Computational Toxicity and QSAR
Supa HannongbuaDepartment of Chemistry, Faculty of Sciece
Kasetsart University, Bangkok [email protected]
Background and MotivationBackground and MotivationWhat is QSAR?What is QSAR?QSARQSAR’’ss ApplicationsApplicationsResearch Area in ThailandResearch Area in ThailandConcept proposal Concept proposal CompututationalCompututational Toxicity in Dyes and CosmeticsToxicity in Dyes and Cosmetics
The European Chemicals Bureau provides technical and scientific support for implementation of certain EU legislation
on dangerous chemicals and the preparation for REACH
Action 1311Assessment of Chemicals
Action 1313Support to REACH
Action 1314REACH-IT
&Informatics
Action 1321Computational
Toxicology(QSARs)
http://http://ecb.jrc.itecb.jrc.it
Background and MotivationBackground and Motivation
1311 Assessment of ChemicalsNew and Existing Chemicals*Biocidal ProductsClassification and Labeling*Testing MethodsExport/Import
Actions in FP6 year 2006Actions in FP6 year 2006
The Action mission is to provide the scientific and technical support to the conception, development, implementation and monitoring of EU policies on dangerous chemicals.
5
What is in silico / QSAR ?What is in silico / QSAR ?
In silico and REACHWhat can be done in silico ?An example: in silico CYP inhibitionWhat next ?
6
Levels of testingLevels of testing
in cerebro
in silico
in vitro
in vivo
7
(Q)SARs defined by ECB(Q)SARs defined by ECB
Quantitative structure-activity relationships, collectively referred to as (Q)SARs, are theoretical models that can be used to predict the physicochemical and biological properties of molecules.
A structure-activity relationship (SAR) is a (qualitative) association between a chemical substructure and the potential of a chemical containing the substructure to exhibit a certain biological effect.
A quantitative structure-activity relationship (QSAR) is a mathematical model that relates a quantitative measure of chemical structure (e.g. a physicochemical property) to a physical property or to a biological effect (e.g. a toxicological endpoint).
8
In principle, (Q)SARs could be used to supplement experimental data, or to replace testing:
Supplement to testing:1) To support priority setting of chemicals.2) To guide experimental design (e.g. selection of tests
/doses).3) To provide mechanistic information.
Replacement of testing:4) To group chemicals into chemical categories.5) To fill in data gaps for classification and labelling.6) To fill in data gaps for risk assessment.
Possible applications for QSARs in the Regulatory Assessment of Chemicals
Possible applications for QSARs in the Regulatory Assessment of Chemicals
9
Chemical categories and read-acrossChemical categories and read-across
The underlying premise underpinning all structure-activity relationships (SARs) is the expectation that structurally similar chemicals are likely to have similar physicochemical attributes and biological effects. The approaches of read across and chemical categories are based on this similarity principle.
In the read-across or analogue approach, endpoint information for one chemical is used to make a prediction of the endpoint for another chemical, which is considered to be "similar" in some way. In principle, read-across can be used to assess physicochemical properties, toxicity, environmental fate and ecotoxicity, and it may be performed in a qualitative or quantitative manner.
ECB/JRC 2005
10
The chemical category concept and supporting role of (Q)SARs
The chemical category concept and supporting role of (Q)SARs
11
ContentsContents
What is in silico / QSAR ?In silico and REACHWhat can be done in silico ?An example: in silico CYP inhibitionWhat next ?
