2018

DEUTSCHE LEBENSMITTEL-RUNDSCHAU

114. Jahrgang Mai 2018 Behr’s Verlag l Hamburg l ZKZ 9982

Angewandte Wissenschaft

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Bioanalytical screening for Dioxins and PCBs in EU-regulated foods: New analytical criteria adopted by the European Union in Commission Regulation (EU) 2017/644. Part 3: Assay criteria, validation and quality control, reporting of results, implementation Johannes Haedrich1*, Claudia Stumpf1 and Michael S. Denison2 ¹ Bioassay Research Laboratory, State Institute for Chemical and Veterinary Analysis (CVUA), Bissierstraße 5, 79114 Freiburg, DE 2 Department of Environmental Toxicology, University of California Davis (UCD), One Shields Avenue, Davis, CA 95616, US Abstract Bioanalytical high-throughput and rapid screening methods for detection of dioxins and PCBs can reliably process up to 1.000 samples annually per technical assistant, reducing the workload of a GC/HRMS laboratory by up to 80 %. By sieving out contaminated samples suspected to exceed regulatory limits bioassays help to minimize instrument down time by sparing sensitive components such as detectors and columns while making more efficient use of the expensive instrumentation. Leveraging the benefits of both complementary technologies provides a strong synergistic effect reflected in reduced turn-around times of 1½ to 2½ days for 10 and more samples per lab assistant, with considerable cost savings. The third part of this publication focuses on assay performance, validation, quality control and reporting of bioanalytical results as proposed to the European Commission and adopted into EU legislation by Commission Regulation (EU) 2017/644 of 5 April 2017. We present a new statistical approach to efficient cut-off concentrations which is, in principle, applicable to any analytical screening method, illustrated with a practical example and the necessary mathematical formulae. Analysts interested in applying bioanalytical methods for screening food (and feed) samples for elevated levels of dioxins and PCBs should be aware of how easily the capability can be integrated into an existing analytical laboratory. Some guidance is therefore given on how to implement a bioassay laboratory for dioxin screening. Zusammenfassung Mit schnellen bioanalytischen Hochdurchsatz-Screeningverfahren für den Nachweis von Dioxinen und PCBs können je Labormitarbeiter jährlich bis zu 1000 Proben zuverlässig untersucht werden, was die Arbeitslast eines GC/HRMS-Labors um bis zu 80 % redu-

ziert. Durch Vorauswahl belasteter Proben, die im Verdacht stehen, gesetzliche Grenzwerte zu überschreiten, tragen Bioassays unter Schonung empfindlicher Komponenten wie Detektoren und Säulen dazu bei, Ausfallzeiten kostspieliger Analysengeräte zu minimieren und diese effizienter einzusetzen. Führt man die Vorteile beider komplementären Technologien zusammen, so wird ein starker Synergieeffekt wirksam, der sich insbesondere in verkürzten Durchlaufzeiten von lediglich 1½ bis 2½ Arbeitstagen für mindestens 10 Proben je Mitarbeiter widerspiegelt, verbunden mit erheblichen Kosteneinsparungen. Der dritte Teil dieser Veröffentlichung befasst sich mit Vorgaben zur Durchführung des Assays sowie zur Validierung, Qualitätskontrolle und Berichterstattung bioanalytischer Ergebnisse, wie sie der Europäischen Kommission vorgeschlagen und mit Verordnung (EU) 2017/644 der Kommission vom 5. April 2017 in die EU-Rechtsvorschriften übernommen wurden. Wir stellen einen neuen, prinzipiell für jedes analytische Screeningverfahren geeigneten statistischen Ansatz zur Ermittlung effizienter Cut-off-Konzentrationen vor, veranschaulicht anhand eines praktischen Beispiels und den erforderlichen mathematischen Formeln. Analytiker, die sich für die Anwendung bioanalytischer Methoden zum Screening von Lebensmittel- (und Futtermittel)proben auf erhöhte Dioxin- und PCB-Konzentrationen interessieren, sollten wissen, wie einfach dieses Potenzial in ein bereits bestehendes analytisches Labor integriert werden kann. Den Abschluss bilden daher einige praktische Hinweise zur Einrichtung eines Bioassay-Labors für das Dioxin-Screening.

