David Robin Tennant

Philippe Bequet

Didier Jans

Modelling consumer exposures to nutrients and additives in animal products

Keywords: Bequet, Philippe, DSM Nutritional Products Jans, Didier, Exposure modelling, Risk assessment Additives/Contaminants: Additives general, Residues, Vitamins Food Types: Animal feedingstuffs Feed additives, risk assessment, intakes, dietary exposure 19 modelling 20 21 22

Current guidelines for assessing human exposures to nutrients and other substances 5 used in animal feed are based on methods used for veterinary pharmaceuticals.

6

These methods assume high level daily consumption of animal products and do not 7 take account of differences between species or between consuming humans. A more 8 detailed dietary modelling approach is described, which takes these factors into 9 account as well as high level consumption by different age groups. The proposed 10 approach is evaluated in three case studies, iodine, selenium and astaxanthin, which 11 have previously undergone thorough evaluation by EU authorities. When applied in a 12 tiered approach the proposed model provides results that are consistent with previous 13 assessments and with results obtained using other modelling techniques. The results

14

indicate that the method has the potential to provide a conservative, reliable and 15 flexible alternative to existing approaches to intake estimation.

nutrition (European Commission, 2003a). The Regulation includes provisions to 7 ensure that residual amounts of these additives remaining in animal products have 8 no adverse effects on the health of the consumer. Consumer exposure to nutrients 9 are controlled through the setting of tolerable upper intake levels (ULs) for different 10 age groups by the European Food Safety Authority (EFSA, 2006a). Exposure to 11 other additive residues/metabolites are controlled by determining acceptable daily 12 intakes (ADIs), which are usually expressed as mg/kg bodyweight/day. 13 14 Current guidelines for the assessment of additives in animal nutrition set out in 15 Commission Regulation 429/2008 provide a method for establishing maximum 16 residue levels (MRLs) for marker residues of feed additives based on food 17 consumption data (Table 1) (European Commission, 2008). This method was 18 originally developed for the assessment of MRLs for veterinary pharmaceutical 19 residues, which can be administered orally or by injection and may have acute 20 effects. At the time that the method was developed (during the 1980s), accurate 21 information about the consumption of foods of animal origin was difficult to obtain and 22 so "high arbitrary daily consumption data were applied in order to assure protection 23 of consumers" (European Commission, 2005). The method was adopted for feed 24 additives several years later. When using this method, MRLs and withdrawal periods 25 are proposed in such a way that the total amount of toxicologically significant residue 26 ingested daily should be lower than the ADI. An arbitrary body weight of 60 kg was 27 also adopted. 28

29

The actual quantities proposed for individual food items in the daily diet in the 30

Regulation 429/2008 model may not appear excessive for large portion sizes. 31 However, they assume that these amounts are eaten by every individual every day 32 whereas in reality many of these foods, particularly offal, may be eaten less 33 frequently and so average consumption over the longer term will be much lower, 1 even for high level consumers. Furthermore they do not take interspecies differences 2 into account. For example, particular products may be authorised only in certain 3 species and the consumption of beef, pork or lamb will be significantly lower than the 4 total for mammalian meat. The method also does not take account of variations 5 related to the consumer's age. In particular, infants tend to consume more milk than 6 adults when expressed on a body weight basis. Finally the method assumes that an 7 individual can be a high level consumer of all of these food commodities 8 simultaneously, whereas in reality a high-level consumer of one food is not 9 necessarily a high-level consumer of all others. 10 11

The values used in the Regulation 429/2008 model to represent concentration of 12 nutrients or feed additives in animal products also warrant consideration. The feed 13 additive guidelines relate to the use of tissue concentrations identified in efficacy 14 trials but not all products from treated animals will contain the maximum recorded 15 amount and not all animals in the food chain will have received supplements or feed 16 additives. As a consequence, most consumers will be exposed to average 17 concentrations much lower than the maximum values used in this model over the 18 longer term. Only in certain exceptional circumstances, for example where meat is 19 always obtained from the same source, could it be assumed that the entire supply 20 contains the supplement or additive at the maximum level. 21

22

The present Regulation 429/2008 model therefore represents a worst case and could 23 be used in situations where there are concerns about acute exposure (amount 24 consumed in any single day) to a substance of particular toxicological concern or in 25 other exceptional circumstances as a maximum estimate of short-term intake 26 (MESTI). However, in the majority of cases, where concern is related to exposures 27 averaged over the longer term a more detailed model might produce more relevant 28 results. This paper will review some simple approaches to intakes modelling and 29

proposes a structure where they can be applied in a tiered approach so that the best 30 use can be made of available data. Conservative estimates of high level intake can 31 then be compared with Upper tolerable Limits (ULs) or with Acceptable Daily Intakes 32 (ADIs). 33

Materials and methods 1

Methods for modelling intakes of substances from the diet 2 Intakes of substances from the diet can be estimated using the general formula: 3 Daily intake = mass of food (per day) x concentration of substance 4 body weight 5 6 Each of the variables used to estimate intake (food mass, substance concentration, 7 body weight) is subject to variability. Methods that assign one value to each variable 8 (such as the average or maximum) are described as 'deterministic', whilst methods 9 that take account of some aspects of variability (usually food consumption and body 10 weight in original survey data) are 'distributional'. 'Probabilistic' models aim to take 11 account of all sources of variability using more sophisticated models (see Lambe 12 2002, for example). Distributional and probabilistic models require access to raw data 13 from food consumption surveys so that intakes for each individual can be estimated 14 and the population statistics calculated. Because of this they are more time-15 consuming and expensive to perform and are usually applied after completing a 16 tiered assessment scheme, which uses simple yet conservative deterministic models 17 to eliminate substances where there is no cause for concern about intakes. 18

Resources can then be concentrated on those substances where there is real 19 potential to exceed ULs or ADIs. 20

21

A reliable screening method is therefore required that can effectively identify 22 substances that have the potential to exceed ULs or ADIs, whilst applying a simple, 23 deterministic approach. Furthermore, this type of method is very useful for the safety 24 assessment of substances in case of pre-marketing authorisation. Therefore, the 25 method should be shown to maintain conservatism so that it does not fail to identify 26 substances where high intakes might be of concern. To achieve this, the model must 27 take account of multiple exposure routes, high level consumption by individuals and 28 differences related to age. To maintain a conservative approach the model should 29 use high-end estimates, where a range of values is available. 30

31

Where a substance can be present in a number of different food items it is possible 32 to calculate average or high level intake associated with each item using the formula 33 above. High level consumption is usually taken to be an upper percentile (usually 95 th 1 or 97.5 th ) of the distribution of consumption data for consumers of the food (note that 2 non-consumers should not be included in this statistic). Upper percentiles are used to 3 represent maximum levels of consumption because it is unlikely that consumption 4 over the period of the survey (4 or 7 days) is unlikely to be sustained in the longer 5 term. The use of the 97.5 th percentile to represent high food consumption therefore 6 effectively represents the entire population. Estimation of total average intake is 7 straightforward since average per capita intakes from each source can be simply 8 summed. High level intakes from each source cannot be summed because a high 9 level consumer of one food is not necessarily a high level consumer of all foods. 10 Several methods for overcoming this problem, without resorting to a distributional 11 model, have been proposed and three will be considered here. 12 13 Intakes of high level consumers of an individual food can be estimated by taking the 14 97.5th percentile intake for consumers of this food and adding this to the population 15 average intake from all other foods in the diet ('highest plus mean' -model 1) 16 (Verger, 1995). An alternative method has been developed by the UK Pesticides 17 Safety Directorate in which total high level intake is based on taking the two highest 18 97.5th percentiles for consumers of individual foods and adding this to the population 19 average from the rest of the diet ('two-highest plus mean' -model 2) (UK PSD, 20 1995). This method is also recommended in the Guidelines to the European Food 21 Safety Authority's 'concise diet' (EFSA, 2008). However, the Guidelines state that it is 22 very unlikely that there exist individuals being high-level consumers of more than one 23 food category, unless the number of food categories is very high. The Guidelines 24 also warn against the use of these approaches (models 1 and 2) where the diet is 25 broken down into many sub-categories of food. 26 27 A third approach, which is sometimes used in the absence of high level consumption 28 data, is to use three times the population mean to estimate total high level intake 29 ('three-times mean' -model 3) [START_REF] Bernier | Méthode pour assurer, au sein[END_REF]. The aim of each model is to 30 provide a reliable estimate of total high level intake, such as would be produced by a 31 depending on the application. Each of the three models was used to estimate total 8 high level intake for the population and this was compared to the 97.5 th intake 9 calculated using the distributional model, based on the same concentration data 10 (usually maximum levels). Minimum, average, median, interquartile range and 11 maximum estimates of total high level intake generated by each model have been 12

expressed as a percentage of the same estimate generated by the distributional 13 model (Table 2). No systematic differences between the methods regarding age 14 groups or number of food groups or the type of substance were observed. 15 16

The results indicate that the 'three-times mean' model (model 3) tends to under-17 estimate high level intake, in some cases by 80% and so should be used only in the 18 absence of better data. The 'highest plus mean' model (model 1) provides the most 19 accurate estimates but can under-estimate by 20%. This under-estimation is 20 relatively minor in the context of other uncertainties associated with risk assessment 21 and so the model can be used where there are other sources of conservatism in the 22 overall approach. The 'two-highest plus mean' model (model 2) almost always over-23 estimates the true value, sometimes by a factor of up to three. This model would be 24 suited to situations where there are no other sources of conservatism in the overall 25 approach. However, the method is likely to produce a much higher proportion of false 26 positive results, which could undermine the screening strategy. The EU SCOOP 4.2 27 project investigated this method (model 2) and found that it over-estimated food 28 A tiered approach to intake modelling has been developed that can be applied 2 according to the level of detail available about the distribution and levels of additive 3 substances in food (Figure 1). Both tiers of the proposed approach use per capita 4 Tyler 1992). The UK database provides the opportunity for detailed assessment of 9 the consumption of specific food categories by particular age groups, not available 10 from more aggregated data sets such as that used in the EU SCOOP 3.2 project 11 (European Commission, 2003b). This allows different levels of detail to be built into a 12 tiered approach. The data are expressed as daily intakes averaged over the duration 13 of the surveys (7 or 4 days) 14 15

The Regulation 429/2008 MRL model will be used as the basis for an initial (Tier 1) 16 screen and for cases where there are concerns about acute exposures to determine 17 maximum estimates of short-term intake (MESTI) (Table 1). The first tier of the 18 proposed approach (Tier 2) will generate an estimate of theoretical maximum daily 19 intake (TMDI) based on actual consumption of food groups used in the Regulation 20 429/2008 MRL model averaged over several days (Table 3). TMDI food consumption 21 data differ from the Regulation 429/2008 MRL model because all dairy products are 22 included, not just milk. In some cases there were too few consumers to make reliable 23 estimates of consumption of certain minor products or data were not separately 24 reported. In these cases it was assumed that the fat:muscle ratio for mammals was 25 0.167 and the liver:muscle ratio was 0.33, the kidney:muscle ratios was 0.033 and 26 the fat:muscle ratio was 0.3 for birds, as in the Regulation 429/2008 MRL model. At 27 Tier 2 total high level intakes are estimated by taking the highest two 97.5 th 28 percentiles and adding them to average intakes from all other foods (model 2), in 29 order to maintain a high level of conservatism. 30