12
The White PaperThe White Paper
3.2 Research and ValidationDevelopment of alternative methodsOther research priorities
improvement and simplification of risk-assessment proceduresimprovement and development of new toxicological and eco-toxicological methodsparticular reseach efforts need to be made for developing and validating in-vivo and in-vitro test methods as well as modelling (e.g. QSAR) and screening methods for assessing the potential of adverse effects of chemicals on endocrine systems of humans and animals.etc…
13
Use of QSARs under REACH Use of QSARs under REACH
Acceptance of QSAR results - BOTH positive and negative results will be accepted if
Models have been validated Models are adequately documented and meet acceptance criteria for a given application- “fit for purpose” concept
(Q)SARs may support grouping of chemicals Chemical categories and minimise testing
Animal testing is conducted as a last resort
14
RemarksRemarks
Need to use (Q)SARs is explicit in legislative proposal for REACH In silico methods developing fastPredictive power already quite good in some (restricted) areas Future regulatory use of (Q)SARs will be controlled by various acceptance and validity criteria
Alternative approaches can reduce the use of test animals Examples of read-across under 793/93: Flame retardants
TCEP Tris(2-chloroethyl) phosphate 115-96-8
TCCP Tris(2-chloro-1-methylethyl)phosphate 13674-84-5
TDCP Tris[2-chloro-1-(chloromethyl)ethyl]phosphate 13674-87-8
V6 2,2-bis(chloromethyl) trimethylene bis[bis(2-chloroethyl) phosphate 38051-10-4
ENV: UKHH:Ireland
Rapporteur:Germany
Basis for read across
• Structural analogy of chloroalkyl phosphate esters
• lack of data for some substances for some endpoints
• Decision needed for
– Risk Characterisation and
– Classification and Labelling
3
Structural formula:
2.833.692.681.78log Kow
232 mg/l18.1 mg/l1080 mg/l7820 mg/l at 20°CWater Solubility
2.75 x 10-6 Pa at 25°C5.6 x 10-6 Pa at 25°C1.4 x 10-3 Pa1.14 x 10-3 Pa at 20°C
Vapor Pressure252°C (decomp)>200°CCa. 288°C (decomp.)320°C (decomp.)Boiling point
FP < -50.5°C<-20°C<-20°C<-70°CMelting point583430.91327.57285.49Molecular weight:
C13H24Cl6O8P2C9H15Cl6O4PC9H18Cl3O4PC6H12Cl3O4PMolecular formula:
V6TDCPTCPPTCEPSubstance
POO
O O
Cl
Cl
Cl
Comparison phys-chem. Properties
POO
OO
CH3
CH2Cl
CH2ClCH3
ClCH2
CH3ClCH2
CH2ClO POP
O
O
OO
OO
Cl
Cl
Cl
Cl
POO
OO
CH2Cl
CH2Cl
CH2ClClCH2
ClCH2
ClCH2
3
Read across for Sensitization
Guinea pig, M&KGuinea pig, M&KGuinea pig, M&KNo studyStudy:
No classificationNo classificationNo classificationClassification
negativenegativenegativeResults:
V6TDCPTCPPTCEPSubstance
Conclusion:TCEP is regarded as not being sensitizing on basis of read across to structural related substances (TCPP and TDCP) and no observation of sensitizing potential in workers exposed to TCEP.
3
Read across for Carcinogenicity
No studyrat, 2-y-carcinogenitcity study
No study2 y carc. Study in rat and mice
Study:
Carc. Cat. 3 R40 agreedCarc. Cat. 3 R40 proposed
carc. Cat. 3 R40 agreed at TC C&L
Classification:
Renal cortical tumours, testicular interstitial cell tumours, hepatocellular adenomas and adrenal cortical adenomas
Kidney tumours in two species and both sexes;
Results:
V6TDCPTCPPTCEPSubstance
Conclusion:Read across to TCPP from TCEP and TDCP proposed. There are similarities in hydrolysis and one common metabolite. The NOAEL is taken from the study with TDCP. No read across for V6 as it is an alkyl bridged bis-phosphate ester which makes it probably a bulkier and less bioavailablemolecule. (There were proposals from MS to consider V6 as two molecules of TCEP and to take over the same classification).
