Transcript Presentation @ 2pm

NoTox: Toxicity Analysis of
Molecules and their derivatives
Primary Advisor : Professor David J. Wild
Bioinformatics Advisor: Professor Sun Kim.
What is Computational Toxicology
Computational toxicology is an emerging
field that constitutes methodical
approaches to analyze the harmful effects
on the environment and public health due
to exposure to chemicals.
 Computational toxicology may be defined
as the application of the tools developed
using computational biology concepts to
analyze the risk that some chemicals pose
to human health and the environment.

Types of toxicology
The different types of toxicology include
 Toxicogenomics
 Aquatic Toxicology
 Chemical toxicology
 Ecotoxicology
 Environment toxicology
 Medical Toxicology
 Forensic Toxicology
Ref:Wikipedia
How is computational toxicology
studies important to biology
Computational toxicity is an increasingly
important field with implications
throughout biomedical science and drug
discovery. To name a few applications
 Providing molecular profiling approaches
to toxicology
 Drug toxicity
 Gene- Drug interaction studies
 Toxicological analysis of Biological
molecules and their derivatives

Shortcomings
The shortcomings of most computational
toxicology methods are
 Most toxicity methods concentrate on
determining whether a particular compound
has toxic properties or not. The methods do
not necessarily help in understanding whether
modifying a particular molecule synthetically to
reduce toxic effects is possible.
 It also does not concentrate on identifying and
analysing the reason for toxicity of most
molecules.

Problems analyzed
If we have a toxic molecule, can we find a
synthetic alternative that might be non
toxic
 If we have a non-toxic molecule, might it
be metabolized or changed in the body to
something toxic?
 We use some cheminformatics
representations called SMILES and
SMIRKS to deal with these problems

Terminologies - SMILES/SMIRKS
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SMILES is the acronym for Simplified Molecular Input
Line Entry Specification.
Each molecule has a unique canonicalized SMILES
representation (Eg c1ccccc1 represents benzene).
SMIRKS is a reaction transform language.
In general two different kinds of SMIRKS are in use to
show transformations. Functional group transformations
and molecular framework modifications. An example for
SMIRKS would be
[O:1]=[C:2][Cl:3].[N:4][H:5]>>[O:1]=[C:2][N:4]
this is an example of a simple displacement reaction
representation
Drug Guru - Literature Review
Drug Guru is a web based computer software
program that applies medicinal chemistry
transformation reactions to an input structure.
 The transformation reactions are medicinal
chemistry design rules of thumb taken from
other drug discovery programs.
 The output of this program is a list of analogs
that can be used for further synthesis.
Ref: [1]
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Screen shot of drug guru
Drug Guru to NoTox
Drug Guru uses smirks to develop
alternative compounds based on
medicinal chemistry reactions.
NoTox combines the concept of Drug
Guru with a different field – Toxicity.
NoTox
Tool to predict alternatives for a given
molecule based on reaction
transformations
 Tool to test the toxicity of any given
molecule
 Helps in comparing the toxicity of the
original and the derived molecule

NoTox - Input Requirements
The input query (Original molecule)
should be in the form of SMILES.
 The reaction transformations should be in
the form of SMIRKS
 The SMIRKS used are a more specific
form of the reaction.

Flowchart of NOTOX
Smirksgen
The program generates derivatives for a
given molecule based on the specified
reaction transformation rules.
 Openeye software for library generation
 The smirksgen program is developed in
Python and it takes in input from a GUI
developed using HTML

Toxicity prediction using Toxtree
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Toxtree is an open source application that
uses the decision tree approach to identify
the toxicity of a molecule represented as
SMILES.
It provides 5 plugins. In NoTox, we make use
of the Cramer's rule decision tree approach
to determine toxicity.
The input molecules are classified in the
increasing levels of toxicity as Class 1, Class
II and Class III
NoTox - Features available
Apart from Smirksgen and Toxtree,
 NoTox Provides access to another tool
WENDI that helps in the profiling of any
particular molecule.
 NoTox also has a feature that helps in
the validation of the input SMILES
Screen shot of NoTox
ATP Example
OBJECTIVE
Identifying a molecule with desirable
properties and activity towards the
human body.
 Observing the Toxicological effects due to
the activity of the identified molecule .