31

The second tier of the proposed approach (Tier 3) will generate an estimate of 32 theoretical estimated daily intake (TEDI). It uses the same raw data as the Tier 2 33 model but sub-divided by animal species (Table 4). 'Other meat' is included to allow for species such as rabbits and 'other poultry' and for minor species such as duck 1 and game. Only fish species suitable for aquaculture are included and 'other farmed' 2 uses halibut as a surrogate because this species is being developed for aquaculture. 3 Food consumption data for infants could not be subdivided because the original data 4 are not accessible. Consumption data were unavailable for poultry kidney and fat and 5 fat in all species and so estimates were taken from Tier 2. 6 7 At Tier 3 total high level intakes are estimated by taking the highest 97.5th percentile 8 and adding this to average intakes from all other foods (model 1). Conservatism is 9 maintained because the intake assessment is based on the maximum levels reported 10 in efficacy studies and not longer-term averages (including levels in foods not 11 treated) to which consumers are actually exposed. Average bodyweights for each 12 age group are available to convert intakes from mg/person/day to mg/kg bw/day for 13 comparison with ADIs (Table 5.) 14 15

Results 16

Evaluation of intake models is difficult in the absence of a 'true' answer. The model 17 developed here for estimating high level intakes of additives (including nutritional 18 additives) from animal products will be evaluated using two approaches. Firstly by 19 comparison of results produced by the method with the conclusions of recent 20 opinions considering intake estimates produced by the EFSA panel on additives and 21 products or substances used in animal feed (FEEDAP). The second approach will 22 assess the outputs of the model in comparison with using the same concentration 23 data in a distributional model. The distributional model will use the same tissue 24 concentration values as the TEDI model but the values will be applied to the 25 consumption values of each individual in the original surveys to calculate his or her 26 total intake and the high level intake value will be the 97.5 th percentile of the entire 27 all other species, respectively) (EFSA, 2005a). They concluded from worst case 1 estimations, that iodine intake from beef, pork or poultry would not exceed 0.1 2 mg/day for an adult person and was probably nearer to 0.05 mg/day, which was well 3 below SCF ULs. Further estimates were therefore limited to model calculations 4 primarily with milk and eggs. Fish was not considered by the FEEDAP panel because 5 the iodine content of farmed fish was below the iodine in marine fish. 6 7

The panel considered theoretical milk and egg consumption patterns appropriate to 8 different age groups. They concluded that with a concentration of 4 mg iodine/kg in 9 complete feed for dairy cows and laying hens, the intake levels would be 0.3 mg/day 10 for adults and adolescents and 0.055 -0.109 mg/day for children 4-6 yrs. 11 12

The screening method, using the same levels of iodine in beef, pork and poultry 13 muscle, liver and kidney and in milk and egg as well as levels in fish, resulted in a 14 total Tier 1 MESTI estimate of 16.7 mg/day. The Tier 2 TMDI estimate, using the 15 same food concentrations gave total high level iodine intakes of 3.9, 2.4, 2.0 and 1.0 16 mg/day for adults, schoolchildren, pre-school children and infants respectively (Table 17 6). The main contributor to MESTI and TMDI estimates was wild sea-caught fish. 18

19

The Tier 3 TEDI estimate, based on species tissue concentrations, gave total high 20 level of iodine intake estimates of 0.45, 0.40 and 0.54 mg/day for adults, 21 schoolchildren and pre-school children (including intake from other sources). The 22 validation of Tier 3 estimates for iodine intake was investigated using the 23 distributional model. This model gave high level (97.5 th percentile) estimates of iodine 24 intakes of 0.44, 0.39 and 0.51 mg/day adults, schoolchildren and pre-school children. 25

The principal source of intake was milk. 26 27

The proposed model produced iodine TEDI intake estimates that were close to 28 estimates provided by the FEEDAP panel based on modelled milk and egg 29 consumption. The TEDI intake estimates were about 0.1 mg/day higher because they 30 also included intakes from meat, fish and other animal products and the results were 31 confirmed in the distributional model. 32 The FEEDAP panel estimated maximum daily intakes of selenium from animal 3 products in terms of a worst case scenario, using maximum levels in beef and pork 4 muscle, liver and kidney and in milk and egg with the theoretical daily consumption of 5 food commodities of animal origin provided in Commission Regulation 429/2008 (see 6

Table 1) (EFSA, 2006b). The total exposure was 0.428 mg/day, which exceeded the 7

ULs established for the different age-based populations. 8 9 A refinement of the figures was achieved using consumption data obtained from 10 twelve EU countries using pig tissue levels as a worst case, average meat, liver and 11 kidney consumption (calculated in same ratio as in Regulation 429/2008), average 12 milk consumption and high level egg consumption data obtained from the SCOOP 13

project (European Commission 2003b

). This gave a maximum daily Se intake of 14 0.105 mg/day, which was below the UL set by the SCF for adults. Intakes for children 15 aged 4-6 yeas were estimated to be 0.039 mg/day. 16 17

The proposed new screening method, using the same levels of selenium in beef and 18 pork muscle, liver and kidney and in milk and egg resulted in an identical Tier 1 19 MESTI estimate, of 0.43 mg/day. The Tier 2 TMDI estimate, gave total high level 20 selenium intakes of 0.16, 0.10, 0.10 and 0.07 mg/day for adults, schoolchildren, pre-21 school children and infants, respectively. The Tier 3 TEDI estimate, based on species 22 tissue concentrations, gave total high level selenium intake estimates of 0.10, 0.09 23 and 0.06 mg/day for adults, schoolchildren and pre-school children. 24 25 As a validation of the Tier 3 TEDI model estimates of selenium intake by consumers 26 of animal products were estimated in a distributional model. This model gave high 27 level selenium intake estimates of 0.10, 0.07 and 0.06 mg/day for adults, 28 schoolchildren and pre-school children. 29

The results obtained with the proposed new tiered approach produced intake 30 estimates that were close to those provided by FEEDAP (Table 7). Though slightly 31 In their 2005 assessment of potential consumer exposures to astaxanthin the 5 FEEDAP panel used three models because they felt that data on the consumption of 6 salmon and trout were insufficient (EFSA, 2005b). In the first model, based on 7 salmon and trout production in Europe, the Panel concluded that with maximum 8 astaxanthin levels in salmon of 10 mg/kg and in trout of 25 mg/kg, daily average 9 astaxanthin intake would be 0.045 mg. The second model used WHO GEMS 10 average consumption data for all fish European consumers, of 34g per day. This 11 would result in astaxanthin intakes of 0.34 -0.85 mg/day. 12 13

The final scenario was based on the SCOOP 3.2 project (European Commission 14 2003b), in which average fish consumption ranged from 13 to 80 g/day and high level 15 fish consumption 103 to 165 g/day for adults. Assuming that all fish are salmon or 16 trout and that all salmon and trout are fed with astaxanthin at the highest level 17 approved resulted in astaxanthin intakes of 0.8 -2.0 mg astaxanthin/day for average 18 consumers and of 1.6 -4.1 mg astaxanthin/day for high consumers. This was 19 regarded as a considerable over-estimate because the consumption figure included 20 all sea-food, including the wild catch. 21 22 Using the same levels of astaxanthin in salmon and trout flesh resulted in a Tier 1 23 MESTI estimate, using the proposed screening method, of 7.5 mg/day. The Tier 2 24 TMDI estimate, based on total fish consumption, gave high level astaxanthin intakes 25 of 2.7, 1.5, 1.2 and 0.4 mg/day for adults, schoolchildren, pre-school children and 26 infants, respectively (Table 8). The Tier 3 TEDI estimate, based solely on salmon and 27 trout consumption, gave high level astaxanthin intake estimates of 1.2, 1.5 and 0.6 28 mg/day for adults, schoolchildren and pre-school children. gave high level astaxanthin intake estimates of 1.06, 0.53 and 0.45 mg/day for 1 adults, schoolchildren and pre-school children. 2 3

The TMDI model gave similar intake estimates to the most conservative model used 4 by FEEDAP, which is to be expected since both are based on total fish consumption. 5

The TEDI model gave lower estimates and these were similar to the results of 6 distributional modeling using the same data, which were based on salmon and trout 7 consumption only. 8 9 Discussion 10 An effective risk assessment scheme, based on tiered default methods, should have 11 two important properties: 12

1
The system should be sufficiently conservative that it always identifies 13 compounds which could exceed toxicological safety limits; and 14

2

The system should keep to a minimum compounds which require costly, 15 detailed evaluations on a case-by-case basis. 16 17

The proposed modelling approach is designed to fit between very simple, highly 18 conservative methods and highly sophisticated distributional and probabilistic 19 methods. Comparison with the results of FEEDAP evaluations of potential intakes of 20 nutrients and feed additives have shown that the proposed method provides 21 consistent results that would form the basis of similar risk assessments. The results 22 of the top tier TEDI estimate were compared with results obtained using distributional 23 modelling using the same input data and found to produce very similar results. 24 25

The present model is based on UK food consumption data that are available for 26 adults and children at per capita average levels and for high level consumers. The 27 model could be extended using data from other national food consumption surveys 28 provided that they were statistically consistent. National variations in food 29 consumption patterns are likely to result in differences in the proportion of people 30 consuming a particular food and the frequency of their consumption. This could result 31 in differences in per capita average consumption. However, differences in individual 32 shown its relevance even when compared with more international database used by 4 the FEEDAP Panel. 5 6 Intake estimates produced by all of the models under consideration provide 7 conservative estimates because they assume maximum concentrations of nutrients 8

or additives in all tissues at all times. In reality, consumers are exposed to a range of 9 possible concentrations, including from animals fed with feeds not containing the 10 additive, which may be zero. If appropriate for further monitoring, intake methods 11 could therefore be further improved by refining data used to represent long term 12 average concentrations in tissues (unless there are concerns about acute 13 exposures). 14 15

The MESTI would be considered as the final exposure assessment in cases of 16 significant toxicological concern, such as coccidiostats and histomonostats, to 17 provide an additional margin of safety or where there are concerns about short-term 18 toxicity. In all other cases the exposure assessment would be refined down to the 19 TMDI or TEDI depending on the availability of data. 20 21

Conclusion 22

Overall the proposed method provides a refinement of the existing Regulation 23 429/2008 method because it takes account of longer term average consumption, 24 allows for different concentrations in different animal species and takes account in 25 differences in age of consumer, including bodyweight. It is consistent with risk 26 modeling in use in comparable areas (pesticides, etc.), while the model in Regulation 27 429/2008 is not. Comparison with recent FEEDAP evaluations has shown that the 28 proposed method successfully meets the need for a more refined approach. The 29 method has the additional advantage that it is much easier to identify sources of high 30 exposure and so tailor risk management actions more precisely. It is therefore 31 concluded that the proposed MESTI/TMDI/TEDI method will provide a useful 32 alternative to existing intake modeling approaches. 33 3

