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Incorporating new technologies and high-
throughput approaches in the design and
selection of chemical alternatives
Antony Williams
U.S. Environmental Protection Agency, RTP, NC
June 15th, 2017
21st Annual Green Chemistry & Engineering Conference, Reston, VA
This work was reviewed by the U.S. EPA and approved for presentation but does not necessarily reflect official Agency policy.
http://www.orcid.org/0000-0002-2668-4821
Paradigm Shift in Chemical Design
(especially in regards to toxicology)
• Chemical Design in the future
– High-throughput modeling
– SAR/QSAR/QSTR models
– Retrosynthetic analysis algorithms for synthesis
• Toxicology
– FEWER animal studies. Maybe NONE??
– Openness in data sharing
– More confidence in computational predictive
models that link chemical structures to adverse
outcomes
1
It will depend on lots of DATA
2
What we need to progress
• Adequate information on potential bioactivity for as
many chemicals as possible
• Read-across, QSAR and QSTR models from large
high-quality curated datasets
3
• National Center for Computational Toxicology
established in 2005 to integrate:
– High-throughput and high-content technologies
– Modern molecular biology
– Data mining and statistical modeling
– Computational biology and chemistry
• Currently staffed by ~60 employees as part of
EPA’s Office of Research and Development
• Home of ToxCast & ExpoCast research efforts
• Key partner in U.S. Tox21 federal consortium
• Multiple cross-division collaborations
National Center for
Computational Toxicology
What we need to progress
• Adequate information on potential bioactivity for as
many chemicals as possible
• High Throughput Screening ToxCast/Tox21
generating and making available bioactivity data
• GREAT progress to date – more to come!
• Read-across, QSAR and QSTR models from large
high-quality curated datasets
• Chemistry data gathering and high-throughput
bioassays are providing the necessary data to
generate these models
5
Data and Modeling
Efforts to Date
ToxCast
ExpoCast
Predictive
Models
Comp
Chemistry
HTTK
• High-throughput in vitro screening of ~4,000 chemicals
across ~1200 assay endpoints (ToxCast) and ~8,000
chemicals in ~60 assay endpoints (Tox21)
• High quality, curated chemical structure and physical
chemical properties database of ~750,000 chemicals
• High-throughput exposure estimates with uncertainty for
~ 7,000 chemicals based on production volume and
chemical use
Data and Modeling
Efforts to Date
• High-throughput in vitro screening of ~4,000 chemicals
across ~1200 assay endpoints (ToxCast) and ~8,000
chemicals in ~60 assay endpoints (Tox21)
• High quality, curated chemical structure and physical
chemical properties database of ~750,000 chemicals
• High-throughput exposure estimates with uncertainty for
~ 7,000 chemicals based on production volume and
chemical use
• Legacy in vivo data from ~6,000 animal toxicology
studies on ~1,110 unique chemicals
• High-throughput toxicokinetic (HTTK) models for ~700
chemicals based on in vitro measurements
• AOPs and virtual tissue models for broad range of
developmental toxicities
ToxCast
ExpoCast
Comp
Chemistry
HTTK
ToxRef
Predictive
Models
Underpinning with chemicals
DSSTox
Measured
Properties
Chemical
KB
Approximately 15 Years of Data…
Growing with daily curation
DSSTox_v2
PublicCurated
5. Low
2. Low
6. Untrusted
1. High
3. High
4. Med
7. Incomplete
Public_Untrusted
Public_Low
Public_Medium
Public_High
DSSTox_Low
DSSTox_High 4535
16K
33K
101K
584K
~ 310K pending
~ 150K pending
Features
Properties
Chemotypes, fingerprints,
physchem properties, ...
Chemical representation levels
supporting data integration
10
Structure
Generic
Substance
Test
Sample
Chemical Name
CASRN
Supplier, Lot/Batch,
physical description
SMILES
InChI
Features
Properties
Chemotypes, fingerprints,
physchem properties, ...
