Transcript Key components of mechanism

Assessing Carcinogenic Potential of Chemicals
Using
OncoLogic Cancer Expert System
Yin-tak Woo, Ph.D., DABT
Office of Pollution Prevention and Toxics
U.S. Environmental Protection Agency
Washington, DC 20460
May 19, 2010
Outline of the Presentation
• Scientific background in development of the
OncoLogic system
• Brief description of the OncoLogic system
• Recent development in updating and
expanding OncoLogic system
SAR/QSAR: Background & Issues
• SAR/QSAR: activity = f (structure)
• Given sufficient data/knowledge on related
compounds  screen well defined endpoint
• Evolution of SAR/QSAR: from human intuition to
cyber sophistication
• Impact of commercial software
• User base: from domain expert to nonscientists
• Pressure of reduction in experimentation
Approaches to SAR/QSAR
• Statistical vs. Mechanistic
• Local/Homogeneous/Congeneric vs.
Global/General/Heterogeneous
• Active/Inactive vs. Ranking vs. Potency
Criteria for assessing scientific soundness
• Selection of endpoint
• Knowledge base/training database/applicability
domain
• Methodology and descriptor selection
• Model validation (predictive accuracy, internal,
external, prospective)
• Transparency and scientific rationale
• Confidence/uncertainty analysis
• Strengths, weaknesses and limitations
Importance of mechanistic understanding
in (Q)SAR modelling
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selection of toxicological endpoint
selection of molecular descriptors
coverage of training database
consideration of database stratification
interpretation of outliers
consideration of human relevance
achieving the goal of statistical association with
mechanistic backing
ADME/Toxicokinetics consideration
Ability of toxicant to reach target tissue/molecule
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Chemical structure/phys-chem on ADME
Route of administration
Facilitating “carrier” molecule
Protective “carrier” molecule
Biological half-life
Mechanistic/Toxicodynamics consideration
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Electrophilic
Receptor-mediated
Disruption of homeostasis
Multiple mechanisms
Future Trend of (Q)SAR
• Critical evaluation of current methods
• Expansion of publicly accessible
databases/knowledge bases
• Expansion of integrative approaches
• Utilization of input from emerging
predictive technologies
Incorporating emerging predictive technologies
Chemical
ADME/Toxicokinetics
Consideration
In silico ADME,
ADME assays
Mechanistic/Toxicodynamics
Consideration
HTS assays
Structural
Functional
Molecular descriptors,
Structural features, etc
Screening assays,
Biomarkers, etc
Prediction
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Major References for (Q)SAR
as a Screening Tool
Woo YT, Lai DY (2003): Mechanism of action of chemical
carcinogens and their role in SAR analysis and risk assessment.
In: Quantitative Structure-Activity Relationship (QSAR) Models
of Mutagens and Carcinogens, R. Benigni, ed., CRC Press,
Boca Raton, FL
Doull D, Borzelleca J, Becker R, Daston G, DeSesso J, Fan A,
Fenner-Crisp P, Holsapple M, Holson J, Llewellyn G,
MacGregor J, Seed J, Walls I, Woo Y, Olin S (2007):
Framework for use of toxicity screening tools in context-based
decision-making. Food Chem.Toxicol. 45:759-796.
Development of OncoLogic Cancer Expert
System: Scientific Background
Introduction to the Cancer Endpoint
• Definitions
– Uncontrolled dividing and growth of cells
– Caused by mutations, ↑ cell proliferation, ↓ cell death, loss of
homeostatic control, etc.
• Two general mechanisms by which a chemical can
induce cancer
– Genotoxic (default)
• Interaction with DNA to cause mutation(s) in genes
– Non-genotoxic
• Variety of mechanisms
Introduction to the Cancer Endpoint
(Cont.)
• Carcinongesis is a multistage/multistep process
– Initiation
• Mutation converts normal to preneoplastic cells
– Promotion
• Expansion of preneoplastic cells to benign tumors
– Progression
• Transformation of benign to invasive malignant tumors
• A potent carcinogen acts directly on all three stages
• A weak carcinogen acts directly on one stage and
indirectly on other
Main
event(s)
Key
mechanistic
consideration
Initiation
Promotion
Progression
Direct DNA
binding
Indirect DNA
damage
Clonal
expansion
Overcoming
suppressions
Cell proliferation
Apoptosis
Differentiation
(e.g., p53,
immune,
angiogenesis)
Electrophile,
resonance
stabilization,
nature of
DNA adduct
Receptor,
cytotoxicity,
gene expression
Free radical,
receptor,
gene
suppression
Signal transduction, homeostasis
SAR/QSAR
mechanistic
descriptors
Electrophilicity,
HOMO/LUMO,
delocalization
energies, ……
2D, 3D, docking,
biopersistence,
methylation, ….