The authors wish to acknowledge the support of the European Association of Feed Additives

4

  In Europe the addition of feed additives to animal feed is governed by Regulation 5 (EC) No 1831/2003 on the placing on the market and use of feed additives in animal 6

  All three models for estimating high level intakes have been compared with results 1 produced by distributional modelling of intakes of 40 examples of nutrients, 2 contaminants, food additives and novel food ingredients conducted in recent years 3 for UK consumers (examples include mineral hydrocarbons (Tennant, 2004) and 4 food colours (Tennant et al 2004 and Tennant and O'Callaghan, 2005)). The original 5 intake estimates were based on consumption by UK consumers (Gregory et al 1995, 6 Gregory 2000 and Henderson et al 2002) and included 5 to 25 food groups, 7

  additive intakes by 2 -70% (European Commission 1997). Overall the 'highest plus 29 mean' model (model 1) produces the most useful results. If results are close to but 30 still below the UL or ADI some further investigation may be required.Development of a screening model for additives in animal products 1

  average and 97.5th percentile food consumption data based on individual data in UK 5 food consumption surveys of pre-school children (1½ -4½ yrs) , schoolchildren (5 -6 18 yrs) and adults (19 -65 yrs) plus published data on food consumption by infants 7 (6-12 months) (Gregory et al 1995; Gregory 2000; Henderson et al 2002; Mills & 8

  30In January 2005 the EFSA FEEDAP panel reviewed the possible detrimental effect 31 on human and animal health or the environment of iodine, used at the current levels 32 authorised under Directive 70/524/EC (4, 20 and 10 mg/kg feed for horses, fish and 33

3

  conservative, the Tier 3 TEDI results were also close approximations to the 1 results obtained by distributional modelling based on the same input data. 2Case study 3 -Astaxanthin 4

29 30

 29 As a validation of the Tier 3 TEDI model estimates of astaxanthin intakes by 31 consumers of salmon and trout were estimated in a distributional model. This model 32

  are less likely to be affected by national variations because 1 there are physiological limits to the amount that any one individual can consume. The 2 proposed model, although based on consumption data from one EU country, has 3

4(

  and Premixtures Operators (FEFANA) in the preparation of this report. population, le non dépassement de la dose journalière admissible des additifs 4 alimentaires. Médecine et Nutrition 1: 9-117. 5 6 EFSA. 2005a. Opinion of the Scientific Panel on Additives and Products or Substances used 7 in Animal Feed on the request from the Commission on the use of iodine in feedingstuffs 8 Question N°EFSA-Q-2003-058). Adopted on 25 January 2005. The EFSA Journal (2005) . Opinion of the Scientific Panel on Additives and Products or Substances used 12 in Animal Feed on the request from the European Commission on the safety of use of 13 colouring agents in animal nutrition PART I. General Principles and Astaxanthin (Question 14 No. EFSA-2003-060). Adopted on 30 th November 2005. The EFSA Journal (2005) 291, 1-. Opinion of the Scientific Committee on Food Scientific Panel on Dietetic 18 Products, Nutrition and Allergies. Tolerable Upper Intake Levels for Vitamins and Minerals.

  . Opinion of the Scientific Panel on Additives and Products or Substances used 22 in Animal Feed on the safety and efficacy of the product Sel-Plex®2000 as a feed additive 23 according to Regulation (EC) No 1831/2003 (Question No EFSA-Q-2005-071). Adopted on 24 19 of April 2006. The EFSA Journal (2006) 348, 1-40.

  , Collins D L, Davies PSW, Hughes JM, and Clarke PC. 1995. National Diet and 19 Nutrition Survey; Children aged 1½ to 4½ years. HMSO, London. 20 21 Gregory J. 2000. National Diet and Nutrition Survey: young people aged 4 to 18 years. 22 Findings: Volume 1. London: The Stationery Office. 23 24 Henderson L, Gregory J and Swan G. 2002. The National Diet and Nutrition Survey: 25 adults aged 19 to 64 years. Types and quantities of foods consumed. Her Majesty's 26 Stationery Office (HMSO). ISBN 0 11 621566 6. 27 28 Mills A and Tyler H. 1992. Food and nutrient intakes of British infants aged 6 -12 months.

4

  Lambe, J. 2002. The use of food consumption data in assessments of exposure to food 1 chemicals including the application of probabilistic modelling. Proceedings of the Nutrition 2 Society, 61, 11-18 3 Tennant DR, Gedrich , K, Godfrey D and Davidson J. 2004. Intakes of beta-carotene from its 5 use as a food additive, fortificant and dietary supplement in France, Germany and the UK.

6

  British Food Journal 106(6):436-456.

7 8

 7 Tennant DR. 2004. The usage, occurrence and dietary intakes of white mineral oils and 9 waxes in Europe. Food and Chemical Toxicology. 42(3):481-492. 10 11 Tennant DR and O'Callaghan M. 2005. Survey of usage and estimated intakes of annatto

  Methodologies for the Monitoring of Food Additive Intake across the European Union.Parliament and of the Council as regards the preparation and the presentation of applications 3 and the assessment and the authorisation of feed additives. OJ L133/1; 22.5.08.

	1	European Commission. 2008. Commission Regulation (EC) No 429/2008 of 25 April 2008 on
	2	detailed rules for the implementation of Regulation (EC) No 1831/2003 of the European
	4
	5
	6	European Commission. 2003a. Regulation (EC) No 1831/2003 of the European Parliament
	7	and of the Council of 22 September 2003 on additives for use in animal nutrition. OJ L
	8	268/29, 18.10.2003
	9 10 11	F European Commission. 2003b. Draft Report from Task 3.2.11: Assessment of the dietary o r exposure to arsenic, cadmium, lead and mercury of the population of the EU Member States.
	12 13	European Commission, Directorate-General Health and Consumer Protection, Reports on P tasks for scientific co-operation, Final draft, 5 December 2003.
	14	e
	15 16	e European Commission. 2005. Notice to applicants and note for guidance. Establishment of r maximum residue limits (MRLs) for residues of veterinary medicinal products in foodstuffs of
		animal origin.	R
			e
			v i e
			w
	25 26		O n l
	27	EFSA. 2008. European Food Safety Authority. Guidance Document for the use of the y
	28	Concise European Food Consumption Database in Exposure Assessment.
	29	EFSA/DATEX/2008/01. Parma, 17 March 2008.
	30
	31	European Commission. 1997. Reports on Tasks for Scientific Cooperation. Report on
	32
	33	Directorate General Industry.

Table 3 .

 3 Methods for the Estimation of Dietary Intakes of Pesticide Residues. UK 15 Pesticides Safety Directorate, Ministry of Agriculture, Fisheries and Food. Food consumption data used in TMDI (Tier 2) model (g/day)

	12
	13	extracts. Food Research International 38:911-917.
	14
	UK PSD. 1995. 16
	17
	18	Verger P. 1995. One example of utilisation of the 'French Approach'. Paper presented at the
	19	ILSI Europe Workshop on Food Additive Intake, 29-30 March, Brussels, Belgium.
	20

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table 4 .

 4 Food consumption data used in TEDI (Tier 3) model (g/day)

			Adults	Schoolchildren	Pre-school children
		Tissue	Mean	97.5th %ile	Mean	97.5th %ile	Mean	97.5th %ile
	Bovine	Muscle	20.92	87.69	11.68	65.81	9.27	46.82
		Liver	0.12	40.5	0.00	4.86	0.02	11.75
		Kidney	0.22	28.06	0.08	15.74	0.07	13.95
	Porcine	Muscle	22.9	88.97	13.65	61.71	7.95	36.48
		Liver	0.56	26.42	0.19	17.50	0.11	21.02
		Kidney	0.01	17.14	0.00	6.53	0.00	0.19
	Ovine	Muscle	3.07	68.93	2.66	39.29	0.88	25.35
		Liver	0.5	44.99	0.05	28.93	0.08	23.10
		Kidney	0.03	34.64	0.44	6.56	0.02	18.94
	Other meat Muscle	0.88	19.29	0.55	12.30	0.36	18.72
		Fat	0.209	1.15	1.72	10.37	1.72	10.37
	Dairy	Milk	242.29	653.15	194.91	598.40	310.10	834.00
		Butter	3.23	14.8	1.36	19.61	1.21	13.63
		Cheese	14.8	61.34	8.19	48.95	5.76	30.65
		Other dairy	1.42	32.49	0.45	16.06	0.34	22.88
	Poultry	Broilers	26.27	99.57	14.09	72.77	6.63	42.04
		Turkeys	3.42	55.74	2.49	36.38	0.64	27.04
		Other poultry	0.38	47.22	0.09	32.41	0.01	6.57
		Liver	0.08	34.67	0.02	22.12	0.03	18.93
		Kidney	0.99	3.44	0.46	2.40	0.00	2.73
		Fat	8.96	31.29	4.23	21.83	1.99	12.61
	Egg	All egg	23.82	78.18	15.23	52.80	13.07	48.24
	Fish	Salmon	3.4	58.68	0.46	41.82	0.28	25.74
		Trout	0.64	47.53	0.12	60.62	0.06	22.98
		Prawns	2.41	48.88	0.50	30.51	0.12	17.31
		Other farmed	0.03	20.6	0.12	60.62	0.01	12.85

Table 5 .

 5 Average bodyweight data.

	Population	Adults	Schoolchildren Pre-school	Infants
	group			children
	Age (yrs)	19 -65	4 -18	1.5 -4.5	0.5 -1.0
	Weight (kg)	70.1	43.3	14.5	8.7

Table 6 .

 6 Results of iodine intake estimation using FEEDAP methods, proposed new tiered approach and distributional modelling, in comparison to SCF ULs

	Age group:	Adult 15 -	11 -	7 -	4 -6	1 -3 yrs
			17	14	10
	SCF UL:	600	500	450	300	250	200 mg/day
	FEEDAP (beef. Pork,	0.05-	-	-	-	-	-	mg/day
	poultry)	0.1
	FEEDAP (eggs, milk)	0.3	0.3	0.3	-	0.06 -0.11	-	mg/day
	New MESTI (entire diet)	16.7	-	-	-	-	-	mg/day
	New TMDI (entire diet)	3.86	2.35	2.35 2.35	2.35	2.01 mg/day
	New TEDI (entire diet)	0.45	0.40	0.40 0.40	0.40	0.54 mg/day
	Distributional model	0.44	0.39	0.39 0.39	0.39	0.51 mg/day

Table 7 .

 7 Results of selenium intake estimation using FEEDAP methods, proposed new tiered approach and distributional modelling, in comparison to SCF ULs

	Age group:	Adult 15 -17 11 -14 7 -10 4 -6 1 -3 yrs
	SCF UL:	0.30 0.25	0.20 0.13 0.09 0.06 mg/day
	FEEDAP (conservative) 0.428	-	-	-	-	-mg/day
	FEEDAP (refined)	0.105	-	-	-	0.039 -mg/day
	New MESTI	0.43	-	-	-	-	-mg/day
	New TMDI	0.16 0.10	0.10 0.10 0.10 0.07 mg/day
	New TEDI	0.10 0.09	0.09 0.09 0.09 0.06 mg/day
	Distributional model	0.10 0.07	0.07 0.07 0.07 0.06 mg/day

Refine concentration and food consumption data. Refine concentration and food consumption data.

