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METABOLISM
in-silico simulation
Laboratory of Mathematical ChemistryUniversity ‘Prof. As. Zlatarov’, Bourgas, Bulgaria
Sabcho DimitrovOvanes Mekenyan
The McKim Conference on the Use QSARs and Aquatic Toxicology in Risk AssessmentJune 27-29, 2006
Research Partners from Regulation Agencies
US EPA, Athens, GA – 5 yrs coopUS EPA, Duluth, MNEnvironment CanadaNITE Japan
Research Partners from Industry
P&GExxonMobilUnileverBASF
Overview
• (Q)SARs• Metabolism of Prokaryotes• Metabolism of Eukaryotes• Simulation of metabolism
(Q)SARsBiodegradationBioaccumulationAcute ToxicityChronic ToxicityHormone ToxicitySkin sensitizationMutagenicity…
OH
O
O
O
SO
O
OH
OH
OH
O
O
OOCH3
HO
OH
OH
OH
O
Metabolism
(Q)SARs
The Five Kingdoms
Acute & Chronictoxicity
Hormone ToxicitySkin sensitizationMutagenicity
BCF
Biodegradation
Metabolism
Energy-generating component: CatabolismProduce energy (as ATP) and simple oxidized compounds
Energy-consuming component: Anabolism Build cell material
Intermediary metabolism
Common energy metabolismtricarboxylic acids cycle, glycolysis
Common biosynthetic pathwayssynthesis of amino acids, lipids, and carbohydrates
Set of reactions involving metabolites that are intermediates in the degradation or biosyntheses of biopolymers
Anabolism
Catabolism
metabolismmetabolismEnzymes often need to be highly substrate specific
IIntermediaryntermediary
Non-iNon-intermediaryntermediarymetabolismmetabolism
1. Response to environment2. Catabolism
Prokaryotes
KingdomMonera
The Kingdom Monera (Prokaryotes)
Constitutive enzymes: always produced by cellsThe 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
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-galactoside permease
Structural gene for β-galactoside transacetylase
Structural gene for β-galactosidase
Lactose operon: contains genes that encode enzymes responsible for lactose metabolism
The Kingdom Monera (Prokaryotes)
Eukaryotes
Kingdoms:ProtistaPlantaFungiAnimalia
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:
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
• Active sites are found on a single, multifunctional peptide chain• Several individual enzymes are non-covalently associated• Attachment to membranes
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 C
O
O
R2
R1 C
OH
O
HO R2++ HO CC C
OH
O
C C
O
O
C
H3C C
O
O
CH2CH3
H3C C
OH
O
HO CH2CH3+
H3C C
O
O
CH CH2
H3C C
OH
O
+ HO CH CH2
HCl2C C
O
O
C
+HCl2C C
OH
O
HO C
Half-lives,250C, pH =7
2 years
7 days
4 minutes
Simulation of molecular transformations
R1 C
O
O
R2
R1 C
OH
O
HO R2+
# Transformation Rate
1
High
2
Moderate
3
Low
+X2C C
O
O
C{sp }2
X2C C
OH
O
HO C{sp }2
+C C
O
O
C{sp }2
C C
OH
OHO C{sp }2
not C{sp2} and CX2; X: F, Cl, Br
+ HO CC C
OH
O
C C
O
O
C
X: F, Cl, Br
not CX2; X: F, Cl, Br
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 metabolismSimulators 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 metabolismSimulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
