The McKim Conference on the Use QSARs and Aquatic Toxicology
in Risk Assessment
June 27-29, 2006
METABOLISM
in-silico simulation
Sabcho Dimitrov
Ovanes Mekenyan
Laboratory of Mathematical Chemistry
University ‘Prof. As. Zlatarov’, Bourgas, Bulgaria
Research Partners from Regulation Agencies
US EPA, Athens, GA – 5 yrs coop
US EPA, Duluth, MN
Environment Canada
NITE Japan
Research Partners from Industry
P&G
ExxonMobil
Unilever
BASF
Overview
•
•
•
•
(Q)SARs
Metabolism of Prokaryotes
Metabolism of Eukaryotes
Simulation of metabolism
(Q)SARs
OH
O
Metabolism
(Q)SARs
O
OH
S
O
OH
CH3
O
O
HO
O
O
OH
OH
O
O
OH
OH
O
Biodegradation
Bioaccumulation
Acute Toxicity
Chronic Toxicity
Hormone Toxicity
Skin sensitization
Mutagenicity
…
The Five
Kingdoms
Hormone Toxicity
Skin sensitization
Mutagenicity
BCF
Acute & Chronic
toxicity
Biodegradation
Metabolism
Energy-generating component: Catabolism
Produce energy (as ATP) and simple oxidized compounds
Energy-consuming component: Anabolism
Build cell material
Intermediary metabolism
Set of reactions involving metabolites that are intermediates in the
degradation or biosyntheses of biopolymers
Common energy metabolism
tricarboxylic acids cycle, glycolysis
Common biosynthetic pathways
synthesis of amino acids, lipids, and carbohydrates
Anabolism
Catabolism
Intermediary
metabolism
Non-intermediary
metabolism
1. Response to environment
2. Catabolism
Enzymes often
need to be highly
substrate specific
Prokaryotes
Kingdom
Monera
The Kingdom Monera (Prokaryotes)
Constitutive enzymes: always produced by cells
The enzymes that operate during glycolysis and the tricarboxylic acid cycle
Inducible enzymes: produced ("turned on") in cells in response to a particular
substrate; they are produced only when needed
The Kingdom Monera (Prokaryotes)
Operon - grouping of genes in bacteria
under the control of the same regulatory
system.
Bacteria tend to group genes that are
functionally related together on the
chromosome.
Control
mechanism
Structural gene for
β-galactosidase
Structural gene
for β-galactoside
permease
Structural gene
for β-galactoside
transacetylase
Lactose operon: contains genes that encode enzymes responsible for lactose metabolism
Eukaryotes
Kingdoms:
Protista
Planta
Fungi
Animalia
Eukaryotes
Multienzyme complex
In a number of cases, enzymes that catalyze sequential reactions in
the same metabolic pathway have been found to be physically
associated:
•
•
•
Active sites are found on a single, multifunctional peptide chain
Several individual enzymes are non-covalently associated
Attachment to membranes
The formed enzyme complexes allow channeling of reactants
between active sites – the product of one reaction can be transferred
to the next active site.
•
•
Speed up the reactions of the pathway
Protect unstable intermediates
Separate enzymes with freely
diffusing pathway intermediates
(glycolysis)
Cytosolic multienzyme complex with
intermediates channelled between
enzymes
(pyruvate dehydrogenase complex)
Membrane bound
multienzyme complex
(electron transport system)
Summary
1. The application of metabolic transformations is strongly organized.
2. This is a premise for development of metabolic simulators.
Simulation of molecular transformations
R1
R1
C
O R2
C
O
O
O
O
+
C C
C C
HO R2
O C
OH
+
HO C
OH
Half-lives,
250C, pH =7
O
O
H3C C
H3C C
O CH2CH3
HO CH2CH3
+
2 years
OH
O
O
H3C C
H3C C
O CH CH2
+
O
HCl2C C
O C
7 days
OH
O
HCl2C C
HO CH CH2
+ HO C
OH
4 minutes
Simulation of molecular transformations
O
O
R1
R1
C
C
OH
O R2
#
Transformation
X2C C
X2C C
Rate
O
O
1
HO R2
+
+
High
HO C{sp2 }
OH
O C{sp 2}
X: F, Cl, Br
O
O
2
C C
C C
O C{sp 2}
3