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants


Luana Maggi

Valeria ; Mazzoleni

Mª Maria Daria Fumi

Rosario Salinas

Daria

Transformation ability of fungi isolated

Keywords: cork taint, fungi, 2,4,6-trichloroanisole, 2,4,6-trichlorophenol 12

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The bark of cork oak (Quercus suber L.) is the material used to produce cork stoppers 15 for wine bottles, especially for high quality wines. Fungi present on oak bark can 16 affect the quality of cork stoppers and consequently affect the wine if their 17 metabolism causes the formation of unpleasant moldy off-flavours (Lee and Simpson 18 1993). Cork taint is one of most serious problems for the wine and cork industries: it 19 causes financial losses every year and damages the reputation of the wine industry. It 20 has been estimated that the proportion of bottles affected by cork taint ranges from 1-21 5% or even higher, although conclusive evidences that the wine has been tainted by 22 the cork closures is lacking [START_REF] Tanner | Zur analytischen Differenzierung von Muffon 22 und Korkgeschmack in Wein[END_REF][START_REF] Prescott | Estimating a "consumer rejection 23 threshold" for cork taint in white wine[END_REF]; Sefton and 23 [START_REF] Sefton | Compounds causing cork taint and the factors 8 affecting their transfer from natural cork closures to wine -a review[END_REF]. Even if chloroanisoles and especially TCA have been considered the 24 primary compound responsible for at least 80% of the cases of cork taint, also other 25 substances have been identified as implicated in this problem [START_REF] Butzke | Detection of cork taint in wine using 20 automated solid-phase microextraction in combinated with GC/MS-SIM[END_REF]; 1 Quercus 1999). These compounds include geosmine, guaiacol, 1-octen-3-one, 2-2 methoxi-3,5-dimethylpyrazine [START_REF] Simpson | Off-flavour in wine caused by guaiacol[END_REF][START_REF] Amon | Compounds responsible for the cork 15 taint in wine[END_REF]Capone et al. 3 2002;[START_REF] Riu | Determination of 2,4,6-trichloroanisole 4 in wine by headspace SPME and GC-ECD[END_REF][START_REF] Simpson | Isolation and identification of 2-19 methoxy-3,5-dimethylpyrazine, a potent musty compound from wine corks[END_REF]). Recently [START_REF] Chatonnet | Identification and 4 responsability of 2,4,6-tribromoanisole in musty, corked odors in wine[END_REF] have 4 observed that 2,4,6-tribromoanisole (TBA) can be responsible for the cork taint when 5 insufficient amounts of TCA were detected. (TCP) is recognized as one of the greatest environmental contaminants. Significant 13 efforts have been made to isolate and characterize the microorganisms that are able to 14 transform TCP into TCA. Some fungal strains have been isolated from food-15 packaging materials [START_REF] Tindale | Fungi isolated from 1 packaging materials: their role in the production of 2,4,6-trichloroanisole[END_REF][START_REF] Whitfield | Effect of relative-humidity and 4 incubation-time on the O-methylation of chlorophenols in fiberboard by 5 Paecilomyces variotii[END_REF]) and several fungi 16 isolated from drinking water [START_REF] Nyström | 20 Drinking-water off-flavor caused by 2,4,6-trichloroanisole[END_REF]) to methylate TCP to produce TCA. 17

In these cited studies, TCA has been the most important compound, and no comments 18 have been found about other possible products formed although other compounds are 19 detected but at traces levels. Various hypotheses have been suggested to explain how 20 TCA is generated: [START_REF] Maujean | Explications bioquimiques et 15 metaboliques de la confusion entre goût de bouchon et goût de moisi[END_REF], justified the formation of TCA with the 21 hypochlorite wash of cork, but today this process has fallen into disuse. Other authors 22 indicated the catabolism of pentachlorophenol (PCP), 2,3,4,6-tetrachlorophenol 23 (TeCP) and higher chlorinated compounds such as the possible origin of TCA (Tanner 24 On the other hand, the bark of the cork oak contains unchlorinated phenolic 4 compounds to defend the oak from microbial attacks, and these substances in the 5 presence of chlorine can turn into chlorophenols [START_REF] Maujean | Explications bioquimiques et 15 metaboliques de la confusion entre goût de bouchon et goût de moisi[END_REF]Codina et al. 6 1993). For this reason, their formation can occur at any time during cork stopper 7 processing, from the forest to shipping (Ducan et al. 1997). Moreover, the 8 contamination of the atmosphere in wineries by chlorophenols and chloroanisoles can 9 be caused by agents arising from wood preservatives, paints, cardboards, etc. Later, 10 these substances can pass into the stored materials, i.e. adjuvants, bottles, cork 11 stoppers, and can come into contact with the wine, thereby causing cork taint (Amon 12 et al. 1989;[START_REF] Barker | Absorption of 2,4,6-trichloroanisole by wine corks via the vapour phase in an 18 enclosed environment[END_REF]. 13 14 Numerous studies have been carried out on TCA but few on TCP, its phenolic 15 precursor, which can be present in cork, producing cork taint in the presence of fungal 16 strains able to form TCA [START_REF] Álvarez-Rodríguez | Cork taint of wines: role of the filamentous fungi 12 isolated from cork in the formation of 2,4,6-trichloroanisole by O methylation of 13 2,4,6-trichlorophenol[END_REF]. Until now there were no 17 studies dealing Botrytis cinerea, a microorganism that can frequently be found on 18 grapes and in winery atmospheres. 19 20 In a previous study, different fungal strains isolated from cork were identified and 21 their ability to produce volatile compounds was tested both on a solid medium and on 22 cork [START_REF] Caldentey | Volatile compounds produced 23 by microorganisms isolated from cork[END_REF]). Hence, the characterization of the same fungal strains 23 growing in presence of TCP, on both solid medium and on cork, was also carried out 24 with regard to their ability to consume TCP and to produce TCA and to increase our 25 Agar (MEA) for fungi maintenance and growth was from Oxoid (Hampshire, United 11 Kingdom). 12 13 Raw cork stoppers were washed with water and subjected to two autoclave treatments 14

for 15 min at 130 ºC. Later they were analysed by the optimized method and proved 15 free from TCP and TCA. So they were powdered by grinding (Model 4, Thomas-16 Wiley, U.S.A.) after freezing under liquid nitrogen and the fraction with a diameter of 17 ≤1 mm was retained. 18

Microorganisms 19

The fungal strains used were from the Istituto di Enologia e Ingegneria agro-20 alimentare culture collection. They were isolated from raw one-piece cork stoppers 21 and the identification was made by macroscopic observations (shape, size, colour of 22 colonies) and microscopic observations, as reported in previous studies (Caldentey et 23 al. 1998;Samson et al.1995). This method is not able to identify at the level of species 24 Trichoderma, so the isolates were distinguished by number. 2

The following fungi were considered: 3 different Penicillium spp (I 1, I 2, I 3 ), 2 different 3 Aspergillus spp (I 1, I 2 ), 1 Trichoderma sp and 1 Chrysonilia sitophila. Moreover 1 4

Botrytis cinerea, isolated from grapes, was considered. 5

Purity of the each strain was observed microscopically by looking at morphology of 6 reproductive structures (Philips XL-30 ESEM). 7

2,4,6-Trichloroanisole biosynthesis from 2,4,6-trichlorophenol by fungal growth on 8

MEA medium 9

The spores of each active fungal strain were inoculated with 10 5 CFU in a flask on 10 100 ml of MEA containing TCP (1 mg/l). Duplicate flasks in two independent 11 experiments were incubated statically for 20 days at 25 °C. 12 Blank experiments were carried out, as described, on MEA, on MEA with no addition 13 of TCP and inoculated with fungi, and on MEA added with TCP and not inoculated. (aqueous ethanol solution 1) was added. Then a second extraction was carried out on 1 the same flask, with 100 ml of absolute ethanol for 48 hours at 25°C. The alcoholic 2 solution was then filtered through filter paper and 50 ml of distilled water (aqueous 3 ethanol solution 2) was added. The two aqueous ethanol solutions (1 and 2) were 4 separately extracted twice with 20 ml of n-hexane. Each hexane extract was analyzed 5 separately by gas chromatographic analysis as described below. 6

TCA and TCP analysis 7

The hexane extracts were analysed using gas chromatography equipment GC 1000 8 (DANI Instruments S.p.A., Italy) coupled to an electron capture detector (ECD). 9

Splitless mode injection (0.5 µl) was made into an EC-5 column (30 m x 0.25 mm 10 i.d.; 0.25 µm film thickness; Alltech, U.S.A.). The operating conditions were: injector 11 temperature of 200 °C, a detector temperature of 300 °C, carrier gas N 2 at 1.0 ml/min, 12 make up gas N 2 at 30 ml/min. The oven temperature programme was of 5 min at 60 13 [START_REF] Zalacain | Stir bar sorptive 7 extraction for the analysis of wine cork taint[END_REF]. [START_REF] Pereira | Cork taint in wine: 14 scientific knowledge and public perception -a critical review[END_REF] 14

Results and discussion 15

The analysis of blank samples from experiments carried out on MEA (only MEA, 16

MEA with no addition of TCP and inoculated with fungi, and MEA added with TCP 17 and not inoculated) and on cork (only cork, cork with no addition of TCP and 18 inoculated with fungi, and cork with addition of TCP and not inoculated) did not show 19 any TCA level, as TCP had not been transformed. The recovery of the overall method 20 of extraction and analysis, determined on MEA and cork, was 72% (RSD 4%) and 21 61% (RSD 4%) respectively. Data reported in Table 1 show the ability of the tested 22 microorganisms to transform TCP when growing on synthetic medium (MEA), 23 analyzed by GC/ECD, and cork, determined by GC/ECD and STD-GC/MS. 24 All the strains showed the ability to decrease TCP both in synthetic medium and in 1 cork. On MEA, the three Penicillium spp (I 1, I 2, I 3 ), and Botrytis cinerea were the most 2 active; the two Aspergillus spp (I 1, I 2 ) showed a medium level of transformation; 3 Chrysonilia sitophila and Trichoderma sp were the least active. On cork, all the fungi 4 were able to decrease TCP in higher amounts than on synthetic medium, except two 5

Penicillium strains that showed an equal (Penicillium sp I 2 ) or minor (Penicillium sp 6 2 show the ability to 17 produce TCA for tested fungal strains on MEA, analysed by GC/ECD, and on cork, 18 determined by GC/ECD and STD-GC/MS. In the same way that Álvarez-Rodríguez et 19 al. (2002) reported, we can see that not all TCP is transformed in TCA (Table 2). The 20 ability to produce TCA varied among the tested strains: Chrysonilia sitophila did not 21 have the ability to produce TCA, both on synthetic medium and cork, as already 22 showed in the work of Silva Pereira et al. (2000a). A low level of TCA was obtained 23 by all the Penicillium spp, but Penicillium spp I 1 and I 2 were more active on MEA 24 while Penicillium sp I 3 behaved to the contrary. For these fungi, the methylation of 25 Botrytis cinerea showed the highest capability to produce substancial amounts of 2 TCA on MEA, while they were different on cork: Botrytis cinerea produced the same 3 level of TCA on both substrates; Aspergillus sp I 1 was more active on cork, and 4

