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TransnationalAction Program on Emerging Substances

The role of in silico methods in the context of water treatment The role of in silico methods in the context of water treatment Simona Kovarich, PhDS-IN Soluzioni Informatiche

Transnational Action Program on Emerging Substances

Outline

• About S-IN

• Background

• In silico methods

- In silico approaches

- QSAR applications within Regulatory framework

- QSAR applications within water treatment framework

• Conclusions


About S-IN Soluzioni Informatiche

S-IN is an Italian company (SME) offering a range o f services in scientific

informatics: consultancy, contract research, traini ng, in silico predictions, and

acting as distributors of specialist software for Life Sciences.

Pillars:

• Professionals with long-term experience and extensi ve technical/scientific

background in: Chemistry, Biology, Chemoinformatics, Statistics, Toxicology,

Informatics, Environmental science � Molecular Informatics

• Collaboration attitude: Working groups to address complex projects

Located in Vicenza (Italy) – Established in 1994


Areas of expertiseMolecular Modeling and (Q)SARvirtual screening, hit-to-lead and lead optimizationthrough structure and ligand based design, ADMETprofiling predictions and QSAR Analysis.

Computational Toxicologyin silico predictions (for regulatory purposes),training and consultancy on in silico methodologiesand software tools.

Data Storage & Managementsupport and software, desktop and enterprise solutions, for the storage, management and analysis of chemistry based data.

Quality by Designconsulting services to optimize products and processes; multivariate data analysis (by selecting the most appropriate methods) and results interpretation.

Contracted research• EFSA Projects• EU FP7 COSMOS Project

Training activities on methodologies or software tools (QSAR, expert rule-based methods and read-across)

In silico predictions in compliance with regulations• (eco)toxicological properties• physico-chemical properties


Outline

• About S-IN

• Background





• Conclusions


BackgroundWater treatment and emerging pollutants

• The quality of water, whether used for drinking, domestic purposes, food

production etc.., has an important impact on human and environmental

health.

• Increased detection of emerging pollutants (e.g., pharmaceuticals and

personal care products, pesticides, endocrine disruptors, industrial

compounds) in water sources and drinking water.

• Advanced water treatment systems, such as activated carbon adsorption,

nanofiltration membranes, and advanced oxidation processes (AOPs),

have shown satisfactory results for the removal/reduction of these priority

pollutants.


BackgroundWater treatment and emerging pollutants

• The efficiency of water treatment systems for the removal of emerging

pollutants is often unknown a priori and laboratory testing can be very

expensive and time-consuming.

• Large amount of emerging pollutants with unknown removal efficiency for

different water treatment processes.

In silico structure-based approaches (e.g., QSARs) to predict key process properties:• Estimation of removal efficiency• Identification of the “best” water treatment systems

(Q)SARs


Outline

• About S-IN

• Background




- QSAR applications within Water Treatment framework

• Conclusions


In silico methods

Computer-based prediction models that rely on the development of a relation

between a chemical (sub)structure of a compound and the biological effects

(or properties) elicited by chemicals containing this (sub)structure.

• (quantitative) structure-activity relationships (Q)SARs

• expert rule-based systems

• analogue approaches (which include grouping and read-across )

The development and application of all kinds of non-testing methods is based

on the “similarity principle”, i.e. hypothesis that structurally similar compounds

should have similar properties/biological activities.

Definition and main approaches


In silico methods

a) (quantitative) structure-activity relationships (Q)S ARs

b) expert rule-based methods

c) analogue approaches (read-across)

Formalisedmethods

Not formalisedmethod


(Q)SAR / (Q)SPRQuantitative Structure Activity/Property Relationship

A QSAR is a mathematical model (often a statistical correlation) relating one or more

quantitative parameters derived from chemical structure to a quantitative measure of a

property or activity (e.g. a (eco)toxicological endpoint).

