environmental decision analysis for nanomaterials · • toxic response • exposure assessment •...
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NSF: DBI‐0830117
Environmental Decision‐Analysis for Nanomaterials
Yoram Cohen, Rong Liu, Haven Liu , Robert Rallo,Hilary Godwin, Andre Nel
University of California, Los Angeles
Center for Environmental Implications of Nanotechnology
UC CEINSafe Design of NMRaw Materials Safe Design of NM
Risk evaluation as a basis Processing & Manufacturing
for NM safe design
• Emissions• Hazard identification
Emissions from Processing
Product Use and Disposal
Hazard identification• Toxic response• Exposure assessment• Scoring analysis
Nanomaterial CharacterizationFate and
Transport
M it iHazard Scoring analysis
• Risk ranking• Quantitative risk scoring• Expert opinion
MonitoringIdentification
Toxicological Response
Exposure
Expert opinion• Risk perception• Integrated weighted
scoring
Scoring(Hazards & Risks)
Ranking of Potential Risks
g• Safe design tools
Potential Risks
Safe Design & Risk
Reduction
A Possible Process of Assessing the Environmental Impact of NanomaterialsImpact of Nanomaterials
Cohen et al., “In Silico Analysis of Nanomaterials Hazard and Risk,” Accs. Chem. Research, doi:10.1021/ar300049e
Is this Engineered Nanomaterial Environmentally Safe?Is this Engineered Nanomaterial Environmentally Safe?
Physicochemical CharacterizationPhysicochemical Characterization
ExposureAssessment
ExposureAssessment
HazardIdentification
HazardIdentification
gem
ent
gem
ent Experi
Experi
In VitroIn VivoToxicity
In VitroIn VivoToxicity
Transport andFate studies/ Transport andFate studies/
In SilicoToxicityIn SilicoToxicity MonitoringMonitoringa
Man
aga
Man
agm
ental Sm
ental S
ToxicityToxicity
LT Exp.LT Exp.
ModelingModelingyy
QuantitativeNano-SAR
QuantitativeNano-SAR
MechanisticConceptualMechanisticConceptualat
ion/
Dat
aat
ion/
Dat
a
HT Exp.HT Exp.
Studies / Studies / pp
EnvironmentalConcentrationsEnvironmentalConcentrations
pp
• Dose- Response• Hazard Thresholds• Dose- Response• Hazard ThresholdsIn
form
aIn
form
a pp Models
Models
Environmental Impact AssessmentEnvironmental Impact Assessment
Decision Analysis
Product manufacturing & use approval
Product/process redesign
Exposure control
Cohen et al., “In Silico Analysis of Nanomaterials Hazard and Risk,” Accs. Chem. Research, doi:10.1021/ar300049e
Multimedia Environmental Distribution of Nanomaterials
The environment is collection of compartments
Unlike dissolved/gaseous chemicals, the multimedia transport/partitioning of Particle size distribution
• linked by mechanistic intermediatransport processes
NPs is typically not constrained by phase equilibria
affects eNM transport
AtmosphereeNMs input Dispersion C ti
TiO2 NP Size distributionp
AerosolizationDry/wet Deposition
Resuspension
Dispersion Convection
Dry/wet Deposition
Resuspension
FloodingAdsorption
Aggregation
DisaggregationAdsorption
DesorptionRunoff
Water Body
SedimentSoil Sedimentation
Resuspension
Liu et al., , Env. Sci.Technol., 2011, 45 (21): 9284–9292.
Multimedia Environmental Distribution of Nanomaterials
The environment is collection of compartments
Unlike dissolved/gaseous chemicals, the multimedia transport/partitioning of Particle size distribution
• linked by mechanistic intermediatransport processes
NPs is typically not constrained by phase equilibria
affects eNM transport
Origin
Air Water Soil Sediment
Air
Convection,dispersion sorption Aerosolization Wind –Resuspension N/A
on
A dispersion, sorption, particle/particle
Aerosolization Wind Resuspension N/A
Water Dry/Wet
deposition
Convectiondispersion
sorption, dissolution, ppt,i l / i l
Runoff (particulate & dissolved)
Resuspensiondesorption
Destina
tio particle/ particle
Soil Dry/Wet
DepositionFlooding
Infiltrationsorption, dissolution, ppt,
particle/particleN/A
Infiltration,
Sedim.
