Treatment Process Modeling by QSAR Approach Biodegradation

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Treatment Process Modeling by QSAR Approach -
Biodegradation

• Sung Kyu (Andrew) Maeng

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Contents

• QSAR Introduction
• QSBR Introduction
• Results and discussion
• Current QSAR project in UNESCO-IHE

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Introduction to the (Q)SAR concept

• Chemicals with similar molecular structures have
  similar effects in physical and biological
  systems
• ? qualitative model (SAR)
• The extent of an effect varies in a systematic
  way with variations in molecular structure
• ? quantitative model (QSAR)

Biodegradation index 4.066-0.007MW-0.314H/C r
0.866, r2 0.750, Sig. lt 0.005, n 156
Activity depends on chemical structure
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SAR vs QSAR

• SAR is based on the similarity principle
• The principle is assumed, but in the reality it
  is not always true
• - Similarity of structures
• - Similarity of descriptors
• The authenticity depends on the type of the
  relationship between descriptors (numerical
  representation of chemicals) and activity
• The type of the relationship should be known (or
  derived)

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SAR vs. QSARhow could we say there is a
difference ?

• Three common things to this point
• Both methods use numerical representation of
  chemical compounds
• Both methods need to decide which representation
  to use
• Both methods need to derive the relationship
  between numerical representation (descriptors,
  etc.) and activity.

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QSAR in water treatment processes

• Results obtained from valid qualitative or
  quantitative structure-activity relationship
  models can provide the removal of PhACs in
  drinking water and the process selection for
  target compounds. Results of QSAR may be used
  instead of testing if results are derived from a
  QSAR model whose scientific validity has been
  established

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QSAR in water treatment processes

• In principle, QSARs can be used to
• - provide information for use in priority
  setting treatments for target compounds
• - guide the experimental design of a test or
  testing strategy
• - improve the evaluation of existing test data
• - provide mechanistic information (e.g. to
  support the grouping of chemicals into
  categories)
• - fill a data gap needed for classification

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OECD Principles for QSAR Validation

• QSAR should be associated with the following
  information
• - a defined endpoint
• - an unambiguous algorithm
• - appropriate measures of goodness-of-fit,
  robustness and predictivity
• - a mechanistic interpretation, if possible

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QSBR

• Development of Quantitative Structure-Biodegradati
  on Relationships (QSBRs)
• - QSBRs has been developed to predict the
  biodegradability of chemicals released to natural
  systems using their structure-activity
  relationships (SAR)
• - The development of QSBRs has been relatively
  slow compared with proliferation of QSARs because
  of the nature of the biodegradability endpoint
• - QSBR is very complex because
• 1. Chemical structure
• 2. Environmental conditions
• 3. Bioavailability of the chemical

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QSBR

• - Limitations often associated in developing
  QSBR
• 1. Only within cogeneric series of chemicals
• 2. The absence of standardised and uniform
  biodegradation databases
• - Recent years, a very intensive development of
  new and better qualitative and quantitative
  biodegradability models was observed
• - How many QSBR have been developed ?
• A literature search on QSBR was performed
  including literature published showed more than
  84 models
• - However, only a few models provided an
  acceptable level of agreement between estimated
  and experimental data

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QSBR

• - All QSBR models until 1994 were reviewed by
  several researchers for their applicability
• 1. Group contribution method (OECD, PLS,
  BIOWIN, MultiCASE)
• 2. Chemometric methods (CART)
• 3. Expert system (BESS, CATABOL, TOPKAT)
• - According to the previous studies, the group
  contribution method seems to be the most applied
  and successful way of modeling biodegradation

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Group Contribution Method

• OECD hierarchical model approach
• Multivariable Partial Least Approach (PLS) model
• BIOWIN
• MultiCASE anaerobic program

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What Does the BIOWIN Model Do?

• Provide estimates of biodegradability useful in
  chemical screening under aerobic condition
  (1,2,5,6)
• Provide approximate time required to biodegrade
  in a stream (3,4)
• Recently, BIOWIN was updated and now it can
  estimate anaerobic biodegradation potential (7)

BIOWIN has 7 models (U.S. EPA, 2007)
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Materials and method

• Finding Molecular Descriptors
• Sofrware Delft Chemtech, Dragon, Chem3D etc
• Selection of Molecular Descriptors
• 1. PCA (SPSS)
• 2. Genetic Algorithm-Variable Subset Selection
  (Mobydigs)

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Principal Component Analysis
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Principal Component Analysis (PCA)

• Variables MW, MV, log Kow, dipole, length,
  width, depth, equiv width, HL surface, polar
  surface are
• Assessment of the suitability of the data for PCA
• - KMO gt 0.6 (KMO 0.6), Barletts Test of
  Sphericity lt 0.05 (lt0.005)
• Determination of the number of factors by Kaise
  criterion, scree plot and Montecarlo parallel
  analysis

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Classification PhACs - PCA
HL-neu
HL-ion
HP-neu
HP-ion
The two-component solution explained a total of
67 of the variance with Component 1 contributing
46 and Component2 contributing 21 Component 1
SIZE and component 2 Hydrophobic/Hydrophilicity
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Biodegradation (Aerobic)

• HP and HP-ionic compounds were not feasible to
  come up with equation because of collinearity
  problem in variables
• (Violation in MLR assumptions)

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Innovative system for removal of micropollutants
RBF and NF membrane
days
RBF
days - weeks
weeks
weeks - months
months
Membrane
longer
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QSAR Models Decision Support Framework
Organic micropollutants
QSAR
BIOWIN
Biological treatment
Physical/Chemical Treatment
Kow
KO3
ARR
RBF /DUNE
Membrane
GAC
AOP
MW
NF
RO
Cl2
O3
Process selection and comparative performance
assessment
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Current QSAR project
2008
GIST
Analysis of PhACs LC-MS / AUTO SPE
QSAR Tools
Selection of Target compounds
Selection of Target compounds
Physical-chemical characteristics Vs. Water
treatments
2009
Selection of Water Treatments
Selection of Water Treatments
Selected water Treatments
PhACs removal using selected water treatments by
UNESCO-IHE
PhACs removal using selected water treatments by
GIST
Classification, Database, Model development
A decision support tool for PhACS removal for
water utility
2010