High Throughput Discovery at the Nano/Bio interface for

Risk Assessment and Nano EHS Decision Making

André Nel   M.B.,Ch.B;  Ph.DProfessor 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

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.   

NSF: DBI ‐0830117

• Provide wide coverage of potential toxicants• Use a robust scientific platform for testing (instead of a

descriptive approach in whole animals) • Predictive in vitro tests that utilize toxicity pathways and

mechanistic systems biology approaches• High content or high throughput screening to facilitate testing

of large batches of materials• In vitro hazard to be confirmed

in vivo

US National Academy of Science’s Transformative Approach to Toxicity 

testing in the 21st Century

http://www.nap.edu/catalog.php?record_id=11970

-

In Vivo Adverse Outcomes

Validity ofpredictions

QSARs

• mechanism of injury• toxicological pathway

-

Validity ofpredictions

ENM Library physicochemical

propertiesValidity

QSARs

toxicological pathway

(102 observations days-months)

(102 – 105 observations per day)

Cellular or Bio-molecularEndpoints

Data acquisition, transformationFeature analysisIn silico decision making toolsIncremental learning ,

Multimedia Analysis

In vivo screening-Organisms, populations,mesocosms

Compositions and Properties

Hazard rankingQSARsExposure modelingEnvironmental Decision AnalysSafer-by-design strategies

FateTransport

Exposure

HTS

Cell & biomolecularscreening

Nanomateriallibraries

Multi-disciplinary NanoSafety PlatformNSF: DBI ‐0830117

Property/Activity

relationships

Life Cycle Analysis

In vitro/In vivo

predictions

Rapid throughputdiscovery at thenano/bio interface

Property-based discoveryallowing safe-by-designnanomaterial synthesis

Property-based discoveryof new biological

applications

Property-based discoveryof hazardous material properties- lung and

environment

Property-based discovery

of safer and more efficacious nano

therapeutics

Faster, smarter, predictive Nano/bio Platforms

+Nano

Materials

Luminescence

Fluorescence microscopy

Mitochondrial damage, ROS generation, stress response, cellular apoptosis

Reporter genes for sublethal effects

Cells & Model Organisms UV/Vis Spectroscopy Cell Growth

Cellular High Throughput Screening in CEIN

Assessment ofInflammation

MultiplexCytokine &Chemokineassay

NSF: DBI ‐0830117

Cationic toxicity, e.g. cationic polystyrene,

PEI-MSNP

+ ++++

++ +

++++

+ ++++

+

+ ++++

++ +

++++

mitochondria

lysosome

Photoactivation e.g. TiO2

Conduction Band

Valence Band

-

+

ΔEg

hν

Embryo hatchinginterference

e.g. CuO

Dissolution, shedding, toxic Ions e.g. ZnO, CuO

N

Metal Metal ions

O2· – O2

e–

h+

SHSH

S

S

Redox activity and ROSe.g., TiO2, CuO, CoO

Lung Fibrosis e.g. CNTs

Membrane Lysise.g. Si nanoparticles,

Ag-plates

Inflammasome activation

e.g., CNT, CeO2 rods

NucleusNucleus

InflammasomeInflammasome

ILIL--11ββ

ILIL--11ββ

propro--ILIL--11ββ

NALP3NALP3

ENM Toxicological ParadigmsNSF: DBI ‐0830117

George et al, ACS Nano, 2010George et al, ACS Nano, 2011

Multi-parameter HTS assay to assess contemporaneous sub-lethal and lethal cytotoxic cellular responses

384 well plates

Epithelial cellsand Macrophages

epifluorescence

ROS

Toxic

ions[Ca2+]i

Mitochondria

Gene activation

↓MMP

Lysosomes

Caspaseactivation

Apoptosis

CytokinesChemokines

Membraneleakage

1h 24h

200

0.4

PI Ca O2- H2O2 MMP

Heatmap rankSimilaritiesor differences

NSF: EF‐0830117

HTS Data Analysis & in silico Nano Informatics Tools

HTS NP

Data

HTS NP

Data

Pre-processing

Pre-processing

Heat-mapsHeat-maps

Self-organizing mapsSelf-organizing maps

data normalizationdata normalization

Feature analysisFeature analysis

ExplorationExplorationStructure-

activity analysisStructure-

activity analysis Modelvalidation

Modelvalidation

Incremental learningIncremental learning

Similar behavior(Cluster)

