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