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Cite This: J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Benchmarking the Current Codex Alimentarius International Estimated Short-Term Intake Equations and the Proposed New Equations Cheryl B. Cleveland,*,† Carrie R. Fleming,‡ Jason E. Johnston,§ Angela S. Klemens,⊥ and Bruce M. Young¶ †
BASF Agricultural Solutions, 26 Davis Drive, Research Triangle Park, North Carolina 27709, United States Corteva Agriscience, the Agriculture Division of DowDuPont, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States § Bergeson & Campbell, P.C., 2200 Pennsylvania Avenue, Northwest Suite 100W, Washington, DC 20037-1701, United States ⊥ FMC Agricultural Solutions, Stine Research Center, 1090 Elkton Road, Newark, Delaware 19711, United States ¶ Bayer Crop Science, 2400 Ellis Road, Research Triangle Park, North Carolina 27709, United States
J. Agric. Food Chem. Downloaded from pubs.acs.org by 79.110.19.75 on 03/16/19. For personal use only.
‡
ABSTRACT: The International Estimated Short-Term Intake IESTI equations are used during the establishment of Codex Maximum Residue Limits. A recent proposal to revise the equations sparked international debate regarding selection of residue inputs and the appropriate level of consumer protection. The 49th Codex Committee on Pesticide Residues meeting recommended benchmarking the IESTI equations against distributions of actual exposures. Using publicly available data and models, this work compares dietary exposures for strawberries, tomatoes, and apples at five levels of refinement to place these equations into context relative to real-world exposures. Case studies were based on availability of robust USDA PDP monitoring data, which is uniquely suited to refine dietary exposures for a population. Benchmarking dietary exposure involves several decision points. Alternate methodology choices are not expected to impact the large margins observed between the probabilistic estimates and the IESTI equations or to change the overall conclusion that existing IESTI equations are conservative and health-protective. KEYWORDS: IESTI, CARES-NG, JMPR, MRL, USDA PDP, Codex, dietary exposure, residue monitoring, pesticide
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INTRODUCTION Dietary risk assessment is a fundamental part of the regulatory process that ensures potential residues on food or feed that may result from the use of a pesticide active ingredient in crop production will not pose an unacceptable risk to consumers. Because risk is a function of both the exposure and the hazard of the active ingredient, an acceptable (i.e., passing) risk assessment results when the dietary exposure is below a regulatory safety threshold, also known as a reference dose (RfD), which is based on the toxicology. Dietary risk assessments support the establishment of maximum-residuelimit values (MRLs): the maximum legally allowed residue on food or feed. The establishment of MRLs is an essential regulatory tool that ensures food safety and helps with food security, because it facilitates trade between exporting and importing countries. However, within the general system of dietary risk assessment, the methodology and inputs used for conducting the dietary assessment can vary among regulatory bodies that derive or approve MRLs. Dietary exposure is dependent on the concentration of the substance (i.e., active ingredient and/or toxicologically relevant metabolites or degradates) in food, the amount of the food consumed, and the frequency and duration of consumption. Dietary assessments are typically conducted to cover repeated exposure (chronic) timeframes and also short-term (acute) exposures, as appropriate. Chronic exposures are averaged over a long duration of time (repeated doses over a year and may be © XXXX American Chemical Society
considered to represent a lifetime). Acute exposures are single exposure events or exposures that occur within a short period of time (typically 24 h). Short-term assessments are only conducted when an acute reference dose (ARfD) is established on the basis of the toxicological profile. This paper is focused on the methodology options for acute (short-term)-exposure assessments. This work arises from recent and differing interpretations over the need to revise the International Estimated Short-Term Intake (IESTI) equations used for short-term dietary assessments. The IESTI equations are used during establishment of Codex Maximum Residue Limits (CXLs): the Joint FAO/ WHO Meeting on Pesticide Residues (JMPR) estimates maximum residue levels, and subsequently the Codex Committee on Pesticide Residues (CCPR) proposes CXLs for adoption by the Codex Alimentarius Commission (CAC). Specifically, this paper focuses on the 2015 Geneva workshop proposal1 to change the JMPR version of the IESTI equations and the ensuing recommendations from the 49th Codex Committee on Pesticide Residues (CCPR)2 meeting to compare (i.e., benchmark) the outcomes of the proposed IESTI equations to probabilistic distributions of actual Received: October 9, 2018 Revised: January 25, 2019 Accepted: March 4, 2019
A
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry Table 1. Current and Proposed IESTI Equationsa current IESTI
proposed IESTI
Case 1
LP × (HR or HRP) bw
LPbw × MRL × CF × PF
Case 2a
(Ue × HR or HRP × v) + [(LP − Ue) × HR or HRP] bw
LPbw × MRL × v × CF × PF
Case 2b
LP × (HR or HRP) × v bw LP × STMR or STMRP bw
LPbw × MRL × v × CF × PF
Case 3
LPbw × MRL × CF × PF
a
LP, large portion size or high-end consumption in a day (97.5th percentile, consumers only); LPbw, large portion size divided by body weight for an individual (97.5th percentile, consumers only); HR, highest residue or residue from highest composite from field trials; HRP, HR for processed commodities; bw, mean body weight from the country from which the LP was reported; CF, conversion factor to account for cases when the residue definition for enforcement is different from the dietary-intake-assessment residue definition; PF, processing factor; Ue, edible portion or unit weight of RAC; v, variability factor or factor applied to a composite sample to estimate residue in a high-residue unit (a variability factor of v = 3 is used at the JMPR); STMR, supervised-trials median residue or median residue from field trails (only for blended commodities) derived from the same set as the HR (e.g., cereals and tea); STMRP, STMR for processed commodities.
