Front page for deliverables
Project no.
003956
Project acronym
NOMIRACLE
Project title
Novel Methods for Integrated Risk Assessment of
Cumulative Stressors in Europe
Instrument
IP
Thematic Priority
1.1.6.3, ‘Global Change and Ecosystems’
Topic VII.1.1.a, ‘Development of risk assessment
methodologies’
Deliverable reference number and title:
D.2.3.12 Report on first prototype of model for predicting biodegradation
pathways in soil
Due date of deliverable: 31 January, 2007 Actual submission date: 23 January, 2008
Start date of project: 1 November 2004
Duration: 5 years
Organisation name of lead contractor for this deliverable: LMC
Revision [draft, 1, 2, …]: Final Report
Project co-funded by the European Commission within the Sixth Framework Programme (2002-2006)
Dissemination Level
PU
PP
RE
CO
Public
Restricted to other programme participants (including the Commission Services)
Restricted to a group specified by the consortium (including the Commission Services)
Confidential, only for members of the consortium (including the Commission Services)
X
D. 2.3.12
Authors and their organisation:
Gergana Dimitrova, Sabcho Dimitrov, Ovanes Mekenyan
Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bulgaria
(Partner 35)
Deliverable no:
Nature:
D. 2.3.12.
Report
Status: Final Report
Reviewed by (period and name):
February 4-18, 2008, Ralph Kühne
Dissemination
level: PU
Date of delivery:
21 February 2008
Date of publishing:
February 2008
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Contents
Page
1. Introduction
4
2. Materials and methods
4
2.1. Observed metabolic pathways in soil
4
2.2. Simulation of metabolism
5
2.3. Similarity of metabolic pathways
5
3. Results
8
4. Summary
16
5. References
17
D. 2.3.12
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1. Introduction
The aim of this study is to build a prototype of simulator for terrestrial biodegradation
using the database with observed biodegradation pathways in soil. The modeling methodology is
based on multipathway probabilistic approach for simulation of metabolism. The core of the
approach is a library of hierarchically ordered individual transformations (catabolic steps) and
matching substructure engine providing their subsequent performance.
A collection of
biodegradation pathways for 183 chemicals in soil were used to adapt the currently available
simulator for terrestrial environment. The quality of simulator was quantified by the degree of
reproducibility between observed and generated metabolites.
2. Materials and methods
2.1. Observed metabolic pathways in soil
An electronic database with documented biodegradation pathways in soil were collected
from different sources including monographs, scientific articles and public web sites. The most
significant sources for these data were [1-3].
The created database includes observed
biodegradation pathways for 183 chemicals, mainly pesticides.
Amongst them there are
herbicides, insecticides, fungicides, acaricides etc. The latter includes substances with a variety
of chemical functionality, such as acid amides, aniline and nitrobenzenes, dithio and
thiolcarbamates, five- and six-rings heterocyclic compounds, phenyl (aryl) carbamates,
phosphoro(di)thiolates, sulfonylureas, etc. Additional information for microbial species, bacteria
strain and presence or absence of oxygen was also implemented into the database. The catabolic
D. 2.3.12
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pathways database for soil was used to extract new transformations and to adapt the currently
available simulator for terrestrial environment.
2.2. Simulation of metabolism
The modelling methodology is based on the probabilistic approach.
Catabolism is
simulated via the principal molecular transformations extracted from the metabolic pathway
database.
The molecular transformation consists of parent sub-molecular fragment,
transformation products and inhibitory functional groups (masks). The latter serves as a reaction
inhibitor. If the fragment assigned as a mask is attached to the target sub-fragment the execution
of the transformation on parent chemical is prevent. Recently, a multi-pathway approach for
simulating molecular transformations of chemicals in biotic and abiotic conditions was proposed
[4-6]. The development of this approach was conditioned by the fact that along the most
probable pathway chemicals could be metabolized by a number of less probable pathways.
Initially, the parent chemical is submitted to the list of transformations and all transformations
meeting the associated sub-structures are implemented on the parent producing the list of the
first level metabolites. Each of the generated metabolites is then submitted to the same list of
transformations to produce the second level of metabolites, etc.
The hierarchy of the
transformations and their probabilities are used to control the propagation of the catabolic maps
of the chemicals. If the probability to obtain certain metabolite is less than a critical value (0.001
in the current version of the simulator) the propagation of metabolism is stopped and this
metabolite is an end product.
2.3. Comparison of metabolic pathways
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An important characteristic of the metabolism simulator is its ability to correctly
reproduce the observed metabolic maps. The predictability of the simulator is estimated on the
basis of the union of the observed and predicted metabolic maps as is illustrated in Figure 1.
+
Observed catabolism
=
Predicted catabolism
Union of pathways
Figure 1. Predicted and observed catabolism and their union.
In order to quantify the metabolism predictability of the model we distinguished four
types of nodes in the observed and simulated maps as follows:

- Root of the tree (parent chemical),

- Observed and predicted metabolites;

- Observed and not predicted metabolites (false negatives);

- Predicted and not observed metabolites (false positives);
SObs  SPred
,
where SObs and SPred are the sets of observed and predicted metabolites.
S Obs \ S Pred
S Pred \ SObs
.
,
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Based on the union of metabolic trees the following statistics can be applied for evaluating
the performance of the simulator:

Probability that the metabolite is truly observed, given that the metabolite is predicted
(predictability):
Pred 

Card S Obs  S Pred 

Card S Pred 

(1)
Probability that the metabolite is predicted, given that the metabolite is truly observed
(sensitivity):
Sens 

Card S Obs  S Pred 

Card S Obs 

(2)
Probability that the metabolite is not predicted given that the metabolite is truly observed
(false negatives):
Fneg

Card SObs \ S Pred 

Card SObs 

 1  Sens
(3)
Probability that the metabolite is predicted given that the metabolite is truly not observed
(false positives):
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Fpos
Card S Pred \ SObs 

Card S Pred 

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 1  Pr ed
(4)
In equations (2 - 4) parent chemical is not taken into account.
3. Results
Collected data with documented biodegradation pathways in soil for about 180 chemicals
was used to adapt the currently available simulator for biodegradation in water. All statistics
presented below are calculated on the bases of these training chemicals. The number of the
transformations in the old version of the simulator was 609. To evaluate its performance we
have used the collected data set with 183 observed biodegradability pathways, using as a
measure for simulator performance the sensitivity - probability that the metabolite is predicted,
given that the metabolite is truly observed and the predictability - probability that the metabolite
is truly observed, given that the metabolite is predicted.
The results with the simulator
developed to predict metabolism in water showed very low sensitivity and predictability – 57%
and 45%, respectively. In order to improve the performance of the simulator new reactions were
included and some specific masks were added to the existed transformations. As a result of these
modifications the number of the transformations increased up to 689. Table 1 illustrates some of
the new entered reactions and their inhibiting masks.
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Table 1. List with new entered reactions and their inhibiting masks.
#
Principal transformations
1
Aromatic ring cleavage
Masks
Probability
1.00
OH
HO
C
O
C{sp3}
C{sp3}
O
OH
C
HO
C
O
C
C
O
C
2
Nitrile and amide hydrolysis
CH
CH2
C
HO
3
N
C
0.8143
CH
O
C
H2C
NH
C
Aromatic ring oxidation
0.1157
SH
OH
H
CH
C
C
Hal
Hal
Hal = F, Cl, Br, I
4
Oxidative O-dealkylation
0.0100
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N
O
N
CH3
N
5
N
OH
Oxidative C - S bond cleavage
C
0.0100
O
O
S
C
C
HO
O
S
+
C
O
OH
O
Imidic – Amide tautomerization
N
C
0.0100
HN
C
7
H3C
N
O
6
OH
+
N
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OH
C
N
C
O
N
Aromatic ring oxidation
0.0100
O
N
N
N
O
HO
H
N
O
C
O
8
Aliphatic sulphur oxidation
0.0100
C
C
N
N
C
C
S
N
CH
9
O
Oxidative N-dealkylation
S
N
CH
0.0100
D. 2.3.12
C
C
N
N
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C
H
NH
C
N
+
HO
C
H
C
10 Nitrogen formylation
0.0000
HC
C
NH2
C
O
O
CH3
O
NH
N
O
The improvement in the simulator performance is presented in Figure 2. As can be seen
from the figure the overall sensitivity increased up to 72% in comparison with the version for
biodegradation in water (57%).
Sencor
(A) Water system
(B) Water and soil compartment
Figure 2. Comparison of predictability of biodegradation simulators for water (A) and
water&soil (B) compartment.
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Sencor is an example of a chemical which sensitivity increased from 0% to 100% as a
result of the modifications of the model. As can be seen from Figure 2 Sencor, predicted with the