Reduction
potential, 2D, 3D,
……
Difficulties of (Q)SAR of carcinogenicity
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Complex, mechanism-dependent (Q)SAR
Local vs. global models
Data scarcity and variability
Feedback and validation issues
Need for integrative approach
Historical development of OncoLogic
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TSCA and New Chemicals Program (PMN)
Structure-Activity Team approach
Need to provide guidance to industries
OncoLogic Team (Joseph Arcos, Mary Argus,
David Lai, Yin-tak Woo)
• LogiChem coop version
• Current version
• Future developments
OncoLogic: A mechanism-based expert
system for predicting carcinogenic potential
• Developed by domain experts in collaboration
with expert system developer
• Knowledge from SAR on >10K chemicals
• Class-specific approach to optimize predictive
capability
• Consider all relevant factors including biological
input when possible
• Predictions with scientific rationale and
semiquantitative ranking
Major Sources of Data/Insight Used to
Develop Cancer Knowledge Rules
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The OncoLogic Team and members of SAT
Chemical Induction of Cancer monograph series
IARC monograph series
NCI/NTP technical reports
Survey of compounds which have been tested for
carcinogenic activity, PHS Publ. 149
• Non-classified EPA submission data from various EPA
program offices
• Current literature and ad hoc expert panels
Profile of most potent carcinogens
• Ability to reach target tissue
• Reasonable lifetime of ultimate carcinogen
• Persistent and site-specific interaction with
target macromolecule
• Ability to affect all three stages of
carcinogenesis
Development of rules for each class
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Gather all available data and information
Brainstorming to determine key factors
Determine need for subclassification
Assign concern levels to known carcinogens
Determine mechanism-based modification
factors for substituents
• Develop rationale for conclusion
Concern Levels
OncoLogic
Concern
Definition
Low
Unlikely to be carcinogenic
Marginal
Likely to have equivocal carcinogenic activity
Low – Moderate
Likely to be weakly carcinogenic
Moderate
Likely to be a moderately active carcinogen
Moderate – High
Highly likely to be a moderately active
carcinogen
High
Highly likely to be a potent carcinogen
Critical Factors for SAR
Consideration
• Electronic and Steric Factors
– Resonance stabilization
– Steric hindrance
• Metabolic Factors
– Blocking of detoxification
– Enhancement of activation
Critical Factors for SAR
Consideration
• Mechanistic Factors
– Electrophilic vs. receptor- mediated
– Multistage process
• Physicochemical Factors
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Molecular weight
Physical state
Solubility
Chemical reactivity
OncoLogic®
Factors Affecting Carcinogenicity of Aromatic Amines
• Number of aromatic ring(s)
• Nature of aromatic ring(s) - homocyclic vs. heterocyclic
- nature and position of heteroatoms
• Number and position of amino or amine-generating
groups(s) - position of amino group relative to longest
resonance pathway - type of substituents on amino
group
• Nature, number, position of other ring substituent(s) steric hindrance - hydrophilicity
• Molecular size, shape, planarity
Some Hydrocarbon Moieties Present in
Carcinogenic Aromatic Amines
R-NO2
R-NO
R-NH-OAc
CH3
CH 3
C
CH 3
H
C
CH
R-NH-OH
R-NH2
[R-N(CH3)2]
Molecular Mechanism for Generation of
Resonance-stabilized Reactive Intermediates
from N-acyloxy Aromatic Amines
Ac
Ac
N
O Ac
N
+
Ac
N
Ac
+
N
+
Carbonium ion
Amidonium ion
-O
Ac
Synoptic Tabulation of Structural Requirements for Carcinogenic
Activity of 4-Aminobiphenyl and Benzidine Derivatives
5’
H
Active if:
--NH2
--NH•OC•CH3
--NO2
Weakly active if:
--C6H5
Inactive if:
--CH3
--Cl
--Br
Active if:
--S-Inactive if:
--NH--
6
5
4’
4’
3’
Very active if:
--F
6’
2’
2
3
For 4-aminobiphenyl:
For benzidine:
Very active if:
3-methyl
3,2’-dimethyl
3,3’-dimethyl
3-fluoro
3’-fluoro
Very active if:
2-methyl
3,3’-dihydroxy
3,3’-dichloro
Active if:
3-chloro
3-methoxy
3,2,5’-trimethyl
3,2’,4’,6’-tetramethyl
Weakly active if:
3-hydroxy
Inactive if:
2-methyl
2’-methyl
2’-fluoro
3-amino
NH2
Weakly active if:
3,3’-dimethyl
3,3’-dimethoxy
Inactive if:
2,2’-dimethyl
3,3’-bis-oxyacetic acid
Very active if:
OC•CH3
--N
OH
Active if:
--NH•OC•CH3
--N(CH3)2
--NO2
--OCH3
Inactive if:
--F
R
|
--CH-Transition to
diphenylmethane
and triphenylmethane amines
--CH==CH-Transition to aminostilbenes
--N==N-Transition to amino
azo dyes
OncoLogic® Prediction vs. NTP Bioassays
Aromatic Amines and Related Compounds
NTP
#
Chemical
24 4,4’-Diamino-2,2’-stilbene
disulfonic acid
42 p-Nitroaniline
26 p-Nitrobenzoic acid
9 p-Nitrophenol
33 4-Hydroxyacetanilide
32 2,4-Diaminophenol
dihydrochloride
40 3,3’-Dimethylbenzidine
43 o-Nitroanisole
C
S
N
NT
+
Eq
--
=
=
=
=
=
=
=
Bioassay Results
Oncologic
Rat Mouse “Call” Evaluation
N/N
N/N
-L
NT
N/S
NT
N/E
N/N
E/N
N/N
N/N
N/N
S/N
Eq
+
-Eq
+
mar
mar
LM
LM
M
C/C
C/C
NT
C/C
+
+
HM
HM
Clear evidence of carcinogenicity
Some evidence of carcinogenicity
No evidence of carcinogenicity
Not tested
At least one test = C or S
No C or S, and E must appear at least once
No C, S, or E
Examples of how “Knowledge Rules”
can be used in chemical design
H2N
NH2
OncoLogic Cancer Concern = High
Strategies to Designing Safer Chemicals:
- Steric hindrance
- Nonplanarity
- Electronic insulation
- Hydrophilicity
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential
H2N
Example
Action
H2N
N
NH2
NH2
N
Introduce bulky
substituent(s) ortho to amino
/ amine-generating group(s).
Introduce bulky Nsubstituent(s) to amino /
amine-generating group(s).
Effect on Cancer
Concern/Justification
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential
H2N
Example
NH2
Action
H2N
NH2
Effect on Cancer
Concern/Justification
Introduce bulky
Provide steric hindrance to
substituent(s) ortho to amino inhibit bioactivation.
/ amine-generating group(s).
Concern = Marginal
N
N
Introduce bulky Nsubstituent(s) to amino /
amine-generating group(s).
Make it a poor substrate for
the bioactivation enzymes.
Concern = Marginal
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
H2 N
NH2
Action
NH2
Introduce bulky
groups ortho to
intercyclic linkages.
Effect on Cancer
Concern/Justification
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
H2 N
NH2
Action
NH2
Introduce bulky
groups ortho to
intercyclic linkages.
Effect on Cancer
Concern/Justification
Distort the planarity of the
molecule making it less
accessible and a poorer
substrate for the
bioactivation enzymes.
Concern = Marginal
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
NH2
Action
H2N
NH2
Replace electronconducting intercyclic
linkages by electroninsulating intercyclic
linkages.
Effect on Cancer
Concern/Justification
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
NH2
Action
H2N
NH2
Replace electronconducting intercyclic
linkages by electroninsulating intercyclic
linkages.
Effect on Cancer
Concern/Justification
1. Reduce length of conjugation
path and thus the force of
conjugation, which
facilitates departure of
acyloxy anion.
2. Less resonance stabilization
of electrophilic nitrenium
ion.
Concern = Marginal
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
Action
SO3
H2N
NH2
SO3
NH2
Ring substitution with
hydrophilic groups (e.g.,
sulfonic acid); especially
at ring(s) bearing amino /
amine-generating
group(s).
Effect on Cancer
Concern/Justification
Molecular Design of Aromatic Amine Dyes
with Lower Carcinogenic Potential (Cont.)
H2N
Example
Action
SO3
H2N
NH2
SO3
NH2
Ring substitution with
hydrophilic groups (e.g.,
sulfonic acid); especially
at ring(s) bearing amino /
amine-generating
group(s).
Effect on Cancer
Concern/Justification
Render molecule more
water-soluble thus
reducing absorption and
accelerating excretion.
