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Claude Beigel, PhD.
Exposure Assessment Senior Scientist
Research Triangle Park, USA
Practical session metabolitesPart III: plenary discussion of results
2
Results Example 1Visual Evaluation of Goodness of fit (Parent + Metabolite1)
ParentMetabolite1
Example 1 data set (SFO)
0 10 20 30 40 50 60 70 80 90 100 110 120
Time (days)
0
10
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100
110
Su
bst
ance
(%
AR
)
Example 1, parent residuals
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0 20 40 60 80 100 120
Time (days)
Res
idu
al (
% A
R)
Example 1, metabolite1 residuals
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-2
-1
0
1
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3
0 20 40 60 80 100 120
Time (days)
Res
idu
al (
% A
R)
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Hands-on Example 1Visual Assessment (Parent + Metabolite1)
Graph Assessment / Remarks
ParentOverall fit
Good, initial scattering
Residuals Random distribution
Metabolite1
Overall fit Excellent
Residuals Random distribution
4
Hands-on Example 1Statistical Indices (Parent + Metabolite1)
2-test Relevant Parameters
Estimated (y/n)Number of Parameters
Minimum 2 Error
Percentage
ParentPini
kP
y
y2 9.2
Metabolite1ffM1
kM1
y
y2 4.9
t-test Estimated Value
Standard Error
Number of Data Points
Number of Estimated
ParametersP-value Conclusion
kP 0.0508 0.0033 38 4 <0.001 Significant
kM1 0.1018 0.0320 38 4 <0.001 Significant
5
Hands-on Example 1Conclusion and Endpoints (Parent + Metabolite1)
SFO model is considered appropriate for both parent and metabolite
Trigger endpoints for Metabolite 1:
DT50 = 6.8 d and DT90 = 22.6 d
Modeling endpoints:
kP = 0.0508 d-1 (equivalent to half-life of 13.7 d), ffM1= 0.5881 and kM1= 0.1018 d-1 (equivalent to half-life of 6.8 d)
kP_M = 0.0299 d-1 (equivalent to half-life of 23.2 d), kP_S = 0.0209 d-1 (equivalent to half-life of 33.1 d), and kM1= 0.1018 d-1
(equivalent to half-life of 6.8 d)
or
6
Hands-on Example 1Parent + Metabolite1+ Metabolite2
Initial fit with flow from Metabolite 1 to sink results in formation fraction ffM2 of 0.98 (stepwise fit, parent and M1 parameters fixed) or >1 (simultaneous fit, all parameters free)
The question is: should we remove or keep this flow (does Metabolite 1 degrade exclusively to Metabolite 2, or does it form other metabolites and/or bound residues too)?
Let’s assume that additional information, e.g. a degradation study conducted with Metabolite 1 also suggests 100% formation of Metabolite 2, ffM2 is fixed to 1, i.e. the flow from Metabolite 1 to sink is removed
Parent Metabolite1
Sink
fP_M1
fP_S
Metabolite2
fM1_M2
fM2_S
Parent Metabolite1
Sink
fP_M1
fP_S
Metabolite2
fM1_M2
fM2_S
fM1_S
7
Results Example 1Visual Evaluation of Goodness of fit (Parent + Met1 +Met2)
ParentMetabolite1Metabolite2
Parent + metabolite1 + metabolite2, SFO-SFO-SFO
0 10 20 30 40 50 60 70 80 90 100 110 120
Time (days)
0
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% A
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Example 1, parent residuals
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Time (days)
Re
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Example 1, metabolite1 residuals
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1
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3
0 20 40 60 80 100 120
Time (days)
Res
idu
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% A
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Example 1, metabolite2 residuals
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Time (days)
Res
idu
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% A
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Hands-on Example 1Visual Assessment (Parent + Met1 +Met2)
Graph Assessment / Remarks
ParentOverall fit
Good, initial scattering
Residuals Random distribution
Metabolite1
Overall fit Excellent
Residuals Random distribution
Metabolite2 Overall fit Excellent
Residuals Random distribution
9
Hands-on Example 1Statistical Indices (Parent + Met1 +Met2)
2-test Relevant Parameters
Estimated (y/n)Number of Parameters
Minimum 2 Error
Percentage
ParentPini
kP
y
y2 9.2
Metabolite1ffM1
kM1
y
y2 5.0
Metabolite2ffM2
kM2
n (fixed to 1)
Y1 3.9
t-test Estimated Value
Standard Error
Number of Data Points
Number of Estimated
ParametersP-value Conclusion
kP 0.0507 0.0021 56 5 <0.001 Significant
kM1 0.0999 0.0091 56 5 <0.001 Significant
kM2 0.0114 0.0014 56 5 <0.001 Significant
10
Hands-on Example 1Conclusion and Endpoints (Parent + Met1 +Met2)
SFO model is considered appropriate for parent and both metabolites
Trigger endpoints
Metabolite1 DT50 = 6.9 d and DT90 = 23.1 d
Metabolite2 DT50 = 61.0 d and DT90 = 203 d
Modeling endpoints:
kP = 0.0507 d-1 (equivalent to half-life of 13.7 d), ffM1= 0.5813 and kM1= 0.0999 d-1 (equivalent to half-life of 6.9 d), kM2= 0.0114 d-1
(equivalent to half-life of 61.0 d),
kP_S = 0.0212 d-1 (equivalent to half-life of 32.7 d), kP_M = 0.0295 d-1 (equivalent to half-life of 23.5 d), kM1_M2 = 0.0999 d-1 (equivalent to half-life of 6.9 d), and kM2_S = 0.0114 d-1 (equivalent to half-life of 61.0 d)
or
11
Results Example 2Visual Evaluation of Goodness of fit (Parent FOMC)
ParentMetabolite
Example data set 2, parent + metabolite, FOMC-SFO
0 10 20 30 40 50 60 70 80 90 100 110 120
Time (days)
0
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% A
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Example 2 (parent FOMC), parent residuals
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Time (days)
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Example 2 (parent FOMC), metabolite residuals
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Time (days)
Res
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Hands-on Example 2, parent FOMCVisual Assessment
Graph Assessment / Remarks
ParentOverall fit Excellent
Residuals Random distribution
Metabolite
Overall fit Excellent
Residuals Random distribution
13
Hands-on Example 2, parent FOMCStatistical Indices
2-test Relevant Parameters
Estimated (y/n)Number of Parameters
Minimum 2 Error
Percentage
Parent
Pini
P
P
y
y
y
3 5.5
MetaboliteffM
kM
y
y2 4.1
t-test Estimated Value
Standard Error
Number of Data Points
Number of Estimated
ParametersP-value Conclusion
kM 0.0200 0.0019 38 5 <0.001 Significant
14
Hands-on Example 2Conclusion and Trigger Endpoints (Parent FOMC)
SFO model is considered appropriate for metabolite in combination with FOMC model for parent
Trigger endpoints for Metabolite:
DT50 = 34.7 d and DT90 = 115 d
Endpoints for PEC soil calculations:
P = 0.9425, P = 4.436, ffM= 0.8018 and kM= 0.0200 d-1
15
Results Example 2Visual Evaluation of Goodness of fit (Parent DFOP)
ParentMetabolite
Example data set 2, parent + metabolite, DFOP-SFO
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Time (days)
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% A
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Example 2 (parent DFOP), parent residuals
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Time (days)
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ual
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Example 2 (parent DFOP), metabolite residuals
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Time (days)
Res
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% A
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Hands-on Example 2, Parent DFOPVisual Assessment
Graph Assessment / Remarks
ParentOverall fit
Excellent up to DT90, slight overestimation
afterward
ResidualsRandom distribution
up to DT90
Metabolite
Overall fit Excellent
Residuals Random distribution
17
Hands-on Example 2, Parent DFOPStatistical Indices
2-test Relevant Parameters
Estimated (y/n)Number of Parameters
Minimum 2 Error
Percentage
Parent
Pini
g
k1
k2
y
y
y
y
4 6.5
MetaboliteffM
kM
y
y2 3.6
t-test Estimated Value
Standard Error
Number of Data Points
Number of Estimated
ParametersP-value Conclusion
k1 0.3227 0.0613 38 6 <0.001 Significant
k2 0.0340 0.0064 38 6 <0.001 Significant
kM 0.0216 0.0021 38 6 <0.001 Significant
18
Hands-on Example 2Conclusion and Modeling Endpoints (Parent DFOP)
SFO model is considered appropriate for metabolite in combination with DFOP model for parent
Modeling endpoints (higher Tier approach based on parent DFOP):
g = 0.5509, k1 = 0.3227 d-1 (equivalent to half-life of 2.15 d), k2 = 0.0340 d-1 (equivalent to half-life of 20.4 d), ffM= 0.8332 and kM= 0.0216 d-1 (equivalent to half-life of 32.0 d)
19
Results Example 2Visual Evaluation of Goodness of Fit (Metabolite Decline)
Metabolite
Example data set 2, metabolite decline
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Time (days)
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% A
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Example 2, metabolite decline
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Hands-on Example 2, Metabolite DeclineVisual Assessment
Graph Assessment / Remarks
Metabolite decline
Overall fitGood, slight
underestimation at last time points
Residuals No distinct pattern
21
Hands-on Example 2, Metabolite DeclineStatistical Indices
2-test Relevant Parameters
Estimated (y/n)Number of Parameters
Minimum 2 Error
Percentage
MetaboliteMmax
kM
y
y2 5.7
t-test Estimated Value
Standard Error
Number of Data Points
Number of Estimated
ParametersP-value Conclusion
kM 0.0216 0.0021 12 2 <0.001 Significant
22
Hands-on Example 2Conclusion and Endpoints (Metabolite Decline)
SFO model is considered appropriate for metabolite decline
Metabolite decline rate may be used as worst-case estimate for trigger endpoints
Trigger endpoints: DT50 = 49.7 d and DT90 = 165 d (compared to DT50 = 34.7 d and DT90 = 115 d from actual degradation rate)
Decline rate may also be used as modeling endpoint for metabolite, if calculated from maximum observed
Modeling endpoint: kM= 0.0139 d-1 (equivalent to half-life of 49.7 d)