26-27 jan 2005 page 1 focus kinetics training workshop chapter 7 recommended procedures to derive...
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26-27 Jan 2005 Page 1
FOCUS Kinetics training workshop
Chapter 7
Recommended Procedures to Derive Endpoints for Parent Compounds
Practical Exercise - Answers
Ralph L. Warren, Ph.D.DuPont Crop Protection
Delaware, USA
26-27 Jan 2005 Page 2
FOCUS Kinetics training workshop
Goal of the exercise
The goal of this exercise is to calculate the 2 error and to conduct a visual assessment for kinetic model fits to measured levels of parent compound in soil. Based on this information, the most appropriate kinetic model and endpoints for comparison with regulatory triggers and for use in regulatory exposure modelling should be identified.
You will need• ModelMaker 4 results from this morning for Example 1 and Example 2. Try Example 3 if time allows (you will need to first do the optimization using MM4).
• Excel file “Parent degradation kinetic training unprotected.xls”
• Excel file “t_test.xls”
• Excel file “DFOP_DT50.xls”
26-27 Jan 2005 Page 3
FOCUS Kinetics training workshop
General instructions for the exercise• Follow the parent only flow chart for triggers, then the flow chart for modelling to determine which fits are needed.
• Generate optimized results from ModelMaker (record necessary information!)
• Create plots for observed versus fitted values and for residuals using Excel (record necessary information!)
• Calculate the 2 error percentages using Excel (record!).
• Decide which kinetic model and endpoints to use for triggers and for modelling.
• Record your conclusions and be prepared to report them to the class.
In the interest of time, we will not iteratively modify the fitting routines (e.g. excluding outliers, constraining MO, weighting).
Also assume that there are no experimental artifacts (e.g. microbial die off).
Page 4
Triggers flowchart
FOCUS Kinetics training workshop
NO
YES see text
YES
RUN SFO, FOMC
Data entry M0 free, use all data, no weighting
SFO more appropriate than FOMC and gives
acceptable fit?
RUN DFOP (unmodified &
modified fitting routine)
Does the best-fit model give an acceptable description
of the data?
STEP 1: SFO appropriate?
STEP 2: Identify best model other than SFO
Deviation from SFO due to experimental
artifact/decline in microbial activity?
NO
Case-by-case decision (see text)
Determine which of the models (FOMC, DFOP)
is best
NO
YES STOP
STEP 3: Evaluate goodness of fit
NO
Modify fitting routine stepwise: 1. Exclude outliers 2. Constrain M0 3. Weighting
RUN modified fitting
SFO more appropriate than FOMC & fit acceptable?
(modified fitting)
YES STOP
STOP
26-27 Jan 2005
26-27 Jan 2005 Page 5
Modelling flowchart
FOCUS Kinetics training workshop
NO YES
RUN SFO
Data entry M0 free, use all data, no weighting
SFO statistically and visually acceptable? Modify fitting routine for
SFO stepwise: 1. Exclude outliers 2. Constrain M0 3. Weighting until best SFO fit achieved
STEP 1: SFO appropriate?
RUN modified SFO
Use SFO DT50 for fate modelling
Aim: modelling fate of parent only?
YES
YES 10% initially measured concentration reached
within experimental period?
NO RUN FOMC
RUN HS or DFOP
Use DT50 from slow phase of HS of DFOP
model for fate modelling
Case-by-case decision (see text)
NO
HS or DFOP statistically and
visually acceptable?
YES
FOMC statistically and visually acceptable?
YES
Back-calculate DT50 from DT90 for FOMC (DT50 = DT90 / 3.32)
Case-by-case decision (see text)
NO
YES
Use SFO DT50 (modified fitting routines) for fate modelling
NO
Bi-phasic pattern? (assess experimental
artefacts!)
SFO statistically and visually acceptable?
YES
Case-by-case decision (see text)
NO
STEP 2:Correction procedure
Aim: modelling metabolite fate linked to
parent?
see text
YES
YES
Page 6
Time(days)
Obs.(% AR)
Calc.(% AR)
00
22
77
1414
2121
2929
4545
6464
8989
119119
96.7105.0
83.397.5
81.987.2
46.343.1
35.236.5
24.519.7
9.89.3
4.13.0
1.11.6
0.30.2
103.365
93.3763
72.4271
50.7491
35.5594
23.6809
10.4985
3.99416
1.11992
0.2435
EXAMPLE 1 – SFO
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
k
103.365
0.0507544
2.90251
0.00329378
Not required
Yes
Endpoint Value(days)
DT50
DT90
13.6
45.4
Fitting statistic Value(%)
2 error (%) 9.2
Page 7
EXAMPLE 1 - SFODraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? No
Does the line cross the y-axis near the Day 0 data? Yes
Other comments?
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? No
Are the residual magnitudes large? Only at early time points.
Other comments? Points randomly scattered about the
0 line.
