Application of AOPs to the development of Defined Approaches (DA) and Integrated Approaches to

Testing and Assessment (IATA)

Gavin Maxwell, Unilever, SEAC, Bedford, UK

+44-1234-264-888; gavin.maxwell@unilever.com; tt21c.org

• ACD – Allergic Contact Dermatitis

• ADME – Absorption Distribution Metabolism Excretion

• DA – Defined Approach

• DC – Dendritic Cell

• DNCB – 2,4-Dinitrochlorobenzene

• IATA – Integrated Approaches to Testing and Assessment

• DPRA – Direct Peptide Reactivity Assay

• GARD – Genomic Allergen Rapid Detection

• GPMT – Guinea Pig Maximization Test

• hCLAT – human Cell Line Activation Test

Abbreviations• HRIPT – human repeat insult patch test

• hTCPA – human T Cell Proliferation Assay

• LLNA – mouse Local Lymph Node Assay

• MHC – Major Histocompatibility Complex

• NAM – New Approach Methodologies

• Q(SAR) – Quantitative Structure Activity Relationship

• TIMES-SS – TImes MEtabolism Simulator platform for predicting Skin Sensitization

• TK – Toxicokinetic

• TD – Toxicodynamic

• TcR – T cell receptor

Outline

• Skin Sensitisation AOP, NAMs & DAs

• DA example : ‘qAOP’ TKTD model of skin sensitization

• DA example 2: Skin Allergy Risk Assessment (SARA) model

• IATA example: NexGen Skin Allergy Risk Assessment

• Learnings and insights

• Questions?

Why Skin Sensitisation?• first human health AOP to be defined (2012)

• useful case study for considering how OECD has developed and applied Integrated Approaches to Testing and Assessment (IATA) (2014-16) and Defined Approach (DA) (2017-present) concepts

• lots of published examples demonstrating the value of the AOP for driving the application of Novel Approach Methodologies (NAMs)

Covalent Protein Binding leading to Skin Sensitisation AOP: https://aopwiki.org/aops/40

Key Event 1 (KE1) KE2 KE3 KE4 Adverse Outcome (AO)

Predictive Chemistry

For example:• DEREK-NEXUS• OECD QSAR Toolbox• TIMES• ToxTree

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

Protein Reactivity*

OECD TG 442C

Includes:• ADRA• DPRA

* Other non-OECD test guideline methods also available

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

Keratinocyte Activation*

OECD TG 442D ARE Nrf2 luciferase test

Includes:• KeratinoSens™• LuSens

* Other non-OECD test guideline methods also available; e.g. SENS-IS®, SenCeeTox®

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

Dendritic Cell Activation*

OECD TG 442E

Includes:• h-CLAT• IL-8 Luc Assay• U-Sens™

* Other non-OECD test guideline methods also available; e.g. GARD™

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

T Cell Proliferation

For Example:• Artificial Lymph Node

Tissue model• Human T cell

proliferation assays (hTCPA)

Skin Sens NAMs (New Approach Methodologies)

in silico NAM

in chemico/vitro NAM

in vivo evidence

Skin Sensitisation

OECD TG 429: mouse local lymph node assay (LLNA)

& variants TG442A & 442B

OECD TG 406: Buehler & Guinea Pig Maximisation

Test (GPMT)

Human evidence e.g. Human Repeat Insult

Patch Test (HRIPT)

Integrated Approach to Testing and Assessment (IATA)• are pragmatic, science-based approaches for chemical hazard characterisation

that rely on an integrated analysis of existing information coupled with the generation of new information using testing strategies

• follow an iterative approach to answer a defined question in a specific regulatory context, taking into account the acceptable level of uncertainty associated with the decision context

• range from more flexible, non-formalised judgment based approaches (e.g. grouping and read-across) to more structured, prescriptive, rule based approaches [e.g. integrated testing strategy (ITS)]

• can include a combination of methods and can be informed by integrating results from one or many methodological approaches

www.oecd.org/chemicalsafety/risk-assessment/iata-integrated-approaches-to-testing-and-assessment.htm

IATA general workflow

Problem Formulation

Gather existing information

Weight of Evidence Assessment: adequate information for decision-making?

Generate additional information

DecisionAOP Yes

No Yes

OECD GD No. 260: Using AOPs to develop novel testing and assessment approaches

www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=ENV/JM/MONO(2016)67/&doclanguage=en

Problem Formulation

Gather existing information

Weight of Evidence Assessment: adequate information for decision-making?

