an overview of recent skin sensitisation work at lhasa overview of rec… · freund’s complete...
Post on 06-Nov-2020
3 Views
Preview:
TRANSCRIPT
An Overview of Recent Skin Sensitisation Work at Lhasa
Senior Scientist
donna.macmillan@lhasalimited.org
Donna Macmillan
Agenda
• Skin sensitisation and legislation
• Negative predictions for skin sensitisation
• Lhasa’s defined approach using in silico / in chemico / in
vitro data
• Conclusions
Skin sensitisation and legislation
What is skin sensitisation?
• Common occupational disease
• Estimated to cost the EU €600 million and 3 million lostworking days
• High concentrations and/or repeated exposure increasesthe likelihood of becoming sensitised to a specificchemical
• Not life-threatening but is life-long
What is skin sensitisation?
(2) Elicitation:subsequent contact with the samehapten leads to the hapten-proteincomplex triggering the allergen specificT-cells which induces inflammatorycytokines and skin sensitisation
(1) Induction:allergen (hapten) forms a stableconjugate (hapten-protein complex)with carrier proteins within the skininitiating a cascade which ends withproliferation of allergen specific T-cells
Figures taken 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.
• Develops in two stages:
Legislation
Korea REACH
China REACHTurkey KKDIK
Brazil cosmetics ban USA – Amended TSCA
EU REACHCosmetics Regulation
Negative predictions for skin sensitisation
Negative predictions – Methodology
• Trustworthy negative predictions are important as theconsequence of such a prediction being incorrect issevere• False negatives (sensitisers predicted as non-sensitisers)
can have a detrimental impact on human health
• An existing methodology demonstrably works well formutagenicity in Derek, requiring:• A well-developed endpoint
• A reactivity-driven mechanism
• A sufficiently large dataset
R. Williams et al., Regul. Tox. Pharmacol., 2016, 76, 79-86
• A similar methodology for skin sensitisationwas implemented
• An internal skin sensitisation dataset of > 2500chemicals was used
• Dataset consists of human, mouse and guinea pig data• Overall experimental call is derived using a hierarchical
approach
Human Standard animal Non-standard animal Other animal
Non-standard LLNANon-radioactive LLNABuehler (closed patch) testFreund’s complete adjuvant testFreund’s complete adjuvant test (modified)Split adjuvant testSingle injection adjuvant testSingle injection adjuvant test (modified)Maurer optimisation testOptimisation testOpen epicutaneous testClosed epicutaneous test
LLNA GPMT
BgVVBasketter
Draize testDraize test (altered)Mouse ear swelling testpositive data only
Negative predictions – Dataset
Lhasa skin sensdataset
(n > 2500)
Fragment structures
Fragment library(n > 6500)
Classify fragments
Negative predictions – Fragment library
Negative predictions – Outcomes
Query fragment present in Lhasa dataset?
Query fragment contained in known false negatives? Outcome Example
Yes No No misclassified or unclassified features
Yes Yes Misclassified feature
No n/a Unclassified feature
No alertfired
Query chemical
Negative prediction
dataset
• Misclassified and unclassified features occur infrequently and represent
areas of increased uncertainty, which may require further scrutiny
Derek ‘no alert’ Derek with skin negative predictions
How often is each type of prediction correct? How often does eachtype of prediction occur?
Negative predictions – 5-fold CV
• Misclassified and unclassified features occur infrequently and represent
areas of increased uncertainty, which may require further scrutiny
How often is each type of prediction correct?
Derek ‘no alert’ Derek with skin negative predictions
How often does eachtype of prediction occur?