3
(No) read across for Fertility
No study12 weeks fertility study in rabbits, 2 y carcinogenicity study
No study2 generation in miceStudy:
Repro Cat. 3 R62 agreed at TC C&L
repro. Cat. 2 R60 agreed at C&L
Classification:
No effects in rabbits; significant effects on the male reproductive organs in the carcinogenicity study
High dose 90 day study showed no significant effects
Impairment of fertility for both sexes: reduced sperm counts, reduced litter size
Results:
V6TDCPTCPPTCEPSubstance
Conclusion:Read across was not regarded as appropriate on basis of the available data (V6 see also justification for carcinogenicity). A new 2 generation study will be performed for TCPP and V6.
3
(No) read across for Developmental Effects
No studydevelopmental study in ratsdevelopmental study in rats
developmental study in rats and mice
Study:
No classification/no concernNo classification/noconcern
No classification/no concern
Classification:
No significant effectsNo significant effectsNo significant effectsResults:
V6TDCPTCPPTCEPSubstance
Conclusion:Read across was not regarded as appropriate for V6. Further testing will depend on the results of the 2 generation study.
13141314 -- REACH-IT & Informatics (new in 2006)
Actions in FP6 year 2006Actions in FP6 year 2006
Chemical databases, IT for registration, workflow for dossiers, global portal for sharing data
RIP 2RIP 2
REACH-IT Architecture
Intelligent Testing Strategies (ITS)
Endpointinformation
(Q)SARsRead Across
In-vitro
ExposureScenarios
(Annex VII/VIII)
Existinginformation
TESTING
?
Computational Toxicity (QSARs)QSARs = Quantitative Structure Activity relationships
Actions in FP6 year 2006Actions in FP6 year 2006
Development, validation and implementation of (Q)SARs and other estimation methods for the assessment of chemicals Experimental LogLC50 (mol/l)
Pred
icte
d Lo
gLC5
0 (m
ol/l
)
0-1-2-3-4-5-6-7
0
-1
-2
-3
-4
-5
-6
-7
MOA-SetNPNPN
P86P85
P84P83P82
P81 P80
P79P78P77
P76
P75
P74P73
P72P71
P70P69
P68
P67P66
P65
P64
P63P62P61
P60
P59P58
P57
P56P55
P54P53
P52
P51P50
P49
P48
P47
P46P45 P44P43P42P41
P40P39
P38
P37P36P35 P34
P33P32P31
P30P29
P28
P27
P26P25P24
P23
P22
P21
P20P19
P18P17
P16
P15
P14 P13P12
P11P10
P9P8
P7
P6P5
P4
P3
P2
P1
N58
N57
N56
N55
N54
N53
N52
N51
N50
N49
N48
N47N46
N45
N44
N43N42
N41
N40
N39
N38
N37
N36
N35
N34
N33 N32N31
N30
N29N28
N27
N26
N25
N24
N23
N22
N21
N20
N19
N18
N17
N16
N15
N14N13N12
N11
N10
N9N8
N7
N6
N5
N4
N3
N2
N1
QSAR Action (Computational Toxicity)
((Q)SARsQ)SARs:: theoretical models that can be used to predict the physicochemical and biological properties of molecules. They aresometimes called in silico models.
QSARs can be used, in combination with other types of information, to minimize testing (e.g. animal testing)
This does not imply that a single QSAR replaces a single test
Additional animals Use of (Q)SARs, read-across 3.9 million Minimal use
2.6 million Average use (likely scenario)
2.1 million Maximal use
Animal-saving potential: 1.3-1.9 million animalsVan der Jagt et al. (2004).
Alternative approaches can reduce the use of test animals under REACH.
http://ecb.jrc.it
(Q)SARs and REACH: Use of Animals
(Q)SARs and REACH: Testing Costs
Additional cost Use of (Q)SARs, read-across 2.3 billion Euro Minimal use
1.5 billion Euro Average use (likely scenario)
1.1 billion Euro Maximal use
Cost-saving potential: € 800-1130 million
Pedersen et al. (2003). Assessment of additional testing needs under REACH.http://ecb.jrc.it
Computational Computational NanotoxicologyNanotoxicology
Nanotoxicology
•• Increasing importance of nanotechnologyIncreasing importance of nanotechnology
•• Unique risksUnique risks: adverse effects of nano-particles and materials cannot always be predicted from the known toxicity of the corresponding bulk material.