Chemical analogs,
Read-across,
SAR modeling
Chemical representation levels
supporting data integration
11
Structure
Generic
Substance
Test
Sample
Chemical Name
CASRN
Supplier, Lot/Batch,
physical description
SMILES
InChI
Experimental
Endpoint Data
Public toxicity
datasets
Structure searching
& modeling
Integrating in vitro and in vivo data
HTS-In vitro
ToxRef
COSMOS
ToxCast
Tox21
Chemicals
DSSTox
Measured
Properties
Chemical
KB
Biological
Data
Biological
activities
Adding Product Use and Exposure
HTS-In vitro
ToxRef
COSMOS
ToxCast
Tox21
Chemicals
DSSTox
Measured
Properties
Product Use
& Exposure
Chemical
KB
Biological
Data
Biological
activities
High Throughput Measurement
to Characterize Exposure
14
Building Models from the data
HTS-In vitro
ToxRef
COSMOS
ToxCast
Tox21
Chemicals
DSSTox
Biological
Data
Fate & Transport
ADME
Reactivity
Biotransformation
Physchem
properties
Biological
activities
Measured
Properties
ModelsExposure
Data
Toxicity Predictions
Structure-alerts
Chemical
KB
Product Use
& Exposure
Access to Our Data and Models
16
What we have learned…
• Data curation, standardization and
versioning is essential
• Prototype application development suffices
for research projects but production
development requires managed processes
• ODOSOS (Open Data, Open Source and
Open Standards) endows many benefits
• With this in mind…
17
Our INTEGRATION Dashboard:
https://comptox.epa.gov
18
Bisphenol A
19
Chemical Properties
20
Chemical Properties
21
• Consuming and producing open data
Data Distribution
22
• Consuming and producing open data
Data Distribution
23
Modeling Details
24
Calculation Result
for a chemical
Model Performance
with full QMRF
Nearest Neighbors
from Training Set
24
Prediction Details and
QMRF Report
25
Developing “NCCT Models”
• Our approach to modeling:
– Obtain high quality training sets
– Apply appropriate modeling approaches
– Validate performance of models
– Define the applicability domain and model limitations
– Use models to predict properties across our full datasets
• Release as Open Data and Open Models
Workflow Details and Data
27
OPERA Models: https://github.com/kmansouri/OPERA
Making Toxicity Value Data
available
28
Exposure
29
Consumer Product data
30
• Data gathered from multiple online data
sources – text mining and data downloads
• CASRN and name mappings to produce
curated structure set with functional uses
• Produce Functional Use (FuseDB) and
build predicted functional use models
NHANES Exposure data
31
Environ. Sci. Technol., 2013, 47 (15), pp 8479–8488
ToxCast and Tox21 Bioassays
32
ToxCast and Tox21 Bioassays
33
PubChem Bioassay Data Integration
34
PubMed: BIG DATA Literature
Search Integration
35
Linked Directly to PubMed
36
Literature Search Integration
Google Scholar
37
Batch Data Searching
• I have a 5000 CAS Numbers (or Names) –
is there data available?
– Has any Toxcast data been run?
– Are there Toxicity Data values available?
– Are there predicted exposure data?
– Can I get predicted physchem data for my model?
38
We get lots of user feedback..
• Online since April 2016
• Feedback welcomed!
39
“RapidTox” in Development
• Semi-automated decision support tool for
high-throughput risk assessments
• Use Dashboard “architecture”, existing data
streams and add new data – e.g. Global
Hazard Summary and ECHA data
• Combine data streams into quantitative
toxicity values with uncertainty estimates
Risk Assessments Generally Contain a
Standard Set of Components
We are assembling these components to
deliver RapidTox
Phys Chem
Exposure
Hazard
Dose Response, PK, and
PODs
Risk Summary
Uncertainty
Variability
Quantifying Uncertainty & Assessing
Performance of Read-Across - GenRA
• “Chemical-Biological Read-Across” - predict toxicity as a
similarity-weighted activity of nearest neighbors
• Evaluates read-across performance and uncertainty using
available data
Work-in-progress
43
We’re not done yet…
HTS-In vitro
ToxRef
COSMOS
ToxCast
Tox21
Chemicals
DSSTox
Biological
Data
Fate & Transport
ADME
Reactivity
Biotransformation
Phys-chem
properties
Biological
activities
Measured
Properties
ModelsExposure
Data
Toxicity Predictions
Structure-alerts
Chemical
KB
Product Use
& Exposure
Acknowledgements
EPA-RTP
Russell Thomas
Kevin Crofton
Chris Grulke
Ann Richard
Richard Judson
John Cowden
John Wambaugh
Imran Shah
Grace Patlewicz
and…
Many other contributors
from across EPA-ORD