Trichoderma sp showed a very low level of TCA production on cork compared with 5 its ability on MEA. Aspergillus sp I 2 was less active in MEA compared to the 6 Aspergillus sp I 1 , but on cork the two isolata had the same level of TCA production. 7

As proposed by [START_REF] Tindale | Fungi isolated from 1 packaging materials: their role in the production of 2,4,6-trichloroanisole[END_REF] the tested fungal strains can be classified 8 according to their ability to methylate TCP, producing TCA. The methylation ability 9 found for the fungi checked was the same on both the substrates, except in the case of 10

1

  Istituto di Enologia e Ingegneria agro-alimentare, Università Cattolica del Sacro 7 Cuore, Via E. Parmense 84, 29100 Piacenza, Italy 8 2 Cátedra de Química Agrícola, E.T.S.I.Agrónomos, Universidad de Castilla-La 9 Mancha, Campus Universitario, 02071 Albacete, Spain 10

6 7

 6 In relation to these compounds and their metabolic pathway, only a few studies have 8 been carried out with TCA (Álvarez-Rodríguez et al. 2002; Silva Pereira et al. 2000a; 9 Silva Pereira et al. 2000b). Chloroanisoles could arise by methylation of toxic 10 chlorophenol precursors through a detoxification process mediated by microbial 11 species (Allard et al. 1987; Álvarez-Rodríguez et al. 2002), as 2,4,6-trichlorophenol 12

  al. 1981).[START_REF] Neidleman | Biohalogenations: principes, basic roles and 18 applications[END_REF] explained the biosynthesis of TCA by 1 chlorination from anisol or phenol. 2 3

3and methods 4

 4 process. Especially attention is carried out on Botrytis cinerea 1 strains, as until now there were no studies dealing with this microorganism. 2MaterialsReagents and chemicals 5All the solvents, absolute ethanol and n-hexane (analytical reagent grade) were 6 obtained from Carlo Erba Reagenti (Milan, Italy). 2,4,6-Trichloroanisole and 2,4,6-7 trichlorophenol were supplied by Fluka Chemie AG (Buchs, Switzerland). Anhydrous 8 sodium sulphate of analytical reagent grade (Carlo Erba Reagenti, Milan, Italy) and 9 paper filters (Schleicher & Schuell, Dassel, Germany) were also used. Malt Extract 10

  high reliability the isolates belonging to the genus Penicillium, Aspergillus and 1

14 2,4, 6 -

 146 Trichloroanisole biosynthesis from 2,4,6-trichlorophenol by fungal growth on 15 cork 16 20 g of powdered cork were placed in a flask containing 20 ml of distilled water and 17 20 µg of TCP and inoculated the preparation with 10 5 CFU of an active fungal strain. 18 Duplicate flasks in two independent experiments were incubated statically for 110 19 days at 25 °C. 20Blank experiments were done on cork, on cork with no addition of TCP and 21 inoculated with fungi and on cork with addition of TCP and not inoculated. 22Extraction procedure 23Each flask was extracted with 200 ml of absolute ethanol for 48 h at 25 °C; the 24 alcoholic solution was filtered through filter paper and 100 ml of distilled water 25

18 19

 18 °C, 5°C/min up to 200 °C and then 30°C/min up to 280 °C and finally 5 min at 280 14 °C. The chromatographic data were analysed by the Clarity software of DANI 15 Instruments S.p.A. Identification and quantification of TCP and TCA were carried out 16 by calibration curves and comparison of the retention time with of a commercial 17 standard. To confirm the results obtained by GC/ECD the hexane extracts coming from fungal 20 growth on cork were analyzed by a thermal desorption system (STD) and GC/MS. 21 Ten microliters of hexane extract were injected into a stainless steel thermal tube, 22 containing glass wool previously conditioned for 1 min at 300 ºC under a N 2 stream 23 flowing at 100 ml/min. The equipment used was a Perkin-Elmer thermal desorption 24 injector ATD 400 (Norwalk, CT, U.S.A.) coupled to an Hewlett-Packard 6890 gas 25 Hewlett-Packard LC 3D mass detector (Palo Alto, CA, 1 U.S.A.) with a fused capillary column (BP21 stationary phase 50 m length, 0.22 mm 2 i.d., 0.25 µm film thickness) (SGE, Ringwood, Australia). The conditions for thermal 3 desorption were as follows: oven temperature at 330 ºC; desorption time, 4 min; cold 4 trap temperature, -30 ºC; helium inlet flow 45 ml/min. The chromatographic program 5 was set at 50 ºC (held for 5 min), raised to 180 ºC at 2.5 ºC/min (held for 2 min) and 6 to 230 ºC (5 ºC/min) and held for 20 min. For mass spectrometry analysis, electron 7 impact mode (EI) at 70 eV was used. The mass range varied from 35 to 500 u and the 8 detector temperature was 150 ºC. Identification was carried out using NIST library 9 and quantification was based on the calibration curves of respective standards in 10 hexane. The MS analysis was carried out by using the selective ion monitoring mode 11 and the following ions were used: 2,4,6-trichloroanisole, m/z 195, 197, 210, 212; and 12 2,4,6-trichlorophenol, m/z 132, 160, 196

I 3 ) 8 9

 38 ability to transform TCP.Botrytis cinerea and Penicillium sp I 2 decreased TCP in 7 similar amounts on both substrates. Comparing the behavior of the fungi growing on synthetic medium with the species 10 tested by Silva Pereira et al. (2000a), Penicillium spp showed a more homogeneous 11 capability to transform high levels of TCP, whereas Chrysonilia sitophila showed a 12 lower ability. Until now there were not any data concerning the ability of Botrytis 13 cinerea in order to transform TCP to TCA. 14 15 Comparing the results obtained by the two different methods of analysis used, they 16 are consistent and show the same trend. Data reported in Table

  the main detoxification process. Aspergillus sp I 1 , Trichoderma sp and 1

5 proofreading

 5 Trichoderma sp which was much more active on MEA. Aspergillus spp (I 1, I 2 ) and 11 Botrytis cinerea were moderate methylators on both media. According to Tindale et 12 al. (1989), Penicillium spp (I 1, I 2, I 3 ) and Chrysonilia sitophila showed weak 13 methylation ability, confirming the data obtained by Silva Pereira et al. for these 14 strains (2000a). 15 16 With respect to the fungi tested previously, the results confirmed what has already 17 been suggested by other authors: the possible use of fungal strains in the cork stoppers 18 manufacturing process for the transformation of TCP without producing TCA. 19 20 Moreover, there were no previous studies on Botrytis cinerea's ability to transform 21 TCP to TCA, though this microorganism is often situated on grapes and in the 22 atmosphere of cellars. The most outstanding result of the present work is the 23 methylation ability of Botrytis cinerea, which explains partially the cork taint in wine 24 before being bottled. 25 you to DANI Instruments S.p.A. for instrumentation and technical support 3given. Thanks to the financial support given by the Ministerio de Ciencia y 4Tecnología of Spain to the project AGL2004-04609 and to Kathryn Walsh for

  values are the averages for duplicate estimates obtained in two independent experiments made for 1 each fungal strain. Number of CFU per gram of cork after incubation was more than 10 5 CFU.The values are the averages for duplicate estimates obtained in two independent experiments made for 2 each fungal strain. Number of CFU per gram of cork after incubation was more than 10 5 CFU.

	Page 15 of 16		Food Additives and Contaminants		Page 16 of 16
	1			TCP (µg/l)
		Fungi	MEA	TCA (µg/l)	Cork
	2 3 3 4	Fungi Penicillium sp I 1 Penicillium sp I 2 Penicillium sp I 1 Penicillium sp I 3 Penicillium sp I 2 Aspergillus sp I 1 Penicillium sp I 3 Aspergillus sp I 2 Aspergillus sp I 1 Trichoderma sp Aspergillus sp I 2 Chrysonilia sitophila Trichoderma sp Botrytis cinerea Chrysonilia sitophila Botrytis cinerea F o r	GC/ECD MEA 603±7 GC/ECD 682±7 32.6±0.4 768±11 49.1±0.5 451±16 10.0±0.6 555±13 74.4±1.3 356±17 58.8±1.1 354±18 66.9±0.9 659±11 0.0±0.0 125.2±1.6	GC/ECD 987±8 GC/ECD 622±13 11.8±0.1 469±24 6.8±0.1 801±11 35.2±0.5 785±16 83.3±0.8 870±13 80.9±0.9 957±10 10.4±0.1 716±16 0.1±0.0 116.7±1.3	Cork	STD-GC/MS STD-GC/MS 932±13 12.1±0.1 651±16 12.4±0.1 497±18 28.8±0.3 851±10 77.4±1.3 758±14 86.4±0.9 887±13 9.8±0.1 929±11 743±14 0.9±0.0 115.2±0.9
	4	P
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Food Additives and Contaminants

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants


Marta Gratacós-Cubarsí

Massimo Castellari
email: massimo.castellari@irta.es

Angel Valero

Isabel Diaz

Antonio García-Regueiro

TO CONTROL SULFAMETHAZINE MISUSE IN FOOD PRODUCING ANIMALS BY HAIR ANALYSIS

Keywords: Chromatography -HPLC, Regulations, Screening assays, Toxicology -pharmokinetics Additives/Contaminants: Veterinary drug residues -sulphonamides, Veterinary drugs

The presence of sulfamethazine (SMZ) residues in pig and calf hair was 16 compared with the residual levels encountered in the corresponding edible 17 tissues (liver and muscle) as a consequence of drug administration. SMZ up to 18 84.7 mg/kg was found in calf hair samples after a pharmacological treatment, 19 with a significant effect on hair pigmentation. High concentrations of the parent 20 drug were detected in calf hair for four weeks after administration, when SMZ 21 residues were no longer detectable in the corresponding edible tissues. In a 22 similar way, pig hair also accumulated SMZ residues up to 40.5 mg/kg, which 23 was more than the amount detected in the corresponding muscle and liver 24 samples at slaughter. Hair analysis seems a suitable tool to improve the 25 efficacy of regulatory controls, and thus the safety of the food chain and to 26 discourage the improper use of SMZ in animal farming.