Concern Level = Low
OncoLogic® Prediction vs. NTP Bioassays
Aromatic Amines and Related Compounds
NTP
#
Chemical
24 4,4’-Diamino-2,2’-stilbene
disulfonic acid
42 p-Nitroaniline
26 p-Nitrobenzoic acid
9 p-Nitrophenol
33 4-Hydroxyacetanilide
32 2,4-Diaminophenol
dihydrochloride
40 3,3’-Dimethylbenzidine
43 o-Nitroanisole
C
S
N
NT
+
Eq
--
=
=
=
=
=
=
=
Bioassay Results
Oncologic
Rat Mouse “Call” Evaluation
N/N
N/N
-L
NT
N/S
NT
N/E
N/N
E/N
N/N
N/N
N/N
S/N
Eq
+
-Eq
+
mar
mar
LM
LM
M
C/C
C/C
NT
C/C
+
+
HM
HM
Clear evidence of carcinogenicity
Some evidence of carcinogenicity
No evidence of carcinogenicity
Not tested
At least one test = C or S
No C or S, and E must appear at least once
No C, S, or E
Conclusions from NTP Cancer
Bioassays Predictive Exercises
• Most of the best performers are predictive systems
that incorporate human expert judgment and
biological information
• OncoLogic was one of the best performers among
more than 15 methods
Final results of 2nd NTP predictive exercise
(from Benigni and Zito, Mutat. Res. 566, 49, 2004)
FDA Validation of Genetic Toxicity and SAR
Methods for Predicting Carcinogenicity*
Concordance/
Accuracy
False Negatives False Positives
Bacterial rev.
mutation
286/457 (62.5 %)
158/405 (39.0 %) 13/52 (25 %)
Mouse
lymphoma
201/268 (75.0 %)
48/236 (20.0 %)
19/32 (59 %)
Chromosome
aberration
215/342 (62.9 %)
103/298 (34.6 %)
24/44 (55 %)
Ashby-Tennant
structural alert
461/650 (70.9 %)
154/569 (27.1 %)
35/81 (43 %)
Multi CASE
ver. 3.1
491/592 (82.9 %)
85/530 (16 %)
16/62 (26 %)
OncoLogic ver.
4.1
313/354 (88.4 %)
28/325 ( 8.6 %)
13/29 (45 %)
*from Mayer et al.: SAR analysis tools: validation and applicability in
predicting carcinogens. Regulatory Toxicol. Pharmacol. 50: 50-58, 2008
Sensitivity and Specificity of the Genetic Toxicity
and SAR Methods for Predicting Carcinogenicity
Sensitivity
Specificty
(# carcinogens
identified / # tested)
(# noncarcinogens
identified / # tested)
Bacterial rev.
mutation
247/405 (61.0 %)
39/52 (75 %)
Mouse
lymphoma
188/236 (79.7 %)
13/32 (41 %)
Chromosome
aberration
195/298 (65.4 %)
20/44 (45 %)
Ashby-Tennant
structural alert
415/569 (72.9 %)
46/81 (57 %)
Multi CASE
ver. 3.1
445/530 (84.0 %)
46/62 (74 %)
OncoLogic ver.
4.1
297/325 (91.4 %)
16/29 (55 %)
FDA Validation of Genetic Toxicity and SAR
Methods for Predicting Potent Carcinogenicity
Concordance/
Accuracy
False Negatives False Positives
Bacterial rev.
mutation
129/154 (77.9 %)
35/147 (22 %)
1/7 (14 %)
Mouse
lymphoma
53/66 (80 %)
11/62 (18 %)
2/4 (50 %)
Chromosome
aberration
75/97 (77 %)
20/90 (22 %)
2/7 (29 %)
Ashby-Tennant
structural alert
202/250 (80.8 %)
41/239 (17 %)
7/11 (64 %)
Multi CASE
ver. 3.1
221/232 (95.3 %)
10/223 (4.5 %)
1/9 (11 %)
OncoLogic ver.
