

in biological systems to concentration in water, increases with increased chlorination.

Physicochemically, they have low water solubilities and are highly soluble in organic


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solvents. PCBs have been found to accumulate in lipid organs and polar lipid content like

phospholipids. PCBs have been reported to biomagnify in aquatic food chains and higher

trophic levels.

Following is a summary of some pollutants, their mode of action, some physicochemical

properties and permissible limits (Table 1):

Chemical Class Representativ

e Chemical

Uses Biological

Endpoint

Maximum

Permissible

levels

Log

P

Vapor

Pressure

LD50

Organochlorine

s

Aldrin Insecticide Carcinogen

and EDC

0.03 ug/l (WHO

water quality

norms)

7.4 2.31 x 10-5

mm Hg at

20 C

33 mg/kg

body

weight (

guinea

pigs)

Dieldrin Pesticide Carcinogen

and EDC

0.03 ug/l (WHO

water quality

norms)

6.2 1.78 x 10-7

mm Hg at

20 C

37 mg/kg

body

weight (guinea

pigs)

DDT Insecticide Reproductive

Defects,Bioac

cumulation,E

DCs

Banned (except

for a few

countries)

6.9 1.5 x 10-7

mm of

mercury

at 20 C

100

mg/kg

body

weight (

rats)

Organophospha

tes

Malathion Pesticide Cholinesterase

inhibition,

neurotoxic

5 ppm (EU

Norms)

2.89 5.3 mP2

2 t30 C

1375

mg/kg in

rats

Profenofos Pesticide Cholinesteraseinhibition,

neurotoxic

0.05 ppm (EUnorms)

4.44 3.5 x 10-4mm. Hg at

25C

358mg/kg

PCBs Aroclor Electrical

Equipment

manufacturi

ng

Reproductive

toxicity,

bioaccumulati

on

10 ppm (EU

norms)

6.3 0.9-2.5 Pa

at 20 C

2 to 10

g/kg

body

weight of

rats

PAHs Naphthalene Coal tar and

industrial

waste

Carcinogen 1.1 ug/l (WHO

limit)

3.01

-

3.45

0.082 mm

Hg at 25 C

533 mg/k

g in rats

Note :

yThe physicochemical properties like Kow (log P) and Vapor pressure are very

commonly used molecular descriptors in QSAR studies.

yLD50 values are commonly modelled endpoints in QSAR studies.

The source of information for the permissible limits in the above table are the

USEPA, Pesticide Activity Network UK and the World Health Organization.


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4.2) Limitations of Congeneric models

Studies suggested that models based on modes of action were more accurate than those based

strictly on chemical structures. Also observed is that many inert chemicals have observed to

act by nonspecific modes of action like narcosis. Much of this could be attributed to the

lipophilicity of these inert chemicals which may not share any chemical similarity. Another

example of how a diverse class of chemicals can act by the same or similar mode of action

are compounds which act by uncoupling the oxidative phosphorylation enzymes in the

mitochondria. These compounds belong to multiple classes of chemicals like phenols,

anilines, pyridines etc.

Thus, classification of chemicals based upon their mode of actions and models based on such

training sets have gained more importance.

4.3) Classification of Pollutants based upon the modes of action and biological targets is

as follows:

This approach is also known as target-based prediction approach. Target prediction approach

originated from the field of drug designing for developing muti-target drugs and to study non-

target effects of such drugs. Such an approach was also extrapolated to the field of toxicology

to study acute responses of chemicals. All of these methods share the common goal of

establishing links between the chemical structures of a compound and potential protein

targets in biological systems. The concept by which side effects and non-target effects of

drugs were studied is also used in finding out targets molecules for chemicals. Efforts are also

on to integrate protein structural data and toxicological data to improve predictive models.

One such example of how this integration could be achieved was demonstrated by Chen and

co-workers (2001) who devised a technique called INVDOCK (ligand protein inverse

docking). This technique uses an algorithm to explore macromolecular binding site most

suitable to accommodate a target compound in question. The limitation of this method is the

relatively low number of structures for proteins that is currently available. However, with

more and more 3D structures of proteins being resolved with every passing year, better mode

of assessment and target prediction linked to chemical structure could be achieved.