METEOR (Lhasa Ltd)
PPS (UM-BBD)
Simulators of metabolismSimulators 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)
Simulators of metabolismSimulators of metabolism
Rule based systems
Predictions: single best or multiple alternative metabolic pathways
Probabilitykt1/2
COH
OHC O
Geminal diol decomposition
OH
O
OH
O
-oxidation
Cyclohexanone oxidation
O
OO
O
OH
+
O
Ester hydrolysis
C
CNH2
C
CO
Amine decomposition
CH3OH
-Oxidation
C NN C
C NH2 H2N C+
Azo-bond cleavage
Substrate Principal transformations Metabolites
O O
OH
CATABOLCATABOL
Simulation of catabolismSimulation of catabolism
COH
OHC O
P = 1.00
Geminal diol decomposition
OH
O
OH
O
P = 0.99 -oxidation
Cyclohexanone oxidation
O
OO
O
OH
+
O
P = 0.90
Ester hydrolysis
C
CNH2
C
CO
P = 0.75
Amine decomposition
CH3OH
P = 0.40 -Oxidation
C NN C
C NH2 H2N C+ P = 0.001
Azo-bond cleavage
Substrate Principal transformations Metabolites
O O
OH
P = 0.95
COH
OHC O
P = 1.00
Geminal diol decomposition
OH
O
OH
O
P = 0.99-oxidation
O O
OH
P = 0.95
Cyclohexanone oxidation
O
OO
O
OH
+
O
P = 0.90
Ester hydrolysis
C
CNH2
C
CO
P = 0.75
Amine decomposition
CH3OH
P = 0.40
-Oxidation
C NN C
C NH2 H2N C+P = 0.001
Azo-bond cleavage
O
Substrate Principal transformations Metabolites
Geminal diol decomposition
OH
O
OH
O
P = 0.99-oxidation
O O
OH
P = 0.95
Cyclohexanone oxidation
O
OO
O
OH
+
O
P = 0.90
Ester hydrolysis
C
CNH2
C
CO
P = 0.75
Amine decomposition
CH3OH
P = 0.40
-Oxidation
C NN C
C NH2 H2N C+P = 0.001
Azo-bond cleavage
O
Substrate Principal transformations Metabolites
Match? COH
OHC O
P = 1.00
- No!
COH
OHC O
P = 1.00
Geminal diol decomposition
-oxidation
O O
OH
P = 0.95
Cyclohexanone oxidation
O
OO
O
OH
+
O
P = 0.90
Ester hydrolysis
C
CNH2
C
CO
P = 0.75
Amine decomposition
CH3OH
P = 0.40
-Oxidation
C NN C
C NH2 H2N C+P = 0.001
Azo-bond cleavage
O
Substrate Principal transformations Metabolites
OH
O
OH
O
P = 0.99
Match? - No!
COH
OHC O
P = 1.00
Geminal diol decomposition
OH
O
OH
O
P = 0.99-oxidation
Cyclohexanone oxidation
O
OO
O
OH
+
O
P = 0.90
Ester hydrolysis
C
CNH2
C
CO
P = 0.75
Amine decomposition
CH3OH
P = 0.40
-Oxidation
C NN C
C NH2 H2N C+P = 0.001
Azo-bond cleavage
Substrate Principal transformations Metabolites
O O O
OH
P = 0.95RESULT
O
OHMatch? - Yes!
The most plausible biotransformation pathway
k0
k1
k2
k
k
CO2
P2
P
1
O2
1
CO2P1
CO2
2
O2
2
CO2
O2
CO2P
O2
n
n
n
+1n
+1n
n
+1n
-1n
- Parent chemical or metabolite ki
- Transformation and its probabilityPi
- BOD or CO2 production for a single transformation
22 , COi
Oi
Comparison of metabolic pathways
Pathway A Pathway B
IntersectionA∩B
Complement of A in BB\A or B-A
Complement of B in AA\B or A-B
Accounting (or not) for pathway structures
IntersectionA∩B
Complement of A in BB\A or B-A
Complement of B in AA\B or A-B
Measures of similarity/dissimilarity:
Tanimoto,Dice,etc.
