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?