Negative predictions – Member data
Defined approach for skin sensitisation
• 5 in chemico / in vitro assays have OECD guidelines• DPRA, KeratinoSens™, LuSens, h-CLAT, U-SENS™
• Generally agreed that more than one non-animal assay will be required to replace the LLNA or GPMT
Defined approach
Skin sensitisation assay(s)
DPRA
LLNA
h-CLAT / U-SENS™
KeratinoSens™ / LuSens
GPMT / humanOrganism response
Organ response
Cellular response
Molecular Initiating Event Haptenation
T-cell activation
Activation of DC
Stress response
Skin sensitisation
Adverse Outcome Pathway
KE3
KE2
AO
MIE
KE4
Defined approach
Lhasa’s defined approach
• Our hypothesis:
• Use Derek information alongside assay data (grouped into key events in the AOP)
• Apply exclusion criteria to take into account assay limitations and confidence in the Derek prediction
• This ensures the most relevant information source(s) are used for specific chemicals
DPRAh-CLATKeratinoSens™
U-SENS™LuSens
AO MIE KE2 KE3
Summary of 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 reactive Exclusive ✓ ✓ ✗ ✓
The Nrf2-ARE pathway is associated with cysteine binding - lysine-reactive chemicals
may not be reliably predicted
Likelihood Equivocal ✗ N/AAlerts 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
Making use of Derek
• Metabolism
• Lipophilicity
• Lysine reactivity
• Likelihood level
• Negative predictions
Hazard prediction
NH2
NH2
1st assay
1st assay
1st assay
EquivocalNon-sensitiser with misclassified or unclassified features
2nd assay
3rd assay
2nd assay
2nd assay
2nd assay
CertainProbablePlausible
Non-sensitiserDoubted
Improbable
Derek alert
outcome
Chemical of interest
Use Derek outcome to determine decision tree
branch
Prioritise in chemico/in vitro assays using exclusion criteria
Run in chemico/in vitro assays in order of AOP (MIE → KE2 → KE3) unless de-
prioritised by exclusion criteria
Hazard prediction using ‘2 out of 3’
approach
Exclusion criteria
sensitiser
sensitiser
non-sensitiser
non-sensitiser
sensitiser
non-sensitiser
Blue italics = Derek outcomeRed arrow = positive resultGreen arrow = negative result
Potency prediction
Human data1-2
Mouse data(EC3)
Combine and curate
Combined dataset
ℎ𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 > 𝑢𝑢𝑚𝑚𝑢𝑢𝑚𝑚𝑚𝑚
n = 199
n = 672
n = 762
0 1.0
Non-sensitiser
Pote
ncy
Cat
egor
y
Extreme
Strong
0.5
Query compound with predicted
potency category
Tanimoto Similarity
Top 10 Nearest Neighbours
Moderate
Weak
Very weak
Compounds that fire alert
Basketter human potency category
Basketter human potency category name
GHS category
Equivalent EC3 value (%)3
1 extreme 1A < 0.2
2 strong 1A 0.2 – 2
3 moderate 1B 2 – 20
4 weak 1B 20 – 80
5 very weak/non-sensitiser 2 > 80
6 non-sensitiser 2 negative
1. Basketter et al., Dermatitis, 2014, 11-212. Api et al, Dermatitis, 2017, 299-3073. Basketter, 2016,.Altern. Lab. Anim., 431–436
Potency prediction
Potency prediction
modelNH2
NH2
1st assay
1st assay
1st assay
EquivocalNon-sensitiser with misclassified or unclassified features
2nd assay
3rd assay
2nd assay
2nd assay
2nd assay
CertainProbablePlausible
Non-sensitiserDoubted
Improbable
Derek alert
outcome
Basketter 5/6 (GHS 2)
Chemical of interest
Use Derek outcome to determine decision tree
branch
Prioritise in chemico/in vitro assays using exclusion criteria
Basketter 1 (GHS 1A)
Basketter 2 (GHS 1A)Basketter 3 (GHS 1B)Basketter 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 outcomeRed arrow = positive resultGreen arrow = negative result
Dataset compilation
22
Key Event Assay + - Total Discordant data Final total
MIE DPRA 120 72 192 n/a 192
2KeratinoSens™ 122 62 184
5 195LuSens 45 32 77
3h-CLAT 113 50 163
25 186U-SENS™ 130 39 169
4 LLNA 173 60 233 human call taken preferentially 240
AO Human 83 46 129
Exclusion criteria Chemical property Assay(s) excluded
Metabolism Hapten none
Lipophilicity 4.07 KE3
Lysine reactivity Yes KE2