Contact
Antony Williams
US EPA Office of Research and Development
National Center for Computational Toxicology (NCCT)
Williams.Antony@epa.gov
ORCID: https://orcid.org/0000-0002-2668-4821
46

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Incorporating new technologies and High Throughput Screening in the design and selection of chemical alternatives

  • 1. Incorporating new technologies and high- throughput approaches in the design and selection of chemical alternatives Antony Williams U.S. Environmental Protection Agency, RTP, NC June 15th, 2017 21st Annual Green Chemistry & Engineering Conference, Reston, VA This work was reviewed by the U.S. EPA and approved for presentation but does not necessarily reflect official Agency policy. http://www.orcid.org/0000-0002-2668-4821
  • 2. Paradigm Shift in Chemical Design (especially in regards to toxicology) • Chemical Design in the future – High-throughput modeling – SAR/QSAR/QSTR models – Retrosynthetic analysis algorithms for synthesis • Toxicology – FEWER animal studies. Maybe NONE?? – Openness in data sharing – More confidence in computational predictive models that link chemical structures to adverse outcomes 1
  • 3. It will depend on lots of DATA 2
  • 4. What we need to progress • Adequate information on potential bioactivity for as many chemicals as possible • Read-across, QSAR and QSTR models from large high-quality curated datasets 3
  • 5. • National Center for Computational Toxicology established in 2005 to integrate: – High-throughput and high-content technologies – Modern molecular biology – Data mining and statistical modeling – Computational biology and chemistry • Currently staffed by ~60 employees as part of EPA’s Office of Research and Development • Home of ToxCast & ExpoCast research efforts • Key partner in U.S. Tox21 federal consortium • Multiple cross-division collaborations National Center for Computational Toxicology
  • 6. What we need to progress • Adequate information on potential bioactivity for as many chemicals as possible • High Throughput Screening ToxCast/Tox21 generating and making available bioactivity data • GREAT progress to date – more to come! • Read-across, QSAR and QSTR models from large high-quality curated datasets • Chemistry data gathering and high-throughput bioassays are providing the necessary data to generate these models 5
  • 7. Data and Modeling Efforts to Date ToxCast ExpoCast Predictive Models Comp Chemistry HTTK • High-throughput in vitro screening of ~4,000 chemicals across ~1200 assay endpoints (ToxCast) and ~8,000 chemicals in ~60 assay endpoints (Tox21) • High quality, curated chemical structure and physical chemical properties database of ~750,000 chemicals • High-throughput exposure estimates with uncertainty for ~ 7,000 chemicals based on production volume and chemical use
  • 8. Data and Modeling Efforts to Date • High-throughput in vitro screening of ~4,000 chemicals across ~1200 assay endpoints (ToxCast) and ~8,000 chemicals in ~60 assay endpoints (Tox21) • High quality, curated chemical structure and physical chemical properties database of ~750,000 chemicals • High-throughput exposure estimates with uncertainty for ~ 7,000 chemicals based on production volume and chemical use • Legacy in vivo data from ~6,000 animal toxicology studies on ~1,110 unique chemicals • High-throughput toxicokinetic (HTTK) models for ~700 chemicals based on in vitro measurements • AOPs and virtual tissue models for broad range of developmental toxicities ToxCast ExpoCast Comp Chemistry HTTK ToxRef Predictive Models
  • 10. Approximately 15 Years of Data… Growing with daily curation DSSTox_v2 PublicCurated 5. Low 2. Low 6. Untrusted 1. High 3. High 4. Med 7. Incomplete Public_Untrusted Public_Low Public_Medium Public_High DSSTox_Low DSSTox_High 4535 16K 33K 101K 584K ~ 310K pending ~ 150K pending
  • 11. Features Properties Chemotypes, fingerprints, physchem properties, ... Chemical representation levels supporting data integration 10 Structure Generic Substance Test Sample Chemical Name CASRN Supplier, Lot/Batch, physical description SMILES InChI
  • 12. Features Properties Chemotypes, fingerprints, physchem properties, ... Chemical analogs, Read-across, SAR modeling Chemical representation levels supporting data integration 11 Structure Generic Substance Test Sample Chemical Name CASRN Supplier, Lot/Batch, physical description SMILES InChI Experimental Endpoint Data Public toxicity datasets Structure searching & modeling