Authority (EFSA) Pesticide Residue Intake Model (PRIMo)4 for Belgium, Denmark, Ireland, Italy, Spain, Lithuania, and Poland. For IESTI, acute exposures from each food commodity are calculated separately, without summing the exposure from all commodities because it is considered unlikely that a consumer will consume large quantities of two or more different commodities that also contain high levels of the compound on the same day.5 The existing IESTI approach is understood to be conservative (i.e., highly protective) and is therefore recognized to be a screeninglevel approach. Alternately, a probabilistic approach uses a model to account for the full distribution of consumption levels and/or residues on food. Probabilistic models randomly sample from distributions of food consumption and residue data to calculate exposure, and sampling is repeated over multiple iterations to create a distribution of dietary exposures for a population. These models typically require specialized software to incorporate distributions for residues on each food type and distributions of food-consumption values as well as to manage the calculations. Traditionally, the U.S. and Canadian governments have used the Dietary Exposure Evaluation Model (DEEM-FCID)6 to conduct acute probabilistic assessments. Recently, the Cumulative and Aggregate Risk Evaluation System−Next Generation (CARES-NG) model7 has become available as a web-based model for this purpose. Probabilistic models are valuable alternatives for refining dietary-exposure estimates. If an upper-bound screening model suggests unacceptable risk to consumers, then higher-tiered assessments with more refined inputs can provide more realistic exposure estimates for a better balance of food safety and agricultural trade.8 Proposed Changes to IESTI. The IESTI equations are currently used at the JMPR level to support establishment of Codex MRLs (CXLs). In addition, Australia, Japan, and the EU use national versions of the IESTI equations (with regionspecific consumption data or alternate variability factors) for safety assessment, supporting authorization of use and MRL settings within their countries. Proposed changes to the IESTI equations from the 2015 FAO/WHO workshop have been the subject of international debate at the 48th,9 49th,2 and 50th10 CCPR Codex meetings. The IESTI equations are divided into four cases on the basis of the following three considerations: (a) the portion size eaten, (b) how the residue is distributed among the individual units that make up the composite sample used for residue determination, and (c) how the food is
exposures. This paper explores benchmarking case studies in strawberries, tomatoes, and apples at five tiered levels of refinement for dietary exposure. The tiers were designed using different combinations of publically available residue estimates (MRL, field data, and monitoring distributions) and models (deterministic or probabilistic). It compares the current and proposed IESTI calculations to outcomes from refined exposure assessments based on residue-monitoring data and probabilistic methodologies. Key learnings for consideration in benchmarking exercises are noted. Acute-Dietary-Exposure Assessment. Acute dietary assessments are conducted to ensure consumer safety in the case of large-portion consumption of a food with high residues in a short time frame. The exposure estimation has two components: the large-portion-consumption patterns of the population in question and the manner in which the high residue on the food is estimated. Once the exposure is estimated, it is compared against the established ARfD. There are currently two primary approaches used globally for estimating acute dietary exposure. The IESTI equations, developed in the late 1990s, are the most widely used approach for acute dietary assessment. They are a set of deterministic equations (Table 1) divided into four cases that depend on the unit size of the food sample collected for analysis, the amount eaten in the large portion (LP), and whether the food is considered to be bulked or blended. In a deterministic model, the exposure is estimated as a product of discrete numbers; for example, the residue-concentration input may be the highest observed residue (HR) from field trials, or a single value may be used as an upper estimate of food consumption. Practically, the IESTI equations are embedded as formulas in a spreadsheet, and the exposure and risk assessments can be easily conducted and shared within a standalone workbook. For use in the JMPR IESTI spreadsheet,3 the consumption estimates for each commodity reflect the overall highest consumption estimate for that commodity reported from all the countries responding to the surveys for the Global Environment Monitoring System (GEMS). The IESTI equations estimate exposure on the basis of the highest reported 97.5th percentile single-day intake reported as “consumers only” by participating countries. Until recently, IESTI contained information from 12 countries: Australia, Brazil, China, Germany, Finland, France, Japan, Netherlands, Thailand, United Kingdom, United States, and South Africa. The August 2017 version of IESTI also contains new separate consumption estimates from the European Food Safety B
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry
anticipated.16 A complete set of publicly available LP/bw data will likely take years to gather, approve, and release. Because the 2015 Geneva proposal would result in a JMPR exposure assessment that is always greater than the current approach, concerns have been raised2,9,10,17 that implementation of the proposed changes will result in the unwarranted loss of CXLs. Any regulatory proposal to change existing approaches to exposure and subsequent risk assessments that could negatively impact international trade must be carefully considered with respect to the balance between conservatism in the exposure and risk assessments and impact on agricultural economies. Fewer CXLs ultimately translate into the loss of crop-protection-product alternatives available for rotation and resistance-management practices as part of Integrative Pest Management, which can be particularly challenging for developing nations when they rely directly on Codex MRLs for trade. In developed nations, adoption and use of active ingredients by growers heavily depends on having international MRLs (including those at the Codex level) to support trade. Benchmarking versus Risk Assessment. Although the current IESTI is generally accepted as conservative and protective, the degree of conservatism is not well-characterized. To quantify the level of protection of the current and proposed IESTI equations, a methodical comparison to benchmark (or calibrate) the IESTI approach against the more realistic and refined estimates provided with probabilistic models and realworld data can aid understanding. At the 49th CCPR meeting,2 a request was made for an FAO/WHO workgroup to conduct and report on a benchmark of the outcomes of IESTI equations “to a probabilistic distribution of actual exposures”. In anticipation of the FAO/WHO exercise, the authors have undertaken a similar exercise based on publicly available data and models. The authors used routine methodologies already developed within the U.S. regulatory process. Preliminary results were presented at the Global Minor Use Summit (GMUS-3)18 and the 27th Annual Meeting of the International Society of Exposure Science.19 The purpose of benchmarking needs to be distinguished from risk assessment. Regulatory risk assessments are designed to be health-protective and to avoid any underestimation of potential consumer exposures. Many of the same tools and information are used in the benchmarking exercise. However, the goal of benchmarking is to understand all of the potential estimates of exposure ranging from the most conservative screening-level approach to a refined (e.g., realistic) level at the dinner plate of the consumer. To achieve a meaningful calibration, refined data for residue concentrations and consumption are needed to cover the full range of calculated exposures. Furthermore, benchmarking exercises should reflect the assessed methodology. In this case, because the IESTI equations evaluate high exposure to single commodities only, any benchmarking exercise using probabilistic tools should similarly focus on single commodities. Finally benchmarking for exposure should not include full risk-assessment comparisons to ARfD values, because the toxicological differences among active ingredients have nothing to do with the construction of the IESTI equations for residue exposure.
distributed in the food chain. In Case 1, residues in the composite sample generally reflect residues in the large portion for consumption (e.g., berries). Cases 2a and 2b apply to foods that are eaten in discrete units (e.g., apple and watermelon), and a variability factor is used to account for variation in residues among the individual units. In Case 2a, the large portion contains more than one unit (e.g., apple), whereas in case 2b, the large portion size is smaller than the unit size (e.g., watermelon). Case 3 is for bulked or blended commodities (e.g., grains). The current and proposed IESTI equations are shown in Table 1. The mathematical-formula changes between the current and proposed equations are most distinct in Case 2a, in which the large portion consumed is smaller than the composite sample size (e.g., a large portion for consumption may be several apples, but the composite sample for residue testing uses many more apples). Because multiple units may be consumed in the large portion, the current equation uses a variability factor as a multiplier applied to the residue for only the first unit in the large portion. In the proposed Case 2a, the mathematical formula is simplified by removing the adjustment for the individual units, but the simplification creates an increased intake estimate, because the variability factor is applied to the residue of all units in the large portion. The variability factor was included in the IESTI equations to reintroduce unit-tounit variability lost during compositing of samples; therefore, this change of applying the variability factor to all units in the large portion is not consistent with the scientific purpose of the variability factor. For Case 2b, the unit size of the commodity exceeds the large portion size (e.g., watermelon), and the variability factor is applied to the whole unit; this variability factor is applied in the same fashion for both the current and proposed equations in Case 2b. The proposed equations use a higher residue estimate by replacing field-trial residue observations with MRL values while maintaining the use of a variability factor of 3 and adding a new conversion factor (CF) for differences between the definition of residue (DoR) for dietary assessment (which may include a combination of parent compound and degradates or metabolites) versus that for monitoring (typically fewer analytes). The proposed changes are especially impactful for blended or processed commodities (e.g., juice or grains), where it is currently accepted that the field-supervised-trial median residue (STMR) or other central-tendency concentration is representative of residues.5,11,12 Within the international debate, distinctions among the IESTI equations used in different regions are critical to maintain because of the impact of variability factors. The 2015 FAO/WHO Geneva proposal includes assumptions for harmonization for several versions of the IESTI, with an emphasis on the EU. Because the EU currently uses higher variability factors of 5 and 7 for some commodities, the impact of the proposed changes in the EU13 is not equivalent to the impact on the JMPR estimate.14 At the JMPR, the math dictates that the use of MRL will increase all estimated exposures. Although it has been suggested that this exposure increase may be counterbalanced by the expression of LP data on a body-weight (bw) basis, adjusted LP data are not available at this time15 to fully assess the validity of this assertion. A preliminary assessment using consumption data from Australia and Europe found limited impact from the LP parameter, and those authors suggest it may not provide the offset originally
