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Molecular Networks GmbHHenkestraße 9191052 Erlangen, Germanywww.molecular-networks.com

Prediction of Toxicity and Metabolism of Chemicals

Johann Gasteiger

2

Outline

REACH

Representation of chemical structures

Modeling of toxicity

Prediction of metabolism

Risk assessment workflow

Autumn School of Chemoinformatics, Tokyo, JP, 2011-11-16

3

Risk Assessment of Chemicals

REACH – Registration, Evaluation, Authorization and restriction of CHemicals

Only those chemicals used with more than 1 ton/year are allowed to be manufactured or imported into the European Union that are registeredRegistration has to provide a dossier with many data and might need a safety reportLaw since June 1, 2007Chemicals have to be accepted until Dec 1, 2013Applies to about 35,000 chemicals


REACH Dossier

For compounds used in more than 10 t/a a Chemical Safety Report is needed

Harmful effects on human healthHarmful effects on the environmentDetermination of Persistence, Bioacumulation and Toxicity (PBT)Evaluation of exposition

Testing is time-consuming, expensive and might need many animals

4

Use chemoinformatics methods for ranking of chemicals


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Japanese Translation2005

byK. Funatsu, H. Satoh, H. Masui

J. Gasteiger, T. Engel(Editors)

Chemoinformatics - A Textbook -


Handbook of Chemoinformatics

J. Gasteiger (Editor)

65 authors73 contributions

4 volumes1900 pages

Wiley-VCH, Weinheim(August 2003)

From Data to Knowledge

6Autumn School of Chemoinformatics, Tokyo, JP, 2011-11-16

7

Quantitative Structure Activity/Property Relationships

molecularstructure property

structuredescriptors

//

representation model building


8

Structure Representation

Constitution

3D model

Molecular surface

N

N

O

-ONH3+

J. Gasteiger, Of Humans and Molecules, J. Med. Chem., 2006, 55, 6429 - 6434


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Structure Representation - Geometry

Constitution

3D model

Molecular surface

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N

N

O

-ONH3+

CORINA

SURFACE

250,000 structures99.8% conversion rate0.02 s/molecule

Connolly surfacevan der Waals surface


Structure Representation -Physicochemistry

Charge distributionJ. Gasteiger, M. Marsili, Tetrahedron 36, 3219 (1980)

Inductive effectJ. Gasteiger, M. G. Hutchings, Tetrah. Lett. 24, 2541 (1983)

Resonance effectJ. Gasteiger, H. Saller, Angew. Chem. Int. Ed. Engl. 24, 687 (1985)

Polarizability effectJ. Gasteiger, M. G. Hutchings, J. Chem. Soc. Perkin 2, 559 (1984)


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Hierarchy of Structure Representations: ADRIANA.Code

Global molecular properties# H acceptors & donors, molecular weight , TPSA, dipole moment, polarizability, logP, logS

Constitution (topological, 2D)2D autocorrelationAtom properties: q, χ, α

3D model3D autocorrelation, radial distribution functionsAtom properties: q, χ, α

Molecular surfaceAutocorrelation of surface propertiesMEP, HBP, HPP

N

N

O

-ONH3+


MOSES.Descriptors -Community Edition

Most structure descriptors of ADRIANA.Code are now freely available as

MOSES.Descriptors - Community Edition

http://www.molecular-networks.com/ services/mosesdescriptors


Methods for Data Analysis

Inductive learning methodsMachine learningData miningStatisticsPattern recognitionChemometricsNeural networksSupport vector machine


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J. Zupan J. Gasteiger

Neural Networksfor Chemists

Japanese EditionMaruzen, 1996


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MOSES.Descriptors – Areas of Application

Drug designClustering of compounds according to their biological activityLocating biologically active compounds in sets of diverse chemical compoundsQuantitative prediction of biological activitiesAnalysis of results of high-throughput screening...

Prediction of ADME/Tox propertiesAqueous solubility of organic compoundspKa valuesPrediction of major metabolizing CYP450 isoformClassification of toxic mode of action…

Prediction of infrared and 1H NMR spectraDye design...