Generate additional information

DecisionAOP Yes

No Yes

Organise and structure information using AOP as a frame

Use AOP to identify and/or develop targeted testing, testing strategy or assay development

Defined Approach (DA)• consists of a fixed data interpretation procedure (DIP) used to

interpret data generated with a defined set of information sources, that can either be used alone or together with other information sources, to satisfy a regulatory need

Relevant OECD guidance documents :

• No. 255: Reporting of Defined Approaches to be used within Integrated Approaches to Testing and Assessment: www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2016)28&doclanguage=en

• No. 256: Reporting of Defined Approaches and Individual Information Sources to be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitisation www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2016)29&doclanguage=en ;

Annex 1: www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2016)29/ann1&doclanguage=en ; Annex 2: www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2016)29/ann2&doclanguage=en

Case Study submitter

SkinBioavail.

Phys-ChemInfo.

(Q)SAR Protein Binding

Keratinocyte activation

DCactivation

T cell proliferation

AdverseEvent

Other

BASF DPRA - OECDTG 442C

KeratinoSens - OECD TG 442D, LuSens

h-CLAT, m-MUSST

Dupont Yes OECD TG 442C, Glutathione

depletion assay

OECD TG 442D h-CLAT, U937 LLNA - OECD TG429

GPMT - OECD TG 406

e.g. Skin irritation,

Ames

EC-JRC TIMES-SS, Dragon

Givaudan Yes TIMES-SS Cor1C420-assay OECD TG 442D

ICCVAM Yes OECD Toolbox h-CLAT

Kao Corp. OECD TG 442C h-CLAT

Kao Corp. DEREK Nexus OECD TG 442C h-CLAT

L’Oréal Yes Various OECD TG 442C OECD TG 442D, ARE-Nrf2 assay

U-Sens, PGE2 assay

P&G Kasting Skin PBPKmodel

Yes TIMES-SS OECD TG 442C OECD TG 442D h-CLAT, U937 LLNA - TG 429

RIVM Various OECD TG 442C OECD TG 442D, HaCaTgene signature

h-CLAT

Shiseido Yes SH test AREc32 assay h-CLAT

Unilever modified OECD TG 428

modified OECD TG 428

TG No. 256 DA case studies: sensitiser potency

1. Bauch C, Kolle SN, Ramirez T, Eltze T, Fabian E, Mehling A, Teubner W, van Ravenzwaay B, Landsiedel R. Putting the partstogether: combining in vitro methods to test for skin sensitizing potentials. Regul Toxicol Pharmacol. 2012. 63:489-504

2. Patlewicz G, Kuseva C, Kesova A, Popova I, Zhechev T, Pavlov T, Roberts DW, Mekenyan O. Towards AOP Application –implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitisation. RegulToxicol Pharmacol. 2014. 69:529-545

4. Natsch, A. Emter, R., Gfeller, H., Haupt, T. and G. Ellis. 2015. Predicting skin sensitizer potency based on in vitro data fromKeratinoSens and kinetic peptide binding: Global vs. domain-based assessment. Toxicological Science 143:319-332 .

6. & 7. Takanouchi et al. Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK basedon a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals, J Appl Toxicol. 2015. 35. 1318-32

8. Gomes, C., Noçairi, H., Thomas, M., Collin, J.F., Ibanez, F., Saporta, G., 2012. Stacking prediction for a binary outcome.COMPSTAT, 20th International Conference on Computational Statistics, Limassol, Cyprus. 2012: 271-282.

9. Jaworska. J, Dancik Y. Kern P. Gerberick F., Natsch A, A Bayesian integrated testing strategy to assess skin sensitizationpotency: from theory to practice, 2013 J. App. Toxicology 33. 1353–1364.

10. Van der Veen JW, Rorije E, Emter R, Natsch A, van Loveren H, Ezendam J. (2014) Evaluating the performance of integratedapproaches for hazard identification of skin sensitizing chemicals. Regul Toxicol Pharmacol. 69. 371-9.

11. Morihiko hirota et al., Evaluation of combinations of in vitro sensitization test descriptors for the artificial neural network-based risk assessment model of skin sensitization, J. Applied Toxicology. 2015. 35. 1333-47

12. MacKay, C., M. Davies, V. Summerfield and G. Maxwell. From Pathways to People: Applying the Adverse Outcome Pathway(AOP) for Skin Sensitization to Risk Assessment. ALTEX. 2013. 30. 473-486.

OECD TG No. 256 Annex A case study references Annex 1: www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=env/jm/mono(2016)29/ann1&doclanguage=en

Outline

• Skin Sensitisation AOP, NAMs & DAs

• DA example : ‘qAOP’ TKTD model of skin sensitization

• DA example 2: Skin Allergy Risk Assessment (SARA) model

• IATA example:NGRA Skin Allergy Risk Assessment

• Learnings and insights

• Questions?