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METHODS
Exposure estimates from the current and proposed IESTI equations were benchmarked against probabilistic estimates of dietary exposure using a tiered set of variously refined assumptions about residue concentrations in the consumed food. Details on the selection of the C
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry Table 2. Five Different Methods Used in Benchmark Analysis probabilistic benchmarking proposed IESTI data source (consumption) data source (residue) variability factor conversion factorg number of iterations exposure percentile used
a
point value
quasiprobabilistic benchmarking
current IESTI point value
a
monitoring data distribution of consumption datab distribution of monitoring dataf no yes 1000 99.9th percentile per capita exposure
MRL point value
HR point value
yes yes NA
yes NA NA
no yes NA
distribution of consumption datab distribution of field-trial datae no NA 1000
97.5th percentile eatersonly exposure
97.5th percentile eatersonly exposure
97.5th percentile eatersonly exposure
95th percentile per capita exposure
d
c
distribution of consumption datab MRL point valued
field-trial data
Global Environment Monitoring System (GEMS). bWhat We Eat in America (WWEIA). cCodex high residue (HR) from field-trial data. dCodex MRL. eAll Codex field-trial residue data used to calculate Codex MRL. fUSDA Pesticide Data Program (PDP), 2014−2015. gWhere a conversion factor is not used, relevant metabolite-residue values are included by addition to parent-residue value. a
• Quasiprobabilistic exposure modeling using probabilistic consumption data with all residues assumed present at the MRL • Probabilistic exposure modeling with probabilistic consumption data and one of the following: • Distribution of residues from the field trials used to establish the MRL (i.e., field trials conducted at the critical “good agricultural practice”, GAP) • Distribution of residues from a recent monitoring program The probabilistic exposure modeling was performed using the dietary module of the recently developed CARES-NG model. The dietary module incorporates food-consumption data from the What We Eat in America (WWEIA) survey,25 which is the dietary-interview component of the National Health and Nutrition Examination Survey (NHANES).26 WWEIA provides national 2 day food-consumption survey data for approximately 10 000 individuals in each 2 year cycle of the survey, and it is designed and weighted to be statistically representative of food-consumption patterns in the United States. These survey data are coupled with the U.S. Environmental Protection Agency’s (EPA) Food Commodity Intake Database (FCID),27 which uses a database of recipes to translate “foods as eaten” reported in the WWEIA survey to component raw-agriculturalcommodity (RAC) equivalents. The survey data in CARES-NG represents consumption patterns from the 2005−2010 surveys. For probabilistic runs, the modeled population age range was matched to the age group associated with high consumption of the raw commodity of interest in the IESTI tool (i.e., ages 1−6 years for tomato and apple and ages 3−6 years for strawberry). Details on the five-tiered approach and assumptions used for each scenario in the case studies follow. Specific input for each Case are in Tables 4−6 for strawberry, tomato, and apple, respectively. 1. Current IESTI. For each crop, acute exposures were modeled using the current IESTI equations using the “Template for the evaluation of acute exposures (IESTI)”.3 The assumptions for residue concentrations followed the current practice of using the HR (based on the residue definition for dietary risk assessment) for Case 1 and 2 commodities; HR values were selected from the JMPR Report associated with the Codex MRL for the crop or commodity of interest. The IESTI calculated exposure results from the spreadsheet column “All data, all population groups” was used. 2. Proposed IESTI. The proposed IESTI changes include use of large-portion data expressed as a function of individual body weight (LPbw), but such data are not publicly available for use in the IESTI JMPR spreadsheets, so benchmarking this aspect of the proposed change was not currently possible. For this exercise, the large-portion data was used as presented in the current IESTI (this is generally understood to be large-portion data expressed per person, coupled with a population-group-standard-body-weight assumption). Residues were assumed to be at the Codex MRL for all food consumed, with
case studies and the assumptions used follow. Each intake assessment is performed for a specific active-ingredient and commodity combination. The active ingredients were selected by applying a set of defined inclusion criteria to the available data sets. An active ingredient was included in a crop case study only if it met all three data criteria (see below). Selection of Case Studies. Case studies were developed for one IESTI Case 1 commodity (strawberries) and two IESTI Case 2a commodities (tomatoes and apples). Emphasis was placed on Case 2a commodities rather than 2b commodities because the proposed changes have a greater impact on Case 2a than on Case 2b because of the application of the variability factor to all units in the large portion. The crops chosen for case studies were selected because they are common foods known to be highly consumed by children20 and thus have robust data sets for both consumption and active-ingredient residues. For each of the chosen crops, active ingredients were included for development of case studies on the basis of the following criteria: • Codex MRLs are established for the active ingredient on the crop of interest per the online Codex Alimentarius Pesticide