CHEMICAL STRUCTURE

BIOLOGICAL ACTIVITY (e.g. toxicity)

PHYSICO-CHEMICAL / FATE PROPERTIES


(Q)SAR / (Q)SPRQuantitative Structure Activity/Property Relationship

BIOLOGICAL ACTIVITY (e.g. toxicity)

PHYSICO-CHEMICAL / FATE PROPERTIES

CHEMICAL STRUCTURE

Molecular descriptor representation

(1D-2D-3D descriptors, structural fragments)

(MLR, PLS, ANN, 3D-QSAR, etc …)


Q)SAR / (Q)SPRQuantitative Structure Activity/Property Relationship

COMPOUNDS with known experimental data (Y)

COMPOUNDS with known experimental data (Y)

Molecular DescriptorsMolecular Descriptors

QSAR ModelY = f (descriptors)

X

1

.

.

.

.n

X

1

.

.

.

.n

xn... Y

INPUT DATAChemometrical Tools

COMPOUNDS with unknown experimental data (Y)

COMPOUNDS with unknown experimental data (Y)

Molecular DescriptorsMolecular Descriptors

Predicted Data ( Ŷ)Predicted Data ( Ŷ)


Expert systems

Expert systems are system which makes use of rules compiled by

human experts and stored in a knowledge base.

• A typical rule might take the form: if-then-else’ rules

• Arguments for and against toxicity from such rules are

weighed up against each other to arrive at an overall assessment.

Judson et al, Journal of Chemical Information and Computer Sciences 43 1364-1370 2003.

Decision TreesExpert systems


Read-across

Technique for data-gap filling where endpoint information from one chemical is used to

predict the same endpoint for another chemical which is considered to be similar in some

important aspect relating to that endpoint (e.g. mode of action, toxicokinetics, metabolism)*

Analogue Target

PropertyReliable data point

Missing data point

Analogue 1 Target Analogue 2

Property

*ECHA Guidance on information requirements and chemical safety assessment, Chapter R.6: QSARs and grouping of chemicals.


Why different methodologies ? (Q)SARs

• usually higher accuracy

• can be use for preliminary research when mechanism of action is unknown

• usually difficult to interpret• the mechanistically reasoning is

not always provided• often non-transparent to the end-

user

• provide hints on the mechanism of action

• provide a reasoning

• usually cannot explain differences of the activity within a chemical class

• applicability domain often restricted and/or ill-defined

• The acceptance of the read-across to fill data gap is growing in the regulatory framework

• Detailed guidance on how to perform it are now available.

• Strongly depends on expert judgment

• It is not a formalized approach, therefore it cannot be encoded in a reproducible algorithm

Expert systems Read-across


Consensus approach«Essentially, all models are wrong, but some are useful»

Each modelling technique has its own

advantages and weaknesses, an obvious

approach to get the best out of several

worlds, is to combine predictions from

different models.

Empirical Model-Building and Response Surfaces (1987)George Edward Pelham Box, Professor Emeritus of Statistics at the University of Wisconsin


QSAR Applications

• Drug discovery : virtual screening and lead optimization (3D-QSARs,

ligand-based approaches)

• Green chemistry : benign by design approach

• Predictive toxicology : to gain insights into the mechanism/mode of

action (mechanistic-based QSARs)

• Chemical Safety Assessment (industrial chemicals, agrochemicals,

food, cosmetics, etc…): prediction of physicochemical and fate

properties, toxicological and eco-toxicological effects


QSAR ApplicationsRegulatory framework

Multiple legislations requiring information on chemical properties and (eco-

)toxicological effects (e.g., EU REACH, CLP Regulation, ICH M7 guideline,

Biocide and Cosmetic regulations) promote the use of in silico methods:

o to support priority setting procedures (i.e. provide the basis for

further testing)

o to supplement the use of experimental data in weight-of-evidence

approaches

o to replace animal testing

The ways in which in silico methods are used depend on the possibilities

foreseen by the regulatory framework


QSAR ApplicationsRegulatory framework

To consider a QSAR prediction ADEQUATE for a regulatory purpose:

i. the model used is shown to be scientifically valid ;

ii. the model used is applicable to the chemical of interest;

iii. the prediction (result) is relevant for the regulatory purpose;

iv. appropriate documentation on the method and result is given. REACH Annex XI Conditions for (Q)SARs