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Following are a few classes of pollutants based upon their modes of action and

biological endpoint:

4.2.1) Mutagenic and Carcinogenic Compounds :

Compounds are generally described as carcinogenic or mutagenic depending upon their

toxicological endpoint (eventual effect). With more and more chemical compounds being

generated and released into the environment and newer compounds being introduced into

nature, the major challenge is to identify compounds which can be potential carcinogens and

mutagens and also to identify the levels at which they show the adverse effect.

The classification of carcinogens is as follows:

i) those causing damage to the DNA directly, known as genotoxic carcinogens

which mainly act by causing a mutation. (mutagens)

ii) Epigenetic carcinogens which do not covalently bind to the DNA and they show

negative results in mutagenecity tests. These carcinogens act by disrupting

regulatory mechanisms and transcriptional mechanisms. As an example they may

bind to transcription related proteins to exhibit carcinogenicity.

Heavy metals such as Fe,Zn,,Cu,Mg,Pb,Cr and aromatic hydrocarbons like phenanthrene,

anthracene, naphthalene, pyrene etc have been observed to cause considerable chromosomal

alterations in aquatic organisms like molluscs. Polyaromatic hydrocarbons also get

metabolized to forms which are carcinogenic.

4.2.2) Endocrine Disrupting Chemicals or EDC`s :

EDC`s are those compounds which interfere in hormone biosynthesis, metabolism or activity

thereby causing adverse effects to the homeostatic mechanisms and reproductive systems of

higher organisms. According to a study concucted by the Kandarakis et3 l (4

009) it has been

found that EDC`s have effects on male and female reproduction, breast development and

cancer, prostate cancer, neuro-endocrinology, thyroid, metabolism and obesity, and

cardiovascular endocrinology. Endocrine disruptors act by multiple pathways and thus exists

the challenge of detecting these compounds and predicting their endocrine disrupting activity.

Another complicating factor is that in certain cases, the parent compound may not show any

endocrine disrupting activity but their metabolic products may show biological activity.

Also, different kinds of EDCs may also produce similar biological effects. Endocrine systems

do not show any immunity or resistance against these chemicals because of their structural

similarity to endocrine hormones, shared receptor sites and their ability to bind to the


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enzymes involved in metabolizing them. Earliest recorded examples of the ill effects of

EDCs were the thinning of eggshells of fish-eating birds and impairment of reproductive

processes in birds like seagulls. Some EDCs have been detected in commonly used products

such as personal-care products like soaps and cosmetics (some contain nonylphenol

compounds and parabens), industrial by-products, plastics (phthalates) and pesticides. When

these products are used, disposed of, or excreted by people or animals, they typically end up

in either stormwater or wastewater. While wastewater treatment processes can remove a

significant amount of these compounds, small concentrations of some are discharged into

surface waters. Below are a few examples of EDCs along with their targets:

Table 2: Different environmental pollutants showing Endocrine disrupting or hormone

tagetting activity

Chemical Uses Target

Hormone

Organisms Affected

Polybrominated

Diphenyl

Ethers

Flame

Retardants

Thyroid Mammals.Birds,Reptiles,Fish

DDTs Insecticide Estrogen Mammals,Birds,Reptiles,Amphibians,Fish,Inverteb

rates

PCBs Industrial Cortisol Mammals,Birds,Reptiles,Amphibians,Fish,Invertebrates

Cadmium Batteries Adrenaline Mammals,Birds,Fish

Fenoxycarb Insecticide Juvenile Invertebrates

Bisphenol A Plasticizer Estrogen Mammals,Birds,Amphibians,Fish

Nonylphenol Plasticizer Estrogen Mammals,Birds, Invertebrates

EDC`s may not only affect the target organism but also impact future generations due to

modification in factors which affect gene expression, e.g DNA methylation and histone

acetylation (Anway, Skinner5

006).


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CHAPTER 5: CASE STUDY

5.1) Estrogenic Activity of Anthraquinone Derivatives: In Vitroand In SilicoStudies

(FeiLietal.,2010)

Backgroundofthe Study

Fei Li et al. (2010) published their studies in the Chemical Toxicology Journal where in the

effect of anthraquinone derivatives on biological systems were discussed. The publication

discussed both in-vitro and in-silico (QSAR) studies and compared the results of the two. The

study focused primarily on the estrogenic activity of anthraquinone derivatives.