biodegradation simulator for water, belongs to the bar with structures having sensitivity values
between 0% and 10%. The same chemical predicted with the new simulator for biodegradation
in soil achieves the sensitivity value of 100% as a result of reproduction of all documented
metabolites. The observed and predicted biodegradation pathways in soil for this chemical are
presented in Figure 3A and 3B, respectively. As can be seen from Figure 3 the modified
simulator generates correctly all metabolites from the observed biodegradation pathway.
D. 2.3.12
(A) Observed metabolism
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(B) Simulated metabolism
Figure 3. Observed (A) and simulated (B) biodegradation pathway in soil for Sencor;
green – observed and predicted metabolites, yellow – not observed but predicted intermediates,
grey – predicted metabolites not accounted for similarity.
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Although the number of chemicals from the bars with low sensitivity presented in Figure
2 decreased significantly with the modified simulator there are still six structures for which the
sensitivity remains 0%.
Among these chemicals are Chloroanisidine, Thiophanate methyl,
Clethodim, Tetradifon, Alloxydim sodium, Tebufenozide.
The reasons for their low
predictability could be summarized as follows: 1) incomplete documented biodegradability
pathways with missing intermediates and 2) software limitation resulting in impossibility at this
stage to reproduce some of the transformations, for example – reactions of condensation. These
problems impose the necessity of collecting new biodegradation information in soil for the
problematic chemicals and implementation of a new logic of metabolism in the software in order
to reproduce the documented pathways.
In Figure 4 as an example is shown one part of the documented biodegradation pathway
for Chloroanisidine for which the simulator is unable to reproduce the formation of a dimmer.
The structures indicated with red represent the observed but not predicted metabolites.
D. 2.3.12
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Figure 4. Part of the documented biodegradation pathway for Chloroanisidine, including
the formation of dimmer.
An example of a chemical with incomplete observed metabolic pathway is illustrated in
Figure 5.
The biodegradation pathway for Alloxydim sodium does not present the full
catabolism of the chemical and can not be used for the extraction of a reasonable transformation.
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Figure 5. An example of a chemical with incomplete observed metabolic pathway.
4. Summary
The work presented here describes the first prototype of the model for predicting
biodegradation pathways in soil. Collected data with observed biodegradation pathways in
soil for 183 chemicals were used to adapt the CATABOL model to simulate their catabolism.
80 new reactions were included to the currently available simulator for biodegradation in
water. The quality of the simulator was quantified by the degree of reproducibility between
observed and generated metabolites. The results showed significant improvement of the
sensitivity that increased from 57% to 72%.
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5. References
1.
Aizawa, H., Metabolic Maps of Pesticides: Ecotoxicology and Environmental Quality
Series, Academic Press, New York, 1982.
2.
Aizawa, H., Metabolic Maps: Pesticides,
Environmentally Relevant Molecules and
Biologically Active Molecules , Academic Press, San Diego, 2001.
3.
Pesticides: Benefaction or Pandora 's Box? A synopsis of the environmental aspects of
243 pesticides, J. B. H. J. Linders, J. W. Jansma, B. J. W. G. Mensink, K. Otermann,
Report no 679101014, March 1994.
4.
O.G. Mekenyan, S.D. Dimitrov, T.S. Pavlov, G.D. Veith. Curr. Pharmaceut. Design, 10,
1273 (2004).
5.
O.G. Mekenyan, S. Dimitrov, R. Serafimova, E. Tompson, S. Kotov, N. Dimitrova, J.
Walker. Chem. Res. Toxicol., 17, 753 (2004).
6.
S.D. Dimitrov, T.S. Pavlov, R. Vasilev, O. Mekenyan. Simulation of abiotic
transformations by CATABOL, poster presented at SETAC Europe 15th Annual Meeting,
Lille, France, 21-26 May (2005).