0 20 40 60 80 100 120
t (days)
0
20
40
60
80
100
% A
R
-15
-10
-5
0
5
10
15
0 20 40 60 80 100 120
t (days)
resi
du
al
Page 8
Time(days)
Obs.(% AR)
Calc.(% AR)
00
22
77
1414
2121
2929
4545
6464
8989
119119
96.7105.0
83.397.5
81.987.2
46.343.1
35.236.5
24.519.7
9.89.3
4.13.0
1.11.6
0.30.2
103.367
93.3726
72.4165
50.7378
35.5318
23.6784
10.506
4.00194
1.12497
0.245697
EXAMPLE 1 - FOMC
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
alpha
beta
103.367
1504.36
29623.2
2.99666
7182.86
141191
Not required
Not required
Not required
Endpoint Value(days)
DT50
DT90
13.6
45.4
Fitting statistic Value(%)
2 error (%) 9.6
Page 9
EXAMPLE 1 - FOMCDraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? No
Does the line cross the y-axis near the Day 0 data? Yes
Other comments?
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? No
Are the residual magnitudes large? Only at early time points.
Other comments? Points randomly scattered about the
0 line.
0 20 40 60 80 100 120
t (days)
0
10
20
30
40
50
60
70
80
90
100
% A
R
-15
-10
-5
0
5
10
15
0 20 40 60 80 100 120
t (days)
resi
du
al
26-27 Jan 2005 Page 10
FOCUS Kinetics training workshop
Conclusions – Example 1
Triggers flowchart
Most appropriate kinetic model (SFO, FOMC, other): SFO
Most appropriate endpoint values (days): DT50 = 13.6 DT90 = 45.4
Modelling flowchart
Most appropriate kinetic model (SFO, FOMC, other): SFO
Most appropriate endpoint values (days): DT50 = 13.6
Page 11
Time(days)
Obs.(% AR)
Calc.(% AR)
00
11
33
77
1414
2828
4242
6161
9191
118118
96.7102.5
71.278.6
51.069.4
42.741.5
28.522.4
18.614.3
10.38.4
6.35.6
6.02.8
2.93.0
88.6733
80.7067
66.8566
45.8789
23.7369
6.35307
1.69978
0.283231
0.0167182
0.00130959
EXAMPLE 2 - SFO
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
k
88.6733
0.0939487
4.03138
0.0122743
Not required
Yes
Endpoint Value(days)
DT50
DT90
7.38
24.5
Fitting statistic Value(%)
2 error (%) 15.5
Page 12
EXAMPLE 2- SFODraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? No
Are there obvious over or under predictions? Yes
Does the line cross the y-axis near the Day 0 data? No, low
Other comments? Fitted line under predicts much of the
data points, especially after Day 20.
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? Yes
Are most of the residual points above or below 0? Yes, below
Are the residual magnitudes large? Yes, most >5%
Other comments?
0 20 40 60 80 100 120
t (days)
0
10
20
30
40
50
60
70
80
90
100
% A
R
-15
-10
-5
0
5
10
15
0 20 40 60 80 100 120
t (days)
resi
du
al
Page 13
Time(days)
Obs.(% AR)
Calc.(% AR)
00
11
33
77
1414
2828
4242
6161
9191
118118
96.7102.5
71.278.6
51.069.4
42.741.5
28.522.4
18.614.3
10.38.4
6.35.6
6.02.8
2.93.0
96.9914
79.8848
59.3269
39.5089
25.216
14.8529
10.6187
7.70828
5.42071
4.29718
EXAMPLE 2 - FOMC
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
alpha
beta
96.9914
0.930673
4.32709
2.9586
0.17099
1.4789
Not required
Not required
Not required
Endpoint Value(days)
DT50
DT90
4.8
47.0
Fitting statistic Value(%)
2 error (%) 5.5
Page 14
EXAMPLE 2- FOMCDraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? No
Does the line cross the y-axis near the Day 0 data? Yes
Other comments?
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? Trend > 0
Are the residual magnitudes large? Only at early time points.
Other comments?
0 20 40 60 80 100 120
t (days)
0
10
20
30
40
50
60
70
80
90
100
% A
R
-15
-10
-5
0
5
10
15
0 20 40 60 80 100 120
t (days)
resi
du
al
Page 15
Time(days)
Obs.(% AR)
Calc.(% AR)
00
11
33
77
1414
2828
4242
6161
9191
118118
96.7102.5
71.278.6
51.069.4
42.741.5
28.522.4
18.614.3
10.38.4
6.35.6
6.02.8
2.93.0
97.4133
79.4295
57.7849
39.7511
28.4258
16.8358
10.0414
4.97992
1.64559
0.607452
EXAMPLE 2 - DFOP
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
g
k1
k2
97.4133
0.513067
0.39253
0.0368533
3.19895
0.0827071
0.117258
0.00783263
Not required
Not required
Yes
Yes
Endpoint Value(days)
DT50
DT90
DT50 fast phase
DT50 slow phase
4.5
43.0
1.8
18.8
Fitting statistic Value(%)
2 error (%) 6.4
Page 16
EXAMPLE 2- DFOPDraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? Slight
Does the line cross the y-axis near the Day 0 data? Yes
Other comments? Slight under prediction at the last two
time points, which are below 10% of
the initial measured value.
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? No
Are the residual magnitudes large? Only at early time points.
Other comments? Slight trend for residuals to be negative
after Day 60. However, the associated
magnitude of the residuals is small
(5% or less).
0 20 40 60 80 100 120
t (days)
0
10
20
30
40
50
60
70
80
90
100
% A
R
-15
-10
-5
0
5
10
15
0 20 40 60 80 100 120
t (days)
resi
du
al
26-27 Jan 2005 Page 17
FOCUS Kinetics training workshop
Conclusions – Example 2
Triggers flowchart
Most appropriate kinetic model (SFO, FOMC, other): FOMC
Most appropriate endpoint values (days): DT50 = 4.8 DT90 = 47.0
Modelling flowchart
Most appropriate kinetic model (SFO, FOMC, other): FOMC
Most appropriate endpoint values (days): DT50 = 47.0/3.32 = 14.2
Page 18
Time(days)
Obs.(% AR)
Calc.(% AR)
0
7
14
28
56
84
112
292
380
91.5
64.1
53.6
68.8
25.6
14.0
18.6
1.2
0.04
82.7476
73.348
65.0162
51.0842
31.5368
19.4693
12.0193
0.54112
0.118841
EXAMPLE 3 - SFO
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
k
82.7476
0.0172133
7.17998
0.00383787
Not required
Yes
Endpoint Value(days)
DT50
DT90
40.3
134
Fitting statistic Value(%)
2 error (%) 19.0
Page 19
EXAMPLE 3 - SFODraw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? Day 0?
Does the line cross the y-axis near the Day 0 data? Yes
Other comments? Day 0 could be under predicted.
Check Day 0 recoveries from the study (e.g. application monitor
data) to rationalize. If ~85% confirmed then okay. If ~100%
supported then consider constraining M0 in the fitting.
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? No
Are the residual magnitudes large? Several, up to Day 125.
Other comments? Keep in mind that this is a field study,
which typically have more variable data than lab
studies. This variability is reflected in the residual
magnitude. The residual pattern is okay.
0 50 100 150 200 250 300 350 400
t (days)
0
10
20
30
40
50
60
70
80
90
100
Pa
ren
t (%
AR
)
-20
-15
-10
-5
0
5
10
15
20
0 50 100 150 200 250 300 350 400
t (days)
resi
du
al
Page 20
Time(days)
Obs.(% AR)
Calc.(%AR)
0
7
14
28
56
84
112
292
380
91.5
64.1
53.6
68.8
25.6
14.0
18.6
1.2
0.04
83.8357
73.3257
64.3321
49.9532
31.0844
20.0656
13.3661
1.66168
0.750887
EXAMPLE 3 - FOMC
Parameter Optimized value
Standard error
Different than 0 by
t-test?
M0
alpha
beta
83.8357
5.6639
292.783
9.04697
25.615
1476.18
Not required
Not required
Not required
Endpoint Value(days)
DT50
DT90
38.1
147
Fitting statistic Value(%)
2 error (%) 20.0
Page 21
EXAMPLE 3 - FOMC
0 50 100 150 200 250 300 350 400
t (days)
0
10
20
30
40
50
60
70
80
90
100
Pa
ren
t (%
AR
)
-20
-15
-10
-5
0
5
10
15
20
0 50 100 150 200 250 300 350 400
t (days)
resi
du
al
Draw the fitted line to the observed data points.
Does the fitted line adequately describe the data? Yes
Are there obvious over or under predictions? Day 0?
Does the line cross the y-axis near the Day 0 data? Yes
Other comments? Day 0 could be under predicted.
Check Day 0 recoveries from the study (e.g. application monitor
data) to rationalize. If ~85% confirmed then okay. If ~100%
supported then consider constraining M0 in the fitting.
Draw in the residual points (approximate).
Do the residuals have a distinct pattern? No
Are most of the residual points above or below 0? No
Are the residual magnitudes large? Several, up to Day 125.
Other comments? Keep in mind that this is a field study,
which typically produce more variable data than lab
studies. This variability is reflected in the residual
magnitude. The residual pattern is okay.
26-27 Jan 2005 Page 22
FOCUS Kinetics training workshop
Conclusions – Example 3
Triggers flowchart
Most appropriate kinetic model (SFO, FOMC, other): SFO
Most appropriate endpoint values (days): DT50 = 40.3 DT90 = 134
Modelling flowchart
Most appropriate kinetic model (SFO, FOMC, other): SFO
Most appropriate endpoint values (days): DT50 = 40.3