Anthraquinones are derivatives of anthracene, a polyaromatic hydrocarbon. They are used in

the making of organic dyes and by papermaking industries. Other applications of

anthraquinones includes its use in hydrogen peroxide production and in synthesis of drugs

like antimalarial drugs, laxatives and also in some anti-neoplastic medicines (anti-cancer).

Hence, waste discharge from these industries would lead to anthraquinone entry into water

sources and ground, thus causing adverse environmental effect. In September 2007, the

California Environmental Protection Agency also added anthraquinone to the list of

chemicals known to cause cancer. The public listing of known cancer-causing agents is

required by the Safe Drinking Water and Toxic Enforcement Act, commonly known as

Proposition 65.

Anthraquinones are also known for their xeno-estrogen activities which lead to unregulated

activation of estrogen receptors thereby disrupting the balance of endocrine hormone

functioning. The anthraquinones exert biological effects by their interaction with estrogen

receptor, mainly ER1 which is predominantly expressed in uterine, kidney and ovarian cells.

The vast number of xenoestrogens necessitates the need to develop computational models

which would enable screening and prediction of their estrogen mimicking tendencies.

In order to develop a QSAR model on these anthraquinones for the prediction of

xenoestrogenic activities of anthraquinones, the primary need is to understand the interaction

between the xenobiotics and the estrogen receptor. In this study, this interaction was studied

using a computational biological approach called as `Molecular Docking .


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Molecular docking is commonly used to study the interaction between a biomolecular target

such as a receptor protein and a drug/pollutant molecule. The associations between

biologically relevant molecules such as proteins, nucleic acids and various chemical

molecules play an important role transduction of biological signals. Furthermore, the relative

orientation of the two interacting partners may affect the pattern and type of signal produced

(e.g.,agonism vs antagonism). Therefore docking is useful for predicting both the strength

and type of signal produced when a pollutant molecule is bound to a target. For example, a

receptor protein molecule in this case study. Other studies which used the technique of

docking to study the interaction between ligands and receptors were those ofCelik et al.

(2008) who found that some PCBs, pesticides and plasticizers could interact with the sterol

binding site of the estrogen receptor.

The in-vitro experiments included the use of 20 Anthraquinone (AQ) model compounds to

determine estrogenic activity using a yeast cell based assay. Molecular docking was used to

define an interaction model between the ligand and the estrogen receptor. By observing the

ligand-receptor interactions, appropriate molecular descriptors were chosen for the purpose of

QSAR modelling.

Techniques used:

In-vitro Assay (Recombinant Yeast-based Assay)

20 Anthaquinones were selected on the basis of their occurrence in environmental and

biological matrices. DNA Sequence of estrogen receptor, ER and a reporter gene lac-Z (forenzyme -galactosidase) were integrated into the yeast genome. The yeast strain used here

was Saccharomyces cerevisiae.

The principle behind this assay is that once the DNA construct is integrated into the yeast

genome, the yeast would express -galactosidase enzyme activity in presence of an estrogen,

i.e whenever an enstrogenic molecule binds to the ER receptor, the operon lac-Z leads to

production of -galactosidase enzyme. The amount of -galactosidase produced is thus

proportional to the estrogenic activity.

Activity of -galactosidase was , U was calculated as per the following relationship:

U = (OD(test) OD`blank) x D

(t x V x OD600)


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Where,

D = dilution factor,

V = volume of the culture,

ODtest= optical density measured for enzymatic action supernatant at 420nm

OD`blank= optical density measured for blank at 420nm

t = incubation time

The EC50 (half maximal effective concentration) was calculated from the dose response

curves. Estrogenic activities of the chosen anthraquinones were thus expressed in terms of

relative potency (RP). Relative Potency of the anthraquinone was calculated using the

expression :

RP = ______EC50 (E2)_______ x 100

EC50(reference compound)

EC50(E2) is the concentration which gives 50% induction of galactosidase activity.

RP is the relative potency of the anthraquinone.

In-silico method:

Molecular docking study was used to predict the binding of the various anthraquinone model

compounds to ER receptor. CDOCKER was the docking algorithm used to study the

binding of AQs to the receptor. For purpose of docking, the 3D crystal structure (coordinates)

of ER was obtained from the Protein Data Bank (PDB), USA. The structure of the ligand

and random conformations of the ligand were generated using molecular dynamics method.