4.1
149/154 (96.8 %)
4/150 ( 3 %)
2/4 (50 %)
OncoLogic® - Benefits
• Allow non-experts to reach scientifically supportable
conclusions
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Expedites the decision making process
Allows sharing of knowledge
Reduces/eliminates error and inconsistency
Formalize knowledge rules for cancer hazard
identification (SAT-style)
• Bridge expertise of chemists and toxicologists for most
effective hazard evaluation
• Provide guidance to industries on elements of concern
for developing safer chemicals
Some Notable Uses of OncoLogic
• OPPT (new chemicals, design for environment,
green chemistry, existing chemicals)
• Guidance to industries (Sustainable Future
program)
• OW (disinfection byproduct prioritization) and
other EPA program offices
• FDA (food contact notification) and other
governmental agencies
Strengths
• Developed by recognized
domain experts
• Knowledge not just data
• Local models with strong
mechanistic basis
• Integrates biological input
when possible
• Semiquantitative ranking
with scientific rationale
• Proven performance in
prospective and external
validations
• Industrial chemicals
Limitations
• Users need to have some
organic chemistry
background
• Coverage limited by
available knowledge
• No batch mode
• Some updates are needed
• Current coverage mainly
on established carcinogen
classes
• Limited receptor-based
SAR
• Pharmaceuticals?
Overall Structure of The Cancer Expert System
Chemical compound
Structural arm
Functional arm
Physical state
Consideration of available
non-cancer toxicological data
known to correlate with
carcinogenic activity
Molecular weight
No
breakdown
Fibers
Polymers
products
Organic
compounds
Metal / metalloid?
releasable
organic
ligands/
moieties
Initial cancer hazard evaluation
FINAL EVALUATION
Yes
Metal /
metalloid
Running OncoLogic®
• Two methods to predict carcinogenicity
– SAR Analysis
• Knowledge rules
– Functional Analysis
• Uses results of specific mechanistic/non-cancer studies
SAR Analysis
• Four modules
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–
–
–
Organics
Metals
Polymers
Fibers
• Different method used to evaluate each type
Running OncoLogic® :
Organics Module
• Organics
– Enter information on chemical identity
– Choose appropriate chemical class
– Enter chemical name, CAS#, or chemical
structure
Running OncoLogic®:
Organics Module
• Select chemical class
– 48 total
– Description in Manual
– Hit “F1” to view sample
structures
• Absence of structure in
OncoLogic provides
suggestive, but not
definitive, evidence of
lack of major cancer
concern. Functional arm
should be used if
possible.
Running OncoLogic®:
Organics Module
• Pick Correct Backbone Structure if Provided
• Draw chemical
Running OncoLogic®:
Organics Module
Running OncoLogic®:
Organics Module
Perform evaluation
OncoLogic® Justification for
Organics Module
OncoLogic®(R) Justification Report
CODE NUMBER: Isodecyl Acrylate Example
SUBSTANCE ID: 1330-61-6
The final level of carcinogenicity concern for this acrylate when
the anticipated route of exposure is inhalation or injection is
MARGINAL.
JUSTIFICATION:
An acrylate is a potential alkylating agent which may bind, via
Michael addition, to key macromolecules to initiate/exert
carcinogenic action. The alkylating activity of acrylates can be
substantially inhibited by substitution at the double bond,
particularly by bulky or hydrophilic groups..........................
Other Chemicals
• In addition to SAR analysis, OncoLogic includes
evaluations of approximately 90 specific
chemicals that do not fit into any OncoLogic class
Other Chemicals (Cont.)
• Locate chemical by CAS number or by name
Running OncoLogic®:
Metals
• Similar to running the organics module
• Pick the metal to be evaluated
– OncoLogic® will then either ask a series of
questions needed to evaluate the chemical or
provide a database of related compounds
Information Needed to Run the
Metals Module
• Nature/form of the metal / metalloid
– Organometal, metal powder
• Type of chemical bonding (e.g., organic, ionic)
• Dissociability / solubility
– Valence / oxidation state
• Crystalline or amorphous
• Exposure scenario
• Breakdown products (e.g., organic moieties)
Running OncoLogic®
Polymers
• Polymer must consist of covalently linked
repeating units and have a number average
molecular weight >1000
• OncoLogic® asks a series of questions
designed to aid in evaluation of
carcinogenicity of the polymer
Polymers Module:
Information Needed to Evaluate Polymers
• Percentage of polymer with molecular weight <500 and <1000
• Percent of residual monomer
• Identification of Reactive Functional Group(s)
• Solubility
• Special features
– Polysulfation, "water-swellability"
• Exposure route
• Breakdown products (e.g., hydrolysis)
Fibers Module
Evaluations are based on physical dimensions and
physicochemical properties
Physical dimensions
Diameter, length, aspect ratio
Physicochemical properties
High density charge, flexibility, durability, biodegradability,
smooth and defect-free surface, longitudinal splitting
potential
Presence of high MW polymer, low MW organic moiety,
metals/metalloids
Fibers Module (Cont.)
