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Claude Beigel, PhD. Exposure Assessment Senior Scientist Research Triangle Park, USA Practical session metabolites Part III: plenary discussion of results.

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Presentation on theme: "Claude Beigel, PhD. Exposure Assessment Senior Scientist Research Triangle Park, USA Practical session metabolites Part III: plenary discussion of results."— Presentation transcript:

1 Claude Beigel, PhD. Exposure Assessment Senior Scientist Research Triangle Park, USA Practical session metabolites Part III: plenary discussion of results

2 1 Results Example 1 Visual Evaluation of Goodness of fit (Parent + Metabolite1)

3 2 Hands-on Example 1 Visual Assessment (Parent + Metabolite1) GraphAssessment / Remarks Parent Overall fit Good, initial scattering ResidualsRandom distribution Metabolite1 Overall fitExcellent ResidualsRandom distribution

4 3 Hands-on Example 1 Statistical Indices (Parent + Metabolite1) 2 -test Relevant Parameters Estimated (y/n) Number of Parameters Minimum 2 Error Percentage Parent Pini kP yyyy 29.2 Metabolite1 ffM1 kM1 yyyy 24.9 t-test Estimated Value Standard Error Number of Data Points Number of Estimated Parameters P-valueConclusion kP <0.001Significant kM <0.001Significant

5 4 Hands-on Example 1 Conclusion 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 = d -1 (equivalent to half-life of 13.7 d), ffM1= and kM1= d -1 (equivalent to half-life of 6.8 d) kP_M = d -1 (equivalent to half-life of 23.2 d), kP_S = d -1 (equivalent to half-life of 33.1 d), and kM1= d -1 (equivalent to half-life of 6.8 d) or

6 5 Hands-on Example 1 Parent + 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)? Lets 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

7 6 Results Example 1 Visual Evaluation of Goodness of fit (Parent + Met1 +Met2)

8 7 Hands-on Example 1 Visual Assessment (Parent + Met1 +Met2) GraphAssessment / Remarks Parent Overall fit Good, initial scattering ResidualsRandom distribution Metabolite1 Overall fitExcellent ResidualsRandom distribution Metabolite2 Overall fitExcellent ResidualsRandom distribution

9 8 Hands-on Example 1 Statistical Indices (Parent + Met1 +Met2) 2 -test Relevant Parameters Estimated (y/n) Number of Parameters Minimum 2 Error Percentage Parent Pini kP yyyy 29.2 Metabolite1 ffM1 kM1 yyyy 25.0 Metabolite2 ffM2 kM2 n (fixed to 1) Y 13.9 t-test Estimated Value Standard Error Number of Data Points Number of Estimated Parameters P-valueConclusion kP <0.001Significant kM <0.001Significant kM <0.001Significant

10 9 Hands-on Example 1 Conclusion 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 = d -1 (equivalent to half-life of 13.7 d), ffM1= and kM1= d -1 (equivalent to half-life of 6.9 d), kM2= d -1 (equivalent to half-life of 61.0 d), kP_S = d -1 (equivalent to half-life of 32.7 d), kP_M = d -1 (equivalent to half-life of 23.5 d), kM1_M2 = d -1 (equivalent to half-life of 6.9 d), and kM2_S = d -1 ( equivalent to half-life of 61.0 d) or

11 10 Results Example 2 Visual Evaluation of Goodness of fit (Parent FOMC)

12 11 Hands-on Example 2, parent FOMC Visual Assessment GraphAssessment / Remarks Parent Overall fitExcellent ResidualsRandom distribution Metabolite Overall fitExcellent ResidualsRandom distribution

13 12 Hands-on Example 2, parent FOMC Statistical Indices 2 -test Relevant Parameters Estimated (y/n) Number of Parameters Minimum 2 Error Percentage Parent Pini P yyyyyy 35.5 Metabolite ffM kM yyyy 24.1 t-test Estimated Value Standard Error Number of Data Points Number of Estimated Parameters P-valueConclusion kM <0.001Significant

14 13 Hands-on Example 2 Conclusion 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 = , P = 4.436, ffM= and kM= d -1

15 14 Results Example 2 Visual Evaluation of Goodness of fit (Parent DFOP)

16 15 Hands-on Example 2, Parent DFOP Visual Assessment GraphAssessment / Remarks Parent Overall fit Excellent up to DT90, slight overestimation afterward Residuals Random distribution up to DT90 Metabolite Overall fitExcellent ResidualsRandom distribution

17 16 Hands-on Example 2, Parent DFOP Statistical Indices 2 -test Relevant Parameters Estimated (y/n) Number of Parameters Minimum 2 Error Percentage Parent Pini g k1 k2 yyyyyyyy 46.5 Metabolite ffM kM yyyy 23.6 t-test Estimated Value Standard Error Number of Data Points Number of Estimated Parameters P-valueConclusion k <0.001Significant k <0.001Significant kM <0.001Significant

18 17 Hands-on Example 2 Conclusion 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 = , k1 = d -1 (equivalent to half-life of 2.15 d), k2 = d -1 (equivalent to half-life of 20.4 d), ffM= and kM= d -1 (equivalent to half-life of 32.0 d)

19 18 Results Example 2 Visual Evaluation of Goodness of Fit (Metabolite Decline)

20 19 Hands-on Example 2, Metabolite Decline Visual Assessment GraphAssessment / Remarks Metabolite decline Overall fit Good, slight underestimation at last time points ResidualsNo distinct pattern

21 20 Hands-on Example 2, Metabolite Decline Statistical Indices 2 -test Relevant Parameters Estimated (y/n) Number of Parameters Minimum 2 Error Percentage Metabolite Mmax kM yyyy 25.7 t-test Estimated Value Standard Error Number of Data Points Number of Estimated Parameters P-valueConclusion kM <0.001Significant

22 21 Hands-on Example 2 Conclusion 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= d -1 (equivalent to half-life of 49.7 d)


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