Index21 or the Bryant Christie Global MRL Database.22 • The active ingredient is an analyte within the USDA Pesticide Data Program (PDP),23 and there is at least a 5% detection rate of observable residues in the monitoring data. • An acute reference dose (ARfD) applicable to children has been established by JMPR.24 On the basis of these criteria, a total of 12 active ingredients were included for strawberries (Case 1), 16 were included for tomatoes (Case 2a), and 8 were included for apples (Case 2a). The active ingredient carbendazim initially satisfied the selection criteria for all crops, but carbendazim residues observed in the monitoring data can originate from multiple pesticide sources, so carbendazim was removed from the benchmarking exercise. Approach to Benchmarking Case Studies. For benchmarking, exposure estimates were calculated five different ways (Table 2), representing increasing levels of refinement in the exposure estimate (i.e., a tiered approach). The approach is illustrative of general trends in exposure when progressing from deterministic models to probabilistic models with monitoring data. The following procedures, ranging from the most conservative (i.e., least refined or upper bound) to the most refined, were used to estimate exposures: • IESTI (August 2017 version) • Proposed IESTI approach using MRLs as residue input (the proposed change to use LPbw data was not achieved in this assessment because of the lack of public data for the international equations) • Current IESTI approach using HR data as residue input D
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry the variability factor of 3 applied to all units for the Case 2a commodities. Conversion factors were calculated when the residue definitions for MRL enforcement and dietary intake differed. To date, the proposal for changes to the IESTI equations acknowledges the need to calculate conversion factors, but it does not establish how this will be achieved. For this benchmarking exercise, simple conversion factors were calculated as follows: (A) The unrounded MRL based on the residue definition for enforcement (e.g., parent only) was calculated in the OECD MRL calculator using the residue data from the applicable JMPR report (“Parent”). (B) An unrounded MRL was calculated using the residues for the dietary-intake residue definition (e.g., parent and metabolites) in the OECD MRL calculator (“Total”). (C) The conversion factor was calculated as ratio of the unrounded “Total” to “Parent” MRLs. The conversion factors calculated for the six crop−active-ingredient combinations with different enforcement and dietary-intake residue definitions are shown in Table 3. For several cases (fluxapyroxad−
in the dietary-intake assessment by JMPR was used for benchmarking. Links to the Codex dossiers referenced for each case are in Tables 4−6. Exposures were calculated for the raw commodity (e.g., tomato, uncooked) in the CARES-NG model, using the full distribution of the field-trial residue data in the probabilistic exposure modeling, with no adjustments included for percent crop treated. In accordance with EPA long-standing practice for application of probabilistic modeling of slightly refined dietary exposures, exposure is presented as the 95th percentile exposure per capita.28 The field-trial residue distributions for dietary intake already included any metabolites of regulatory interest, so no CF was needed. 5. Probabilistic Benchmarking Using Monitoring Data. A final refined assessment was performed using the distribution of residues observed in recent monitoring conducted for the Pesticide Data Program (PDP). PDP is a national residue-monitoring program operated by the United States Department of Agriculture (USDA). PDP monitors fresh and processed food commodities, including fruits, vegetables, grains, dairy, meat, and poultry, and it focuses on foods consumed by infants and children. Different foods are selected annually for monitoring on a rotating basis, such that commonly consumed foods are generally sampled in 3−5 year cycles. Among monitoring programs, PDP is unique in that the primary goal is not targeted MRL enforcement but is rather to gain an understanding of the distribution of active-ingredient residues present in the U.S. food supply. Samples are collected from terminal markets and large distribution centers, and sampling strategies for each commodity are designed to ensure that data collected are representative of consumed commodities. During routine commodity sampling, typical collection involves over 700 samples, and collection can often exceed 1 year. For example, within the 2015 PDP program, fruit and vegetable samples were analyzed for over 450 parent pesticides, metabolites, degradates, and isomers. Detailed data by crop and active ingredient are published in an online electronic database.23 The combination of a sampling design aimed at informing consumer exposures, and the availability of individual sample data make PDP an ideal residue data source for probabilistic assessment of real-world dietary exposures to pesticide residues. For each crop selected as a case study, the full distribution of residue samples from the 2014 and 2015 PDP sampling periods (i.e., the most recent available data at the time these case studies were initiated) was used for probabilistic modeling. Summaries of the distribution of PDP monitoring data are shown in Table 7. The full distributions are available online.23 By focusing on analytes with >5% detections, our methodology selects for relevant active ingredients with high-end exposure. Samples below the limit of detection were assumed to be present at 1/2 the limit of detection (LOD), with no adjustments made for percent crop treated (i.e., no true zeros were used). In accordance with EPA common practice for application of probabilistic modeling for refined dietary exposures, exposure is presented as the 99.9th percentile exposure per capita.28 MRL conversion factors >1.0 (Table 3) were included in the assessment.
