transformation products of organic contaminants – relevant risk
TRANSCRIPT
Transformation products of organic contaminants –Relevant risk factors?
Kathrin Fenner
Eawag, Swiss Federal Institute of Aquatic Science and Technology
Occurrence of transformation products
• Some herbicide transformation products detected in higher concentrations and more frequently than the parent pesticides in groundwater and surface water
Infotag 2009
Gilliom et al. (2005) USGS Circular 1291
Transformationproducts
5 herbicides(triazines, chloro-acetanilides)
Sinclair and Boxall (2003) Environ Sci Technol 37: 4617-4625
Parent compound ecotoxicity (EC50) (mM)
• 30% of transformation products more toxic than parent pesticide to fish, daphnids and algae
0.0001 0.01 1 100 10000
100000
1000
10
0.1
0.001
Tran
sfor
mat
ion
prod
uct e
coto
xici
ty(E
C50
) (m
M)
Transformation product10x more toxicTransformation product100x more toxic
Toxi
city
low
high lowToxicity
Transformation productequally toxic
How toxic are transformation products?
• Pesticide Directive (91/414/EEC):– Identification of relevant transformation products
mandatory– Clear guidance on assessing their relevance
• Industrial chemicals (REACh):– Identification of transformation products requested for
products > 100 t/y– No guidance on assessment
• Human and veterinary medicines (EMEA):– Consideration of environmental transformation
products subject to expert judgment
How are transformation products regulated?
• Research goals– Assess exposure to transformation products– Identify relevant transformation products
• Challenges– Which products?– Lack of analytical standards– Scarcity of experimental fate data
• Opportunities– Read-across from parent compound properties
⇒ Develop toolbox to adequately and efficiently assess transformation products
Open questions, challenges and opportunities
SBR
HR-MSMS
Fate models
Field studies
Readacross
Exposure assessmentPEC/MEC
Transformation products
Ris
k in
dica
tor
Risk assessment
Effect assessmentPNEC
Identification
Transformation product 2
Transformation product 1
Transformation product 3
Relevant transformation
product!
Identification
Strategy for assessing transformation products
• Artificial intelligence tools
• University of Minnesota Pathway Prediction System (UM-PPS)
– About 200 transformation rules– Derived from database of experimentally elucidated
biotransformation pathways (UM-BBD)– Publicly available, transparent, continuously developed– http://umbbd.msi.umn.edu/predict/index.html
Set of transformation rules
Structure-biodegradation relationships
University of Minnesota Pathway Prediction System (UM-PPS)
Structure-biodegradation relationships
Method to limit combinatorial explosion needed!
127 10 510
2nd step - # of predicted metabolites
Benzamide
bt0027bt0011
bt0012bt0013 bt0353
The challenge: Combinatorial explosionStructure-biodegradation relationships
“Known” products
• Use knowledge from experimentally elucidated pathways to learn about rule priorities
• Define rule priorities: If two rules are applicable, only apply the one more likely according to known pathways
• Find pairs of rules with clear rule priority over all known transformations
alcohol oxidation > aliphatic hydroxylation
⇒ 110 rule priorities
bt0063 > bt0036bt0022 > bt0036
“Unknown” products
>
Finding rule priorities through data miningStructure-biodegradation relationships
Formed in batch studywith activated sludge
Fenner et al., Bioinformatics, 2008
UM-PPS: Results from implementation of rule prioritiesStructure-biodegradation relationships
n Predictedreactions
OriginalPesticides 24 280With relative reasoningPesticides 24 240
• 15% reduction
-14.3%
UM-PPS: Results from implementation of rule prioritiesStructure-biodegradation relationships
Fenner et al., Bioinformatics, 2008
n Predicted Known products Sensitivity Selectivityreactions predicted not
predicted(%) (%)
OriginalPesticides 24 280 43 15 74.1 15.4With relative reasoningPesticides 24 240 43 15 74.1 17.9
• 15% reduction• Sensitivity stable• Selectivity improved
UM-PPS: Results from implementation of rule prioritiesStructure-biodegradation relationships
Fenner et al., Bioinformatics, 2008
Bioreactor considerations:Control pH, temperature, dissolved oxygenSludge (active biomass) concentrationSpiked compound concentrationSorption and abiotic controls
m/z
Inte
nsity
High resolution mass spectrometry
Sample preparation considerations:Sample volume – SPECompound sorption to syringeCompound sorption to filter materialSample storage
t= 0 6h 12h 1d 21d
…
Analytical method:Previously developed screening methodUM-PPS predicted TP massesIdentification with exact mass and MS/MSNo need for reference standards
Experimental setupExperimental elucidation of rule priorities
Unsubstituted or monosubstituted aromatics