The CDOCKER interaction energy between the AQs and the ER (Ebinding) was then

calculated. Also calculated were the electrostatic parameters for the ligand-binding site, using

the same docking software.

Estrogenic activities of anthraquinones were hypothesized to function depending on two keyprocesses:

i.) Ability of the AQs to penetrate the biological membrane and reach the target site

ii.) interactions between AQs and the receptor.


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Using the information gained from the docking studies, a total of 15 parameters were selected

to characterize the process.

i) The log Kow values were chosen in order to characterize the ability of the AQs to

cross the membrane. Parameters like molecular volume (V), average molecular

polarizability () and the polarizability term were also selected because of their

correlation with log Kow.

ii) The parameters which were chosen from the docking studies in order to

characterize molecular interactions were as follows

yEnergy of the highest occupied orbital (EHOMO)

yEnergy of the lowest unoccupied orbital (EHUMO)

yThe most positive hydrogen atom in the molecule (qH+)

y

The most negative formal charge in the molecule (q

-

)yElectrophilicity index ()

yMost positive and most negative values of molecular surface potentials

(Vsmax and Vs

min)

y Averages of positive and negative potentials on the molecular surface

(V+, V

-)

Relationship between the above parameters and the interacting molecules is the basis of the

binding affinity of these estrogen derivatives. EHOMO, ELUMO, qH+ and q- are all used to

characterize the electron donating/accepting nature of the molecule. Also, the electrophilicity

index measures the ability of a compound to accept electrons. It has been observed that in

many cases, the binding affinity directly correlates to the electrophilicity. Surface potentials

describe the charge distribution on the molecule.

Ebinding, above all was found to be the most important parameter in characterizing binding

affinity. All these parameters were calculated using the Gaussian 09 program.

QSAR modeling

The 20 AQs were randomly divided into 2 sets, one for training the model (training set) and

the other for testing it (validation set) as shown in table 3.


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Table 3. Observed and predicted estrogenic activities of various AQs.

Results :

Simple linear model based on log RP and E binding

The binding free energies (Ebinding) calculated from the docking study are listed in table 3.

When the relationship between the log RP and Ebinding was analyzed, it was found to have a

simple linear relationship. This indicated that the binding to ER was the key step in

exerting the estrogenic activity. It must be noted that, in this case log RP is the biological

endpoint and Ebinding is the molecular descriptor. However, Ebinding alone was not a good

predictor for log RP. Hence, a multi-parameter model was then developed using the

parameters found to be essential in binding interactions between the molecule and the

receptor and thus its activity.


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Fig 13. A plot showing linear relationship between Ebinding and logRP

Finally, an optimal QSARmodel was developed. The model was ofthe form :

log RP = -8.08 + 4.511 1.84 x 10-2Ebinding + 1.36 x 10

-2 6.70 x 10-1q-- - 6.82Vs-

The predicted log RP values are listed in table 3. The R2

values ofthe QSARmodel was 0.85,

indicating a reliable model. The predicted log RP values were close to the observed values for

both the validation and training sets.

The training set also had good representativeness and hence the model can be used to predict

estrogenic activity of other anthroquinones not a part ofthis dataset.

Matsuda et al (2001). Studied the estrogenic activity of emodin, 2,6-

dihydroxyanthraquinone, daidzein and genistein using in-vitro binding assays. The log RP

values predicted by this QSARmodel forthe above compounds was -1.50, -0.59 and -0.97

respectively, thus indicating that 2,6- dihydroxyanthraquinone has the most potent estrogenic

activity. It was observed thatthese predictions were consistent with the observations of

Matsuda et al(2001).

Conclusions of the study and significance of Anthraquinone estrogenic activity:

The case study used both protein structural data as well as the electronic data ofthe ligand in

orderto characterize the ligand-receptorinteraction. Hydrogen bonding, hydrophobic and -

interactions between the ligand and the receptor govern the estrogenic activity ofthe AQs.

Hence, comprehension of binding interactions is of greatimportance in developing QSAR

models based on toxicity mechanisms. The study also demonstrates how protein structural


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