  • 13. Integrating in vitro and in vivo data HTS-In vitro ToxRef COSMOS ToxCast Tox21 Chemicals DSSTox Measured Properties Chemical KB Biological Data Biological activities
  • 14. Adding Product Use and Exposure HTS-In vitro ToxRef COSMOS ToxCast Tox21 Chemicals DSSTox Measured Properties Product Use & Exposure Chemical KB Biological Data Biological activities
  • 15. High Throughput Measurement to Characterize Exposure 14
  • 16. Building Models from the data HTS-In vitro ToxRef COSMOS ToxCast Tox21 Chemicals DSSTox Biological Data Fate & Transport ADME Reactivity Biotransformation Physchem properties Biological activities Measured Properties ModelsExposure Data Toxicity Predictions Structure-alerts Chemical KB Product Use & Exposure
  • 17. Access to Our Data and Models 16
  • 18. What we have learned… • Data curation, standardization and versioning is essential • Prototype application development suffices for research projects but production development requires managed processes • ODOSOS (Open Data, Open Source and Open Standards) endows many benefits • With this in mind… 17
  • 22. Chemical Properties 21 • Consuming and producing open data
  • 23. Data Distribution 22 • Consuming and producing open data
  • 25. Modeling Details 24 Calculation Result for a chemical Model Performance with full QMRF Nearest Neighbors from Training Set 24
  • 27. Developing “NCCT Models” • Our approach to modeling: – Obtain high quality training sets – Apply appropriate modeling approaches – Validate performance of models – Define the applicability domain and model limitations – Use models to predict properties across our full datasets • Release as Open Data and Open Models
  • 28. Workflow Details and Data 27 OPERA Models: https://github.com/kmansouri/OPERA
  • 29. Making Toxicity Value Data available 28
  • 31. Consumer Product data 30 • Data gathered from multiple online data sources – text mining and data downloads • CASRN and name mappings to produce curated structure set with functional uses • Produce Functional Use (FuseDB) and build predicted functional use models
  • 32. NHANES Exposure data 31 Environ. Sci. Technol., 2013, 47 (15), pp 8479–8488
  • 33. ToxCast and Tox21 Bioassays 32
  • 34. ToxCast and Tox21 Bioassays 33
  • 35. PubChem Bioassay Data Integration 34
  • 36. PubMed: BIG DATA Literature Search Integration 35
  • 37. Linked Directly to PubMed 36
  • 39. Batch Data Searching • I have a 5000 CAS Numbers (or Names) – is there data available? – Has any Toxcast data been run? – Are there Toxicity Data values available? – Are there predicted exposure data? – Can I get predicted physchem data for my model? 38
  • 40. We get lots of user feedback.. • Online since April 2016 • Feedback welcomed! 39
  • 41. “RapidTox” in Development • Semi-automated decision support tool for high-throughput risk assessments • Use Dashboard “architecture”, existing data streams and add new data – e.g. Global Hazard Summary and ECHA data • Combine data streams into quantitative toxicity values with uncertainty estimates
  • 42. Risk Assessments Generally Contain a Standard Set of Components We are assembling these components to deliver RapidTox Phys Chem Exposure Hazard Dose Response, PK, and PODs Risk Summary Uncertainty Variability
  • 43. Quantifying Uncertainty & Assessing Performance of Read-Across - GenRA • “Chemical-Biological Read-Across” - predict toxicity as a similarity-weighted activity of nearest neighbors • Evaluates read-across performance and uncertainty using available data
  • 45. We’re not done yet… HTS-In vitro ToxRef COSMOS ToxCast Tox21 Chemicals DSSTox Biological Data Fate & Transport ADME Reactivity Biotransformation Phys-chem properties Biological activities Measured Properties ModelsExposure Data Toxicity Predictions Structure-alerts Chemical KB Product Use & Exposure
  • 46. Acknowledgements EPA-RTP Russell Thomas Kevin Crofton Chris Grulke Ann Richard Richard Judson John Cowden John Wambaugh Imran Shah Grace Patlewicz and… Many other contributors from across EPA-ORD
  • 47. Contact Antony Williams US EPA Office of Research and Development National Center for Computational Toxicology (NCCT) Williams.Antony@epa.gov ORCID: https://orcid.org/0000-0002-2668-4821 46