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MOlecular Structure Encoding System

C++ based Chemoinformatics toolkithigh performance available for many platforms (Windows, Linux, Unix)

Python interface provides easy access to the full functionality of MOSESideally suited for the development of client / server solutions

under active development since 2001Computer-Chemie-Centrum, Universität Erlangen-NürnbergMolecular Networks GmbH

300,000 lines of codewell documented and tested


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Modeling toxicity of chemicals

Classification of toxic Mode of Action


Baseline Toxicity

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0 1 2 3 4

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

log P

log(

1/LC

50)

LC50 (fish species Pimephales promelas) of a series of aliphatic compounds versus lipophilicity (log P)

lipophilicity

toxicity


The Larger Picture

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-1 0 1 2 3 4 5 6

-10

12

34

5

log P

log(

1/LC

50)

baseline toxicants (nonpolar)

inhibitors of AChE

SH-alkylating agents

baseline toxicants (polar)uncouplers

inhibitors of photosynthesis

reactives

estrogenic compounds


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Prediction of Toxicity

Global QSAR models are of limited predictive power because of different toxic modes of action (MOA)

First classify compounds according to toxic MOA

Then develop a local QSAR model for this MOA


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Why Prediction of Toxic Mode of Action (MOA)?

however: and

require different QSAR-equations.

most QSARs in toxicology focus ona certain class of compounds

OH

ClCl

OHCl

ClCl

Cl

polar narcotic uncoupler of oxidative phosphorylation


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Dataset: MOA of Phenols

1. polar narcotics (156 cpds)2. uncouplers of oxidative phosphorylation (19 cpds)3. precursors to soft electrophiles (24 cpds)4. soft electrophiles (22 cpds)

221 cpds

S.Spycher, E.Pellegrini, J.Gasteiger, J. Chem. Inf. Model.,2005, 45, 200-208

A.O.Aytula, T.I.Netzeva, I.V.Valkova, M.T.D.Cronin, T.W.D.Schultz, R.Kühne, G.Schüürmann, Quant. Struct.-Act. Relat. 2002, 21, 12-22.


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Counterpropagation Network Modelsfor Classification of MOA

Estimate of predictive power with 5-fold cross-validation:

RDF(α, q) 2x32 77.4%RDF(χσ) 32 85.5%

RDF(χLP, χσ) 2x32 85.1%

NHdonor, RDF(χLP, χσ) 1 + 2x32 88.7%

RDF(χ LP, χ σ ), HBP surface AC 2x8 + 12 95.9%

S.Spycher, E.Pellegrini, J.Gasteiger, J. Chem. Inf. Model., 2005, 45, 200-208


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Classification in 5-fold Crossvalidation

polar narcotic

OHClCl

OHCl

ClCl

Cl

uncoupler of oxidative phosphorylation

Correct classification !


25

Metabolism of Xenobiotics

Drugs, agrochemicals, food additives


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Oxidations by Cytochrome P450

Aromatic hydroxylation

Aliphatic hydroxylation

Epoxidation

N, O, S-dealkylation, oxidative deaminationN,S-oxidation


27

Development of MOSES.Metabolism

Selectivity between different cytochrome P450 isozymesin particular 3A4, 2C9, 2C19, 2D6, 1A2

Selectivity between different reaction typeschemoselectivity

Selectivity between different reaction sitesregioselectivity

Modeling different Selectivities


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29

Data Set of 3A4, 2D6, and 2C9 Substrates

Training set: 146 drugs, substrate for 3A4, 2D6 or 2C9*

major isoform specified

*Manga, N. et al. SAR and QSAR in Env. Res. 2005, 16, 43-61.

Bufuralol Tramadol Felodipine

O OH

N

OOHN

NH

O

O

O

O

Cl

Cl


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Support Vector Machine (SVM) Model

Training set: 146 drugsDescriptors (242 descriptors by ADRIANA.Code)Automatic variable selection: 12 components

2D-ACidentity(5), 2D-ACqπ(3), 2D-ACqπ(6), 2D-ACχπ(5), 2D-ACqσ(1), 2D-ACqσ(2), 2D-ACχσ(6), 3D-ACidentity([5.8-5.9[Å), nacid_groups, naliphatic_amino ,nbasic_n , r3

PredictabilityTraining: 90.4%5-fold CV: 87.8%


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Validation of the Support Vector Machine Model

External validation set: 233 substrates from the Metabolite database

Predictability: 82.8%

remember: some drugs are metabolized by several isoforms

L. Terfloth, B. Bienfait, J. Gasteiger, J. Chem. Inf. Model. 2007, 47, 1688-1710


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isoCYP Webservice

http://www.molecular-networks.com/online_servicesL. Terfloth, B. Bienfait, J. Gasteiger, J. Chem. Inf. Model. 2007, 47, 1688-1710

