Regulatory Science FACT: FACT: FACT: FACT: 9 Nov. 2013, Y. Yamada, Ph.D. 2 9 Nov. 2013, Y. Yamada, Ph.D. 3 9 Nov. 2013, Y. Yamada, Ph.D. 4 Regulatory
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1 Regulatory Science FACT: FACT: FACT: FACT: 9 Nov. 2013, Y. Yamada, Ph.D. 2 9 Nov. 2013, Y. Yamada, Ph.D. 3 9 Nov. 2013, Y. Yamada, Ph.D. 4 Regulatory Science Basic science Basic scienceinnovative science 9 Nov. 2013, Y. Yamada, Ph.D. 6 Nov. 2013, Y. Yamada, Ph.D. 5 9 Innovative science Regulatory science
2 Regulatory Science Regulatory research Regulatory affairs Regulatory Science regulatory science regulatory science 9 Nov. 2013, Y. Yamada, Ph.D. 7 9 Nov. 2013, Y. Yamada, Ph.D. 8 Regulatory Science RIKLT USDAneed update) USDA NIH 9 Nov. 2013, Y. Yamada, Ph.D. 9 Regulatory Research 9 Nov. 2013, Y. Yamada, Ph.D. 10 Regulatory Research 9 Nov. 2013, Y. Yamada, Ph.D. 11 Regulatory Research 9 Nov. 2013, Y. Yamada, Ph.D. 12
3 AA A (Innovative) A(Regulatory) (Regulatory) 9 Nov. 2013, Y. Yamada, Ph.D. 13 1/5 laboratory 9 Nov. 2013, Y. Yamada, Ph.D. 14 Laboratory Proficiency testing Collaborative studies Single laboratory Regulatory research Innovative science 9 Nov. 2013, Y. Yamada, Ph.D Nov. 2013, Y. Yamada, Ph.D. 16 Regulatory Science Regulatory Science 9 Nov. 2013, Y. Yamada, Ph.D. 17 Regulatory Science Variability Uncertainty : case-bycase Case-by-case 9 Nov. 2013, Y. Yamada, Ph.D. 18
4 9 Nov. 2013, Y. Yamada, Ph.D. 19
5 MRL 21 OECD calculator MRL Proportionality concept 2 Codex 1. GAP EUCodex
6 (GAP) kg ai/ha = kg ai/ll/ha Supervised trials on crops Formulation type EC, WP, WG, SC, SL 7 8 Supervised trials on crops JMPR data from 2000 to 2004 WP, EC, CS, SC0 PHI 7 Maclachlan and Hamilton (2010) OECD calculator MRL EU method NAFTA method EU methodeurope NAFTA methodeu method method OECD RCEG (the Residue Chemistry Expert Group) MRL 10 OECD calculator MRL EUNAFTA 11 OECD calculator Highest residue 95 MRL 95 <LOQ 12
7 OECD calculator 37 MRL High uncertainty of MRL estimate. [Small dataset] 8MRL MRL95 25 OECD calculator 50<LOQ MRL High uncertainty of MRL estimate. [High level of censoring] <LOQ1 >LOQ OECD calculator JMPR 1 Highest residue Mean + 4*SD 3*Mean*CF MRL
8 2 X 10SC / L/10a ( kg ai/ha) 2, 3 (PHI: 3 days), Lettuce, Leaf 2.84, 5.66, 6.14, 11.0 Lettuce, Head 4.38, 4.88, 7.58, , 5.66, 6.14, << , 4.88, 7.58, <x< Chlorfenapyr Lettuce, Leaf Japan 3 days Chlorfenapyr Lettuce, Head Japan 3 days Total number of data (n) 4 Percentage of censored data 0% Number of non-censored data 4 Lowest residue Highest residue Median residue Mean Standard deviation (SD) Correction factor for censoring (CF) Total number of data (n) 4 Percentage of censored data 0% Number of non-censored data 4 Lowest residue Highest residue Median residue Mean Standard deviation (SD) Correction factor for censoring (CF) Proposed MRL estimate Proposed MRL estimate - Highest residue Mean + 4 SD CF x 3 Mean Unrounded MRL Highest residue Mean + 4 SD CF x 3 Mean Unrounded MRL Rounded MRL 20 High uncertainty of MRL estimate. [Small dataset] Calculated by Mr. Ikeda 23 Rounded MRL 30 High uncertainty of MRL estimate. [Small dataset] Calculated by Mr. Ikeda 24