Table 3. Conversion-Factor Calculation for Strawberries and Tomatoes active ingredient
basis
flupyradifurone
total parent total parent total parent
fluxapyroxad penthiopyrad
dinotefuran fluxapyroxad penthiopyrad
total parent total parent total parent
MRL (mg/kg) Strawberry 2 1.5 4 4 3 3 Tomato 0.7 0.5 0.6 0.6 2.0 2.0
unrounded MRL (mg/kg)
conversion factor
1.599 1.362 3.591 3.466 2.798 2.786
1.2
0.687 0.493 0.575 0.519 1.810 1.813
1.4
1.0 1.0
1.1 1.0
strawberry, penthiopyrad−strawberry, and penthiopyrad−tomato), the calculated conversion factor rounded to 1.0, and thus was not ultimately needed. When the conversion factor was >1.0, it was included in the proposed IESTI equations. For apples, none of the active ingredients had differences between the residue definitions for enforcement and dietary intake, so no conversion factors were necessary. 3. Quasiprobabilistic Benchmarking. Although not representative of real-world residues, a quasiprobabilistic exposure estimate assuming that all food items consumed contained residues at the MRL level was included as a first level of refinement to allow for a comparison of probabilistic exposure modeling to the deterministic IESTI exposure estimates. Use of the MRL with the probabilistic consumption is a useful signpost along a tiered approach. Exposures were calculated for the raw commodity (e.g., tomato, uncooked) in the CARES-NG model, assuming that the residue is always at the Codex MRL. To allow for comparison with the IESTI approach, exposures were reported for the 97.5th percentile single-day exposure from users (i.e., “Eaters”) only. MRL conversion factors >1.0 (Table 3) were included in the assessment. 4. Probabilistic Benchmarking Using Field-Trial Data. A slightly refined assessment was performed using the full distribution of residue concentrations observed in the residue field trials. For each activeingredient−crop combination, the same residue data evaluated by JMPR during the process to establish the Codex MRL was used in this work. When two distributions were listed in the JMPR, the one used
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RESULTS A comparison of the various tiered levels of refinement with the current IESTI exposure estimates are presented in Tables 8−10 and Figures 1−3. The table presentation provides numeric comparisons of exposure within the five tiers for each active ingredient. The figures present intakes normalized to the current IESTI equations for each active ingredient individually in order to visually display trends across the full data set. True to the objective of this benchmarking exercise, only exposure is presented; no risk assessments against reference doses are made. Two distinct outcomes are observed in the results: (1) the estimated exposures from the proposed IESTI equations are greater than exposure estimates from the current IESTI equations for all pesticides in all investigated commodities, ranging from 1.3 to 3.2× depending on pesticide−commodity E
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry Table 4. Strawberry Case Study: Summary of Input Information class
Codex MRL (mg/kg)
HR (mg/kg)
ARfD (mg/kg bw)
acetamiprid
I
0.5
0.24
0.1
parent/parent
bifenthrin
I
3
2.3
0.01
sum isomers/sum isomers
fenpropathrin
I
2
1.2
0.03
parent/parent
fenpyroximate
I
0.8
0.59
0.02
parent/parent
flupyradifuronea
I
1.5
0.94
0.2
enforcement: parent
active ingredient
residue definition (enforcement/dietaryintake assessment)
fluxapyroxad
F
7 (group MRL)
3.9
0.3
imidacloprid
I
0.5
0.35
0.4
malathion
I
1
0.59
2
RA: parent + metabolites enforcement: parent RA: parent + metabolites parent + metabolites/ parent + metabolites parent/parent
methoxyfenozide
I
2
1.2
0.9
parent/parent
penthiopyrad
F
3
1.8
1
enforcement: parent
0.75
0.05
RA: sum of penthiopyrad and PAM parent/parent
0.26
1
parent/parent
pyraclostrobin
F
thiamethoxam
I
1.5 0.5 (group MRL)
field-trial-residue data set supporting Codex MRL (mg/kg)
conversion factor