N/ASedimentation
SorptionN/A
,sorption,
dissolution, ppt, particle/ particle
Examples of Physical Mechanisms/Pathways of Nanoparticles Transport (atm/water, atm/terresterial env)
C ll ti (I ti
Dispersion
Collection (Impaction, interception and diffusion)
Precipitation (Wet) Scavenging
DryDeposition
Aerosolization
Wind Resuspension
Environmental Transport & Distribution of Nanomaterials Environmental Transport & Distribution of Nanomaterials (Mend(Mend‐‐Nano) : CloudNano) : Cloud‐‐Based SimulatorBased Simulator
ano
‐ ENMs F&T properties‐ Compartmental data library (i.e., geographical info.)
Model input:
Men
d-N
a‐Meteorological data library‐ Number particle bins in particle size dist.‐ Emission scenarios
ENMs biota/vegetation uptake parametersENMs biota/vegetation uptake parameters
C t t li k d i i t di
System of coupled dynamic compartmental mass balance differential equations
Compartments are linked via intermediatransport processes
ENM Concentrations
mass balance differential equations
Results DisplayResults Display Concentrations
& Mass Distribution
SolverEnvironmental mass distribution of TiO2 in LA County: (Left) emission only to the atmosphere, and (Right) apportionment of emissions as 75% to the atmosphere and 25% to water.
Display(Numerical and Visual)
Display(Numerical and Visual)
http://www.polysep.ucla.edu/mendnano/
Example : TiO2 in Los Angeles(Release of TiO2 ~1% of production rate scaled to LA on the basis of per capita world production)
Dynamic Concentration Profile
Csediment: 9.035Csoil: 9.56x103
Dynamic Concentration Profile
Csoil: 7.13x103
( 2 p p p p )
Csediment: 9.035
C : 0 620
Csediment: 68.055
Cair: 0.012
Cwater: 0.620
Cair: 0.0089
Cwater: 6.622
Release: Air: 865 kg/yr
WaterSedimentW t
SedimentRelease: Air: 649 kg/yr, Water: 216 kg/yr
Air
Water0.16kg0.027%
0.476kg0.08%
Air
Water1.71kg0.38%
3.59kg0.8%
Soil580kg97%
Air15.1kg2.5%
Soil432kg96%
Air13.1kg2.5%
Mass Distribution: TiO2(Releases to air and Water)(Releases to air and Water)
100
80
n (%
) Soil
40
60
Distributio
Sediment
20
40
Mass D
Water
00.1 1 10 100
Air
Percent of Release to Air
Example: Ranking of ENMs based on Environmental Exposure Concentrationsp
100
er
Australia
1
10
[eNM]W
at[ng/L]
0.1Al2O3 CeO2 Fe3O4 SiO2 TiO2 ZnO
[
Uncertainty: Source Release Data
100
1000
10000
Water
/L]
Switzerland
0.01
0.1
1
10
Al2O3 F 3O4 SiO2 TiO2 Z O
[eNM]W
[ng/
Al2O3 Fe3O4 SiO2 TiO2 ZnO
a. Australia: Willcocks, D. NICNAS Information Sheets, Summary of call for information on the use of Nanomaterials; Australian Government:, 2007http://www.nicnas.gov.au/publications/information_sheets/general_information_sheets/nis_call_for_info_nanomaterials.pdf
b. Switzerland: Schmid, K.; Riediker, M., Use of nanoparticles in Swiss industry: A targeted survey. ES&T, 2008, 42, 2253-2260.