1 Introduction

Dioxins are among the most toxic organochlorine compounds and they can cause reproductive and developmental

* Dr. Johannes Hädrich, [email protected]

216  Originalarbeiten  « problems, damage to the immune system, interference with hormones, and can cause cancer. Even the smallest amounts of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) can be extremely toxic, and species differences in their sensitivity and adverse effects have been observed [1]. For the most sensitive species, the acute toxicity of this compound is only exceeded by some natural substances and in animal experiments, diphtheria toxin can be three times as toxic, the tetanus toxin 10,000 times and the botulinus toxin A 30,000 times. Additionally, TCDD can be ten times more toxic than mycotoxins from molds, 500 times more toxic than strychnine and curare, and 1,000 times more toxic than pure nicotine [1]. Dioxins are practically insoluble in water but highly soluble in oils and fats. As a consequence, they readily adsorb to and accumulate in organic matter, sediments, suspended solids, fly ash, soot and the fatty tissues of organisms. Their fat solubility and resistance to metabolic degradation is what accounts for the accumulation of these

Abbreviations aryl hydrocarbon receptor AhR action level AL bioanalytical equivalent BEQ biosafety level biological BL biological safety cabinet BSC CALUX Chemically Activated LUciferase gene eXpression GC/HRMS decision limit, level of interest plus U DL dioxin responsive element DRE EC50 50 % of maximal effective concentration GC gas chromatography HAH halogenated aromatic hydrocarbon high resolution mass spectrometry HRMS inter-laboratory comparison ILC LOD limit of detection LOI level of interest ML maximum level MU measurement uncertainity PAH polycyclic aromatic hydrocarbon PCB polychlorinated biphenyl DL-PCB dioxin-like polychlorinated biphenyl NDL-PCB non-dioxin-like polychlorinated biphenyl PCDD polychlorinated dibenzo-p-dioxin PCDF polychlorinated dibenzofuran PCP pentachlorophenol proficiency testing PT quality control QC REP relative potency (relative response of the CALUX cell system) relative light units RLU RSDr relative standard deviation, repeatability conditions RSDRw relative standard deviation, within-laboratory reproducibility conditions SCoPAFF Standing Committee on Plants, Animals, Food and Feed TCDD 2,3,7,8-tetrachlorodibenzo-p-dioxin TEF toxic equivalency factor TEQ toxic equivalent expanded measurement uncertainty U WHO world health organisation WR working range

chemicals in the food web. Because humans are at the top of the food chain, it is obvious that human tissues may contain relatively high dioxin concentrations [2]. In Germany, milk and dairy products account for the largest share of dioxin exposure (approx. 42 %), followed by meat products (approx. 20 %), fish, crustaceans and molluscs (approx. 17 %), and hen’s eggs (approx. 8 %). The European Commission has implemented an integrated approach [3] to reduce dioxin emissions into the environment, assure an effective control system of feed and food, and evaluate compliance with EU standards to ensure a high level of food safety. Hence, in Germany, dioxin emissions from known sources have dropped by more than 90 percent since the late 1980s until 2004, and 2010 values have shown that the levels have dropped even further [4]. Therefore, the dioxin burden in the most extensively studied food product, cow’s milk, decreased by about 80 percent between 1987 and 2000, from ~2.3 to ~0.4 pg PCDD/F-WHO-TEQ/g milk fat and has remained low with little variation since then [4] (Fig. 1). Thus, the average dioxin burden in milk is now also well below the action level (AL, 1.75 pg PCDD/F-WHOTEQ/g milk fat) [5] and the maximum level (ML, 2.50 pg PCDD/F-WHO-TEQ/g milk fat) [6]. This favourable trend is also found in other EU-regulated foods of animal origin. Human milk is considered one of the best indicators of human exposure to dioxins being rich in fat and therefore indicative of residues of dioxins in human adipose tissue. The 80 % reduction in dioxin levels in human milk since the late 1980s reflects the success of the reduction measures taken in Germany [1,7]. High-throughput, easy-to-run and cost-effective bioanalytical screening methods with turnaround times of less than 3 days implemented upstream of GC/HRMS confirmatory methods are authorized by European legislation [8–15]. Bioassays, such as the Chemically Activated LUciferase eXpression (CALUX) assay, when fully validated according to European standards [14,15] and with the legally required quality control measures effectively in place, considerably reduce the workload of laboratories performing official control of feed and food. A powerful tool for monitoring the sum concentrations of PCDD/Fs, DL-PCBs and PCDD/Fs plus DL-PCBs for which individual maximum levels (MLs) and action levels (ALs) were set by EU legislation [5,6], the CALUX assay identifies samples with elevated levels of PCDD/Fs and DL-PCBs suspected to be non-compliant with the respective legal limits. Hence, for two decades this assay has proven to be an efficient response to reduced staffing levels and shortage of financial resources in a number of official and research laboratories across Europe and also worldwide. Applying bioassays seems especially justified in view of the observed significant reduction of dioxin levels in the environment, feed and food. According to our experience from large numbers of routine samples of all EU-regulated food matrices, only around 20 percent of screened samples are suspected to exceed the current legal limits requiring confir-