• Limited understanding of the potential toxicity of nano-sized materials.
•• NanotoxicologyNanotoxicology: addressing the special toxicity that may be associated with nano-sized materials.
NanoparticlesNanoparticles
NoseNose
SkinSkin GutGut
BrainBrain
BloodBlood
Bone Bone marrowmarrow
SpleenSpleen EndotheliumEndothelium LiverLiver HeartHeart Placenta / Placenta / foetusfoetus
LungLung
Background and MotivationBackground and MotivationWhy Computational Toxicity?Why Computational Toxicity?What is QSAR?What is QSAR?QSARQSAR’’ss ApplicationsApplicationsResearch Area in Thailand Research Area in Thailand
Computational Toxicity in Dyes and Cosmetics Computational Toxicity in Dyes and Cosmetics Concept proposalConcept proposal3D3D--Molecular Database for substances in Dyes and Molecular Database for substances in Dyes and CosmeticsCosmetics
QSARQSAR’’ss ApplicationsApplicationsQSAR for Drug Design (Since 1960, C. QSAR for Drug Design (Since 1960, C. HanschHansch) (Most)) (Most)
QSPR for Materials (Inorganic, etc.)QSPR for Materials (Inorganic, etc.)
QSAR for Agriculture and Toxicology (Pesticide, etc.)QSAR for Agriculture and Toxicology (Pesticide, etc.)
QSARQSAR’’ss ApplicationsApplications
(~100 calculated properties)(~100 calculated properties)
Example: QSAR of Example: QSAR of PolyaromaticPolyaromatic Hydrocarbons (Hydrocarbons (PAHsPAHs))
15 published 15 published QSARsQSARs for drug design by for drug design by HannongbuaHannongbua, S. et al. , S. et al.
QSAR for Materials (InorganicQSAR for Materials (Inorganic))• Inorganic cations toxicity - application of QSAR analysis.
Ind. Environ. Xenobiotics, Proc. Int. Conf. 1981, Meeting Date1980, 83-6.
• Enache M.; Dearden J. C.; Walker J. D. QSAR analysis of metal ion toxicity data in sunflower callus cultures (Helianthus annuus Sunspot ). QSAR & Combinatorial Science, 2003, 22:234-240.
• QSAR in toxicology. III. Its use in the determination of thetoxicity of inorganic cations. Ceskoslovenska farmacie. 1981, 30:7-10.
• Use of structure-activity relationships to estimate toxicity ofinorganic cations. Experientia. Supplementum. 1976, 23:83-4.
The aim of the researchTo investigate the toxicological effects of 13 inorganic and 21
organic compounds are evaluated using the pyriformis FDA esterasetest, the conventional pyriformis population growth impairment assayand the luminescent inhibition test (Microtox test).To Study the relationships between the toxic effects of the
substances tested and the ion characteristics of the metal ions or thehydrophobicity (quantified by the 1-octanol/water partition coefficient,log Kow) of the organic compounds.
Methods• Calculations and Statistical Analyses
The relative toxicity of the tested substances was quantified bythe determination of the IC50
For each toxicant and each assay, the concentrations weretransformed into logarithms, and the IC50 was determined byregression analysis.
The relationships between the Tetrahymena 1-h IC50, 9-h IC50,and the Microtox 30-min EC50 were determined by the Spearmanrank correlation coefficient (P<0.05).
The relationship between toxicity results and ioncharacteristics or lipophilicity of organic substances was testedusing mean square root linear regression analysis (P<0.05).
Statistical analyses were computed with Stat View SE 1.03software.
Results
Results
Results
Results
ConclusionToxicology-based QSARs can be used for the prediction of the
toxic potency of chemicals and the interpretation of mechanismsof action (Cronin and Dearden, 1995).