Relevant manufacturing / processing / use
information
Crystallization, thermal extrusion, naturally
occurring, unknown method
The Functional Arm of OncoLogic®
Functional Arm (Cont.)
• OncoLogic® can use results from some shorter-term tests
to support a cancer concern.
• Results indicate whether chemical may be an initiator,
promoter, or progressor
Use of Non-Cancer Data:
Functional Arm (Cont.)
• Functional Arm predicts whether the chemical is likely to
be a tumor initiator, promoter, and/or progressor
– Possible relevance or contribution to the carcinogenesis process is
indicated in the figure below
Initiator
M/HM
M/HM
HM/H
Promoter
LM/M
Progressor
Major References on OncoLogic®
Woo, Y.-T., Lai, D.Y., Argus, M.F. and Arcos, J.C. Development of Structure
Activity Relationship Rules for Predicting Carcinogenic Potential of Chemicals.
Toxico. Lett. 79: 219-228, 1995.
Woo, Y.-T., Lai, D.Y., Argus, M.F. and Arcos, J.C. Carcinogenicity of
Organophosphorous Pesticides/Compounds: An analysis of their Structure Activity
Relationships. Environ. Carcino. & Ecotox. Revs. C14(1), 1-42, 1996.
Lai, D.Y., Woo, Y,-T., Argus, M.F. and Arcos, J.C.: Cancer Risk Reduction
Through Mechanism-based Molecular Design of Chemicals. In:"Designing Safer
Chemicals" (S. DeVito and R. Garrett, eds.), American Chemical Society
Symposium series No. 640, American Chemical Society Washington, DC. Chapter 3,
pp.62-73, 1996.
Woo, Y.-T. et al.: Mechanism-Based Structure-Activity Relationship Analysis of
Carcinogenic Potential of 30 NTP Test Chemicals. Environ. Carcino. & Ecotox.
Revs. C15(2), 139-160, 1997.
Woo, Y., Lai, D., Argus, M.F., and Arcos, J.C.: An Integrative Approach of
Combining Mechanistically Complementary Short-term Predictive Tests as a Basis
for Assessing the Carcinogenic Potential of Chemicals. Environ. Carcino. &
Ecotoxicol. Revs. C16(2), 101-122, 1998.
Major References - continued
Woo, Y.-T., and Lai, D.Y. : Aromatic Amino and Nitroamino
Compounds and their Halogenated Derivatives. In: Patty’s Toxicology,
5th edn., E. Bingham, ed., Wiley, pp. 969-1106, 2001
Woo, Y-T., Lai, D., McLain, J., Manibusan, M., Dellarco, V.: Use of
Mechanism-Based Structure-Activity Relationships Analysis in
Carcinogenic Potential Ranking for Drinking Water Disinfection
Byproducts. Environ. Health Persp. 110 (suppl. 1): 75-88, 2002.
Woo, Y.-T., and Lai, D.Y. : Mechanism of Action of Chemical
Carcinogens and their Role in Structure Activity Relationships (SAR)
Analysis and Risk Assessment. In: Quantitative Structure-Activity
Relationship (QSAR) Models of Mutagens and Carcinogens. R. Benigni,
ed., CRC Press, Boca Raton, FL., pp. 41-80, 2003.
Woo, YT, and Lai DY: OncoLogic: A mechanism-based expert system
for predicting the carcinogenic potential of chemicals. In: Predictive
Toxicology, C. Helma, editor, Taylor and Francis, Boca Raton, FL., pp.
385-413, 2005.
Downloading and Contact Information
Training and limited technical questions
[email protected]
Scientific questions
[email protected]
Downloading
http://www.epa.gov/oppt/newchems/tools/oncologic.htm
Recent development: OncoLogic 7.0
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Windows 7.0 and XP-compatible
Improved drawing package
Improved printer functionality
Drop-down menus of CAS number and
chemical names within each class/subclass
• Ready for downloading soon
OncoLogic 8.0: Work in progress
• Master list of chemicals
• Limited expansion and updates
• “Low-potential” classes with delineation of
exceptions and precautionary notes
• Nongenotoxic SA; input from HTS/TXG?
• Integrative approaches
• Nanomaterial?
Difficulties of negative predictions
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Scarcity and uncertainty of negative data
Less certain mechanistic basis of negativity
Difficult to exhaust all possible mechanisms
Need for supportive data (e.g, mode of
action, pathways, threshold, spp. difference)
• Regulatory caution of negative predictions
for high exposure chemicals