Sulfonamides (SAs) are an important group of synthetic bacteriostatics which 32 are approved as therapeutic agents for use on farm. The sub-therapeutic 33 addition to feeding to improve growth promotion and food efficiency is permitted 34 in the USA (Animal Health Institute) but is prohibited within the European Union 35 [START_REF] Ec | Regulation (EC) nº 1831/2003 of The European Parliament and the 367 Council of 22 September 2003 on additives for use in animal nutrition[END_REF]. Several years ago the scientific community was alerted to the 36 excessive use of SAs (and other antimicrobials) which can promote the 37 development of microbial resistance (WHO 1997[START_REF] Who | WHO/EMC/ZDI/98.10 Use of Quinolones in Food Animals and 427 Potential Impact on Human Health[END_REF], Cruz et al., 2003;38 Pena et al., 2004;[START_REF] Miko | Molecular mechanisms of 401 resistance in multidrug-resistant serovars of Salmonella enterica isolated from 402 foods in Germany[END_REF] as well as a progressive environmental 39 contamination [START_REF] Verstraete | Bacterial antibiotic resistance levels in agricultural 418 soils as a result of treatment with pig slurry and detection of sulphonamide 419 Page 18 of 24[END_REF]Kay 2005a;2005b). Significantly, 40 sulfamethazine (SMZ), one of the most used SAs, has also been proved to 41 induce tumors in the thyroid gland of rodents [START_REF] Littlefield | Chronic 397 toxicity/carginogenicity studies of sulphamethazine in B6C3F1 mice[END_REF]. 42 43 At the same time, the illegal or improper administration of SAs, such as the non-44 observance of withdrawal time or the use without veterinary prescription, may 45 cause residues in the edible tissues of slaughtered animals that could represent 46 a potential risk to consumer health due to the short-term toxic and allergic 47 phenomena in sensitized consumers (European Medicines Agency). 48 49 Following the rules established for other veterinary drug, a Maximum Residue 50 Level (MRL), which has also been called Safe Level or Tolerance Limit, has 51 been defined for SAs in edible animal tissues or other foodstuffs of animal origin 52 in several countries (Codex Alimentarius, EC 1990, Code of Federal 53 Regulation). The MRL represents a drug residual level which is virtually 54 associated with no toxicological risk for the consumer, and is calculated taking 55 The MRL for the SMZ is set at 100 µg/Kg for all the animal species and tissues 57 [START_REF] Ec | Council Regulation nº 2377/90/ECC of 26 June 1990 laying down a 362 Community procedure for the establishment of maximum residue limits of 363 veterinary medicinal products in foodstuffs of animal origin. Off[END_REF]. 58

59

The official target samples (meat, kidney, fat and liver) are usually taken at the 60 time of slaughter to verify the compliance with withdrawal time and MRL. 61

Nevertheless the post-mortem sampling of edible tissues can only be made on 62 a limited percentage of carcasses and may delay the commercialization of the 63 edible parts. Furthermore the half-life of drug residues in other samples taken 64 from living animals (milk, urine, faeces) is generally shorter compared to the 65 withdrawal period for edible tissues (USP DI), limiting the efficacy of these 66 matrices for screening controls at farm and at slaughter. 67 68 Recently, hair analysis has been proposed as an interesting technique to 69 increase the time window in retrospective detection of veterinary drug residues, 70 offering at the same time the advantage of non-invasive collection on living 71 animals [START_REF] Durant | Gas 357 Chromatography-Tandem Mass Spectrometry determination of 17α-358 Ethinylestradiol Residue in the Hair of Cattle. Application to Treated Animals[END_REF][START_REF] Nielen | Value of alternative sample matrices in residue analysis for 406 stanozolol[END_REF]Dunnett and Lees 2004a;72 2005b). It is well known that hair structure is rather biologically inert allowing this 73 to produce a protected microenvironment where the drug residue would be 74 preserved from degrading agents during a long period of time (Dunnett and 75 Lees, 2004a;2004b). 76 77 At present, hair analysis is considered an effective tool to detect banned drugs 78 as anabolic hormones (Gratacós-Cubarsí et al., 2006c), but it seems also 79 interesting to control the administration of compounds authorized only for 80 administration or intravenous injection in racehorses (Dunnett and Lees, 85 2004b), calf and pig (Gratacós-Cubarsi et al., 2006a) hairs. SAs residues at 86 mg/kg level have been detected in hair several months after administration, and 87 a relationship between administered dose and residue level in hair was 88 established for horsehair (Dunnett and Lees, 2004b) (Barcelona, Spain) while strong cationic exchange cartridges (Accubond II SCX, 119 6cc/500 mg) were acquired from Agilent Technologies S.L. (Madrid, Spain). 120

Animal treatment 121

Six Friesian calves were reared in our experimental farm to 8 months receiving 122 no medication containing sulfonamides. At five weeks before slaughter they 123 were divided into three groups, and kept in separate stalls during the last 124 growing phase. 125

The first group (Control) did not receive any veterinary drug. The second group 126 (WP, see Figure 1), complying with a withdrawal period of 28 days, was given a 127 daily subcutaneous dose of Copisulf (40 mL) per animal for 5 days 128 (corresponding to 150-165 mg sulfamethazine sodium salt/kg live weight). 129 Six Landrace pigs were also reared in the same experimental farm to 3 months 134 receiving no drugs containing sulfonamides. Four weeks before slaughter, a 135 group of three animals (NoWP, see Figure 1) started receiving a daily oral dose 136 of 10 g of Torul S mixed with feeding for 28 days (corresponding to a SMZ dose 137 of 1.40 -1.54 g sulfamethazine sodium salt/Kg live weight). The other group of 138 three animals received no medication (Control). 139

Sampling 140

Pigmented and non-pigmented hair samples were taken (2.0-3.0 g. approx. 141 each) from both treated and control animals before and after the beginning of 142 the pharmacological treatments with SMZ (Figure 1). 143

Sequential samples of the same animal were taken from different zones of the 144 upper area of the neck (to limit external contamination) using a hair clipper 145 ARCO 1854 (MOSER, Germany) with a cut height of 1 mm. 146

In the case of the calves having a withdrawal period (WP), additional samples 147 were taken from the areas of the first sampling, corresponding to the hair shaft 148 grown during the two weeks after the first sampling (NG-1) or the last three 149 weeks before slaughter (NG-2) (Figure 1). 150

At slaughter samples of muscle and liver were taken from both Control and 151 treated animals. These tissues were immediately stored at -20 ºC and analyzed 152 within 3 months. 153

SMZ extraction 155

Hair samples were washed three times using ultra-pure water, dried in an oven 156 at 37 ºC for one hour and then analyzed by HPLC-DAD to quantify the SMZ 157 content as previously described (Gratacós-Cubarsí et al., 2006a). 158

Briefly, 50 mg of finely cut calf hair or pulverized pig hair were extracted with 4 159 ml of aqueous alkaline solution. Crude alkaline extracts were centrifuged and 160 filtered through a paper filter. Centrifuged alkaline extracts were directly loaded 161 onto an OASIS ® -HLB cartridge, washed with 2 mL of 5 % methanol in water. 162 SMZ was eluted with 4 mL acetic acid: acetonitrile (5:95 v/v), and directly 163 loaded onto the SCX cartridge. After washing with 5 mL methanol:acetonitrile 164

(1:1 v/v), SMZ was eluted with 5 mL of ammonium hydroxide: methanol (5:95 165 v/v). 166

Eluates containing SMZ were evaporated under nitrogen at 25 ºC, reconstituted 167 with 400 µL of ammonium acetate 0,01M pH 4.5: acetonitrile (88:12 v/v) and 168 filtered through a 0.45 µm nylon filter. 169

To evaluate the SMZ residues in muscle and liver, 4 g of sample were extracted 170 with 20 mL of acetonitrile and centrifuged. One mL of the supernatant was 171 evaporated with nitrogen, reconstituted with 0.5 mL of the mobile phase, filtered 172 with 0.45 µm nylon membranes and analyzed by HPLC (Gratacós-Cubarsi et 173 al., 2006b). 174

215

SMZ accumulation in hair 216

None of the hair samples obtained from "Control" animals (C1, C2) or taken 217 before the pharmacological treatment (WP1, NoWP1) contained detectable 218 residues of SMZ. On the contrary, SMZ residues were detected, in a range from 219 0.4 to 84.7 mg/kg, in the hair samples obtained from treated calves and pigs 220 (Table 1). Figure 2 The mean SMZ levels in non-pigmented hair of pigs and calves not having the 224 withdrawal period (NoWP2) comprised between 15.9 and 40.5 mg/Kg in pig and 225 between 7.2 and 24.0 mg/Kg in calves (Table 1) The levels of SMZ residues found in calf and pig hair samples are in agreement 248 with those reported by Dunnet & Lees (2004b), who detected SA residues up to 249 25 mg/kg in racehorse tails from treated animals. The similar levels of SMZ 250 residues in calf and pig hairs is quite a surprising result, taking into account the 251 higher quantities of SMZ per kg of live weight that were administered to pigs 252 (about ten fold more than calves), but the different type of SMZ administration 253 (oral in pigs and subcutaneous in cattle) should also be considered as it may 254 have influenced the drug bioavailability (USP DI), the maximum plasmatic levels 255 and finally the drug deposition in hair. 256 257 The high variability observed for the SMZ accumulation in hair of different 258 animals, even if statistically significant, should be further investigated with 259 specific studies on a larger population. Anyway, several factors (drug 260 metabolism, hair growth rate, hair composition, feed intake, live weight), which 261 may vary significantly between animals, have been previously described as 262 affecting the drug deposition rate in hair [START_REF] Henderson | Mechanisms of drug incorporation into hair[END_REF]. 263 264 Pigmented hairs accumulated higher quantities of SMZ than the non-pigmented 265 ones, in agreement with previous studies which underlined the binding capacity 266 of SMZ (a basic drug) with the melanin pigments to form adducts (Dunnett and 267 Lees, 2004b). Moreover, in non-pigmented samples (WP2, WP3) and in the 268 new growing samples (NG1, NG2) the concentration of SMZ progressively 269 decreased in the weeks following the treatment, while the corresponding 270 pigmented calf hair gradually accumulated more SMZ residues. This different 271 time-course in pigmented and non-pigmented samples could be explained 272 mechanisms which may control the drug deposition in the hair shaft 274 [START_REF] Henderson | Mechanisms of drug incorporation into hair[END_REF]. According to the literature on this matter, if drug 275 incorporation follows the "passive diffusion" model, the drug concentration in the 276 blood is the primary factor which controls deposition [START_REF] Henderson | Mechanisms of drug incorporation into hair[END_REF]. As the 277 drug molecules diffuse from blood to the growing cells of the follicle they are 278 trapped in the hair structure during the keratogenesis. After a variable length of 279 time from the drug administration the portion of hair containing the drug residue 280 appears over the skin surface and can be sampled and analyzed. A more 281 complex mechanism has also been suggested -"multiple pool" model -to take 282 into account that drugs may also be transferred to hair shaft from sweat and 283 sebum or from external environment after the hair has emerged from the skin 284 [START_REF] Henderson | Mechanisms of drug incorporation into hair[END_REF]. Considering that the average growth rate of calf hair is of 6-285 7 mm/month and that the skin thickness is 2.5-3.0 mm [START_REF] Durant | Gas 357 Chromatography-Tandem Mass Spectrometry determination of 17α-358 Ethinylestradiol Residue in the Hair of Cattle. Application to Treated Animals[END_REF], our 286 experimental data are in agreement with the hypothesis that the prevalent 287 mechanism for SMZ accumulation in calf hairs is the formation of bonded forms 288 with the melanin during the keratogenesis in the follicles under the skin 289 ("passive diffusion"). Other ways of incorporation from the surface of the skin 290 can justify the premature detection of SMZ residues in calf hair 7 days after the 291 administration (NoWP2) and are probably the predominant mechanism in non-292 pigmented hairs. 293 294 Finally, a very interesting result is that, when SMZ residues were not more 295 detected in edible tissues of calves which had the withdrawal period (WP3), 296 they were found at mg/Kg levels in both pigmented and non-pigmented hairs of 297 These results demonstrate that SMZ was accumulated in calf and pig hair in a 302 stable way at ng/mg level, more than in the corresponding edible tissues. 303 Furthermore, SMZ residues have been detected in calf hair since the first seven 304 days and up to four weeks after the subcutaneous administration, when SMZ 305 residues where no longer detected in edible tissues of the same animal. 306 307

The high concentration of SMZ residues (mg/kg) accumulated in hair simplifies 308 the interpretation of analytical results, one of the principal drawbacks of hair 309 analysis.