Knowledge generation and Data Mining
1000’s/year 10 000’s/day 100 000’s/day100’s/year 1000 s/year 10,000 s/day 100,000 s/day
High Throughput Bacterial,Cellular, Yeast, Embryo or
Immediate Relevance, , y
Molecular Screening Prioritize in vivo testing
at increasing trophic levels
Exposure/Life cycle/bio-accumulation
Knowledge Extraction from Toxicity Studies
Preprocessing Exploration Modeling Application
ProgressiveLearning
Raw Data Visualization
HTS/LTS Data
p g p g
Normalization Hit‐Identificationp
Activity‐Activity Relationships
pp
‐Toxicity Metrics‐Hazard Ranking
Outlier Removal Heatmaps/clusteringp
Structure‐Activity Relationships g
ExperimentalDesign
based
yping
Physico‐chemical Properties
Self‐organizing maps
Web‐based HTS Data Analysis Tool AssociationRules
HTS – cytotoxicity/i li h
Image‐b
Phen
oty
S
signaling pathways
Nano-SARs
Zebrafish‐based HTS
High Throughput ExperimentsHigh Throughput Experiments
Nanoparticle size characterization (e.g., DLS, imaging, zeta potential; temporal variability)
Nanoparticle toxicity screening:
Concentration
Nanoparticle toxicity screening:• Different particle types and properties•Multiple Assays (e.g., cytotoxicity, cell signaling pathways)
•Multiple cell lines
Nan
opar
ticl
e
Multiple cell lines• Various environmental conditions; temporal variability)
Empty wellUntreate d Sample (control)Treate d Sample (control) Replicate s
Automated Phenotype Recognition to Accelerate Automated Phenotype Recognition to Accelerate in vivoin vivo toxicity screening of ENMStoxicity screening of ENMS
Image
Zebrafish phenotypes: “dead”, “hatched” or “unhatched” Ph t iti f 97% b d th
descriptor histogram
Phenotype recognition accuracy of >97% based on three vectorial image descriptors
Identified Co‐Occurrence (Association Rule) of Impacted Pathways from “Heatmap” Datap y p
E2F signaling suggests definite detection of p53 and Myc
DataData--Driven Hypothesis GenerationDriven Hypothesis Generation
Ri: Support/Confidence
detection of p53 and Myc
RAW Cell LineBEAS-2B Cell Line
f
Myc signaling suggests that p53 willMyc signaling suggests that p53 will also be detected (92% probability). However if Mito is encountered with Myc then it is definite that p53 will also be encounteredwill also be encountered
NP triggering of one or more pathways can imply triggering of other associated pathways,
i t t ith bi l i l t lkconsistent with biological cross-talk between cellular signal transduction and transcriptional regulation pathways.
Web‐Based HTS Data Analysis Tools (HDAT)
•Outlier Removal•Data Normalization•Data SummarizationData Summarization•Hit Identification•Data Visualization (Heatmap, Self‐Organizing Map)l l•Clustering Analysis
•Direct Interface with the CEIN Data Management System
Hit‐IdentificationIdentify significant effects
FNL=1 Φ 3Threshold for “hit” (b):
Φ : Standard normal distribution
SSMD=3, False Negative Level<5%
AP1
CRE
E2F
HIF1A
Myc
NFA
T
NFkB
p53
SMAD
SRF
Mito
JC1
Fluo
PI AP1
CRE
E2F
HIF1A
Myc
NFA
T
NFkB
p53
SMAD
SRF
Mito
JC1
Fluo
PI
HitNon-hit
Identify significant effects Φ : Standard normal distribution : Standard deviation of theMaximum Likelihood Estimate of SSMD ( )
Ag
A C E H M N N p S S M J F P A C E H M N N p S S M J F P
Al2O3
Au
Pt
SiO2
ZnO
PathwaysPathways CytotoxicityCytotoxicity PathwaysPathways CytotoxicityCytotoxicity
RAW Cell Line BEAS‐2B Cell Line
ZnO
NPs are from Five Categories CLIO
Regulatory Based Class Definition via SOM ClusteringRegulatory Based Class Definition via SOM Clustering- NPs are from Five Categories CLIO-,
PNP-, MION-, QD-, and Iron-based, Conc: 0.01 -0.3 mg/L
- Four toxicity-related assays: caspaseapoptosis assay (APO) mitochondrialC1 apoptosis assay (APO), mitochondrialmembrane potential (JC1), C12 resazurin(RES), TiterGlo ATP content (CTG)
Four Cell Types: aortic endothelial (AO)
C1
C3 - Four Cell Types: aortic endothelial (AO),vascular smooth muscle (SM), liverhepatocytes (HEP), andmonocyte/macrophage (MP)
C3
C2 NPs are grouped in the hexagonal SOM cells according to their similarity. Similar SOM cells are grouped to form mega-clusters SOM cells are color rendered
Cluster C2 contains NP24 (Feridex IV) and NP25 (Ferrum Hausmann)
clusters. SOM cells are color rendered with red indicating high activity.
