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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.

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assessing carcinogenic potential of chemicals using oncologic cancer expert system

Assessing Carcinogenic Potential of Chemicals UsingOncoLogic 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
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: 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
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
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
Importance of mechanistic understanding in (Q)SAR modelling
  • 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
ADME/Toxicokinetics considerationAbility of toxicant to reach target tissue/molecule
  • Chemical structure/phys-chem on ADME
  • Route of administration
  • Facilitating “carrier” molecule
  • Protective “carrier” molecule
  • Biological half-life
mechanistic toxicodynamics consideration
Mechanistic/Toxicodynamics consideration
  • Electrophilic
  • Receptor-mediated
  • Disruption of homeostasis
  • Multiple mechanisms
future trend of q sar
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
Incorporating emerging predictive technologies


In silico ADME,

ADME assays



















HTS assays



Molecular descriptors,

Structural features, etc

Screening assays,

Biomarkers, etc


major references for q sar as a screening tool
Major References for (Q)SARas 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.

introduction to the cancer endpoint
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
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)

Direct DNA


Indirect DNA


Clonal expansion

Cell proliferation



Overcoming suppressions (e.g., p53, immune, angiogenesis)

Key mechanistic


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
Difficulties of (Q)SAR of carcinogenicity
  • 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
Historical development of OncoLogic
  • 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
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
Major Sources of Data/Insight Used to Develop Cancer Knowledge Rules
  • 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
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
Development of rules for each class
  • 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
critical factors for sar consideration
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 consideration24
Critical Factors for SAR Consideration
  • Mechanistic Factors
    • Electrophilic vs. receptor- mediated
    • Multistage process
  • Physicochemical Factors
    • Molecular weight
    • Physical state
    • Solubility
    • Chemical reactivity
oncologic factors affecting carcinogenicity of aromatic amines
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





Carbonium ion

Amidonium ion

Molecular Mechanism for Generation of Resonance-stabilized Reactive Intermediates from N-acyloxy Aromatic Amines











Very active if:




Active if:





Inactive if:





Transition to


and triphenyl-

methane amines


Transition to amino-



Transition to amino

azo dyes

Very active if:


Active if:




Weakly active if:


Inactive if:




Active if:


Inactive if:


For 4-aminobiphenyl:

Very active if:






Active if:





Weakly active if:


Inactive if:





For benzidine:

Very active if:




Weakly active if:



Inactive if:


3,3’-bis-oxyacetic acid

Synoptic Tabulation of Structural Requirements for Carcinogenic Activity of 4-Aminobiphenyl and Benzidine Derivatives
oncologic prediction vs ntp bioassays aromatic amines and related compounds

C = Clear evidence of carcinogenicity

S = Some evidence of carcinogenicity

N = No evidence of carcinogenicity

NT = Not tested

+ = At least one test = C or S

Eq = No C or S, and E must appear at least once

-- = No C, S, or E

OncoLogic® Prediction vs. NTP BioassaysAromatic Amines and Related Compounds
examples of how knowledge rules can be used in chemical design
Examples of how “Knowledge Rules” can be used in chemical design

Strategies to Designing Safer Chemicals:

  • Steric hindrance
  • Nonplanarity
  • Electronic insulation
  • Hydrophilicity

OncoLogic Cancer Concern = High

oncologic prediction vs ntp bioassays aromatic amines and related compounds39

C = Clear evidence of carcinogenicity

S = Some evidence of carcinogenicity

N = No evidence of carcinogenicity

NT = Not tested

+ = At least one test = C or S

Eq = No C or S, and E must appear at least once

-- = No C, S, or E

OncoLogic® Prediction vs. NTP BioassaysAromatic Amines and Related Compounds
conclusions from ntp cancer bioassays predictive exercises
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

FDA Validation of Genetic Toxicity and SAR

Methods for Predicting Carcinogenicity*

*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


FDA Validation of Genetic Toxicity and SAR

Methods for Predicting Potent Carcinogenicity

oncologic benefits
OncoLogic® - Benefits
  • Allow non-experts to reach scientifically supportable conclusions
  • 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
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 limitations
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

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


Strengths Limitations
running oncologic
Running OncoLogic®
  • Two methods to predict carcinogenicity
    • SAR Analysis
      • Knowledge rules
    • Functional Analysis
      • Uses results of specific mechanistic/non-cancer studies
sar analysis
SAR Analysis
  • Four modules
    • Organics
    • Metals
    • Polymers
    • Fibers
  • Different method used to evaluate each type
running oncologic organics module
Running OncoLogic® : Organics Module
  • Organics
    • Enter information on chemical identity
    • Choose appropriate chemical class
    • Enter chemical name, CAS#, or chemical structure
running oncologic organics module53
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 module54
Running OncoLogic®: Organics Module
  • Pick Correct Backbone Structure if Provided
  • Draw chemical
oncologic justification for organics module
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



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
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
Other Chemicals (Cont.)
  • Locate chemical by CAS number or by name
running oncologic metals
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
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
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
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
Fibers Module (Cont.)

Relevant manufacturing / processing / use information

Crystallization, thermal extrusion, naturally occurring, unknown method

functional arm cont
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








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
major references on oncologic
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

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
Downloading and Contact Information

Training and limited technical questions

Scientific questions


recent development oncologic 7 0
Recent development: OncoLogic 7.0
  • 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
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
Difficulties of negative predictions
  • 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