Mechanistic models for studying Nanomaterial Safety using High

Throughput and High Content Screening in Zebrafish Embryos and Bacteria

André Nel M.B.,Ch.B; Ph.D Professor of Medicine and Chief of the Division of NanoMedicine at UCLA

Director of the NSF- and EPA-funded Center for the Environmental Implications of Nanotechnology (UC CEIN)

Director of the NIEHS-funded Center for NanoBiology and Predictive Toxicology Associate Editor ACS Nano MODERN Partner

This materials is based on work supported by the National Science Foundation and Environmental Protection Agency under Cooperative Agreement # NSF-EF0830117. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the Environmental Protection Agency.

Copyright 2010 – The Regents of the University of California. All Rights Reserved. Contact cein@cnsi.ucla.edu to obtain permission to use copyrighted material.

Towards a unified decision analysis framework for ENM environmental

assessment

Is this ENM of environmental

concern?

Is there potential for exposure?

Is the ENM toxic?

HTS Data; QSARs

• Are data available? • What is the reliability of those data?

CEIN Approaches, Models and Nanoinformatics Tools Developed for ENMs Environmental Impact Analysis

Exposure Likelihood

Environmental Hazard Ranking

EHR-Nano

Environmental Impact Evaluation

- ENMs F&T prop. - Geographical & meteorological info. - Emissions

Multimedia Analysis M

end-

Nan

o

ENM Concentrations & Mass Distribution

ENMs biota uptake parameters HTS/LTS Analysis Tools

Inter-Plate Normalization

Inter-Plate Normalization

Normalized Activity

Knowledge Extraction: Toxicity Metrics & QSARs

Data Studies

HTS/LTS

NSF: EF-0830117 CEIN MODELING ACTIVITIES

Conceptual/Qualitative • Proposed mechanisms of action (toxicity) • Risk perception • Ranking

Quantitative: Data mining • Knowledge extraction (e.g., pattern recognition, network analysis) • Toxicity: Nano-SARs, dose-response, toxicity ranking

Quantitative: process based • Transport and fate models • Intermedia transport • Distribution of nanoparticles among environmental media

• Dynamic energy budget models • Identifying toxicity mechanisms • Relating organism response to population dynamics

Hybrid (Quantitative/Qualitative) • Decision process analysis ( e.g., is this NM of environmental concern? ) RR

Flow of ENMs through global economy

• 65-90% of ENMs will end up in landfills • ~85% ENMs that pass through a WWTP will end up in biosolids, which

may be applied to soils • Fraction of ENMs going to water bodies and atmosphere are small

Keller et al., J Nanoparticle Res., 2013

Meng et al. ACS Nano. 2009 Nel et al. Accounts Chem Res, 2012 http://www.nap.edu/catalog.php?record_id=11970 http://www.epa.gov/ncct/toxcast

“Toxicity Testing in the 21st Century: A Vision and a Strategy”

US National Academy of

Science (2007)

• Wide coverage of toxicants

• Robust scientific platform for

screening

• Predictive tests utilizing

toxicity mechanisms

• High throughput discovery

• Connectivity to in vivo

Current: One material at a time descriptive animal testing

Proposed: Rapid mechanism-based predictive testing

Meng et al. ACS Nano. 2009 Nel et al. Accounts Chem Res, 2012

Nanomaterial Predictive Toxicology (proportional weighted discovery)

In Vivo Adverse Outcomes

• mechanism of injury

• toxicological pathway

ENM Libraries of different

composition and accentuated

Physchem Properties

Validiation

(102 observations days-months)

(102 – 105 observations/day by HCS and HTS approaches

Cellular or Bio-molecular Endpoints

Mechanistic Toxicological

pathway

Surface Charge

- - - -

Size Shape

AR

Surface chemistry

Eg

CB VB

Eg

CB VB

Eg

CB VB

Band Gap

Dissolution chemistry

- + - + +

+ + +

Surface Functionalization

Crystal Structure

Compo-sitions

Epifluorescence Heat map ranking

1h 24h

PI Ca O2- H2O2 MMP

1 2 3 4 5 6 7

ZnO Oxidative stress

O2· – O2

e–

h+

A

High Throughput Screening

Pathways of Toxicity

Computational analysis

Composition and Combinatorial ENM Libraries Tools to establish Predictive Toxicology