Rong et al., Nano-SAR Development for Bioactivity of Nanoparticles with Considerations of Decision Boundaries, Small, 2013.
approved for in vivo imaging and for iron supplementation, respectively.
(Q)SARs (Q)SARs and and NanoNano‐‐SARs for Correlation/Prediction SARs for Correlation/Prediction of Cellular Response to ENMsof Cellular Response to ENMsof Cellular Response to ENMsof Cellular Response to ENMs
QSARQSAR
Physicochemical NP i i
(Regression)(Regression)
Continuous
descriptors and Env. conditions
ActivityCategorical
SARSAR(Classification)(Classification)
ENMs of similar physicochemical /structural ti lik l t i d i ilproperties are likely to induce similar
biological response when exposed to NPs
Example: A NanoExample: A Nano‐‐SAR for MetalSAR for Metal‐‐Oxide NPsOxide NPsNano‐SAR for metal oxides (24); (BEAS‐2B and RAW264.7 cell lines; 7 assays)Nano SAR for metal oxides (24); (BEAS 2B and RAW264.7 cell lines; 7 assays)
Decision Boundary (DB)• LFN and LFP are penalties (costs) of accepting false(costs) of accepting false negative and false positive predictions.
• Cost of classifying NP x as:
Underlined NPs are Support Vectors (SV)
‐4.12‐4.84
Cost of classifying NP x as: ‐ toxic P(N|x)LFP‐ nontoxic P(T|x)LFN
• DB is given by g yP(T|x)LFN‐P(N|x)LFP=0
• NP is of concern ifP(T|x)LFN‐P(N|x)LFP>0
LFN : LFP
1.0 : 2.71.0 : 1.0
DB ofProbability of a NP being classified as toxic is given by P(T|x)= 1/(1+e-f(x)) ; x (=[x1, x2]) represents the
Penalty of acceptance of false negatives relative to false
f(x)=∑i SV αiexp[-(xi,1-x1)2-(xi,2-x2)2]+b
1.0 : 1.02.7 : 1.0NP identified by its normalized ( [0,1]) descriptor
vector [ΔHhyd, EC]; SVM: Support Vector Machine.positive predictions
Rong et al., Development of Structure-Activity Relationship for Metal Oxide Nanoparticles, Small, 2013
Development of NanoDevelopment of Nano‐‐SARs based on HTS Toxicity MetricsSARs based on HTS Toxicity Metrics
QSARs for toxicity of metal oxides ENMs
• Applicability domain• Decision boundaries based on the acceptance ratio of false negatives to false positivesg p
• Single end‐point, multiple end‐points, integrated end‐points
Liu et al., Small, 7(8): 1118-1126 (2011)
Example: Possible Ranking of ENMs based on a Simple Risk Indexp
Risk Index = (Probability of Being Toxic) x (eNM Concentration)
10
100
ility
ng
c0.001
0.01
0.1
1
Al2O3 CeO2 Fe3O4 SiO2 TiO2 ZnO
Prob
abi
of Bein
Toxic
l l d
10
100
1000
10000
M]W
ater
ng/L]10
100
]Water
g/L]
Australia Switzerland
0.01
0.1
1
Al2O3 Fe3O4 SiO2 TiO2 ZnO[eNM [n
0.1
1
Al2O3 CeO2 Fe3O4 SiO2 TiO2 ZnO
[eNM]
[ng
1000100000
0.1
1
10
100
1000
Risk In
dex
0 010.11
10100
100010000
Risk In
dex
0.01Al2O3 CeO2 Fe3O4 SiO2 TiO2 ZnO
R
0.0010.01
Al2O3 Fe3O4 SiO2 TiO2 ZnO
R
a. Australia: Willcocks, D. NICNAS Information Sheets, Summary of call for information on the use of Nanomaterials; Australian Government:, 2007http://www.nicnas.gov.au/publications/information_sheets/general_information_sheets/nis_call_for_info_nanomaterials.pdf