HO C{sp }
not CX2; X: F, Cl, Br
O
C C
O C
Moderate
OH
O
C C
+
2
+
HO C
OH
not C{sp2} and CX2; X: F, Cl, Br
Low
Simulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
BESS (P&G and Michigan State University)
META (MultiCASE Inc)
METEOR (Lhasa Ltd)
CATABOL (P&G and LMC, Bourgas As. Zlatarov University)
TIMES (LMC, Bourgas As. Zlatarov University)
PPS (UM-BBD, http://umbbd.msi.umn.edu/)
MEPPS (under development, Lhasa Ltd)
Simulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
BESS (P&G and Michigan State University)
META (MultiCASE Inc)
METEOR (Lhasa Ltd)
CATABOL (P&G and LMC, Bourgas As. Zlatarov University)
TIMES (LMC, Bourgas As. Zlatarov University)
PPS (UM-BBD, http://umbbd.msi.umn.edu/)
MEPPS (under development, Lhasa Ltd)
Simulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
METEOR (Lhasa Ltd)
PPS (UM-BBD)
Simulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
CATABOL (P&G and LMC, Bourgas As. Zlatarov University)
TIMES (LMC, Bourgas As. Zlatarov University)
Probability
k
t1/2
Substrate
Principal transformations
CATABOL
Metabolites
Geminal diol decomposition
Simulation of catabolism
OH
C
C O
OH
-oxidation
OH
OH
O
O
Cyclohexanone oxidation
O
O
OH
Ester hydrolysis
O
O
O
OH
+
O
O
Amine decomposition
C
C
NH2
C
O
C
-Oxidation
CH3
OH
Azo-bond cleavage
C N
N C
C NH2 +
H2N C
Substrate
Principal transformations
Metabolites
Geminal diol decomposition
OH
P = 1.00
C O
C
OH
-oxidation
OH
P = 0.99
OH
O
O
Cyclohexanone oxidation
O
P = 0.95
O
OH
Ester hydrolysis
P = 0.90
O
O
+
O
OH
O
O
Amine decomposition
C
NH2
P = 0.75
C
O
C
C
-Oxidation
P = 0.40
CH3
OH
Azo-bond cleavage
C N
N C
P = 0.001
C NH2 +
H2N C
Substrate
Principal transformations
Metabolites
Geminal diol decomposition
O
OH
P = 1.00
C O
C
OH
-oxidation
OH
P = 0.99
OH
O
O
Cyclohexanone oxidation
Ester hydrolysis
P = 0.90
O
O
+
O
OH
O
O
Amine decomposition
C
NH2
P = 0.75
C
O
C
C
-Oxidation
CH3
P = 0.40
OH
Azo-bond cleavage
P = 0.001
C N
N C
C NH2
+
H2N C
Substrate
Principal transformations
Metabolites
Geminal diol decomposition
O
OH
P = 1.00
C O
C
Match? - No!
OH
-oxidation
OH
P = 0.99
OH
O
O
Cyclohexanone oxidation
Ester hydrolysis
P = 0.90
O
O
+
O
OH
O
O
Amine decomposition
C
NH2
P = 0.75
C
O
C
C
-Oxidation
CH3
P = 0.40
OH
Azo-bond cleavage
P = 0.001
C N
N C
C NH2
+
H2N C
Substrate
Principal transformations
Metabolites
Geminal diol decomposition
OH
P = 1.00
C O
C
OH
-oxidation
O
OH
Match? - No!
P = 0.99
OH
O
O
Cyclohexanone oxidation
Ester hydrolysis
P = 0.90
O
O
+
O
OH
O
O
Amine decomposition
C
NH2
P = 0.75
C
O
C
C
-Oxidation
CH3
P = 0.40
OH
Azo-bond cleavage
P = 0.001
C N
N C
C NH2
+
H2N C
Substrate
Principal transformations
Metabolites
Geminal diol decomposition
OH
P = 1.00
C O
C
OH
-oxidation
OH
P = 0.99
OH
O
O
Cyclohexanone oxidation
O
O
P = 0.95
O
O
RESULT
Match? - Yes!
OH
OH
Ester hydrolysis
P = 0.90
O
O
+
O
OH
O
O
Amine decomposition
C
NH2
P = 0.75
C
O
C
C
-Oxidation
CH3
P = 0.40
OH
Azo-bond cleavage
P = 0.001
C N
N C
C NH2
+
H2N C
The most plausible biotransformation pathway
O2
k0
- Parent chemical or metabolite
ki
1
P1
1
k1
O2
P2
Pi
CO2
- Transformation and its probability
2
CO2
2
k2
2
Oi2 , CO
i
- BOD or CO2 production for a
single transformation
k n -1
O2
Pn
n
CO2
n
kn
O2
Pn +1
n +1
CO2
n +1
CO2
Comparison of metabolic pathways
Pathway A
Pathway B
Complement of B in A
Complement of A in B
Intersection
A\B or A-B
B\A or B-A
A∩B
Accounting (or not) for pathway structures
Trivial measures of similarity/dissimilarity
pathway structure is not accounted for
Complement of B in A
Complement of A in B
Intersection
A\B or A-B
B\A or B-A
A∩B
Measures of similarity/dissimilarity:
Tanimoto,
Dice,
etc.