Zhang et al. ACS Nano. 2012

O

Cell Redox Couples

e-

Permissible electron transfer based on BG

LUMO

HOMO

Oxygen

Superoxide

CB

VB

e-

•NADP/NADPH •Fe(III) TnsFe—Fe(II) etc cell redox potential (-4.2 – 4.8 eV)

cyto Cred (Fe2+)

cyto Cox (Fe3+)

-12 -11 -10

-9 -8 -7 -6 -5 -4 -3 -2 -1 0

Bandgap energy ↔ bio redox potential

NP Intracellular Redox Interactions

Nel et al. Science. 2006 George et al, ACS Nano. 2010 George et al, ACS Nano. 2011 Zhang et al. Submitted. 2012

Epifluorescence

384 well plates

Epithelial cells and Macrophages

Heat map Ranking (in vitro)

1h 24h

PI Ca O2- H2O2 MMP

1

2

3

4

5

6

7

ZnO

Structure-activity relationships

Low toxicity (nuisance dust)

Moderate

High toxicity

Pulm

onar

y in

flam

mat

ion

Dose (mass, surface area dose, reactive surface area)

Oxidative Stress Paradigm

Predictive HTS-based Paradigm for Oxidative Stress in the Lung (acute injury paradigm)

Rodent Lung

In vivo ranking

Reflects an Occupational

Disease in humans

Cellular SAD

In Vivo

Total Alveolar area = 0.05 m2

brain Total Alveolar area

= 102 m2

Tiered Decision Making and Dose-response Extrapolations

?

ISSD modeling

•1st tier – In vitro – Predictive assays to study specific mechanisms of injury – Rank potency of test materials vs + and - controls – Develop quantitative SAR analysis for in silico predictions

•2nd tier – short term in vivo – Test selected materials within a category/mechanism/SAR – Focused/limited animal studies – In vivo hazard ranking (pathophysiology of disease outcome)

•3rd tier – short-term or 90 day inhalation studies – Test the most potent materials within a tier 2 category/group – Dose-response extrapolation establish OEL’s – Use for read-across regulatory decision making

Zebrafish HTS platform → automated imaging of developmental abnormalities and transgenic responses

Hatching Start feeding

NPs

NPs

Embryonic development Larval effects

0 4 24 48 72 120

Image acquisition @ 24 hr intervals

HTS Brightfield (Developmental, morphological abnormalities)

Ctrl

CuO

ZnO

NiO

Co3O4

Ag

Robotic pick-and-plate system

Xia et al. ACS Nano. 2011 Lin et al. ACS Nano. 2011 George et al. ACS Nano. 2011

Newport Green

ZHE1 Hatching Enzyme

Abiotic ZHE 1 Assay for MOx Dissolution → Predicts Zebrafish Embryo Hatching Interference

Lin et al. ACS Nano. 2011 Lin et al. Small. 2012

Polyhistidine tagged ZHE1 Plasmid

BL21 E. coli strain

pET3c-ZHE1

IPTG induction

MOx ions

Fluo

roge

nic

Subs

trat

e

Enzy

mat

ic a

ctiv

ity *

* * *

1-4

0

0.2

0.4

0.6

0.8

1

1.2

14

* * *

0

20

40

60

80

100.