Even if more studies are required to establish the possible 310 relationships between administered dose and quantities of SMZ accumulated in 311 hair, these data show that hair analysis may be a complementary tool in residue 312 Animal 1 40,5 3,7

Animal 2 15,9 1,4

Animal 3 29,3 2,3 Animal 1 7,2 0,5 7,1 1,6 1,1 0,2 4,2 0,6 1,5 0,3

Animal 2 24,0 1,5 16,8 0,6 5,6 0,7 1,4 0,6 0,4 0,2

Animal 1 5,1 0,7 22,9 4,8 68,5 2,1 37,9

  studies, food daily intake as well as other safety factors. 56

  corticosteroids, antibiotics) which should be dispensed under 81 strict veterinary prescription. 82 83 It has been established that SAs are deposited in the hair structure after oral 84

  received the same treatment with Copisulf just one week before 130 slaughter without having the withdrawal period (NoWP, see Figure1). Treated 131 animals received the daily dose in different zones of the thorax to avoid the 132 development of epithelial lesions. 133

  a diode array detector. 179The analysis of hair extracts were carried out with a Luna ® RP-C 18 (150 mm x 180 2.0 mm i.d.) analytical column (Phenomenex ® , Torrance -CA, US). A binary 181 gradient elution (flow rate 0.35 mL/min) was realized varying the composition of 182 the mobile phase linearly from 88 % A (ammonium acetate 0.01M solution, 183 acidified to pH 4.5 with acetic acid glacial) and 12 % B (acetonitrile) to 82 % A 184 and 18 % B at 12 min. The limit of quantification (LOQ) was 155 µg/Kg. 185(Gratacós-Cubarsi et al., 2006a). 186Muscle and liver extracts were analysed on a Synergy TM MAX-RP 4µm (150 187 mm x 3.0 mm i.d.) analytical column (Phenomenex ® , Torrance, CA-US). Elution 188 of SA was realized with binary linear gradient (flow rate 0.75 mL/min) between 189 solvent A (ammonium acetate 0,01M, pH 4.5) and solvent B (acetonitrile). The 190 LOQ was 55 µg/Kg for all matrices(Gratacós-Cubarsi et al., 2006b). 191The identification of SMZ peak in hair and edible tissues extracts was made 192 comparing both retention time and spectra (collected between 240 and 400 nm) 193 with those of the SMZ pure standard solutions. SMZ was quantified at a 194 wavelength of 270 nm (16 nm bandwidth) with a reference signal of 450 nm (80 195 nm bandwidth) using an external standard calibration curve. 196All samples (hair, muscle and liver) were extracted and injected in duplicate. SMZ analysis in pig samples were submitted to a two-way 200 ANOVA and Tukey test, considering the "kind of matrix" (liver, muscle, non-201 pigmented hair) and animal as independent variables. 202 Data from calf samples were evaluated with two separated three-way ANOVA 203 and Tukey tests. In the first analysis only hair samples were considered, setting 204 animal, sampling point and hair colour as independent variables. In the second 205 analysis the results obtained from samples taken at slaughter were evaluated, 206 with animal, treatment (WP, NoWP) and kind of matrix (liver, muscle, pigmented 207 hair ad non-pigmented hair) as independent variables. 208 Control sample (C1, C2) and WP1, NoWP1 samples were not considered. 209 Residual concentration of SMZ in the different samples was always the 210 dependent variable; replicates of analysis were taken into account. All the 211 statistical treatment was carried out with the software package Statistica 5.0 212 (StatSoft ® Inc. Tulsa, OK-US). 213 214 Results

. 226 227

 226 The SMZ accumulation in hair samples was significantly influenced (p<0.01) by 228 the inter-individual variance both in pigs and calves. The quantities of SMZ 229 accumulated in calf hair samples were also significantly influenced (p<0.01) by 230 both sampling point and hair color. The mean concentration of SMZ detected in 231 pigmented hairs was higher than the corresponding non-pigmented samples, 232 with the exception of the samples taken one week after the beginning of the 233 pharmacological treatment (NoWP2). 234235SMZ content in hair, muscle and liver 236The comparison of the SMZ concentration in hair, muscle and liver at slaughter 237 are shown in Figure3. The statistical analysis indicates a significant effect 238 (p<0.05) of the kind of sample matrix taken at slaughter on the SMZ residual 239 content for both pigs and calves. As showed in figure3, at slaughter the SMZ 240 concentration found in hair samples of pigs and calves which did not have a 241 withdrawal period (NoWP2) was higher than those found in the corresponding 242 muscle or liver samples taken from the same animals. At the same time, SMZ 243 residues were found in hair samples of calves having a withdrawal period 244 (WP3), but not in the corresponding edible tissues.

  binding capacity of the melanin and the multiple 273

Figure 1 .WithdrawalFigure 2 .Figure 3 .

 123 Figure captions 430

on SMZ accumulation in hair samples were also 97 determined, to assess the suitability of hair analysis as a complementary tool in 98 official residues monitoring plans. 99 100 Methods and Procedures 101

	106	acetonitrile liquid chromatography grade), ammonium acetate and acetic acid
	107	glacial were from Merck (Darmstadt, Germany). Ultra-pure water was obtained
	108	using an E-pure system (Barnstead Thermolyne, Dubuque IA-USA).
	109	Two different veterinary drugs were employed for the pharmacological
	110	treatments, an injectable solution (Copisulf, containing 330 mg/mL
	111	sulfamethazine sodium salt, from FEDRA S.A., Girona, Spain) and a soluble
	112 113	F powder (Torul S, containing 550 mg/g SMZ sodium salt, from Lamons, o r Tarragona, Spain).
	89 90 114 115	. To the best of our knowledge, no bibliographic data is available to compare the residue levels in A standard stock solution [1 g/L] of SMZ was prepared by dissolving 100 mg of P e pure standard in 100 mL of methanol. The standard working solution [1 mg/L]
	91 92 116	hair and other biological samples of the same animal after SA treatment. was prepared weekly by dilution of standard stock solution in ammonium e r acetate 0,01M pH 4.
	93	The primary objective of the work reported here was the comparison of the R
	94	residual SMZ levels in edible tissues (liver and muscle) and hair of pigs and e
	95 96 102	calves which received a pharmacological treatment during the rearing. The effects of several factors (hair pigmentation, individual response, time between v i e w O n l administration and sampling) Reagents y
	103	Sulfamethazine (SMZ, min 99 %) [4-amino-N-(4,6-dimethyl-2-pyrimidinyl)-
	104	benzenesulfonamide] and ammonium hydroxide ACS reagents were purchased
	105	from Sigma-Aldrich ® (Madrid, Spain). Organic solvents (methanol and
		6

5: acetonitrile (85:15 v/v) and stored at 4 ºC. 117 SPE HLB-OASIS ® (6cc/200 mg) cartridges were obtained from Waters Corp. 118

Table 1 .

 1 SMZ level (mg/Kg) detected in different hair samples.

	Mean S.D.	Mean S.D.	Mean S.D.	Mean S.D.	Mean S.D.

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.ukFood Additives and Contaminants

chromatograph (Agilent Technologies, Palo Alto, CA -US) equipped with a

analysis of veterinary and human drugs in soils, sediments and sludge. Trends

European Medicines Agency[Internet]. Available from: www.emea.eu.int.

Oostende, Belgium. Abingdon (UK): Taylor & Francis CRC Press. 615 p.


Jan C H Van Eijkeren
email: jan.van.eijkeren@rivm.nl

Marco J Zeilmaker

Martine I Bakker

A simple steady-state model for the carry-over of aflatoxins from feed to cows milk

Keywords: aflatoxin B 1, aflatoxin M 1, steady-state model, animal feed 24 25 26

A simple steady-state model is derived from two kinetic one-compartment models for 12 the disposition of aflatoxin B 1 (AFB 1) and aflatoxin M 1 (AFM 1 ) in the lactating cow. 13

The model relates daily intake of AFB 1 in feed of dairy cattle and the cow's lactation 14 status to resulting concentration levels of AFM 1 in milk. Moreover, assuming a linear 15 relation between the cow's lactation status and feed intake, the model relates daily 16 milk production and AFB 1 concentration in total feed to AFM 1 levels in milk. The 17 model explains similar experimental outcomes from different experiments investigat-18 ing carry-over of aflatoxins from feed to milk. Although it is concluded that it is diffi-19 cult to set a limit for AFB 1 in feed once and for all, in Europe the European Union 20 (EU) limit of 5 µg AFB 1 /kg of concentrate until now has proved to be appropriate to 21 prevent exceeding the EU-limit of 0.05 µg AFM 1 /kg milk. 22 23

Aflatoxins are known for their carcinogenic, mutagenic and teratogenic properties 28 (Van Egmond, 1989). They have been found, amongst others, in groundnuts, cotton-29 seed and corn, which are constituents in different formulations of concentrate rations 30 fed to dairy cattle. Aflatoxin B 1 is the most abundant of the aflatoxins. They are sec-31 ondary metabolites of some Aspergillus fungi. In mammals AFB 1 is metabolised to 32 AFM 1 , which is excreted in milk. Both AFB 1 and AFM 1 are very toxic and potent 33 liver carcinogens. Several authors, e.g., references in Van Egmond (1989), Frobish et 34 al. (1986), [START_REF] Veldman | Carry-over 334 of aflatoxin from cows' food to milk[END_REF], [START_REF] Chopra | Carry-over of aflatoxin M 1 in milk of cows fed aflatoxin B 1 contaminated ra-321 tion[END_REF], have investigated the relation 35 between intake of aflatoxins in feed of dairy cattle and the resulting AFM 1 levels in 36 milk, depending on (equivalent) AFB 1 level in (total) feed, daily intake of AFB 1 and 37 lactation status. However, no modelling effort, unifying the different investigations 38 and explaining common observations, was made. 39 40 A model based on mechanistic considerations was developed. This includes the frac-41 tional absorption of daily ingested AFB 1 over the gut wall, its metabolism to AFM 1 42 and its elimination by other ways, the corresponding formation of AFM 1 and the ex-43 cretion of AFM 1 through milk and its elimination by other ways. From the steady-44 state analysis of this model, the carry-over rate (COR) from AFB 1 to AFM 1 , i.e. the 45 ratio of the daily amount of AFM 1 excreted by milk and the daily amount of AFB 1 46 ingested was modelled. Additionally, the bioconcentration factor (BCF), i.e. the ratio 47 of AFM 1 concentration in milk and AFB 1 concentration in feed, follows from the 48 steady state analysis. When it is assumed that feed intake depends linearly on daily 49 milk production, then the BCF could be expressed in terms of lactation status only.

