Human Health Models Skin sensitizationoasis-lmc.org/media/63111/Skin metabolism tool_May...
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Laboratory of Mathematical Chemistry, Bulgaria
Skin sensitization
Human Health Models
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Outline
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Predicting skin sensitization in TIMES
TIMES-SS model Applicability domain in TIMES (Q)SAR models
Model performance
Conclusions
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Epidermis
Dermis
Hypodermis
Vein
Protein conjugates
Penetration
Protein conjugates
Metabolism
Lymph
Subject of modeling
Assumptions: 1. Chemicals always penetrate stratum corneum 2. Formation of protein conjugates is a premise for ultimate effect 3. Metabolism may play significant role in skin sensitization
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Mechanism of skin sensitization
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• In order to predict skin sensitisation effect taking into account metabolic activation of chemicals in same platform are combined:
Toxicokinetics – specific metabolism
− Pre-electrophilic activation – Autoxidation reactions
− Pro-electrophilic activation – Phase I and Phase II reactions
Toxicodynamic – interaction with macromolecules
Dimitrov, et al., Skin sensitization: Modeling Based on Skin Metabolism Simulation and Formation of Protein Conjugates, Internatational Journal of Toxicology. 24, 189-204, 2005.
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Predicting skin sensitization in TIMES Main concept
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Predicting skin sensitization in TIMES
Parent
Pro-electrophilic activation
Phase II Pre-electrophilic
activation (Autoxidation)
Reactive species
Reactive species
S-Pr
W sensitization
S-Pr
W sensitization
S-Pr
S sensitization
S-Pr
S sensitization
No sensitization
QSAR
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1) Estimates skin sensitisation potency by integrating a simulator for skin metabolism with (Q)SARs.
2) Training set of 875 chemicals with experimental data from three sources (436 LLNA, 568 GPMT and 171 BfR).
3) Predicts potency as one of three classes: strong, weak or non sensitising.
4) A multi-step applicability domain is incorporated into the model.
Predicting skin sensitization in TIMES TIMES-SS model
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• Skin metabolic simulator contains around 380 hierarchically ordered transformations based on empiric and theoretical knowledge and peer-reviewed by human experts:
• Non-enzymatic transformations:
o Hydrolysis of salts
o Autoxidation reactions
• Enzyme-mediated reactions (Phase I and Phase II):
o C-hydroxylation
o Glucoronidation
o ..............................
• Protein binding reactions (PBR)
TIMES-SS model Skin metabolic simulator
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TIMES-SS model Different type of principal transformations
• Phase I reactions Ester hydrolysis
Schiff base formation with aldehydes
HC O
CH
NPr
CC
C C
CO
OO
HO
HO OH
OH
O
C
O
OH
R CH2
Hal
C, -CN, -C=C, -C=S, -C=O, -NO2
R CH2
S
Pr
R = -C
Hal = Cl, Br, I
C O
O C
C O
H O
+ O H C
• Phase II reactions Glucoronidation
•Proteins binding reactions Nucleophilic substitution on halogenated C sp3 atom
• Autoxidation reactions Allylic hydroperoxide formation
CH3
CH3
HC
C
CH3
H2C
HC
C O
OH
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• Covalent interactions of chemicals/metabolites with skin proteins are described by almost 160 protein binding reactions (transformations).
• 3D-QSARs are applied for some of these transformations.
• Transformations characterized by same mechanism of interaction are combined in 97 alerts.
• The reliability of the reactions and alerts is assessed
TIMES-SS model Protein binding reactions
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Parent
Pro-electrophilic activation
Phase II Pre-electrophilic
activation (Autoxidation)
Reactive species
Reactive species
S-Pr
W sensitization
S-Pr
W sensitization
S-Pr
S sensitization
S-Pr S sensitization
No sensitization
QSAR
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TIMES-SS model 3D QSAR models
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• COREPA models of reactivity
# Alerting group Chemical class Descriptors in the
corresponding COREPA model
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Aldehydes EHOMO
MW
2 Conjugated
systems with
electron
withdrawing
groups
Electronegativity
EGAP
log KOW
Accept DLC
R
CHO
R = H, alkyl
C
CO
R
C
R = OC, C, N, S
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TIMES-SS model 3D QSAR models
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• The Autoxidation reactions included in the skin simulator are “switched off”
• Autoxidation (AU) transformations are simulated by using an external AU simulator
• The external simulator containing more than 240 transformations, extracted from documented autoxidation pathways in the scientific literature, is applied on parent chemicals
• All generated unique autoxidation products are then submitted to skin sensitization model
• Parent structure is also submitted to skin model assuming that it is not fully autoxidyzed (kinetic data is not used)
TIMES-SS model Pre-electrophilic activation
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TIMES-SS using an external Autoxidation simulator Predicted metabolism of isoeugenol
Autoxidation products
Phase I reactions: Oxidation to quinone methide (quinone)
Binding to proteins
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Applicability domain of TIMES (Q)SAR models
I. General requirements SDGR
Screened
chemicals S
SGR
ST SSD
ST
SMS
GRSDMHMS SD
GRSDMHMSS
GRSDMHS
GRSDMH SD
GRSDS
GRSD SD II. Structural domain
III. Mechanistic domain
IV. Metabolic simulator domain
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DOMAIN: • General Requirements + - • Structural domain + - • Mechanistic domain
o Interpolation space + +
Parents Metabolites
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Applicability domain of TIMES (Q)SAR models
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DOMAIN: • General Requirements + - • Structural domain + - • Mechanistic domain
o Interpolation space + +
Parents Metabolites
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Applicability domain of TIMES (Q)SAR models
The ranges of the specified parameters (log Kow and MW) are defined on the bases of parent structures from the training set which are correctly predicted by the model.
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DOMAIN: • General Requirements + - • Structural domain + - • Mechanistic domain
o Interpolation space + +
Parents Metabolites
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Applicability domain of TIMES (Q)SAR models
Structural domain is represented by the list of atom-centred fragments (accounting for the first neighbours) extracted from the parent structures in the training set which were split into two subsets: chemicals correctly predicted by the model and incorrectly predicted chemicals.
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DOMAIN: • General Requirements + - • Structural domain + - • Mechanistic domain
o Interpolation space + +
Parents Metabolites
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Applicability domain of TIMES (Q)SAR models
This stage of the applicability domain of the model holds only for chemicals which have an alert group however an additional COREPA model is required. This layer of model domain is applied on parents and metabolites because the alert groups interacting with proteins could be obtained after metabolism of the parent chemical.
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Performance of the PBR and alerts Reliability Criteria
1. Performance: • Transformation Performance - the ratio between the number of correct
(Strong, Weak and Non sensitizers) predictions and the total number of chemicals within the local training set associated with the transformation
• Alert Performance - the ratio between the number of correct (Positive and Negative) predictions and the total number of chemicals within the alert training set
• In general, the alert performance is higher than the performance of the constituting transformations, because chemicals are classified as:
Strong/weak/non – when transformation reliability is determined
Positive/negative – when alert reliability is determined
2. Number of chemicals
3. Mechanistic justification
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Based on their performance PBR/alerts have been classified with:
• High performance (perf. ≥ 60% and n ≥ 5)
• Low performance (perf. ≤ 60%)
• Undetermined (1< n <5)
• Undetermined theoretical
Performance of the PBR and alerts Reliability Criteria
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Summary: Total number of PBR in the TIMES-SS model is 158
• High performance – 33 • Low performance – 4 • Undetermined (1< n <5) – 65 • Undetermined theoretical – 56
Performance of the PBR and alerts Reliability of PBR
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Options for minimizing the number of:
PBRs with low performance - the PBRs will be reassessed by
expert and adjusted in TIMES-SS if necessary. PBRs with Undetermined performance - If the expert is certain
in the reactivity of the PBR it will be flagged as “Reliable according to 3rd party expert”.
Performance of the PBR and alerts Reliability of PBR
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PBRs have been review by Dr. David Roberts and coded by LMC in TIMES SS model
After expert review reliable alerts increased up to 68%
The following improvement have been reached
Performance of the PBR and alerts Reliability of PBR
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Performance of the TIMES-SS model
TIMES-SS - using the external Autoxidation simulator
Training set: 875 chemicals
Sensitivity:
Strong sens. 91%
Weak sens. 52%
Specificity: 70%
Concordance: 76%
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Conclusions
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Prediction of skin sensitization is provided in a single modeling platform accounting for specific metabolism and protein adducts formation.
Criteria for reliability of protein binding reactions have been introduced.
Numbers of protein binding reactions with low and undetermined reliability have been reassessed by human expert (Dr. Roberts).
Sets of protein binding reactions having the same mechanism of interaction have formed different alerting groups.