Trivial measures of similarity/dissimilaritypathway structure is not accounted for
IntersectionA∩B
Complement of A in BB\A or B-A
Complement of B in AA\B or 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)
Non-trivial measures of similarity/dissimilaritypathway structure is accounted for
Observed and predicted catabolism
- Observed and predicted metabolites, PredObs SS
- Observed and not predicted metabolites, PredObsPredObs \or SSSS
- Predicted and not observed metabolites, ObsPredObsPred \or SSSS
=+
Observed versus simulated pathwaysObserved versus simulated pathways
Union of pathways
CATABOL, mathematical formalismCATABOL, mathematical formalism BOD or CO2 production
%57BOD%63CalcBOD
Lyons, C. D., S. Katz, R. Bartha, Appl Environ Microbiol, 1984, 48, N 3, pp. 491-496
Simulated catabolism
CATABOL, mathematical formalismCATABOL, mathematical formalism
Quantities of metabolites
nm
mnCalcn PPQ
11 parent mol/mol ,1
k0
k1
k2
k
k
CO2
P2
P
1
O2
1
CO2P1
CO2
2
O2
2
CO2
O2
CO2P
O2
n
n
n
+1n
+1n
n
+1n
-1n
CATABOL, mathematical formalismCATABOL, mathematical formalism
Ultimate half-life
ktBOD exp1100
k2lnt 2/1
28/100/1ln
2ln
282/1 Calc
dBODt
First order kinetics k0
k1
k2
k
k
CO2
P2
P
1
O2
1
CO2P1
CO2
2
O2
2
CO2
O2
CO2P
O2
n
n
n
+1n
+1n
n
+1n
-1n
28/100/1ln 28Calc
dBODk
days 202/1 t
-1day 036.0k
%63CalcBOD
Training data
CATABOLCATABOLTDTD – time dependent model – time dependent model
Probabilistic approach First order kinetics
(7)
(8)
(9)
(10)
[S]0 – initial quantity of S
[S] – quantity of S at time t[M] – quantity of M at time tP – probability of transformation
(7’)
(8’)
(9’)
(10’)
[S]0 – initial quantity of S
[S] – quantity of S at time t[M] – quantity of M at time tk – first order kinetic constant
MSP
P 1SS 0
P 1S
S
0
P0SM
P
0S
M
MSk
kt expSS 0
ktexpS
S
0
kt exp1SM 0
kt exp1S
M
0
ktPt exp1
tPk t 1ln
Probabilistic approach & First order kinetics
CATABOLCATABOLTDTD – time dependent model – time dependent model
1. Primary half-life – half-life of parent chemical2. Ultimate half-life – half-life by BOD3. Biodegradation as a function of time4. Metabolites quantity as a function of time5. Biodegradation within 10 days window
CATABOLCATABOLTDTD – time dependent model – time dependent model
The model is able to predict:
Metabolite
Parent: CAS 31570-04-4
Phenol 2,4-bis(1,1-dimethylethyl), phosphite (3;1)BOD=0.038LC50=145835248 mg/l
Metabolite:BOD=0.016LC50=0.34 mg/l
Biodegradation
MetaboliteBOD=0.39LC50=1.2.106 mg/l
Parent: CAS 124-28-7N-n-Octadecyl-N, N-dimethyl amineBOD=0.83LC50=0.94 mg/l
Biodegradation
Fish liver simulator Bioccumulation
Parent
Metabolism
Phase II
Phase II
Reactivespecies
Reactivespecies
Reactivespecies
S-PrW sensitization
S-PrW sensitization
S-Pr
S sensitization
S-PrS sensitization
No sensitization
QSAR
* Dimitrov et al, 2005. Skin sensitization: Modeling based on skin metabolism simulation and formation of protein conjugates. International Journal of Toxicology 24, 189-204.
TIMES Skin Sensitization Model*
Predicted metabolism of isoeugenol
OH
O
OH
OH
O
O
.
O
O
Pr
O
O
SO
O
OH
O
O
Pr S
OH
OH
+ CH3OH
CH3 O
S
OHO
O
OOCH3
HO
OH
OH
OH
O
Glucoronidation
Sulphate conjugation
Dealkylation
Formation of semiquinone free radicals
Michael addition on quinones Free radical reaction on proteins
Skin sensitization
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
O
O
OHHO
HO
OOH
O
O
OHO
O
NH
SO
OHO
N
CH3
N O
OH
N
CH3
NO
O
NH O
OH
OHO
H3C N
N OH
C+
N-Nitrosoamine Aliphatic C-Oxidation
N-Nitrosoamine Oxidative N-DealkylationN-Nitrosoamine Oxidative N-Dealkylation
Electrophilic Species GenerationAliphatic C-Oxidation
O-Glucuronidation
Amino Acid Conjugation
Reacting with DNA
Mutagenicity
Laboratoryconditions
cDNA-expressedIndividual CYPs
Rat liver homogenate(S9 fraction)
Primary hepatocytes
MicrosomesCytosol
Liver slices In vivo
IntactOrganism
Intact Cells, Tissue, OrganismSub-cellular fractions
Next Major Activity
Research Partners from Regulation Agencies
US EPA, Athens, GAUS EPA, Duluth, MNEnvironment CanadaNITE Japan
Research Partners from Industry
P&GExxonMobilUnileverBASF
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