1Zurich 05.06.07

Computational nanotoxicology - towards a

structure-activity based paradigm for investigatingthe activity of nanoparticles

Andrew WorthEuropean Chemicals Bureau (ECB)

Institute for Health & Consumer Protection (IHCP)Joint Research Centre (JRC), European Commission

21020 Ispra (Va), Italy

E-mail: andrew.worth@ec.europa.eu

2Zurich 05.06.07

•

Role of the European Commission’s Joint Research Centre and its European Chemicals Bureau

•

Basic concepts: (Q)SAR, read-across, grouping and ranking•

Applicability of (Q)SAR methods under REACH•

Development of a structure-activity based paradigm for NP toxicity•

Case studies•

Conclusions

Overview

3Zurich 05.06.07

The European Commission’s Joint Research Centre

Directorates-General

European Commission

Directorates or Institutes

Units European Chemicals Bureau (ECB) European Centre for the Validation of Alternative Methods (ECVAM)Nanotechnology and Molecular Imaging (NMI) unitPhysical & Chemical Exposure (PCE) unit

JRC

4Zurich 05.06.07

The European Chemicals Bureau and its CompuTERM

Project

General aim of CompuTERM (Computational Toxicology and Environmental

Risk Modelling) :To promote the availability of reliable computer-based estimation methods suitable for the regulatory assessment of chemicalsThis includes methods for predicting human health and environmental effects, and their distribution and fate within the environment and biological organisms.Work programme

includes:

•

Direct policy support•

Research in support of policy•

Training, Enlargement and Integration

5Zurich 05.06.07

CompuTERM:Computational Toxicology and Environmental Risk Modelling

Website Website http://http://ecb.jrc.itecb.jrc.it/QSAR//QSAR/

•

Background information on (Quantitative) Structure(Quantitative) Structure--Activity Activity RelationshipsRelationships

[(Q)SARs]

•

Freely downloadable software tools for research and regulatory purposes

• ECB reports and publications

6Zurich 05.06.07

PHYSICAL PROPERTIES

TOXICITY

ENVIRONMENTAL DISTRIBUTION

BIOKINETIC PARAMETERS

In silico methods can be used to predict the effects of chemicals on human health and the environment, as well as their distribution and fate within the environment and biological organisms

CHEMICALSTRUCTURE

SARs: Structure-Activity Relationships

ANDQSARs: Quantitative Structure Activity Relationships

… collectively referred to as (Q)SARs

… are theoretical models that relate the structure of chemicals to their properties

In silico approaches

7Zurich 05.06.07

The (Q)SAR concept: activity depends on structure

1.

Chemicals with similar molecular structures have similar effects in physical and biological systems→ qualitative model (SAR)

2.

The extent of an effect varies in a systematic way with variations in molecular structure→ quantitative model (QSAR)

Log (1/MTOX) = 0.0344 log P - 0.150 σ

+ 0.120

Maternal toxicity

in Chernoff

/ Kavlock

assay

(Kavlock, 1990)

O

R3R2

R4R1

Reprotoxicpotential

8Zurich 05.06.07

Results obtained from valid qualitative or quantitative structure-activity relationship models ((Q)SARs) may indicate the presence or absence of a certain dangerous property. Results of (Q)SARs

may be used instead of testing when the

following conditions are met:

Regulatory Context : Annex XI of REACH on (Q)SARs

•

results are derived from a (Q)SAR model whose scientific validity has been

established•

the substance falls within the applicability domain of the (Q)SAR model

•

results are adequate for the purpose of classification and labelling and/or risk

assessment, and •

adequate and reliable documentation of the applied method is provided

The Agency in collaboration with the Commission, Member States and interested parties shall develop and provide guidance in assessing which (Q)SARs

will meet

these conditions and provide examples.

