Approaches to Skin

Sensitisation Prediction52nd ICGM

Senior Scientist

Rachael.Tennant@lhasalimited.org

Dr Rachael Tennant

Overview

• Skin sensitisation• Drive towards non-animal methods

• Adverse Outcome Pathway (AOP)

• OECD-validated assays

• Derek Nexus

• Lhasa’s defined approach• Results

• Conclusions

What is skin sensitisation?

• Common occupational disease

• Estimated to cost the EU €600 million and 3 million lost working days

• High concentrations and/or repeated exposure increases the likelihood of

becoming sensitised to a specific chemical

• Not life-threatening but is life-long

• In vivo assays no longer permitted for predicting skin sensitisation of

cosmetics in the EU

• In chemico / in vitro assays now used where possible

• Assays cannot be used alone - must be used in combination with other

information sources - known as a defined approach (DA)

Skin sensitisation AOP

Figure adapted from OECD 2012, The Adverse Outcome Pathway for Skin Sensitisation Initiated by

Covalent Binding to Proteins Part 1: Scientific Evidence, Series on Testing and Assessment, No. 168.

MIE = Molecular Initiating Event

KE = Key Event

DC = Dendritic Cells

AO = Adverse Outcome

Organism responseOrgan responseCellular responseMolecular Initiating

Event (MIE)

Haptenation T-cell activation

Activation of DC

Stress response

Skin sensitisation

Skin sensitisation Adverse Outcome Pathway (AOP)

KE3

KE2

AOMIE/KE1 KE4

Chemical structure &

properties

Metabolism & penetration

Electrophilic substance

a DA consists of a fixed data interpretation

procedure (e.g. decision tree) using a defined set

of information sources to derive a prediction (e.g.

in silico predictions, in chemico, in vitro data)

Skin sensitisation AOP

• In silico tools can be used as an information source

• Derek Nexus (Derek) covers multiple aspects of the AOP and would be a

valuable complement to a defined approach

• Lhasa’s defined approach combines in chemico/in vitro assay data with

Derek predictions to provide a predicted skin sensitisation outcome

Figure adapted from OECD 2012, The Adverse Outcome Pathway for Skin Sensitisation Initiated by

Covalent Binding to Proteins Part 1: Scientific Evidence, Series on Testing and Assessment, No. 168.

MIE = Molecular Initiating Event

KE = Key Event

DC = Dendritic Cells

AO = Adverse Outcome

Organism responseOrgan responseCellular responseMolecular Initiating

Event (MIE)

Haptenation T-cell activation

Activation of DC

Stress response

Skin sensitisation

Skin sensitisation Adverse Outcome Pathway (AOP)

KE3

KE2

AOMIE/KE1 KE4

Chemical structure &

properties

Metabolism & penetration

Electrophilic substance

Derek Nexus

• In silico toxicity prediction tool which covers multiple KEs in the skin sensitisation AOP

• Consists of a knowledge base (KB) containing structural alerts (patterns) written by

Lhasa expert scientists

• Provides hazard as well as potency predictions (EC3%)

• Fulfils the OECD (Q)SAR validation principles

• Consists of three main components:

• Alerts

• Potency predictions

• Negative predictions

OECD. 2007. Guidance Document on the Validation of (Quantitative)Structure-Activity Relationships [(Q)SAR] Models.

Limitations Not applicable for:

Alerts based mainly on public data (and some

proprietary data donated by Lhasa Limited members)

Substances of unknown or variable composition, complex

reaction products or biological materials (UVCBs)

Potency predictions not provided for all alerts

Derek Nexus: Typical alert

Derek Nexus: Potency predictions

• EC3 data from the local lymph node assay (LLNA) is used for model

• EC3 - effective concentration required to cause a 3-fold increase in T-cell

proliferation

• Measure of potency (lower EC3 = more potent sensitiser)

All EC3 data in model

Canipa et al., 2017. J. Appl. Toxicol. 37, 985–995.

Derek Nexus: Potency predictions

• Derek’s alerts are used to form a mechanistic domain for query chemicals

• Sensitisers undergoing the same mechanism expected to have similar potencies

• EC3 is predicted using the weighted mean of the nearest neighbours’ (NN)

potency values

Canipa et al., 2017. J. Appl. Toxicol. 37, 985–995.

EC3 data within same

alert/mechanistic domain

Derek Nexus: Negative predictions

Query fragment present in

Lhasa dataset?

Query fragment contained

in known false negatives?Outcome Example

Yes No

Non-sensitiser with

no misclassified or unclassified features

Yes Yes

Non-sensitiser with

misclassified features

No N/A

Non-sensitiser with

unclassified features

No alert

fired

Ionic form not present

in fragment libraryFragment similar to

known false negative

Fragment

library

Defined Approach: Hypothesis

• Use a Key Event (KE) approach

• Assays measuring the same KE will have similar limitations and applicability

domains

• Apply exclusion criteria to chemicals:

• Based on known assay limitations and confidence in Derek predictions

• Ensure the most relevant information source(s) are used for a given chemical

(class)