1-4

% h

atch

ing

*

Abiotic Enzyme Assay

Automated Zebrafish HTS

Marine Mesocosm: From Organism to Ecosystem

Marine Pelagic

1

2

tropic levels

3

Lenihan & Miller et al. PLoS one. 2012

Danio rerio

Oncorhynchus mykiss

Takifugu rubripes

Oryzias latipes

Oreochromis niloticus

Salmo salar Lin et al. Small. 2012

NanoCu-based fungicides using a modeled waste-water treatment system and zebrafish embryo HTS

Life Cycle Considerations

Keller, A.A. et al., Environ. Sci. Technol. Lett. 2013

e.g. nanoCu-based fungicide

Simulated septic tank

Zebrafish embryo

HTS -20.0%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

0 0.5 1 1.5

nano Cu Micro Cu nano CuO Micro CuO CuPro Kocide

Elemental Cu Concentrations (ppm)

% h

atch

ing

Micro CuO

Micro Cu

Nano CuO

CuPro

Kocide Nano Cu

Sharon Walker, Sijie Lin et al. In preparation

• With more than 2000 known species, bacteria form

the basis of most ecosystems & can serve as a sentinel species for ecosystems impacts

• Bacteria play a critical role in cycling nutrients (carbon, nitrogen, sulfur); they are the primary source of nitrogen fixation on earth

• Cyanobacteria are responsible for the majority of O2 production on earth

• Bacteria play a critical role in decomposition (including WWTPs)

• More complex organisms (like us) digest their food and fight off colonization by harmful bacteria (microbiome)

NSF: EF-0830117

Why study nanotoxicology in bacteria?

Bacterial Membrane

Cytoplasm

NADH cyt

cyt

cyt O2 + H+

H2O

e-

e-

e-

e-

H+

H+

H+

H+

ADP

ATP

NP

•Electron Transport Chain (ETC) •Lyon & Alvarez. ES&T (2008)

•Membrane Potential (MP) Lyon & Alvarez. ES&T (2008)

NP

ROS H2O2 OH• O2

-

•Membrane Integrity (MI) Priester et al. ES&T (2009), Su et al. Biomaterials (2009)

•Reactive Oxygen Species (ROS) Nel et al. Science (2006) Priester et al. ES&T (2009)

4. Assays: stress/damage

Allison Horst

Bacterial Nanotoxicology Approaches 1. Bacterial strain selection

3. NM Dispersion

Horst, et al. J.Nanopart. Res. 2012 Horst et al. Small. 2013

2. Oligotrophic media

Direct Interference

Indirect Interference

HCS: Assessment of Fluorescence in Colorimetric Assays

Horst et al. Small. 2012

Bacterial HTS: Cationic NPs Destabilize Membranes and Inhibit through ROS

Ivask et al. ES&T. 2012.

• Each particle has its own “fingerprint” of pathways involved in bacterial response

• Although the magnitude of the toxicity of Ag NPs correlates with the amount of dissolved Ag+, the mechanism of toxicity is highly dependent on the size and coating/surface charge of the particles

• Positively charged Ag NP (Ag-BPEI) exhibits mechanisms of toxicity that are more similar to the cationic polystyrene particles than to other Ag NPs

NSF: EF-0830117

Conclusions for bacterial Ag NP studies:

Ivask et al, submitted

AFM

• From a regulatory perspective, it is important that Ag NPs that are being marketed for antimicrobial applications be subjected to testing beyond what it is required for traditional (“bulk”) Ag+ formulations

• Use of colloidal silver as an “alternative medicine”/ “dietary supplement” could have significant (unintended) impacts on gut microbiome

Implications

Is this Engineered Nanomaterial Environmentally Safe?

Physicochemical Characterization

In Vitro In Vivo Toxicity

LT Exp.

Exposure Assessment

Transport and Fate studies/

Modeling

Environmental Concentrations

Hazard Identification

In Silico Toxicity Monitoring

Quantitative Nano-SAR

Mechanistic Conceptual

• Dose- Response • Hazard Thresholds In

form

atio

n/D

ata

Man

agem

ent

HT Exp.

Environmental Impact Assessment

Experimental Studies / M

odels

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

Acknowledgements Nel Laboratory: Andre Nel Saji George Huan Meng Xiang Wang Ning Li Haiyun Zhang Sijie Lin Ruibin Li Meiying Wang Yu-Pei Liao

Grant support: NSF- and EPA-funded CEIN

Collaborators: Tian Xia Lutz Maedler Suman Pohkrel Jeff Zink Ivy Ji Hilary Godwin Robert Damoiseaux Yoram Cohen Ron Lui Rober Rallo

CEIN MEMBERS