9Zurich 05.06.07

Results of (Q)SARs may be used instead of testing when the following

conditions are met:

Conditions for the regulatory use of (Q)SARs

•

results are derived from a (Q)SAR model whose scientific validity has been established

•

the substance falls within the applicability domain of the (Q)SAR model

•

results are adequate for the purpose of classification and labelling and/or

risk assessment, and

•

adequate and reliable documentation of the applied method is provided

⇒ OECD guidance on QSAR validation

⇒ methods for applicability domain assessment

⇒ QSAR Reporting Formats for models and predictions

⇒ REACH guidance document (RIP 3.3)

10Zurich 05.06.07

Scientific validity of (Q)SAR prediction

A (Q)SAR should be associated with the following information:

1.

a defined endpoint2.

an unambiguous algorithm

3.

a defined applicability domain 4.

appropriate measures of goodness-of-fit,

robustness

and predictivity

5.

a mechanistic interpretation, if possible

•

Principles adopted by 37th Joint Meeting of Chemicals Committee and Working Party on Chemicals, Pesticides & Biotechnology; 17-19 Nov 2004

•

ECB Guidance Document published in Nov 2005•

OECD Guidance Document published in Feb 2007

11Zurich 05.06.07

In order for a (Q)SAR result to be adequate for a given regulatory purpose, the following conditions must be fulfilled:

–

the estimate should be generated by a valid (relevant and reliable) model

–

the model should be applicable to the chemical of interest with the necessary level of reliability

–

the model endpoint should be relevant for the regulatory purpose

(Q)SAR modelapplicable to query chemical

Scientificallyvalid QSARmodel

(Q)SAR model relevant to regulatory purpose

Adequate(Q)SAR result

Reliable(Q)SAR result

Adequacy of (Q)SAR prediction

12Zurich 05.06.07

Substances whose physicochemical, toxicological and ecotoxicological properties are likely to be similar or follow a regular pattern as a result of structural similarity may be considered as a group, or “category”

of substances.

Application of the group concept requires that physicochemical properties, human health effects and environmental effects or environmental fate may be predicted from data for reference substance(s) within the group by interpolation to other substances in the group

(read-across approach). This avoids the need to

test every substance for every endpoint.

The Agency, after consulting with relevant stakeholders and other interested parties, shall issue guidance on technically and scientifically justified methodology for the grouping of substances sufficiently in advance of the first registration deadline for phase-in substances.

Annex XI of REACH – grouping and read-across (1)

13Zurich 05.06.07

In all cases results should:

•

be

adequate for the purpose of classification and labelling

and/or

risk assessment

•

have adequate and reliable coverage of the key parameters

addressed in the

corresponding test method referred to in Article 13(3)•

cover an exposure duration

comparable to or longer than the corresponding

test method referred to in Article 13(3) if exposure duration is

a relevant parameter, and

•

adequate and reliable documentation of the applied method shall be provided

If the group concept is applied, substances shall be classified and labelled on this basis.

Annex XI of REACH – grouping and read-across (2)

14Zurich 05.06.07

Known information

on the property

of a substance

(source

chemical)

is

used

to

make

a prediction

of the same

property

for

another substance

(target chemical) that

is

considered

“similar”

Source

chemical Target chemical

Property

Reliable data

Missing data

Predicted to be harmful

1,2-Benzenedicarboxylic acid, bis(2-ethoxyethyl) ester

Known to be harmful: 1 < log LC50 < 2

Acute fish toxicity?

diethyl phthalate

Data gap filling by read-across

15Zurich 05.06.07

Illustration of the chemical category approachChemical 1 Chemical 2 Chemical 3 Chemical 4

Property 1

Property 2

Property 3

Property 4

Activity 1

Activity 2

Activity 3

Activity 4

reliable data pointmissing data point

SAR / read-acrossinterpolationextrapolation

QSAR

16Zurich 05.06.07

Ranking methods•

Chemometric

approaches can be applied to sort chemicals according to properties of concern, or to identify different profiles of concern

•

Total order ranking

(all substances comparable)•

Partial order ranking

(some substances incomparable due to conflicting criteria)