b. Switzerland: Schmid, K.; Riediker, M., Use of nanoparticles in Swiss industry: A targeted survey. ES&T, 2008, 42, 2253-2260.
CEIN Approaches, Models and Nanoinformatics Tools Developed for ENMs Environmental Impact Analysis
HTS/LTS‐ ENMs F&T prop.‐ Geographical &meteorological info.‐ Emissions-N
ano
DataStudiesHTS/LTS
Emissions
Men
d-
ENMs biota uptake parametersENMs biota uptake parametersHTS/LTS Analysis Tools
Multimedia Analysis
ENM Concentrations
Inter‐Plate Normalization
XX
1st Quartile
3rd QuartileMedian
Largest Value*
Outliers
EHR-Nano
ENM Concentrations& Mass DistributionInter‐Plate Normalization
Normalized Activity
Smallest Value*
X Outlier
Exposure Likelihood
EnvironmentalEnvironmentalHazard Ranking
Environmental Impact EvaluationKnowledge Extraction:
Toxicity Metrics & QSARs
SummaryEnvironmental impact assessment will require integration of toxicity metrics with exposure concentrations to arrive at suitable hazard ranking.
The range of environmental exposure concentrations can be estimated based on mechanistic multimedia models, but this will require data on NP release rates, transformations, and
ti l i di t ib ti d i t l ditiparticle size distribution under environmental conditions
Use of nano‐SARs for environmental impact assessment requires establishment of a meaningful applicability domain and suitable decision boundaries for regulatory purpose.
HTS can be effective for rapid assessment of the potential i i ENM i h h f bli hi i f ltoxicity ENMs with the next step of establishing meaningful
relationships between in vivo and in vitro evidence/assays.
Effective data sharing model development and deploymentEffective data sharing, model development and deployment will be aided by the rapidly developing field of nanoinformatics
Minimum standards for data sets, high‐throughput data collection (Metadata Standards; Data Files
Acceptance Workflow;
Safe and Sustainable implementation of Nano‐technology will require effective tools for data
Informatics in the traditional scientific lifecycle
Acceptance Workflow; Nanocatalog)
effective tools for data mining and sharing
Observation, i i
Data analysis, visualization &
data mining (CEIN Semantic search,
Federated
experimentation, computation
Data analysisHypothesesHDAT), predictive models (e.g., Mend‐Nano, QSARS), topic mapping
databases, Ontologies/ Taxonomies
Data analysisHypotheses
NanoinformaticsNanoinformaticsmapping
PreprintsDiscovery
Data repositories (e.g., NanoTab, CEIN Data
Text mining, information
(
ArticlesCEIN participates in the National N i f ti
( g , ,Repository), data citati on &
author attributionextraction (CEINNano‐Crawler)
Nanoinformatics 2020 Roadmap. http://eprints.internano.org/607/
Nanoinformatics Effort
QUESTIONS?QUESTIONS?