Nanoparticle Phys-Chem descriptors

In vitro to in vivopredictions

Structure-activityrelationships

Safe bydesign

Initial Pool of 14 NPdescriptors

Bio-catalytic responses

New data

QuantitativeStructure-activity

analysis

R. Liu, R. Rallo, S. George, Z. Ji, S. Nair, A.E. Nel, and Y. Cohen, Small, 2011. 7(8): 1118-1126.

High volume data generation by cellular HTS allowing hazard ranking and prioritization of in vivo testing in zebrafish

S. George et al. ACS Nano 2011

‐NSF: DBI 0830117

High Content Imaging - bright field(Developmental, morphogical abN)

AutomatedImageAnalysis Lin et al. ACS Nano. 2011

Zebrafish HTS in Embryos and Larvae

Hatching Start feeding

NPs NPs

Embryonic development

Larval effects

0 4 24 48 72 120

Image accusation @ 24 hr intervals

CuO∅ neg ∅ pos

High Content Imaging – fluorescence(Transgenic Fish)

Robotic pick-and-plate system

Heat shock protein 70

NiO

ZnOCuO∅

Co3O4

Ag

NSF: DBI ‐0830117

Elucidation on MOx Dissolution Chemistry as basis for interference in Zebrafish Hatching

Control

ZnO

Newport Green- Zn ions

TiO2

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

ZHE-1 Hatching Enzyme

NSF: DBI ‐0830117

Iron doping decreases ZnO dissolution

George et. al. ACS Nano, 2010Xia et al. ACS Nano, 2011

Safer Design Feature for nano-ZnO

Metal Ions

ZnO

C57BL/6

BA

L PM

N c

ount

(x10

4 )

Control

ZnO2%

Fe10

% Fe

*

0

4

8

12

16

20

24

*

NSF: DBI ‐0830117

Major Nano EHS Impact GloballyNanomaterial libraries High throughput screening

OrganismAnimal testing

PrioritizeCompareSpeed up Validate

DosimetryRefine

COOH‐MWCNT

CompositionalMe Oxides

MetalsCNTs

Risk IdentificationImmediate measures to• reduce risk• consider regulations• dosimetry calculations• safer design

Property accentuationSize, Shape, AR

DissolutionBand gap

Cells, bacteria, yeasts, zebrafishembryos

Similar behavior(Cluster)

In silico decisions, in vitro ranking

Low toxicity(nuisance dust)

Moderate

High toxicity

Pulm

onar

y in

flam

mat

ion

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

In vivo hazard ranking

NSF: DBI ‐0830117

Inflammasome activation

Predictive Toxicological Paradigm for CNTs

The epithelial-mesenchymal trophic cell unit

pg/m

L

In v

itro

0

200

400

600

800

1000

pg/m

L *

∅ MWCNT

IL-1βmacrophage

0

20

40

60

80

100

*

MWCNT

TGF-β1epithelial

∅

0

20

40

60

80

100

MWCNT

pg/m

L

*

IL-1βDay 1

0

10

20

30

40

50

pg/m

L

MWCNT

TGF-β1Day 21

*

∅ ∅0

20

40

60

80

100

120

140

pg/m

L

*

MWCNT

PDGF-AADay 21

∅

In v

ivo

Type I epithelium

Macrophage

FibroblastProliferation

IL-1βCollagen

DepositionTGF-β1

PDGF AA

Bronchiole epithelium

MWCNT

TGF-β1PDGF BB

N

pro-IL-1β IL-1β

lysosome

IL-1βNALP3

Macrophage

Wang et al. ACS Nano 2011

HTS in Nano Therapeutics - in vitro/in vivo modelingTo improve Efficacy and Safety

NP1- in RES

NP3- in Tumor

++

+

++

++

+

NP1130 nm

NP250nm-PEG

NP350nm-PEI-PEG

Size tuningPore adapted for cargoFunctionalized surface

- Steric hindrance- Electrosteric repulsion- Protein corona/Ligation- Imaging tags

In vitro/cell testing• uptake• localization• release• apoptosis• safety

In vivo/animal testing• ↓ RES uptake• ↑ bio-distribution• optimize EPR effect• visualize/imaging• efficacy• safety

Iterative testingOptimizeAdd/tune functionsImprove safety

Huan Meng et al ACS Nano 2011

AcknowledgementsNel Laboratory:Andre NelTian XiaSaji GeorgeHuan MengXiang WangNing LiHaiyun ZhangMeiying WangYu-Pei Liao

Grant support: NSF- and EPA-funded CEIN

Collaborators:Lutz MaedlerSuman PohkrelJeff ZinkMin XueIvy JiKen BradleyRobert DamoiseauxYoram Cohen

CEIN MEMBERS

NSF: DBI0830117