Level 3a b e

Level 2

Level 1

c

d

ad

a

ab

hazard

Level 3a b e

Level 2

Level 1

c

d

ad

a

ab

hazard

•

a, b and e are incomparable•

a, c and d are comparable and form a chain•

b, c and d are comparable and form a chain1

3

2

7

4

23

14

6

1

3

1

12

1

9

0 5 10 15 20 25

0.278

0.334

0.389

0.445

0.5

0.556

0.611

0.667

0.722

0.778

0.833

0.889

0.944

1

PBT Hazard score for 87 chemicals. Total order ranking by the utility function

CH3

O

OO

O

CH3

CH3 OH

17Zurich 05.06.07

Toxmatch – an ECB tool for read-across and grouping

Soon to be available as a free download from ECB website

•

Encodes different similarity indices

•

Fragment-based similarity indices

•

Endpoint dependent

•

Applicability domains

18Zurich 05.06.07

DART – an ECB tool for chemometric

ranking

Available as a free download from ECB website

•

Designed to prioritise and rank lists of substances

•

Different total order ranking techniques are implemented in DART

•

Partial ranking by Hasse diagram technique is also implemented

19Zurich 05.06.07

StateState--ofof--thethe--art: art: Current theoretical studies:Current theoretical studies: predictions of optical, mechanical, chemical, and electronic

properties.Almost nonnon--existent computational studies for predicting toxicityexistent computational studies for predicting toxicity

Need to extend the traditional (Q)SAR paradigm to NPs:Need to extend the traditional (Q)SAR paradigm to NPs:

Predicting the intrinsic properties and toxicity of NPs

20Zurich 05.06.07

Predicted toxicity

Toxicological endpoint

Image from:http://www.nano-lab.com/nanotube-image2.html

How can we apply read-across to nanoparticles?

Known toxicity

carbon nanotubes

Suitable analogue?

21Zurich 05.06.07

On what basis should we group chemicals?

Adapted from Maynard & Aitken

(2007). Proposed working classification scheme for nanostructured

particles.

A. Spherical or compact particles

B. High aspect ratio particles

C. Complex non-spherical particles

D. Compositionally heterogeneous particles –

core surface

variation

E. Compositionally heterogeneous particles –

distributed

variation

F. Homogeneous agglomerates

G. Heterogeneous agglomerates

H. Active particles

I. Multifunctional particles

22Zurich 05.06.07

Extension of the traditional (Q)SAR paradigm to nanoparticles

Aim: To develop a rational structure-activity based paradigm for assessing the

toxicological hazard of NPs.

What do we need?• Data: from the characterisation of NPs and on toxicological endpoints of

interest (oxidative stress/inflammation, immunotoxicity, and genotoxicity)• Structural descriptors: physicochemical properties and calculated structural

descriptors• Methods:

•

Multivariate statistical methods that relate the structural descriptors to toxicological endpoints

• Molecular modelling methods• Probabilistic statistical methods

23Zurich 05.06.07

•

Development of an inventory of NPs with a greatest potential for environmental

exposure and subsequent identification of a shortlist of NPs with varying structure and with different chemical nature

•

Compilation of available experimental and computational properties for shortlisted

NPs (physicochemical and structural, toxicity data)

•

Identification of structural descriptors suitable for modelling NP reactivity

•

(Q)SAR modelling studies exploring the relationships between properties and toxicity,

using multivariate data analysis techniques

•

Modelling the interaction of NP with biological systems

by means of computational

approaches including quantum chemistry methods, molecular modelling and docking

techniques

•

Exploring the feasibility of developing categories of NPs based on

physicochemical, structural and toxicological properties

Extension of the traditional (Q)SAR paradigm to nanoparticles –

possible roadmap

24Zurich 05.06.07

•

Size and size distribution•

Shape•

Surface area (especially biologically relevant surface area involved in biological interactions)

•

Surface structure •

Surface chemistry associated with biological reactivity•

Chemical composition •

Crystallinity

(purity)•

Porosity•

Charge (in biological fluid)•

Solubility•

Aggregation / Agglomeration

Identification of structural descriptors for the toxicity of NPs

Experimentally derived physicochemical properties

25Zurich 05.06.07

•

Geometric and topological descriptors: Molecular volume, STERIMOL

parameters, Molecular surface area, Charged partial surface area•

3D descriptors: Receptor Surface Analysis (RSA) Descriptors, Molecular Field

Analysis (MFA) Descriptors, Molecular Shape Analysis (MSA) Descriptors, Hydrophilic and hydrophobic surface areas

•

Hydrophobic Descriptors: Partition Coefficient (log P)