Non-trivial measures of similarity/dissimilarity
pathway structure is accounted for
Complement of B in A
Complement of A in B
Intersection
A\B or A-B
B\A or B-A
A∩B
A in B = Card(A∩B)/Card(A)
B in A = Card(A∩B)/Card(B)
A out of B = Card(A\B)/Card(A)
B out of A = Card(B\A)/Card(B)
Observed versus simulated pathways
Observed and predicted catabolism
+
Union of pathways
=
- Observed and predicted metabolites, SObs  SPred
- Observed and not predicted metabolites, SObs  S Pred or SObs \ S Pred
- Predicted and not observed metabolites, SPred  SObs or SPred \ SObs
CATABOL, mathematical formalism
Simulated catabolism
BOD Calc  63%
BOD or CO2 production
Lyons, C. D., S. Katz, R. Bartha, Appl Environ
Microbiol, 1984, 48, N 3, pp. 491-496
BOD  57%
CATABOL, mathematical formalism
Quantities of metabolites
Calc
n
Q
 1  Pn1   Pm , mol/mol parent
m 1n
O2
k0
1
P1
CO2
1
k1
O2
P2
2
CO2
2
k2
k n -1
O2
Pn
n
CO2
n
kn
O2
Pn +1
n +1
CO2
n +1
CO2
CATABOL, mathematical formalism
First order kinetics
BOD  1001  exp  kt
t 1 / 2  ln 2 k
O2
k0
1
P1
BOD
Calc
 63%
CO2
1
k1
O2
P2
2
CO2
2
k2


Calc
k   ln 1  BOD 28
 d / 100 / 28
k  0.036 day -1
k n -1
O2
Pn
n
CO2
n
kn
Pn +1
Ultimate half-life
t1/ 2
n +1
CO2
n +1
ln 2

Calc
 ln 1  BOD 28
 d / 100 / 28

O2

t1/ 2  20 days
CO2
CATABOLTD – time dependent model
Training data
CATABOLTD – time dependent model
Probabilistic approach & First order kinetics
Probabilistic approach
First order kinetics
P
k
S M
S M
S  S0 1  P
(7)
S  1  P 
S0
(8)
M  S0 P
M   P
S0
(9)
(10)
[S]0 – initial quantity of S
[S] – quantity of S at time t
[M] – quantity of M at time t
P – probability of transformation
S  S0 exp kt
S
S0
 exp  kt 
(7’)
(8’)
M  S0 1  exp  kt (9’)
M   1  exp  kt
S0
(10’)
[S]0 – initial quantity of S
[S] – quantity of S at time t
[M] – quantity of M at time t
k – first order kinetic constant
Pt  1  exp  kt 
k   ln 1  Pt  t
CATABOLTD – time dependent model
The model is able to predict:
1.
2.
3.
4.
5.
Primary half-life – half-life of parent chemical
Ultimate half-life – half-life by BOD
Biodegradation as a function of time
Metabolites quantity as a function of time
Biodegradation within 10 days window
Metabolite
Biodegradation
Metabolite:
BOD=0.016
LC50=0.34 mg/l
Parent: CAS 31570-04-4
Phenol 2,4-bis(1,1-dimethylethyl), phosphite (3;1)
BOD=0.038
LC50=145835248 mg/l
Biodegradation
Metabolite
BOD=0.39
LC50=1.2.106 mg/l
Parent: CAS 124-28-7
N-n-Octadecyl-N, N-dimethyl amine
BOD=0.83
LC50=0.94 mg/l
Fish liver simulator
Bioccumulation
TIMES Skin Sensitization Model*
S-Pr
S sensitization
Phase II
Reactive
species
Metabolism
Parent
Phase II
Reactive
species
Reactive
species
QSAR
No sensitization
S-Pr
W sensitization
S-Pr
S-Pr
W sensitization
S sensitization
* Dimitrov et al, 2005. Skin sensitization: Modeling based on skin metabolism simulation and formation of protein conjugates.
International Journal of Toxicology 24, 189-204.
Skin sensitization
Predicted metabolism of isoeugenol
Sulphate conjugation
OH
O
OH
S
O
O
Dealkylation
O
O
OH
Formation of semiquinone
free radicals
O
+ CH3OH
CH3
OH
O
OH
S
O
OH
O
.
CH3
O
O
O
HO
O
Glucoronidation
O
OH
OH
O
Pr
O
Pr
S
O
Free radical reaction on
proteins
OH
OH
Michael addition on quinones
N OH
OH
N
CH3 O
Mutagenicity
N
CH3
H3C N
N
N O
OH
N
O
N
OH
CH3
CH3
N
N
O
N
CH3
N O
N-Nitrosoamine Aliphatic C-Oxidation
N OH
H3C N
O
N-Nitrosoamine Oxidative N-Dealkylation
OH
N
O
N-Nitrosoamine Oxidative N-Dealkylation
N
CH3
N OH
N OH
OH
O
O
Aliphatic C-Oxidation
N
O
H3C N
H3C N
N
Electrophilic Species Generation
CH3
C+
C+
N OH
H3C N
O
O
HO
Reacting with DNA
O-Glucuronidation
N OH
H3C N
C+
Amino Acid Conjugation
O
O
HO
C+
Next Major Activity
Intact
Organism
Laboratory
conditions
Sub-cellular fractions
cDNA-expressed
Individual CYPs
Microsomes
Cytosol
Rat liver homogenate
(S9 fraction)
Intact Cells,
Tissue,
Primary
hepatocytes
Liver slices
Organism
In vivo
Research Partners from Regulation Agencies
US EPA, Athens, GA
US EPA, Duluth, MN
Environment Canada
NITE Japan
Research Partners from Industry
P&G
ExxonMobil
Unilever
BASF