2° or 3°aliphatic Aliphatic methyl
Ether AmineAmide
Aromatic methyl
Selection of pertinent rule prioritiesExperimental elucidation of rule priorities
OR3
NR2
R3O
R1
OHO
R1
NHR2
R3
NHR2
O
R1OH
R3
+
+
Hydrolysis products
Dealkylation productsOR
Case study: AmidesExperimental elucidation of rule priorities
• No transformations with priorities over amide fragments within UM-PPS
• Specific biodegradation pathway of amides remains ambiguous• Starting hypothesis: 1° and 2° amides hydrolyze rapidly, 3° amides
dealkylate
NH2
O
O
OH
NH
CH3
CH3
NHO
Cl
O
OOH
CH3 CH3N
OCH3
NHN
N
N
CH3
CH3
OOH
CH3
N
O
N
O
H
H CH3
N
Cl
N
OO
ONH
CH3
O
NH2
CH3
CH3
OCH3
Hydrolysis
Dealkylation
Other
Experimental elucidation of rule priorities
Helbling et al., in prep., 2009
NO
CH3
CH3
Cl
NO
CH3
CH3
Cl
NO CH3
CH3
N
O
CH3
CH3
CH3
NO CH3
Cl
CH3
NHO
CH3CH3
Hydrolysis
Dealkylation
Other
Experimental elucidation of rule priorities
Helbling et al., in prep., 2009
O
R1
NR2 R3
R1
[C,H], (A,a) [C,H], (A,a)
NHR2 R3
[C,H], (A,a) [C,H], (A,a)+
O R2R1(A,a) (A)
OHR1(A,a)
O R2
(A)
+
Outlook and further workStructure-biodegradation relationships
• New case study for amines and ethers
• UM-PPS mirror at ETH Zürich– More “environmental realism”– Validation and further development based on data from soil
and activated sludge simulation studies
• Feedback loop into UM-PPS
SBR
HR-MSMS
Fate models
Field studies
Readacross
Exposure assessmentPEC/MEC
Transformation products
Ris
k in
dica
tor
Risk assessment
Effect assessmentPNEC
Identification
Transformation product 2
Transformation product 1
Transformation product 3
Relevant transformation
product!
Strategy for assessing transformation products
Mass balance model to predict concentration ratios of parent pesticide and transformation products
Gasser et al., ES&T, 2007; Kern et al., in prep., 2009
Measure for exposure to transformation product:Relative concentration in last box of river model
P TPC TP
EmissionRiver, 30 km
PCTP
Field study La Petite Glâne;Chemical analysis for 12 events and 6 pairs of parent compounds and transformation products
ToolsFinding products with relevant aquatic exposure
Application period 2nd period 3rd periodChloridazonFinding products with relevant aquatic exposure
0.01
0.10
1.00
10.00
100.00
1000.00
Azoxystrobin Atrazine Metolachlor Metamitron Sulcotrione Chloridazon
Rat
iotra
nsfo
rmat
ion
prod
uct :
par
ent p
estic
ide Application
2nd period
3rd period
Baseflow
n.d. n.d.
Atrazine Metolachlor
Summary of field study resultsFinding products with relevant aquatic exposure
AzoxystrobinAtrazine
Metolachlor
Sulcotrione
0.01
0.10
1.00
10.00
100.00
1000.00
0.01 0.10 1.00 10.00 100.00 1000.00
Rat
io T
P :
PC
Mea
sure
men
ts
Chloridazon
Metamitron
Ratio TP : PCModel predictions
Comparison model – measurementsFinding products with relevant aquatic exposure
SBR
HR-MSMS
Fate models
Field studies
Readacross
Exposure assessmentPEC/MEC
Transformation products
Ris
k in
dica
tor
Risk assessment
Effect assessmentPNEC
Identification
Transformation product 2
Transformation product 1
Transformation product 3
Relevant transformation
product!
Strategy for assessing transformation products
Membrane-water partition coefficient(log Dmw, pH 7)
Toxi
city
(log
1/E
C50
)
Theoretical relation-ship for baseline toxicity
Effect and risk assessmentPredicting toxicity range of transformation products using read-
across
Measured, specific toxicity of parent compound
Transformationproduct 1
Transformationproduct 2
Parent compound
diuron
DCPMU
DCPU
DCA
MCPDMU
Effect and risk assessmentCase study diuron
2
3
4
5
6
7
8
2.3 2.5 2.7 2.9 3.1
log Dmw (pH 7)
log (
1/E
C50
(M))
Alg
en,
72 h DCPMU
diuron
MCPDMUDCPU DCA
0
20
40
60
80
100
1 2 3 4 5
Relative concentrations
Diuron DCPMU MCPDMU DCPU DCAConce
ntr
atio
ns/
risk
contr
ibutions
(%)
Relative risk contributions (baseline toxicity)Relative risk contributions (specific toxicity)
Risk contribution:EC50 (product)
Concentration (product)EC50 (diuron)
Concentration (diuron)
Effect and risk assessmentModeling effects, concentrations and risk contributions
• Prediction of biodegradation products– Restriction of prediction space through
data mining and targeted experiments
• Characterization of exposure to transformation products– Combination of modeling and monitoring – Importance of groundwater component!
• Procedure for risk assessment of transformation products– Prediction of relative concentrations– Prediction of toxicity range through read-
across– Targeted toxicity studies
Con
cent
ratio
n
Lipophilicity
Toxi
city DCPMU
diuron
MCPDMUDCPU DCA
Conclusions
Thank you!
KoMet team:• Susanne Kern• Judith Neuwöhner• Beate Escher, Juliane
Hollender, Heinz Singer
Swiss Federal Office for the Environment (Bafu):
• Michael Schärer• Paul Liechti, Christof Studer,
Reto Muralt, Christian Pillonel, Daniel Traber