Prediction of major metabolizing CYP450 isoform(2D6, 3A4, 2C9)


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A Data-Driven Approach to Metabolism Prediction

Extract reaction types from a metabolic reaction database (Metabolite by MDL/Symyx/Accelrys)

For each reaction type develop a statistical evaluation based on the number of observed reactions /

the number of conceivable reactíons

Use this ratio for assigning a likelihood to a reaction type

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L.Ridder, M.Wagener, ChemMedChem, 2008, 3, 821-832


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MOSES.Metabolism Reaction Rules

117 reaction rulesReaction types covered:

Aromatic hydroxylationAliphatic hydroxylationN- and O-dealkylationHydrolysis (ester, amides)Conjugation reactions (glucuronidation, sulphation, glycination, acetylation)Oxidation reactions (alcohols, aldehydes, etc.)

Empirical score for likeliness of a reaction based on literature data


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Derivation of a Rule Base for Metabolite Prediction

Define reaction rules, e.g. for an acetylation

Calculate reaction probabilities based on a reaction database (Metabolite, MDL-Symyx)

Conceivable metabolites 1223Observed metabolites 122Probability 122/1223 = 0.10

RNH2

RNH

O


3737

Rules Relevant for Atorvastatin Metabolism

Rules were derived forAromatic hydroxylationHydroxylation of aromatic aminesAromatic hydroxylation of 1,4-substituted phenyl ringsN-dealkylation of substituted pyrroleHydrolysis of amides


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Predicted Ranks of Atorvastatin Lactone Metabolites

N

F

O

NH

O

O

OH

Rank 3

Rank 1

Rank 2


Experimentally observed Metabolite of Atorvastatin Lactone

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Metabolite predicted for atorvastatin with highest rank corresponds to the experimental observations

N

F

O

NH

O

O

OH N

F

O

NH

O

O

OH

OH

CYP450 3A4

Rank 1


Observed and Predicted Metabolites of Lumiracoxib

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NHOH

O

F

ClOH

NHOH

O

F

Cl

NHOH

O

F

Cl

OH

NH

O

F

Cl

OOH

OHOH

OH

O

Lumiracoxib4‘-hydroxy derivativeRank 4

precursor of 5-carboxy derivativeRank 1

GlucuronidationRank 2

The 3 observed metabolites are high in the ranking position (1, 2, 4)


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In silico Toxicity and Metabolism Predictionin the Risk Assessment Workflow Using the

Chemoinformatics Platform


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Areas of Applications

Hazard and risk assessment of chemicalsProduct safety of pharmaceuticals, cosmetics, food ingredients and other chemicalsComputational toxicologyRegistration of chemical substances, e.g., REACH initiativeCompound profiling


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Workflow of Risk Assessment

Collection of Data CategorizationPrediction

PBT Assessment

ChemicalSpeciation

O

O O

O

• get data• read-across• QSAR prediction

• phys-chem prop• toxicity• biological assays

• reactivity• degradation• metabolism

• biodegradation...• eco-toxicity... • human health..

• query• representation

Slide courtesy Dr. Chihae Yang

PersistenceBioaccumulationToxicity


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• Analog search• TTC analysis• Toxicity prediction• Metabolism prediction

Report generation

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Analog searching

Specify cut off

Similarity criteria – MDL fingerprints

Run search

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Analogs with data

List with analogs

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Toxicity prediction

48

Metabolism prediction

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List with metabolites

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Toxicity prediction for query compound andall metabolites

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Features & Functionality

Knowledge base for hazard and risk assessment of chemicals Database lookup by text-based, analog and similarity searches Retrieval of available study information for query compound and analogs Generation and evaluation of metabolites of query and analogs (including CYP isoform specificity) Analysis tools for query, analogs and their metabolites QSAR predictions of toxicity endpoints (e.g., Ames mutagenicity) Report generation Fully web-based, easy-to-use user interface


Summary

Chemoinformatics can help us better understand chemistry

We can learn from data about the relationships between chemical structure and toxicityInformation in reaction databases can help us model metabolismRisk assessment of chemicals can profit from chemoinformatics methods


Acknowledgements

eTOX project funded by EU-IMI (Innovative Medicine Initiative)7 academic groups5 SMEs11 pharmaceutical companies

FDA – CFSAN (Center for Food Safety and Nutrition)Development of the CERES systemCOSMOS project funded by EU and COLIPA (The European Cosmetics Association)CollaborationDr. Chihae Yang, Altamira LLC, USA


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