Two defined approaches are presented for predicting ocular toxicity of liquids. Defined Approach 1 combines physico-chemical properties with in vitro bottom-up testing, including the RhCE and BCOP tests. Defined Approach 2 combines the STE test with the BCOP test in a bottom-up approach. Examples are provided to demonstrate how each approach works in a step-wise manner to classify chemicals. The inclusion of physico-chemical properties improves the performance and predictive capacity of the bottom-up approaches.

1. We personally care
We personally care
Two Defined Approaches for Ocular Toxicity
Predictions Based on in vitro Bottom-Up Approach
Combined with Physico-Chemical Properties
Dr Bertrand DESPREZ
Cosmetics Europe Science
& Research Department

2. We personally care
What this presentation will let you know
Why?
→ why defined approaches are needed
What?
→ what defined approaches are
available
How?
→ how to build defined approaches with testing and
non-testing methods
→ how to select relevant physico-chemical properties
→ how to incorporate them in a bottom-up sequence
→ how to use 2 defined approaches
with 2 examples / defined approach

3. We personally care
Why Defined Approaches?
Category 1 Category 2 No Category
UN GHS Categories & OECD Test Guidelines

4. We personally care
Why Defined Approaches?

5. We personally care
What Defined Approaches do we have?
DA #1:
Combining Physico-Chemical
Properties to a Bottom-Up
Approach that includes RhC and
BCOP LLBO
→ For neat liquids
DA #2:
Combining STE with BCOP
LLBO in a Bottom-Up
Approach
→For liquids (neat & diluted)
2 Defined Approaches (DA) for liquids from Cosmetics Europe (CE):

6. We personally care
How: Build Defined Approaches Based On OECD IATA Elements
Method suitable for No Cat.
Identification e.g. OECD TG 492
(RhC)
Method suitable for Cat. 1
Identification e.g. OECD TG 437
(BCOP)
-
+
+
-
No Cat.
Cat. 1
Cat. 2
Not No Cat.
Not No Cat.
& Not Cat.1
Testing Part of a DA,
‘Bottom-Up’ for instance
In vitro test 1
In vitro test 2

7. We personally care
How: Build Defined Approaches Based On OECD IATA Elements
Non-Testing Part of a DA:
Physico-chemical properties (for instance)
In vivo literature data
In vitro data
Physico-chemical
properties
Data
collection
Principal
Component
Analysis
(PCA)Representation & Quantification
of physico-chemical properties
role for:
- Separating liquids & solids
- Relationships with UN GHS
categories
- Relationships with in vitro
test predictions

8. We personally care
Physico-Chemical Properties in DA #1
On 250 liquids & solids
Explain separation liquids vs. solids

9. We personally care
Physico-Chemical Properties in DA #1
Focus on Liquids, for RhC
predictions test, for
instance SkinEthic™HCE
(OECD TG 492):
• Water Solubility
• Surface Tension
• Partition Coefficient
• Vapor Pressure

10. We personally care
DA #1 = 3 steps, how it works
Step 1: “exclusion
rules” from physico-
chemical properties
Non-Testing Part
Physico-chemical
properties
Testing Part
(if needed)
A Bottom-Up
Sequence
Step 2: RhCE test
(OECD TG 492)
SkinEthic™
Or
EpiOcular©
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol

11. We personally care
DA #1: Example #1: 1,3-di-isopropyl benzene (CASRN 99-62-7)
Step 1: “exclusion
rules” from physico-
chemical properties
Step 2: RhCE test
(OECD TG 492)
SkinEthic™
Or
EpiOcular©
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol
WS < 0.02
verified
→ Predicted
No Cat.
UN GHS: No Cat.

12. We personally care
DA #1: Example #2: 2-Ethyl-1-Hexanol (CASRN 104-76-7)
Step 1: “exclusion
rules” from physico-
chemical properties
Step 2: RhCE test
(OECD TG 492)
SkinEthic™
Or
EpiOcular©
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol
Not verified
cannot conclude as
No Cat.
UN GHS: Cat. 2
Step 2:
TG 492 result +
→ Not No Cat.
Step 3:
BCOP LLBO result -
→ Not No Cat.
→ Not Cat. 1
→ → Cat. 2

13. We personally care
DA #2 = 3 steps, how it works
Steps 1 and 2: STE
(OECD TG 491)
modified protocol
for highly volatile
chemicalsTesting Part
A Bottom-Up
Sequence
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol

14. We personally care
DA #2: Example #1 = 2-Methyl-1-Pentanol (CASRN 105-30-6)
Steps 1 and 2: STE
(OECD TG 491)
modified protocol
for highly volatile
chemicals
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol
UN GHS: Cat. 2
Cell viability >70% both
at 5% and 0.05%?
NO → Not No Cat.
Cell viability ≤70% both
at 5% and 0.05%?
NO,
≤70% at 5% but >70% at
0.05%
BCOP LLBO opacity
>145? NO
→ Not No Cat.
→ Not Cat. 1
→ Cat. 2

15. We personally care
DA #2: Example #2 = 2-Hydroxy-Isobutyric acid ethyl ester (CASRN 105-30-6)
Steps 1 and 2: STE
(OECD TG 491)
modified protocol
for highly volatile
chemicals
Step 3: Optimized
prediction model on
BCOP (TG 437) for
LLBO protocol
UN GHS: Cat. 1
Cell viability >70% both
at 5% and 0.05%?
NO → Not No Cat.
Cell viability ≤70% both
at 5% and 0.05%?
NO,
≤70% at 5% but >70% at
0.05%
BCOP LLBO opacity
>145? YES
→ Not No Cat.
→ Cat. 1

16. We personally care
What’s next?
EXPERT
GROUP
REVIEW
ON GOING
PROCESS
OECD Test Guideline Project (on going)
Development
of potential
new DA on
solids
To be
continued… ☺

17. We personally care
DA Team & Acknowledgements
DA team:
Pauline McNamee; Els Adriaens; Nathalie Alépée;Takayuki Abo;
Dan Bagley; Jalila Hibatallah; Karsten Mewes; Uwe
Pfannenbecker, Àlvar Sala; An Van Rompay; Sandra
Verstraelen; Bertrand Desprez
Acknowledgements:
Lead country at OECD – France, Pascal Pandard & Anne Braun
OECD Secretariat: Anne Gourmelon

18. We personally care
If time allows ☺ DA#1 Performance
Probabilities with and without
phys-chem that a negative
prediction is an actual No Cat.
99.% & 99.7% respectively
Probabilities with and without
phys-chem that a positive
prediction is an actual Classified
(Cat.1 & 2):
BU only: 45.5%, the 55.5%
remaining are positive
predictions being actual NoCat
BU w/ phys-chem: 60.6%, the
39.4% remaining are positive
predictions being actual NoCat
Inclusion of physico-chemical
properties:
- Improve significantly the
performance of bottom-up
- Highly protective for human health

19. We personally care
If time allows ☺ DA#2 Performance
Probability that a negative
prediction is an actual No Cat.
96.7%
Probabilities that a positive
prediction is an actual Classified
(Cat.1 & 2):
68.6%, the 31.4% remaining are
positive predictions being actual
NoCat
DA #2 is also highly protective for
human health