Derek likelihood Plausible none
Derek negative prediction n/a -
Example 1in vivo
SensitiserBasketter category 1
GHS 1A
CN
OHHO
O
O
O
S
Example 1
Potency prediction
model
1st assay
1st assay
1st assay
EquivocalNon-sensitiser with misclassified or unclassified features
2nd assay
3rd assay
2nd assay
2nd assay
2nd assay
CertainProbablePlausible
Non-sensitiserDoubted
Improbable
Derek alert
outcome
Basketter 5/6 (GHS 2)
Chemical of interest
Use Derek outcome to determine decision tree
branch
Prioritise in chemico/in vitro assays using exclusion criteria
Basketter 1 (GHS 1A)
Basketter 2 (GHS 1A)Basketter 3 (GHS 1B)Basketter 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 outcomeRed arrow = positive resultGreen arrow = negative result
in vivoSensitiser
Basketter category 1GHS 1A
Defined approachSensitiser
Basketter category 2GHS 1A
CN
OHHO
O
O
O
S
AOP event Assay / model Outcome
AO DX plausible
KE1 DPRA positive
KE2 deprioritised
KE3 deprioritised
Exclusion criteria Chemical property Assay(s) excluded
Metabolism prehapten none
Lipophilicity 3.25 none
Lysine reactivity no none
Derek likelihood equivocal none
Derek negative prediction n/a -
CH3
CH3
CH3
HO
Example 2in vivo
Non-sensitiserBasketter category 5/6
GHS 2
Example 2
in vivoNon-sensitiser
Basketter category 5/6GHS 2
Defined approachSensitiser
Basketter category 4GHS 1B
Potency prediction
model
1st assay
1st assay
1st assay
EquivocalNon-sensitiser with misclassified or unclassified features
2nd assay
3rd assay
2nd assay
2nd assay
2nd assay
CertainProbablePlausible
Non-sensitiserDoubted
Improbable
Derek alert
outcome
Basketter 5/6 (GHS 2)
Chemical of interest
Use Derek outcome to determine decision tree
branch
Prioritise in chemico/in vitro assays using exclusion criteria
Basketter 1 (GHS 1A)
Basketter 2 (GHS 1A)Basketter 3 (GHS 1B)Basketter 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 outcomeRed arrow = positive resultGreen arrow = negative result
CH3
CH3
CH3
HO
AOP event Assay / model Outcome
AO deprioritised
KE1 DPRA positive
KE2 KeratinoSens™ / LuSens negative
KE3 h-CLAT / U-SENS™ positive
H3C
OHO
OO
S
Exclusion criteria Chemical property Assay(s) excluded
Metabolism n/a -
Lipophilicity 4.99 KE3
Lysine reactivity n/a -
Derek likelihood n/a -
Derek negative prediction No misclassified or unclassified features -
Example 3in vivo
Non-sensitiserBasketter category 5/6
GHS 2
Example 3
in vivoNon-sensitiser
Basketter category 5/6GHS 2
Defined approachNon-sensitiser
Basketter category 5/6GHS 2
Potency prediction
model
1st assay
1st assay
1st assay
EquivocalNon-sensitiser with misclassified or unclassified features
2nd assay
3rd assay
2nd assay
2nd assay
2nd assay
CertainProbablePlausible
Non-sensitiserDoubted
Improbable
Derek alert
outcome
Basketter 5/6 (GHS 2)
Chemical of interest
Use Derek outcome to determine decision tree
branch
Prioritise in chemico/in vitro assays using exclusion criteria
Basketter 1 (GHS 1A)
Basketter 2 (GHS 1A)Basketter 3 (GHS 1B)Basketter 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 outcomeRed arrow = positive resultGreen arrow = negative result
H3C
OHO
OO
S
AOP event Assay / model Outcome
AO DX Non-sensitiser
KE1 DPRA positive
KE2 KeratinoSens™ / LuSens negative
KE3 deprioritised
Performance
Results
30
Conclusions
• A decision tree defined approach has been designedusing exclusion criteria based on known limitations of inchemico/in vitro assays and Derek Nexus
• The defined approach correctly predicts:
• The Basketter potency category for 64%
• The GHS classification for 77% of chemicals in theevaluation dataset
• Martyn Chilton
• Everyone at Lhasa Limited
Acknowledgements
Lhasa Limited
Granary Wharf House, 2 Canal Wharf
Leeds, LS11 5PS
Registered Charity (290866)
Company Registration Number 01765239
+44(0)113 394 6020
info@lhasalimited.org
www.lhasalimited.org
Thank you very much for your attention
Are there any questions?
top related