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Estimating Reactivity From Structure Using the OECD (Q)SAR Application Toolbox. T. W. Schultz Presented at the Logan Workshop March 23-24, 2010. Topics. Background & The Problem Michael Acceptors: An Example  Toolbox Applications Pathways Application Summary.

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estimating reactivity from structure using the oecd q sar application toolbox

Estimating Reactivity From Structure Using the OECD (Q)SAR Application Toolbox

T. W. Schultz

Presented at the Logan Workshop

March 23-24, 2010

topics
Topics
  • Background & The Problem
  • Michael Acceptors: An Example
  •  Toolbox Applications
  • Pathways Application
  • Summary
coding reactivity from structure
Coding Reactivity From Structure
  • Currently no universal QSAR model
      • Local models for simple congeneric groups
  • 2D structure
  • Qualitatively easy
    • Main drawback false positives
  • Quantitatively more difficult
    • Iso-reactive groups (lumping vs. splitting)
    • Sub-grouping and sub-sub-grouping
2d coding michael acceptors
2D Coding Michael Acceptors

Qualitatively Predicted

as any Polarized

a,b-Unsaturated Compound

for example, C=CC(=O)

2d coding michael acceptors1
2D Coding Michael Acceptors

Quantitative Predictions Impacted By Substitution at

C1=C2(Pg)C3

Reactivity is extended fragment -based

structural variation in reactive potency of esters
Structural Variation in Reactive Potency of Esters

TYPE STRUCTURE RC50(mM)

Acetylenedicarboxylates ROC(=O)C#CC(=O)OR 0.025

Propiolates C#CC(=O)OR 0.1

Vinylene dicarboxylates ROC(=O)C=CC(=O)OR 0.2

Acrylates C=CC(=O)OR 0.8

Alkyl 2-alkynoates RC#CC(=O)OR 1.5

Crotonates CC=CC(=O)OR 15.0

Methacrylates C=C(C)C(=O)COR >30.0

variations in rc 50 values for substituted acrylates c cc o or
Variations in RC50Values for Substituted Acrylates, C=CC(=O)OR

Derivative RC50 (mM)

_______________________________________

Ethyl 0.48, 0.55

Vinyl 0.11, 0.11

2-Hydroxyethyl 0.25, 0.29

n-Propyl 0.80, 0.92

Propargyl 0.19, 0.24

n-Hexyl 0.88, 0.76

Phenyl 0.016, 0.015

to quantitatively predict reactivity must be able to separate
To Quantitatively Predict Reactivity: Must Be Able to Separate

C#CC(=O)

C=CC(=O)

CC=CC(=O)

C=C(C)C(=O)

to quantitatively predict reactivity must also be able to separate
To Quantitatively Predict Reactivity: Must Also be Able To Separate

C=CNO2

C=CC#N

C=CC(=O)C

C=CC(O)OC

C=CC(=O)NH2

toolbox application
Toolbox Application
  • Use reactive to group chemicals into categories and to facilitate the selection of chemical analogues, which allows the integrates of the mechanism of reaction in defining the best category or sub-category
  • Then do read-across
slide11

Read Across with GSH & LLNA Data

EC3 = 0.01

RC50 = 0.03

RC50 = 0.05

RC50 = 0.05

RC50 = 0.09

RC50 = 0.02

protein binding in toxicity
Protein Binding in Toxicity

Mechanisms of Protein

Binding

In Vitro

Measurements

In Chemico

Measurement

Hazard

Assessment

Endpoints

In vitro

effects

Reactive

Potency

Michael

addition

SN2

SNAr

In vivo

effects

In silico

modeling

application reactivity to catgorizing an inventory
Application Reactivity to Catgorizing an Inventory
  • ≈ 1500 substances on the List of Flavor and Fragrance Related Substances
  • ≈1300 discrete substances of which:
    • 79 Fast- to moderate-reacting Michael-acceptors;
    • 19 Slow-reacting Michael-acceptors;
    • 57 Schiff-base aldehydes;
    • 29 Acetals;
    • 15 Disulfide formers;
    • 11 Cyclic addition diones;
    • 9 Disulfide exchangers;
    • 3 O-heterocyclic ring openers.

>40 pro-electrophiles

pathway applications
Pathway Applications
  • Screening Tool
    • Many be used in isolation
  • Risk Assessment
    • Must be used as part of an ITS
    • Represents the molecular initiating event of an adverse outcome pathway
pathway for allergic contact dermatitis
Pathway for Allergic Contact Dermatitis

1. Haptenation; 2. Epidermal inflammation & LC activation; 3. LC migration; 4. DC: T cell interaction; 5. T cell proliferation; 6. Increase in hapten-specific T cells; 7. Hapten re-exposure; 8. Acute inflammation; 9. T cell-mediated inflammation

Karlberg et al.Chem. Res. Toxicol. 2008, 21, 53-69.

slide18

LLNA-tested Michael Acceptor

SUBSTRUCTURE MESSAGE

C=CC=O Vinyl or vinylene with a carbonyl

[CH2]=C(C)C=O -C-atom alkyl-substituted with

a carbonyl

O=CC=CC=O -C-atom substituted with a second carbonyl

[CH]=C(C(=O))C=O -C-atom substituted with a second carbonyl

C=[CH]c1ccncc1 Para-vinyl azaarene

O=C1[CH]=CC(=O)C=C1 Para-quinone

slide19

Michael Acceptor Not Tested in LLNA

SUBSTRUCTURE MESSAGE

C#CC=O Ethylnylene or acetylenic with a carbonyl

C=CN(=O)=O Olefinic nitro

C#CS(=O) Ethylnylene or acetylenic with a S=O C=CS=O Vinyl or vinylene with a S=O

C=CC#N Olefinic cyano

C#Cc1ncccc1 Ortho-ethylnylene azaarene

C=[CH]c1ncccc1 Ortho-vinyl azaarene

C#Cc1ccncc1 Para-ethylnylene azaarene

C=[CH]C(=O)[OX1] Vinylene carboxylic acid

O=C1C=C[CH]=CC1=O Ortho-quinone

[CH2]=[CH][CH]=O Acrolein

subcategorization of michael acceptors by reactivity
Subcategorization of Michael Acceptors by Reactivity
  • Extremely fast: quinones, propiolates ,

1-alken-3-ones

  • Fast:acrylates, 2-alkenals,

3-alken-2-ones

  • Moderately Fast: alkyl 2-alkynoates
  • Slow: crotonates
  • Very Slow: methacryates, tiglates
  • Non-Reactive: non-,-unsaturated
summary
Summary
  • We have a 2D modeling strategy
  • Quantitative reactivity data is available for QSAR development
  • We have an application scheme