strawberry: 0.03, 0.04, 0.05, 0.06, 0.09, 0.11, 0.12, 0.24, 0.24, 0.24 strawberry: 0.27, 0.3, 0.31, 0.33, 0.33, 0.34, 0.34, 0.36, 0.41, 0.46, 0.46, 0.48, 0.51, 0.59, 0.86, 0.86, 0.88, 2.1, 2.3 strawberry: 0.26, 0.38, 0.39, 0.48, 0.48, 0.55, 0.63, 0.65, 0.69, 1.2 strawberry: 0.07, 0.08, 0.19, 0.19, 0.24, 0.24, 0.28, 0.53 strawberry: 0.23, 0.33, 0.38, 0.38, 0.43, 0.51, 0.54, 0.54, 0.58, 0.62 strawberry: 0.33, 0.38, 0.43, 0.43, 0.48, 0.57, 0.59, 0.59, 0.63, 0.9 blueberry: 1.3, 1.7, 2.4, 2.4, 3.8 blueberry: 1.3, 1.7, 2.4, 2.4, 3.8
none
Acetamiprid 2011
none
Bifenthrin 2010
none
Fenpropathrin 2014
none
Fenpyroximate 2013 Flupyradifurone 2016
strawberry: 0.12, 0.14, 0.15, 0.15, 0.17, 0.17, 0.21, 0.32, 0.35 strawberry: 0.09, 0.16, 0.19, 0.25, 0.39, 0.53, 0.59 strawberry: 0.18, 0.2, 0.21, 0.24, 0.43, 0.49, 1.2 strawberry: 0.37, 0.41, 0.46, 0.62, 0.77, 0.87, 1.0, 1.4, 1.8 strawberry: 0.39, 0.43, 0.47, 0.64, 0.8, 0.89, 1.1, 1.4, 1.8 strawberry: 0.06, 0.12, 0.13, 0.15, 0.16, 0.2, 0.24, 0.31, 0.43, 0.73, 0.75 strawberry (WG formulation): 0.02, 0.02, 0.05, 0.05, 0.06, 0.14, 0.22, 0.26
1.2
JMPR report
1 (none needed)
Fluxapyroxad 2015
none
Imidacloprid 2008
none
Malathion 1999
none
Methoxyfenozide 2009 Penthiopyrad 2012
1 (none needed)
none
Pyraclostrobin 2011
none
Thiamethoxam 2010
a
http://www.fao.org/fao-who-codexalimentarius/sh-proxy/fr/?lnk=1&url=https%253A%252F%252Fworkspace.fao.org%252Fsites%252Fcodex %252FMeetings%252FCX-718-49%252FREPORT%252FREP17_PRe.pdf.
combination, and (2) the probabilistic dietary-exposure estimates are lower than those estimated using IESTI and generally decrease as the assessment becomes more realistic (i.e., as it moves from the quasiprobabilistic approach to the probabilistic approach with monitoring data). The results for each commodity case study are discussed separately. Strawberry: Case 1 Commodity. A search of the online Codex Alimentarius Pesticide Index21 and Bryant Christie Global MRL22 online databases for “strawberry/ies” or “berries and other small fruit” identified CXLs for 68 active ingredients. Of these, 25 active ingredients had ≥5% detects in PDP monitoring data, and a final 12 had an ARfD assigned by JMPR (Table 4). A comparison of the various tiered levels of refinement with the current IESTI exposure estimates for strawberries is presented in Table 8 and Figure 1. The proposed IESTI changes increased exposure estimates 1.3− 2.1× over the current IESTI exposure estimates. Relative to the current IESTI exposure estimates, the probabilistic exposure estimates were 1.1−1.8× lower for the quasiprobabilistic approach, 13−47× lower for the probabilistic approach with field data, and 4−110× lower for the probabilistic approach with monitoring data. Tomato: Case 2a Commodity. A search of the online databases for “tomato” or “fruiting vegetables” identified CXLs for 106 active ingredients. Of these, 29 active ingredients had ≥5% detects in PDP monitoring data, and a final 16 had an ARfD assigned by JMPR (Table 5). A comparison of the tiered levels of refinement with the current IESTI exposure estimates for tomatoes is presented in Table 9 and Figure 2. The
proposed IESTI changes increased exposure estimates 1.4− 3.2× above the current IESTI exposure estimates. Relative to the current IESTI exposure estimates, the probabilistic exposure estimates were 1.6−3.7× lower for the quasiprobabilistic approach, 23−120× lower for the probabilistic approach with field data, and 45−1750× lower for the probabilistic approach with monitoring data. Apple: Case 2a Commodity. A search of the online databases for “apple” or “pome fruits” identified CXLs for 98 different active ingredients. Of these, 17 active ingredients had ≥5% detects in PDP monitoring data, and a final 8 had an ARfD assigned by JMPR (Table 6). A comparison of the various tiered levels of refinement with the current IESTI exposure estimates for apples is presented in Table 10 and Figure 3. The proposed IESTI changes increased exposure estimates 1.7−2.8× higher than the current IESTI exposure estimates. Relative to the current IESTI exposure estimates, the probabilistic exposure estimates were 1.6−2.8× lower for the quasiprobabilistic approach, 8−31× lower for the probabilistic approach with field data, and 5−260× lower for the probabilistic approach with monitoring data.