•

Electronic descriptors: Polar descriptors (Intermolecular forces, Molecular

polarizability and molar refractivity, Ionization constants); Energetic Descriptors;

Reactivity indices (EHOMO

, ELUMO

)•

Absorption-Distribution-Metabolism-Excretion (ADME) Descriptors•

Other descriptors: Quantum Similarity Indices

Computed descriptors

Identification of structural descriptors for the toxicity of NPs

26Zurich 05.06.07

•

QSAR has been widely used to predicting toxicity of substances in bulk form•

To date, QSAR has not been carried out for predicting nanoparticle toxicity

•

ECB project with Institute of Occupational Medicine (Dr Lang Tran) and Edinburgh University (Prof Ken Donaldson)

•

Aim: Development of a test protocol that generates “QSAR-ready” data

1) Selection of a panel of 20 nanoparticles

in key categories (carbon-based, metal oxides, silicon oxides)

2) Physicochemical and structural characterisation of these materials (spectroscopic and microscopic methods)

3) Application of an in vitro assay to assess oxidative stress and inflammatory response (cytotoxicity

and pro-inflammatory gene transcription in A549 lung epithelial cells)4) Development and validation of a QSAR model

Feasibility study: QSARs for NP toxicity prediction

27Zurich 05.06.07

Case study: structure-activity relationship for carbon nanoneedles

Structure of ultrathin carbon nanoneedle

(CNN) affects its reactivity and conductivity

Poater

& Gallegos Saliner

(2007)

0

1

2

3

4

5

6

7

8

9

10

1 2 3 4 5 6 7 8 9 10 11

Number of layers

HO

MO

-LU

MO

gap

(eV)

C4 C6 C8

C4 CNN C6 CNN C8 CNN

No o

f lay

ers

Structure of CNN: geometry optimized

using density functional theory at the B3LYP/6-31G level using Gaussian 03

28Zurich 05.06.07

Case study: a nanoparticle-protein interaction

• NP ⇒

fullerene

• State-of-the-art:

a number of fullerene derivatives function as enzyme inhibitors

by interacting with proteins.

• Little knowledge about how native proteins bind fullerenes

• Protein ⇒ HIV-1 proteaseThe HIV-1 Protease (PR) hydrolyzes viral polyproteins

into functional protein products that are essential for viral assembly and subsequent activity.

• Computational technique: Docking

⇒ prediction of binding of one molecule to another, usually a protein

⇒ based on the fitting of both molecules in 3D space

29Zurich 05.06.07

Docking study of fullerene to HIV-protease

• Structure of HIV-protease taken from PDB (PDB ID: 1aid)

• Structure of fullerene: geometry optimized

using density functional theory at the B3LYP/6-31G level using Gaussian 03

• Docking study using SYBYL v 7.3, Surflex-

Dock module (Tripos Inc)

• Active site

identified: hydrophobic cavity

between the two monomers• Binding affinities

of fullerene derivatives

calculated

Case study: fullerene – protein interaction

Docking process: Search algorithm: Optimisation

procedure to find all possible orientations and conformations

of the ligand into the ligand-protein complexScoring function: Evaluates the binding affinity by estimating the ligand-receptor free energy of binding

Tsakovksa

et al. (2007)

30Zurich 05.06.07

•

Mechanistic understanding of NP-protein interactions can be derived by applying molecular modelling

methods (e.g. docking)

-

Need prior knowledge of specific molecular interaction

•

To develop a rational structure-activity based paradigm for predicting NP toxicity, the applicability of traditional (Q)SAR approaches need to be assessed and combined in a weight-of-evidence approach:

-

Need to supplement current molecular descriptors with a set of “nanoparticle

descriptors”-

QSAR modelling should in principle be possible, provided that relevant descriptors can be identified and sufficient experimental data can be obtained

-

SAR / read-across approaches for specific endpoints could be applied when quantitative QSAR models cannot be derived

-

Grouping of NPs according to similar physicochemical and toxicological properties results in categories of NPs

that integrate predicted and experimental data-

Data gap filling

in categories is a weight-of-evidence approach based on read-across and application of (Q)SARs

-

Need to develop suitable classification scheme(s) for the meaningful grouping of NPs

Concluding remarks