9 Y 15EC 912 1/ L/10a ( kg ai/ha) 17 1/2000, 200 L/10a (0.15 kg ai/ha) 2, 1 (PHI: 1 day), 25 Tomato 0.34, 0.42, 0.48, Cherry tomato 0.42, 0.5, 0.5, <x< Total number of data (n) 4 Percentage of censored data 0% Number of non-censored data 4 Lowest residue Highest residue Median residue Mean Standard deviation (SD) Correction factor for censoring (CF) Proposed MRL estimate Tolfenpyrad Tomato Japan 1 day - Highest residue Mean + 4 SD CF x 3 Mean Unrounded MRL Total number of data (n) 4 Percentage of censored data 0% Number of non-censored data 4 Lowest residue Highest residue Median residue Mean Standard deviation (SD) Correction factor for censoring (CF) Proposed MRL estimate Tolfenpyrad Cherry tomato Japan 1 day - Highest residue Mean + 4 SD CF x 3 Mean Unrounded MRL Rounded MRL 1.5 High uncertainty of MRL estimate. [Small dataset] Calculated by Mr. Ikeda 27 Rounded MRL 2 High uncertainty of MRL estimate. [Small dataset] Calculated by Mr. Ikeda 28 Proportionality Concept for estimation of MRLs Application rate GAP25 GAP rate Application rate Scaled residue = Measured residue (GAP rate / Trial application rate) Proportionality Concept for estimation of MRLs JMPR 1146 side-by-side trials MacLachlan and Hamilton, trials2306 side-by-side datasets
10 Principle and Guidance 31 Principle and Guidance GAP0.34 cgap <LOQ 25% 32 Principle and Guidance 33 Principle and Guidance GAPMRL proportionality MRL 34 MRL(2011JMPR) 35 36
11 Codex Stone fruits (Prunus) 1 MRL 41 Codex code Peaches FS 2001 * Peach Prunus persica FS 0247 Nectarine Prunus persica var. FS 0245 nectarina Plums FS 0014 Apricot Prunus armeniaca FS 0240 * Japanese apricot Prunus mume FS 2237 Plum, Japanese Plum Prunes Prunus salicina Prunus domestica Prunus domestica FS 2234 Sloe Prunus spinosa FS 0249 Plum, Damson, Bullace Prunus institia FS 0241 Cherry Plum Prunus ceresifera FS 0242 Plum, Chicksaw Prunus angustifolia FS 0248 Jujube, Chinese ZiZiphus jujuba FS 0302 Plumcot Prunus domestica x FS 2236 P.americana) Cherries FS 0013 Cherry, Sweet Prunus avium FS 0244 Cherry, Sour, Morello Prunus cerasus FS 0243 Korean cherry Prunus japonica Cherry, Nanking Prunus tomentosa FS
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JMPR 及び CCPR に関する勉強会 2013/8/23 残留農薬基準 (MRL)) の推定の実際 23 th Aug 消費 安全局農産安全管理課 農薬対策室入江真理 農薬使用基準 (GAP) 使用する作物 ( 作物群 ) ごとに以下を設定 1 使用量 ( 散布液濃度 散布量 ) k
残留農薬基準 (MRL)) の推定の実際 23 th Aug. 2013 消費 安全局農産安全管理課 農薬対策室入江真理 農薬使用基準 (GAP) 使用する作物 ( 作物群 ) ごとに以下を設定 1 使用量 ( 散布液濃度 散布量 ) kg ai/ha = kg ai/l L/ha 2 使用回数 3 使用時期 ( 収穫前日数 :PHI 等 ) 4 使用方法 ( 散布 土壌処理 種子処理等 ) 1 1
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Information Science Japan Advanced Institute of Science and Technology, Announcement of Researchers School of Information Science Shirai Laboratory Research Interests Associate Professor Kiyoaki Shirai