The relative toxicity of metal ions and organic compoundsdetermined with the two Tetrahymena biotests is predictableusing two ion characteristics, the softness index σp and χ2
m r,and the hydrophobicity coefficient log Kow, respectively.
Background and MotivationBackground and MotivationWhy Computational Toxicity?Why Computational Toxicity?What is QSAR?What is QSAR?QSARQSAR’’ss ApplicationsApplicationsResearch Area in Thailand Research Area in Thailand
CompututationalCompututational Toxicity in Dyes and CosmeticsToxicity in Dyes and Cosmetics
QSARQSAR’’ss application in Cosmeticapplication in Cosmetic
The aim
Collect information on all hair dye substances used inpermanent or temporary hair dyes in Europe and then to rankthese substances according to their estimated potency, theperspective being to supplement the current diagnostic work-upfor hair dye allergy with new potential allergens.
Methods• Prediction of sensitization potency
Structures identified were then imported into a molecular spreadsheet TSAR(Version 3.3, Accelrys Ltd., Cambridge, UK).
• Ranking the substances according to their sensitization potentialThe TOPS-MODE QSAR model was used in order to estimate the likely
sensitization potency• Tonnage amount
The European Cosmetic Toiletry and Perfumery Association has generatedtonnage data for use in the EU Commission for prioritizing hair dyes in riskassessment and risk management.
• Cluster analysisThe cluster analysis provided a means of grouping substances according to
their chemical properties such that a representative diverse subset could beselected for further work.
Results
Results
Results
ConclusionsThe sensitization potential of each substance was then
estimated by using a quantitative structure–activity relationship(QSAR) model and the substances were ranked according totheir predicted potency.A cluster analysis by using TOPS-MODE descriptors as inputs
helped us group the hair dye substances according to theirchemical similarity. This would facilitate the selection of potentialsubstances for clinical patch testing and would provide someclinical validation of the QSAR predictions.
QSARQSAR’’ss application in Dyesapplication in Dyes
The purpose of this paperDerive quantitative structure-activity/property-activity
relationships (QSAR/QPARs) for the mutagenicity (rev/nmol)of a variety of 4-aminoazobenzene (AAB), N-methyl-4-aminoazobenzene (MAB) and N,N-dimethyl-4-aminoazobenzene (DAB) derivatives in the S. typhimuriumTA98 bacterial strain with S9 activation (TA98+S9); thisparticular bacterial strain is well known to detect frameshiftmutagens.
Methods• The structures optimized at the semiempirical AM1 computational
level as implemented in AMPAC 5.0.• The CODESSA/AMPAC integrated software package was used
to calculate hundreds (>300) of molecular descriptors.• The entire collection of descriptors was then used in conjunction
with the statistical facilities of CODESSA to develop multilinearregression models for the log of the measured mutagenicity(rev/nmol) in TA98+S9, logTA98.
Results
Results
G1 XY Shadow/XY RectangleaE1 WPSA-2 Surface weighted partial positive surface areabE2 Polarity parameter/(distance)2 =(QmaxQmin)/(distance)2Q1 Average electrophilic reactivity index for a N atomQ2 Maximum electron-nuclear attraction for a C–C bond
Q3 Minimum net atomic charge for a C atomQ4 Maximum electron-nuclear attraction for a C atomQ5 Maximum bond order of an N atomH1 Final heat of formation/number of atomsO1 LogP
Results
Results
Conclusions• Developing QSAR/QPARs that correlate the relative mutagenic activity of
aminoazobenzene derivatives with various molecular descriptors can helpidentify factors that alter their relative mutagenicity.
• QSAR/QPAR studies can be useful in establishing biochemicalmechanisms/interactions, and in developing combinatorial strategies forthe synthesis of environmentally safe chemicals. Such studies involvingaminoazobenzene derivatives are particularly important because of theirwidespread use in the textile industry.
Thank you for your attention