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Automated Read-Across for REACH
Dr. Anne BonhoffApril 25, 2017
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UL Collaboration to Develop Predictive Toxicity
Tool for Read-across
UL Collaborates with Prof. Dr. Thomas Hartung,
Chair for Evidence-based Toxicology at Johns
Hopkins Bloomberg School of Public Health,
Baltimore, and University of Konstanz, Germany;
also Director of their Centers for Alternatives
to Animal Testing (CAAT)
and Thomas Luechtefeld,
a PhD student,
and UL’s team of scientists and toxicologists.
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Read-across
Data gap filling
concluding from
(structurally) similar
chemicals
Category approach
Test only
representatives of a
group of similar
chemicals or
complex mixtures
REACH Data-rich
substances registered
2010 and 2013:
75% of dossiers use read-
across
Other alternatives hardly
used
Expertise in industry low
Low acceptance by EChA
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The opportunity of Big Data & Bioinformatics
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Deep Learning
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Big biological data in toxicology
Unified and curated data-base
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7
ALTEX
April
2016
issue
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A marriage of technologiesRead-across• Support weight of
evidence• Circumstantial• Manual• Unclear acceptability
(Q)SAR• Data-mining by
computer• Broader applicability• Can be validated with
enormous consequences for acceptability
Automated Read-Across = “REACH-across”• Mines local “similarity space”• Comprehensive use of available data• Expresses certainty• Validation on the way
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REACHACROSS™
UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
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REACHACROSS™ SOFTWARE PIPELINE
UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
Data Source
+
Similarity
=
Graph Algorithms
0 1 0 0 1 1 0 𝑀(0 1 0 0 . . . , 0 0 1 0 . . .) = −∞ ≤ 𝑥 ≤ ∞
MetricFingerprinter
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SIMPLE MODELS
11UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
A B90%
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NETWORK EFFECT
12UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
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INCREASING RELEVANCE
13UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
Unlabeled EINECS ----- Labeled ANNEX• 1387 ANNEX SMILES• 33383 EINECS SMILES
300 ANNEX Chemicals600 ANNEX Chemicals1387 ANNEX Chemicals
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NETWORK FEATURES
14UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
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?+
+
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++
+
-
-
-
---
-
-
-
-
-
-
-
--
-
-
-
-
--
-
-
--
-
-
-
-
-
- --
++
+
+
+
+
+
+
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+
Local similarity
= prediction
Regional similarity
= uncertainty
-
+
+
+
+
-
-
--
Positive =proximity to positives
Negative =proximity to negatives
Confidence =the more data the closer
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CHEMICAL PLANET
16UL and the UL logo are trademarks of UL LLC © 2017. Proprietary & Confidential.
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Toxic
Non-toxic
# of
substances
By choosing the cut-off, the balance
of sensitivity and specificity is
changed
Test performance characteristics are a choice:
Test Sensitivity vs. Specificity
For REACH-across this is even more complex:Two thresholds (proximity to positives and to negatives)
These choices influence coverage, i.e. for how many
unknown chemicals a prediction can be made
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Skin sensitization (Luechtefeld et al., 2016):
Simple classification by nearest neighbor
Accuracies better than different animal TG
against each other, but coverage drops
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Status of the tool March 2017
All endpoints for REACH
2018 <10 tons/a
Coming soon:
acute fish toxicity Internal validation
for unprecedented
number of
chemicals
Accuracy similar
to animal test
Works for most
chemicals, to
increase with more
databases
incorporated
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ULREACHACROSS.COM
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ULREACHACROSS.COM
Smiles code for Methanol
Select Endpoint
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ULREACHACROSS.COM
Request Name
Smiles code for Methanol
Select Endpoint
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ULREACHACROSS.COM
Select EndpointReport Ready
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REACHACROSS™ REPORT
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QUESTIONS?
ULREACHACROSS.COM
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THANK YOU