• By de-prioritising results from less applicable assays and/or predictions

• Use prioritised results until a concordant result is obtained

• 2 out of 3 majority call

Defined Approach: Exclusion criteriaExclusion criteria Derek MIE KE2 KE3 Comment

Metabolism Prohapten ✓ ✗ ✓ ✓Assays lacking metabolic competency are

deprioritised as they are less likely to predict prohaptens well

logP

> 3.5 ✓ ✓ ✓ ✗Cell-based assays are deprioritised for

chemicals with a logP > 3.5 (KE3) and logP > 5 (KE2) as more lipophilic chemicals may

lack high solubility in these cell-based

assays> 5 ✓ ✓ ✗ ✗

Lysine

reactiveExclusive ✓ ✓ ✗ ✓

The Nrf2-ARE pathway is associated with

cysteine binding - lysine-reactive chemicals may not be reliably predicted

Reasoning

levelEquivocal ✗ N/A

Alerts with a likelihood of equivocal have

less evidence of skin sensitisation potential than other likelihoods (e.g. certain) and are

thus deprioritised

Negative

prediction

Misclassified

features✗ N/A Negative predictions with ‘misclassified

features’ or ‘unclassified features’ are deprioritised as these are associated with

higher uncertainty.Unclassified

features✗ N/A

Defined Approach: Hazard prediction

Potency

prediction

model

1st assay

Equivocal

Non-sensitiser with

misclassified or

unclassified

features

2nd assay

3rd assay

2nd assay

Certain

Probable

Plausible

Non-sensitiser

Doubted

Improbable

Impossible

Derek

alert

outcome

Potency category 5/6

(GHS not classif ied)

QueryUse Derek outcome to

determine decision tree branch

Prioritise in chemico/in vitro

assays using exclusion criteria

Potency category 1 (GHS 1A)

Potency category 2 (GHS 1A)

Potency category 4 (GHS 1B)

Run in chemico/in vitro assays in order of AOP (MIE → KE2 → KE3) unless de-

prioritised by exclusion criteria

Potency prediction using k- nearest neighbours model

Hazard prediction using ‘2 out of 3’ approach

Exclusion

criteria

sensitiser

sensitiser

non-

sensitiser

non-

sensitiser

sensitiser

non-

sensitiser

Blue italics = Derek outcome

Red arrow = positive result

Green arrow = negative result

+

-

+

+

+

1st assay

+

+

+

+2nd assay

1st assay

2nd assay

-

-

-

-

-

-

-

+

Potency category 3 (GHS 1B)

Defined Approach: Potency prediction

Human data1-2Mouse data

(EC3)Combine

and curate

Combined

dataset

ℎ𝑢𝑚𝑎𝑛 > 𝑚𝑜𝑢𝑠𝑒

n = 199 n = 672

n = 762

1. Basketter et al., 2014. Dermatitis, 11-21.

2. Api et al., 2017. Dermatitis, 299-307.

3. Basketter, 2016. Altern. Lab. Anim., 431–436.

0 1.0

Non-sensitiser (6)

Pote

ncy C

ate

gory

Extreme (1)

Strong (2)

0.5

Query compound with predictedpotency category

Tanimoto Similarity

Top 10 Nearest Neighbours

Moderate (3)

Weak (4)

Very weak (5)

Compounds that fire alert

Human potency category

name

GHS

Classification

Human potency

category

Equivalent EC3

value (%)3

extreme 1A 1 < 0.2

strong 1A 2 0.2 – 2

moderate 1B 3 2 – 20

weak 1B 4 20 – 80

very weak/non-sensitiser Not classified 5 > 80

non-sensitiser Not classified 6 negative

Results - Hazard

BA = Balanced Accuracy

Se = SensitivitySp = SpecificityBased on a dataset of 210 chemicals from Urbisch et al., 2015, Regul. Toxicol. Pharmacol., 71, 337–351.

Full analysis published in Macmillan & Chilton, Reg. Toxicol. Pharmacol. 2019, 101, 35-47.

Results - Potency (GHS Classification)

Human

n = 79

Acc = 76%

Defined approach prediction vs in vivo outcome

LLNA

n = 174

Acc = 73%

no cat no cat

no

cat

no

cat

Under-prediction

Over-prediction

Under-prediction

Over-prediction

Based on a dataset of 210 chemicals from Urbisch et al., 2015, Regul. Toxicol. Pharmacol., 71, 337–351.

Full analysis published in Macmillan & Chilton, Reg. Toxicol. Pharmacol. 2019, 101, 35-47.

Defined Approach: Prototype

• Lhasa have developed a prototype application that can be accessed through the web• Data can be input manually or by uploading a Derek report

• Supply in chemico/in vitro data and the prediction is given

• Examples are available in the application with the Derek and in chemico/in vitro data already populated

• Web app available at: https://skinsensda.lhasacloud.org

Conclusions

• Lhasa Limited have developed a defined approach (DA) which incorporates in

silico predictions with results from OECD-validated in chemico / in vitro assays• Correctly predicts LLNA hazard for 82%, and LLNA GHS classification for 73% of

the dataset analysed

• Correctly predicts human hazard for 85%, and human GHS classification for 76%

of the dataset analysed

• The DA is currently being considered by the OECD for inclusion in the

upcoming guidance on DAs

• Future work will focus on:• Using any new applicability domain knowledge to amend the exclusion criteria

• Incorporating results from new OECD-validated assays

• Extending predictions from potency (GHS 1A/1B) to quantitative

predictions which may be more useful for risk assessment

Acknowledgements

• Donna Macmillan

• Martyn Chilton

• Sam Webb