QMRF & QPRFQSAR Model/Prediction

Reporting Formats

OECD principlesfor development and validation of (Q)SARs


OECD PrinciplesFor the validation of (Q)SARs for regulatory purposes

1) A defined endpoint

ENDPOINT = physico-chemical property, biological effect, environmental fate parameter that can be

measured and therefore modeled (definition of the experimental protocol and experimental conditions)

2) An unambiguous algorithm

Transparency in the description of the model algorithm (dataset, descriptors, modelling method, etc…)

3) A defined domain of applicability

In terms of the types of chemical structures, physico-chemical properties and mechanisms of action for

which the models can generate reliable predictions

4) Appropriate measures of goodness-of-fit, robustness an d predictivity

Internal and external validation

5) A mechanistic interpretation, if possible.

OECDPrinciples


QSAR ApplicationsWater treatment framework

• The use of QSAR methodologies in water treatment is limited.

Can QSARs be applied to predict removal efficiency of different water treatment systems and identify the most suita ble method?

Water treatment process Endpoints for QSAR

Nanofiltration membrane processes • membrane rejection

Activated carbon adsorption processes • carbon-water adsorption coefficient (Koc)

Advanced Oxidation Processes (AOP)- e.g. UV/H2O2 oxidation

• photochemical reaction parameters (e.g., OH radical reaction rate constant, quantum yield, molar absorption, photolysis half-life)


QSARs in water treatmentLimits and Potentialities

• Availability of experimental data

• Local models

• Limited Applicability Domain

• No robust internal and external validation

o Small datasets (10-20 compounds)

o Homogeneous datasets of congeneric compounds (e.g., phenols, alcohols, alkanes, PCDD, PBDE) Y-Exp

Y-P

red

Y-ExpY-

Pre

d

Training setPrediction set

PBDEs



• Applicability of QSAR models

Roy, Kovarich, and Gramatica (2011) QSAR Model Reproducibility and Applicability: A Case Study of Rate Constants of Hydroxyl Radical Reaction Models Applied to Polybrominated DiphenylEthers and (Benzo-)Triazoles. J Comput Chem 32: 2386–2396.

PBDEs and fluorinatedcompounds out of AD of OH• degradation rate QSAR model

Applicability to specific chemical classes, emerging

pollutants (e.g. brominated, fluorinated compounds)

• Local models for specific chemical classes (e.g.,

photolysis of PBDEs)

• Correction factors based on the contribution of

specific fragments/functional groups



• Reproducibility of QSAR models

o Transparent algorithm (dataset, molecular descriptors and

modelling method)

o Availability of molecular descriptors (free/commercial tools)

o Update of molecular descriptors

• Update of QSAR models (descriptors, experimental data)

• Implementation in software (commercial or freeware)

� AOPWIN, KOCWIN (implemented in EPI Suite)

� CADASTER QSPR-Thesaurus database

� ToxPredict (OpenTox models)

� MolCode Toolbox

� QSARINS-Chem

There are other software products as well as excellent in-house tools


Outline

• About S-IN

• Background





• Conclusions


Conclusions

• The term “ in silico” methods covers several kind of methodologies (QSAR models, read-across, expert

systems) characterised by a diverse level of complex ity.

• More accurate in silico predictions can be achieved by combining multiple m odels and multiple

methods in a consensus approach.

• The application of QSAR in water treatment is expec ted to significantly increase in the nearest future :

emerging needs for further development and update o f QSAR models, and implementation in tools

(both commercial and freeware).

• Our experience is that in silico methods should NOT be applied automatically: expert ise are needed to

get reliable predictions


Thank you for the attention !

[email protected]

www.s-in.it

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