IATA Case Study:

AOP-based Quantitative Risk Assessment (QRA) for Skin Sensitisation

Dr Gavin Maxwell, UK

12th Feb 2014, OECD Skin Sens. IATA WG

Acknowledgement: research funded by Unilever PLC and performed in collaboration with:

Define Human / HRIPT Threshold No Expected Skin Sensitisation

Induction Level (NESIL)

Apply Sensitisation Assessment Factors (SAFs) :

Inter-individual variability (x10) Vehicle/product matrix effects (x1 - x10)

Use considerations (x1 – x10)

Acceptable Exposure Level (AEL)

Compare AEL with Consumer Exposure Level (CEL)

Identify sensitisation potency LLNA (GPMT, Buehler)

Identify sensitisation potential QSAR / read-across

Other Clinical data

Benchmarking Consumer habits and

practices data

Decision on whether or not to market

Quantitative Risk Assessment (QRA) approach for Skin Sens.

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Adverse

Non-Adverse

allergic immune response

time

No

. CD

8+

T ce

lls

dose Y

dose X

1. Skin Penetration

3-4. Haptenation: covalent

modification of epidermal proteins

5-6. Activation of epidermal

keratinocytes & Dendritic cells

7. Presentation of haptenated protein by Dendritic cell resulting

in activation & proliferation of specific

T cells

8-11. Allergic Contact Dermatitis: Epidermal

inflammation following re-exposure to substance

due to T cell-mediated cell death

2.Electrophilic substance:

directly or via auto-oxidation or metabolism

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Case study: single 7.1cm2 exposure of forearm to varying doses of DNCB (Friedmann et al. 1983)

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Case study: single 7.1cm2 exposure of forearm to varying doses of DNCB (Friedmann et al. 1983)

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Adapted from : MacKay et al. 2013. ALTEX. 30. 473-486

Case study: single 7.1cm2 exposure of forearm to varying doses of DNCB (Friedmann et al. 1983)

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Case study: single 7.1cm2 exposure of forearm to varying doses of DNCB (Friedmann et al. 1983)

Adverse

Non-Adverse

allergic immune response

time

No

. CD

8+

T ce

lls

dose Y

dose X

(Fig. 2)

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Case study: 30 day simulation following 5 day antigen exposure in lymph node

Sheeja Krishnan, Carmen Molina-

Paris & Grant Lythe

Adverse

Non-Adverse

allergic immune response

time

No

. CD

8+

T ce

lls

dose Y

dose X

Adapted from: MacKay et al. 2013. ALTEX. 30. 473-486

In collaboration with:

1. Apply exposure, skin diffusion, protein reactivity & biological information as model inputs

2. Use linked mathematical models to predict human allergic immune response

3. Use model human immune response prediction to inform risk assessment decision

4. If necessary, verify model prediction using additional skin bioavailability or clinical data

Case study: T cell responses in PPD allergic (i.e. diagnostic patch test +ve) patients

Adverse

Non-Adverse

allergic immune response

time

No

. CD

8+

T ce

lls

dose Y

dose X

existing information – chemical exposure

weight of evidence approach

Concs in skin

Chemical conc. in

Skin hapten-pMHC per DC

T cell Activation

rate

Concs in skin

memory T cell pops

Concs in skin DC in

skin/LN

generation of new information required to take a final decision

(consumer safety risk assessment)

Skin pen data

Skin pen data

Blood T cell &/or Skin

patch test data

Skin Diffusion & Protein

Reaction info.

Exposure info. (Dose,

MW, Vol)

AOP-based QRA for Skin Sensitisation: fitted to IATA reporting framework

existing information – biological parameters

Antigen Processing & Presentation

Kinapse & synapse

T-cell dynamics

Skin Physiology