WHY ATP??
ATP acts as a coenzyme in most
intracellular energy transfer reactions.
 ATP acts as a substrate for kinases in
signal transduction pathways.
 It is a major component of the Krebs
cycle.

Mechanism of action of ATP and Its
Uses
The main action of ATP is
phosphorylation.
 A double displacement reaction takes
place between the phosphate molecule in
ATP and the hydroxyl hydrogen of the
reacting molecule.
 The reaction is depicted as follows.
R-OH . ATP >> R-O-phosphate . ADP
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Testing effect of ATP on human
body
Our objective is to study the toxicological
effects of ATP on the human body.
 We use our tool NoTox to study the
toxicological effects due to the reaction
of ATP with other molecules.
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Input data Using Smiles
The test molecules were obtained by
doing a substructure search in pubchem
and the test set was a list of 100
molecules that contained a substructure
that can react with ATP.
 The smiles format was obtained for these
molecules using the molinspiration
software.
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The reaction transformations

500 reactions from Kegg involving ATP
were studied and the mechanism of each
reaction was observed and generalized to
2 SMIRKS rules one each for aromatic
and aliphatic compounds
The result
The toxicity of the input molecule was
usually Class 1 or Class 2
 The toxicity of the derivative was always
found to be Class 3.
 It was observed that the reaction of a
molecule with ATP increases the toxicity
of the derivative.
 This suggests that ATP increases the
toxicity in the body when it reacts with
the biological molecules.
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Evidence for the result
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Journal articles substantiate the observed result that ATP increases
the toxicity of the molecule it reacts with. Some of the examples of
the journal articles are
Mallick.N, Rai .LC. Metal induced inhibition of photosynthesis,
photosynthetic electron transport chain and ATP content of Anabaena
doliolum and Chlorella vulgaris: interaction with exogenous ATP, Biomed
Environ Sci. 1992 Sep;5(3):241-50
Lundy, Paul ; Frew, R. ;Vair, C. ; Nelson, P. ; Gong, W ,Mediation of
Sulfur Mustard Cellular Toxicity by ATP: A Possible Mechanism of Action of
Sulfur Mustard Toxicity, ADA412934
KOBAYASHI K. ; RATAIN M. J. ; Pharmacodynamics and long-term
toxicity of etoposide, Cancer chemotherapy and
pharmacology ISSN 0344-5704 CODEN CCPHDZ
Summary
NoTox can be used to search for non-toxic
alternatives to toxic compounds,
and to find possible toxic metabolites
 Initial experiments indicate the method is
useful
 Further work should be carried out to
evaluate NoTox for both of these
applications
Future Work
An evaluation study that looks at the
following can be performed
 Known toxic compounds and their
derivatives
 Use information from metabolic database
to evaluate the toxic effects due to any
particular metabolic pathway.
References
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Kent D.Stewart, Melisa Shiroda, Craig A. James , Drug Guru: A computer Software
Program for drug design using medicinal chemistry rules, doi: 10.1016/j. bmc. 2006.06.024
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Robert J.Kavlock et.al , Computational Toxicology- A state of the Science mini review , doi:
10.1093/toxsci/kfm297

Mallick.N, Rai .LC. Metal induced inhibition of photosynthesis, photosynthetic electron
transport chain and ATP content of Anabaena doliolum and Chlorella vulgaris: interaction
with exogenous ATP, Biomed Environ Sci. 1992 Sep;5(3):241-50

Lundy, Paul ; Frew, R. ;Vair, C. ; Nelson, P. ; Gong, W ,Mediation of Sulfur Mustard
Cellular Toxicity by ATP: A Possible Mechanism of Action of Sulfur Mustard Toxicity,
ADA412934
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KOBAYASHI K. ; RATAIN M. J. ; Pharmacodynamics and long-term toxicity of etoposide,
Cancer chemotherapy and pharmacology ISSN 0344-5704 CODEN CCPHDZ
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www.daylight.com
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www.eyesopen.com
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http://ambit.acad.bg/toxTree/
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Wikipedia
Acknowledgements
Professor David Wild
 Professor Sun Kim
 The faculty of the Bioinformatics and the
Cheminformatics department.
 Linda Hostetter and Rachel Lawmaster.
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THANK U