Objective: model should be simplest representation of the AOP capable of reproducingthe induction of contact allergy to DNCB to enable prediction of a safe level of skinexposure (MacKay et al. 2013. http://altweb.jhsph.edu/altex/30_4/MacKay.pdf )

DA example : Quantitative AOP (qAOP)/TKTD model

DA example 1: Quantitative AOP (qAOP)/TKTD model

Objective: model should be simplest representation of the AOP capable of reproducingthe induction of contact allergy to DNCB to enable prediction of a safe level of skinexposure (MacKay et al. 2013. http://altweb.jhsph.edu/altex/30_4/MacKay.pdf )

Modelling covalent binding toskin proteins1. diffusion and partitioning

into the stratum corneum and skin

2. sensitiser clearance by dermal capillaries

3. covalent modification of protein nucleophiles by hapten.

DA example : Quantitative AOP (qAOP)/TKTD model

DA input: DNCB skin bioavailability data

- Ex vivo human skin penetration experiment – OECD TG 428 to include multiple time points and chemical free/bound measurements

- Pendlington et al. (2008). Cutan. Ocul. Toxicol. 27, 283-94.- Skin PK model: Davies et al. (2011). Toxicol. Sci. 119, 308–18.- Fitting to experimental data provides parameters for DNCB TK Model

Bespoke method:• Radiolabelled

DNCB in solution incubated with tape-stripped ex vivo human skin for up-to 24hrs

• Reaction diffusion system modelled and reaction rate determined by fitting to experimental data

DA input: DNCB protein binding data

Modelling T cell activation and proliferation4. proteasome processing of protein

nucleophiles to form small peptides and transport to the endoplasmic reticulum (ER)

5. binding of peptides and hapten-peptide complexes to Class I MHC and transport to plasma membrane

6. binding of pMHC and hapten-pMHC to CD8+ T cell receptors

7. activation of naïve specific CD8+ T cells

DA example : Quantitative AOP (qAOP)/TKTD model

proteasome

damaged proteins

peptides

DA input: proteasome processing rate parameter

Clin. Exp. Immunol. (1983) 53, 709-715.

Quantitative relationships between sensitizing dose of DNCB and reactivity in normal subjectsP. S. Friedmann, C. Moss, S. Schuster & J. M. Simpson

- 165 healthy human volunteers divided into five dose groups

- Single exposure to between 62.5 - 1000 µg DNCB applied to 7.1cm2

volar forearm in 100μL acetone vehicle

- Sensitisation assessed four weeks later by DNCB challenge

- Sensitisation varied between 8 - 100% across dose groups

Benchmarking DA output: historical human data

DA simulation of single 16.5µgcm-2 DNCB exposure

Thresholding DA output to predict adverse response

• Threshold defined (using literature data) that predicts the rate of human naïve CD8+ TcR signaling that would be sufficient to confer allergic status

• Threshold defined (using literature data) that predicts the rate of human naïve CD8+ TcR signaling that would be sufficient to confer allergic status

• Reverse Dosimetry used to back-calculate the doses of DNCB that would cross the TcR signal rate threshold/cause contact allergy

Thresholding DA output to predict adverse response

Outline

• Skin Sensitisation AOP, NAMs & DAs

• DA example : ‘qAOP’ TKTD model of skin sensitization

• DA example 2: Skin Allergy Risk Assessment (SARA) model

• IATA example: NGRA Skin Allergy Risk Assessment

• Learnings and insights

• Questions?

DA 2: Skin Allergy Risk Assessment (SARA) model

Protein Reactivity

OECD TG 442C: DPRA

ARE Nrf2 Activation

OECD TG 442D: KeratinoSens™

DC Activation

OECD TG 442E:

• hCLAT• U-Sens™

Historical AO data

• TG 429: LLNA• HRIPT

Objective: model should allow a weight-of-evidence prediction to be made of thehuman skin sensitisation potency using available in vitro and historical in vivo data(Reynolds et al. 2019 https://doi.org/10.1016/j.comtox.2018.10.004)

Model inputs:

DA 2: Skin Allergy Risk Assessment (SARA) model

Protein Reactivity

OECD TG 442C: DPRA Cys/Lys

depletion

ARE Nrf2 Activation

OECD TG 442D: KeratinoSens™

EC1.5

DC Activation

OECD TG 442E: • hCLAT CD54

EC200; CD86 EC150

• U-Sens™ EC150

Historical AO data

• TG 429: LLNA EC3• HRIPT dose, cohort

size, number sensitised

Approach:• ‘SARA’ Bayesian multi-level, hierachical regression model was developed to

estimate the human population skin sensitisation induction threshold, under theconditions of an HRIPT, for 30 chemicals using publicly available data.

• SARA model is probabilistic which allows the uncertainty in the populationthreshold prediction to be explicitly quantified

MIE/KE1 KE2 KE3 AO

DA 2: Skin Allergy Risk Assessment (SARA) model

Estimated human population threshold distributions using: all data (purple); HRIPTonly (green); LLNA only (blue); in vitro data only (red); no chemical specific data (grey)

Estimated human population threshold distributions using: all data (purple); HRIPTonly (green); LLNA only (blue); in vitro data only (red); no chemical specific data (grey)

Estimated human population threshold distributions using: all data (purple); HRIPTonly (green); LLNA only (blue); in vitro data only (red); no chemical specific data (grey)

Estimated human population threshold distributions using: all data (purple); HRIPTonly (green); LLNA only (blue); in vitro data only (red); no chemical specific data (grey)

DA 2: Skin Allergy Risk Assessment (SARA) model

Estimated human population threshold distributions using: all data (purple); HRIPTonly (green); LLNA only (blue); in vitro data only (red); no chemical specific data (grey)

Outline

• Skin Sensitisation AOP, NAMs & DAs

• DA example : ‘qAOP’ TKTD model of skin sensitization

• DA example 2: Skin Allergy Risk Assessment (SARA) model

• IATA example : NGRA Skin Allergy Risk Assessment

• Learnings and insights

• Questions?

IATA general workflow

Problem Formulation

Gather existing information

Weight of Evidence Assessment: adequate information for decision-making?

Generate additional information

DecisionAOP Yes

No Yes

IATA general workflow: applied to NexGen Skin Allergy Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

[Note: assume MDBGN is a new ingredient and no in vivo data or read-across candidates exist]

IATA general workflow: applied to Skin Sensitisation Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

Parameter Face cream

Amount of product used per day (g/day) 1.54

Retention factor 1

Skin surface area (cm2) 565

Amount of product in contact with skin (mg) 1536

Percentage ingredient in product (%) 0.2

Amount of ingredient in contact with skin (µg) 3072

Local dermal exposure (µg/cm2) 5

* Deterministic worse case (90th percentile) for Europe based upon SCCS notes of guidance (SCCS/1602/18)

IATA general workflow: applied to Skin Sensitisation Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

• Exposure predicted: 5µg/cm2 skin

KE1/MIE evidence

• TIMES: pre/pro-SN2

• ToxTree: MA/SN2

• OECD QSAR toolbox: SN2

• Expert view: Pro-MA

• DPRA: 100% depletion*

KE2/KE3 evidence

• KeratinoSens™

– EC1.5 = 7.8*

• hCLAT

– CD54 EC200 = 9.42*

– CD86 EC150 = 10.8*

• USens™

– CD86 EC150 = 3*

*Hoffmann et al. 2018: https://doi.org/10.1080/10408444.2018.1429385

IATA general workflow: applied to Skin Sensitisation Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

• Exposure predicted: 5µg/cm2 skin

• Evidence of sensitiser potential, apply SARA model (Reynolds et al. 2019)

Due to differences between the HRIPT

exposure and consumer use of a

face cream the dose response was adjusted by a factor of 10 fold

Vehicle Frequency / duration

Skin site condition

Dotted line represents product exposure (0.2% in a face cream)

IATA general workflow: applied to Skin Sensitisation Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

• Exposure predicted: 5µg/cm2 skin

• Evidence of sensitiser potential, apply SARA model (Reynolds et al. 2019) to predict human population threshold

Dotted line represents product exposure (0.2% in a face cream)

• If we use all in vitro data SARA prediction, the most likely rate of sensitization for use of a face cream containing 0.2% MDBGN is 1 in 200

• We conclude this exposure is high risk

IATA general workflow: applied to Skin Sensitisation Risk Assessment

• Is 0.2% Methyldibromoglutanitrile (MDBGN) safe for use in a face cream?

• Exposure predicted: 5µg/cm2 skin

• Evidence of sensitiser potential, apply SARA model (Reynolds et al. 2019)

Dotted line represents product exposure (0.2% in a face cream)

• If we use all in vitro data SARA prediction, the most likely rate of sensitization for use of a face cream containing 0.2% MDBGN is 1 in 200

• We conclude this exposure is high risk

– Note: inclusion of historical LLNA data would not alter the outcome

Learnings and insights

• AOP provides a framework for data weighting and integration

• Quantitative modelling can help refine mechanistic understanding by evaluating the value of each piece of information for decision-making

• Bayesian statistical approaches enable a weight-of-evidence prediction to be made even when input data are incomplete or conflicting

• DAs can enhance our ability to make weight-of-evidence risk assessment decisions but do not alter our need for good quality input data and upfront hypothesis generation