■
DISCUSSION The case studies were designed in a tiered approach to reflect five levels of exposure predictions, moving from unrefined (proposed and current deterministic IESTI equations) to quasiprobabilistic (MRL but with consumption distribution) and to refined (distributions for both consumption and residues) assessments. The case studies use public data and F
DOI: 10.1021/acs.jafc.8b05547 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
G
I
F
I I
F
I
I
F
F
dinotefuran
famoxadone
fenpropathrin fenpyroximate
fluxapyroxad
imidacloprid
methoxyfenozide
penthiopyrad
pyraclostrobin
I
F
dimethomorph
thiamethoxam
I F
buprofezin difenoconazole
I
I
bifenthrin
sulfoxaflor
I
class
acetamiprid
active ingredient
1.5 (group MRL) 0.7 (group MRL)
0.3
2 (group MRL)
2
0.5
0.6 (group MRL)
1 0.2
2
1 0.6 (group MRL) 1.5 (group MRL) 0.5 (group MRL)
0.2 (group MRL) 0.3
Codex MRL (mg/kg)
0.47
0.6
0.21
1.6
1.8
0.29
0.44
0.64 0.14
1.1
0.55
1.2
0.52 0.39
0.15
0.14
HR (mg/kg)
1
0.3
0.05
1
0.9
0.4
0.3
0.03 0.02
0.6
1
0.6
0.5 0.3
0.01
0.1
ARfD (mg/kg bw)
parent/parent (separate metabolites)
parent/parent
parent/parent
RA: sum parent and PAM
enforcement: parent
RA: sum parent +2 metabolites sum parent + metabolites/sum parent + metabolites parent/parent
enforcement: parent
parent/parent parent/parent
RA: sum parent +2 metabolites parent/parent
enforcement: parent
sum isomers/sum isomers
sum isomers/sum isomers parent/parent parent/parent
parent/parent
residue definition (enforcement/dietary-intake assessment)
Table 5. Tomato Case Study: Summary of Input Information
pepper: 0.07, 0.07, 0.08, 0.08, 0.08, 0.08, 0.10, 0.12, 0.16, 0.26, 0.47
tomato: 0.052, 0.088, 0.12, 0.12, 0.13, 0.13, 0.14, 0.14, 0.16, 0.19, 0.20, 0.21, 0.26, 0.28, 0.33, 0.56, 0.57, 0.73, 0.94, 1.0, 1.6, 1.8 tomato, sweet pepper, and chili pepper: 0.085, 0.15, 0.15, 0.15, 0.16, 0.17, 0.17, 0.17, 0.17, 0.17, 0.18, 0.19, 0.19, 0.19, 0.2, 0.21, 0.22, 0.22, 0.24, 0.25, 0.27, 0.28, 0.33, 0.36, 0.36, 0.36, 0.36, 0.4, 0.41, 0.41, 0.42, 0.57, 0.68, 0.7, 0.71, 0.77, 0.88, 1.3, 1.4, 1.6 tomato, sweet pepper, and chili pepper: 0.095, 0.16, 0.16, 0.16, 0.17, 0.18, 0.18, 0.18, 0.18, 0.18, 0.2, 0.2, 0.2, 0.2, 0.22, 0.23, 0.23, 0.23, 0.25, 0.26, 0.28, 0.29, 0.34, 0.37, 0.37, 0.38, 0.38, 0.41, 0.42, 0.43, 0.44, 0.58, 0.69, 0.71, 0.73, 0.78, 0.92, 1.3, 1.4, 1.6 tomato: 0.02, 0.03, 0.06, 0.07, 0.07, 0.08, 0.1, 0.11, 0.11, 0.11, 0.11, 0.12, 0.12, 0.12, 0.12, 0.13, 0.13, 0.15, 0.16, 0.17, 0.17, 0.17, 0.19, 0.21 tomato: 0.026, 0.030, 0.035, 0.054, 0.063, 0.11, 0.15, 0.31, 0.41, 0.53, 0.60
tomato, sweet pepper, and chili pepper: 0.030, 0.039, 0.042, 0.051, 0.056, 0.060, 0.069, 0.069, 0.071, 0.084, 0.097, 0.13, 0.13, 0.14, 0.14, 0.14, 0.15, 0.16, 0.16, 0.23, 0.25, 0.27, 0.41 tomato and pepper: 0.050, 0.059, 0.062, 0.071, 0.080, 0.080, 0.089, 0.091, 0.10, 0.10, 0.12, 0.15, 0.15, 0.17, 0.18, 0.18, 0.19, 0.19, 0.20, 0.26, 0.29, 0.50, 0.55 tomato: 0.02, 0.02, 0.02, 0.03, 0.03, 0.03, 0.03, 0.04, 0.04, 0.04, 0.05, 0.05, 0.05, 0.07, 0.08, 0.08, 0.09, 0.1, 0.1, 0.1, 0.1, 0.1, 0.11, 0.11, 0.12, 0.12, 0.15, 0.15, 0.15, 0.16, 0.18, 0.18, 0.2, 0.33, 0.74, 1.1 tomato: 0.05, 0.08, 0.11, 0.18, 0.19, 0.21, 0.27, 0.3, 0.55 tomato: 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.06, 0.07, 0.07, 0.08, 0.09, 0.11, 0.12, 0.08, 0.08, 0.14 tomato, bell pepper, and chili pepper:
