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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
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
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
Transnational Action Program on Emerging Substances
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.
Transnational Action Program on Emerging Substances
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
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
In silico methods
a) (quantitative) structure-activity relationships (Q)S ARs
b) expert rule-based methods
c) analogue approaches (read-across)
Formalisedmethods
Not formalisedmethod
Transnational Action Program on Emerging Substances
(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
Transnational Action Program on Emerging Substances
(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 …)
Transnational Action Program on Emerging Substances
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)
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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.
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
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)
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
QSARs in water treatmentLimits and Potentialities
• 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
Transnational Action Program on Emerging Substances
QSARs in water treatmentLimits and Potentialities
• 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
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
Transnational Action Program on Emerging Substances
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
Transnational Action Program on Emerging Substances
Thank you for the attention !
www.s-in.it
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