Materials and Methods 52

The number of animals per experimental group for the studies utilised in this paper is 53 low. [START_REF] Frobish | Aflatoxin residues in milk of dairy cows after ingestion of naturally contami-325 nated grain[END_REF] used 4 animals per group, [START_REF] Veldman | Carry-over 334 of aflatoxin from cows' food to milk[END_REF] used 12 54 and 8 animals per group and [START_REF] Chopra | Carry-over of aflatoxin M 1 in milk of cows fed aflatoxin B 1 contaminated ra-321 tion[END_REF] used 10 animals in their repeated 55 experiment. But also other authors generally seem to use a number of animals of the 56 same order (van Egmond, 1989) In this study we are interested in the case that contamination is sustained and steady 98 state will be reached. From equation ( 1), setting the rates of change to zero, one ob-99 tains readily the steady state concentrations in plasma: 100 101 : : :

: : B B B M B M B M B M m M dA FD CL CL C dt dA CL C CL P M C dt = - + ⋅ = ⋅ - + ⋅ & & (
: Two additional quantities can be derived from equation ( 5): the carry-over-rate 121 (COR), which is the relation between the daily AFB 1 intake and the daily AFM 1 ex-122 cretion through milk and the bio-concentration factor (BCF), which is the ratio be-123 tween the AFM 1 -level in milk and in AFB 1 -level in total feed: 124 (5) can be expressed in terms of lactation status and feed concentration, and so the 137 corresponding BCF of equation ( 6) is no longer dependent on feed intake and lacta-138 tion status, but on the latter only: 139 140 ( ) ( ) (1978) which clearly indicates that there is a linear relationship between AFB 1 intake 145 and AFM 1 levels in milk. This is consistent with model equation ( 5), be it that it may 146 be expected that the slope of the linear relationship differs from the different investi-147 gations, which it does, mainly depending on the kind of contamination and lactation 148 . Note that, because of their clustering 158 the data do not allow for a definite conclusion that a linear relation exists between in-159 take and production level. However, for the range of milk production levels the model 160 may assumed to be adequate. If so, the model for milk levels and bio-concentration 161 factors of equation ( 8) apply. 162 163 In a second trial, concentrate containing contaminated cottonseed was used again, as 164 well as concentrate containing naturally contaminated corn. In Table 1 observed 165 AFM 1 -levels in milk are compared with levels estimated by applying the models in 166 equation ( 5) and ( 8). For cottonseed all the estimated levels exceed observed level, but 167 within 20% at maximum. The estimations for carry-over from corn are about 1.5-fold 168 greater than determined experimentally. As the animals got the same diet, but with the 169 corn portion contaminated instead of the cottonseed portion, this must be due to dif-170 ference in the typical aflatoxin mix of naturally contaminated corn as compared to that 171 of cottonseed. This implies a difference in the factor α in equation ( 5).

B B B M B M B B M M M m M m B B M FD C CL CL CL C CL FD C CL P M CL P M CL CL = + ⋅ = = ⋅ + + + (
, milk feed A M B A M B C C BCF M M α α β β ⋅ ⋅ + ⋅ ⋅ + = ⋅ = + + F o r P e e r

173

Given the parameterisation in equation ( 5), the maximum carry-over-rate possible 174 α = = 0.032 (3.2%). Values of milk production in the study or cottonseed ranged 175 from a minimum of 14.6 L/day to a maximum of 32.9 L/day, and consequently the 176 model calculated COR (for cottonseed) should range from 0.015 (1.5%) to 0.021 177 (2.1%). Values of 1.38% to 2.33% are reported, except for 15.2 kg milk per day pro-178 ducing lot (which is also exceptional in Figure 2). In concordance with our model, it is 179 remarked that AFB 1 -level in feed did not affect COR (equation ( 6)), but that COR was 180 positively correlated with increased milk production (equation ( 6)). 181 182 Also, it is reported that milk production did not affect AFM 1 -levels in milk. When 183 feed intake is linearly dependent on milk production and equation ( 8) applies, this 184 should imply that, at least within the range of milk production in the study, BCF is 185 (almost) independent of M & . In the range 14.6 32.9

M = - &
, BCF ranges between the 186 values 0.0155 and 0.0165. So, an explanation of the independence of AFM 1 -levels on 187 milk production may lie in the validity of both the models in equation ( 5) (derived 188 from mechanistic considerations) and ( 7) (assuming linear relationship between daily 189 feed intake and milk production). Linear dependence of AFM 1 -level in milk on AFB 1 -190 level in feed is implied both by equation ( 5) and ( 8 In a two experiment study of [START_REF] Veldman | Carry-over 334 of aflatoxin from cows' food to milk[END_REF], concentrate with naturally con-193 taminated groundnuts was used. Note that also in this case, quantitative model calcu-194 lations using the parameters found above, notably the parameter α , need not be simi-195 lar to experimental observations. Qualitative model aspects however, should not dif-196 fer. Nevertheless, application of model equation ( 5) to experimental results of experi-197 ment 1 do not fit: while daily intake for high and low producing cows are similar (39 198 and 34 µg/day, respectively), mean AFM 1 -level in milk of the high producing cows 199 exceeds 1.5-fold the corresponding mean level in low producing cows: one would ex-200 pect the reverse, because of the more efficient excretion route through milk. This ex-201 perimental result is also not consistent with the result of the second experiment, nor 202 with experimental results of other researches. The results of experiment 2 are consis-203 tent with the model, indicating that the BCF of AFB 1 to AFM 1 from groundnuts to be 204 a factor of 1.6 greater than the BCF from cottonseed. 205 206 From the second experiment it is concluded by the authors that 1. COR is positively 207 correlated with milk production (our equation ( 6)), 2. given a fixed AFB 1 intake the 208 corresponding AFM 1 -level in milk is negatively correlated to milk production (our 209 equation ( 5)) and 3. COR is independent of AFB 1 -level in feed (our equation ( 6)). 210 211Model calculations (equation ( 5)) with the parameterization as found above applied to 212 result from [START_REF] Chopra | Carry-over of aflatoxin M 1 in milk of cows fed aflatoxin B 1 contaminated ra-321 tion[END_REF] can reasonably explain their findings. However, cal-213 culated concentrations appear to be 2-fold (±0.5) higher than the concentrations found 214 experimentally. The cows were fed concentrate with naturally contaminated ground-215 nuts, as in the research of Veldman et al.

  in equation (1) shows the rate of change of the body burden of AFB 1 89 / B dA dt after the fractional systemic uptake F of the daily ingested amount D & 90 through contaminated feed and the concurrent clearance from plasma by elimination 91 ( B CL ) and AFM 1 -formation : B M CL . The second line shows the rate of change of the 92 body burden of AFM 1 / M dA dt and its concurrent clearance from plasma by elimina-93 tion ( M CL ) and excretion through milk, m P M & . Note that the daily milk excretion is 94 m M P MC & , which is the product of the daily milk production M & (kg/day) and the con- 95 centration of AFM 1 in milk m M P

  the second line the resulting steady state concentration of AFB 1 is substituted 104 from the first line. 105106The corresponding AFM 1 -level in milk at steady state is: state AFM 1 concentration in milk can be expressed in terms of the 115 "free" variables of daily dose and lactation status:

  the COR is only dependent on lactation status and the BCF only on daily 128 feed intake and lactation status. 129 130 Additionally, when it is assumed that there exists a linear relationship between daily is intake for self maintenance when no milk is produced) then milk C in equation 136

  equation (5) has been fit to data, concerning concentrate containing 151 naturally contaminated cottonseed, from[START_REF] Frobish | Aflatoxin residues in milk of dairy cows after ingestion of naturally contami-325 nated grain[END_REF], see Figure2. The re-152 sulting empirical parameter values found are 0appears to be an outlier: its low milk level would indicate cows at high 154 production, while in fact production is low. Frobish et al. also present AFB 1 levels in 155 total feed and thus daily intake of feed can be estimated. The result of linear regres-156 sion of daily intake versus production status is presented in Figure3

Figure 1 .Figure 2 .Figure 3 .

 123 Figure 1. Kinetic model of the carry-over of AFB 1 to AFM 1 . A fraction F of the daily 341 dose D & enters the system over the gut wall. AFB 1 is cleared by excretion and by bio-342

  The mechanistic model for carry-over of AFB 1 to AFM 1 is shown in Figure1. A frac-63 tion, F of the daily intake D & µg/day of AFB 1 is taken up over the gut wall (in this pa-64 :plasma partition of AFM 1 and M & is the milk production in kg/day. 83 84 A coupled set of mass balances describes the carry-over of AFB 1 in feed to AFM 1 in 85

	76	concentration . AFB 1 is cleared from plasma by urinary excretion (after GSH-
	77	conjugation) or by transformation to metabolites that are of no further toxicological
	78	interest (clearance B CL in L/day) and by transformation to the metabolite of interest
	79	AFM 1 (clearance	: B M CL in L/day). Once AFM 1 has been formed in the liver, it results
	80	in a plasma concentration	C	M	=	/ A V M M	, where M V is the distribution volume of
	57											. A general remark is that the inter-individual vari-
	58 59 81 82	ability regarding the resulting AFM 1 -level in milk is great. Moreover, different studies use different races, different composition of concentrate, different source of contami-AFM 1 . Also AFM 1 is cleared from plasma by elimination through other means than F r o milk ( M CL in L/day) and by excretion through milk: m P M & L/day, where m P is the
	60 61	nation (naturally contaminated corn, cottonseed or groundnuts) and different composi-tion of additional feed. P e
	62 86	milk:					e r
												R (	)
	65 66 68 67	per dotted symbols denote quantities per time unit). This fraction may well depend on the matrix/carrier of AFB 1 and thus can be different for different AFB 1 contamination sources (groundnuts, cottonseed and corn), different concentrate composition and dif-ferent total feed composition. The daily intake D & of AFB 1 -equivalents is the product w e e i v ( )
	69 70	of the total daily feed intake feed I &	(kg feed / day) and the total feed concentration O n l
	71	C	feed	(µg AFB 1 / kg feed):	D I = & &	feed	⋅	C	feed	.	y
	72
	73	In the one-compartment model, the amount B A taken up over the gut wall results in a
	74	plasma concentration	C	B	=	/ A V B B	, where B V is the distribution volume of AFB 1 , i.e.
	75	the equivalent to the body tissues volume of the volume of pure plasma to reach that

milk

Table 1 .

 1 Milk levels of AFM 1 observed, estimated with the model in equation (5) 336

	This last research found concentrations that

& & &

& & & & & &

& & & & & &

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Acknowledgements 311

This study was sponsored by Dr. A. Lam of the Food and Consumer Product Safety