26-27 Jan 2005 Page 1 FOCUS Kinetics training workshop Chapter 7 Recommended Procedures to Derive Endpoints for Parent Compounds Practical Exercise - Answers.

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