Sung Kyu (Andrew) Maeng

Contents

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

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)

Activity depends on chemical structure

Biodegradation index = 4.066-0.007MW-0.314H/C r = 0.866, r2 = 0.750, Sig. < 0.005, n= 156

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)

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.

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

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

QSAR in water treatment processes

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

Development of Quantitative Structure-

Biodegradation 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

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

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

QSBR

OECD hierarchical model approach

Multivariable Partial Least Approach (PLS) model

BIOWIN

MultiCASE anaerobic program

Group Contribution Method

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)

BIOWIN1 BIOWIN2 BIOWIN3 BIOWIN4 BIOWIN5 BIOWIN6 BIOWIN7

linear non-linear Ultimate Primary linear Non-linear

Based on regressions against 36 preselected chemical structures plus molecular weight of experimental biodegradation data for 295 compounds (BIODEG)

Based on regressions of biodegradability estimates from a survey of experts for a suite 200 organic chemicals against the same chemical substructures plus molecular weight

Based on regressions of data from the Japanese MITI database against a modified set of chemical substructures plus molecular weight

Based on BIOWIN fragment contribution approach.

What Does the BIOWIN Model Do?

Materials and method

Finding Molecular DescriptorsSofrware Delft Chemtech, Dragon, Chem3D etc…

Selection of Molecular Descriptors1. PCA (SPSS)2. Genetic Algorithm-Variable Subset Selection (Mobydigs)

Principal Component Analysis

Structure Matrix

.952

.905

.900

.879 -.395

.780

.723

.714 .898

.379 -.855

.444 .720-.358 .713

MWeqwidthwidthMVREJdepthlengthdipoleHL_surflog_Kowpo_surfBiowin3

1 2Component

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization.

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 > 0.6 (KMO = 0.6), Barlett’s Test of Sphericity < 0.05 (<0.005)

Determination of the number of factors by Kaise criterion, scree plot and Montecarlo parallel analysis

Principal Component Analysis (PCA)

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

HP-neuHP-ion

HL-neu

HL-ion

Classification PhACs - PCA

Dependent variable

Independent variables (Indices, Chemical descriptors)

BIOWIN3 MW, MV, log Kow, dipole, length, width, depth, equiv width, % HL surface, polar surface area

R2 STD. Error

Sig.

(p)

Rej. range

(%)

BIOWIN3 range

Equation to predict biodegradation

HL 0.76 0.21 < 0.05 6.70-98.5

(75)

1.86- 3.60

(2.8)

2.842-0.168logKow-0.008MV+1.039length

(-59+170.06width)

HP1 - - - 37.5-99.1

(86)

1.52-2.96

(2.5)

-

198+7.53log_Kow-42.75length-94.09eqwidth

HL-ionic 0.55 0.25 < 0.05 74.8-96.9

(91)

1.86-3.03

(2.6)

3.536-0.009MW+0.934length

(138.81-5.04logKow-13.84length-94.09HL_surf)

HP-ionic1

- - < 0.05 74.8-99.1

(95)

2.16-2.96

(2.7)

-

(198.38+7.53logKow-42.57length-94.09eqwidth)

HL-neutral

0.84 0.19 < 0.05 6.7-98.5

(60)

2.28-3.59

(2.9)

3.323-1.88logKow-0.004MV

(-119.89+4.53logKow+27704eqwidth)

HP-neutral

0.35 0.23 < 0.05 37.5-98.1

(79.7)

1.52-2.68

(2.3)

3.493-4.30logKow

(122.38-32.16logKow+109.73eqwidth-0.78HL_surf)

1. HP and HP-ionic compounds were not feasible to come up with equation because of collinearity problem in variables

(Violation in MLR assumptions)

Biodegradation (Aerobic)

Innovative system for removal of micropollutants – RBF and NF membrane

RBF

Membrane

months

longer

weeks

days

days - weeks

weeks - months

Organic micropollutants

QSAR

Biological treatmentPhysical/Chemical

Treatment

MembraneGAC AOP

NF RO Cl2 O3

ARRRBF /DUNE

BIOWIN

KowK O3

MW

Process selection and comparative performance assessment

QSAR Models Decision Support Framework

GIST

Analysis of PhACsLC-MS / AUTO SPE

Selection of Target compounds

Physical-chemical characteristicsVs. Water treatments

Selection of Target compounds

QSAR Tools

Selection of Water Treatments Selection of Water Treatments

Selected water Treatments

Classification, Database, Model development

PhACs removal using selected water treatments by GIST

PhACs removal using selected water treatments by

UNESCO-IHE

A decision support tool for PhACS removal for water utility

2008

2009

2010

Current QSAR project