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dioxin level [pg TEQ/g milk fat]

matory follow-up. This is under3.5 pinned by the observation that durEU maximum level 1 July 2002 to 31 Dec 2011 ing the past years, MLs and/or ALs 3.0 pg/g fat 3.0 were exceeded only in a very small percentage of samples analysed with EU action level from 1 Jan 2012 1 July 2002 to 31 Dec 2011 GC/HRMS at the CVUA Freiburg. 2.5 2.5 pg/g fat 2.0 pg/g fat PCDD/F and DL-PCB congener from 1 Jan 2012 patterns in samples have been col2.0 1.75 pg/g fat lected and catalogued over the past decades in large numbers related to 1.5 background and elevated levels of contamination in feed and food by 1.0 the German Environment Agency (UBA) in a POP-Dioxins Database preliminary [16]. After implementation of the 0.5 CALUX bioassay the most interesting congener fingerprints of samples 0.0 with elevated levels of contamination (e. g., those declared “susyear pected” from screening) will continue to be assessed by GC/HRMS Fig. 1 Decrease in dioxin levels in cow’s milk in Germany from 1987 to 2017 [7] confirmatory methods and results deposited into the Database. The use of state-of-the-art validated bioassays is also eco- by Commission Regulation (EU) 2017/644 of 5 April 2017 nomically obvious during so-called “dioxin incidents” [17] [14] were discussed in the second part of this paper [21] involving contamination of feed and/or food with dioxins with regards to the nature of bioanalytical results and sample and dioxin-like compounds and when large numbers of analysis. The present third part is focused on requirements samples must be analysed within very short spans of time of- for assay performance, method validation and quality conten overstraining capacities of laboratories running the com- trol for bioassays. prehensive and cost-intensive confirmatory methods. The resulting relief from sample “prescreening” enables the latter to focus on finding the source(s), identifying congener pat- 2 New Criteria for Application of Bioanalytical Screening terns and tracing of contamination pathways, while banned products from temporarily closed farms or production lines 2.1 Assay-related Requirements are monitored with the bioassay until levels of PCDD/Fs and/or DL-PCBs have dropped reasonably below the respec- 2.1.1 Dose-response Curves tive legal limits. All things considered, bioanalytical screen- In order to estimate the concentration of the target analyte(s) ing and confirmatory analysis are complementary methods in an extract of an unknown sample, the cell response meaand thus good friends [18], generating synergistic effects re- sured in the assay is compared to a calibration curve or stanflected in considerably reduced turn-around times and re- dard curve, also called a concentration-response curve. Bioduced financial costs (by approximately 80 percent) [19]. analytical response data follow the shape of a hyperbolic There is, however, even more that a thoroughly validated receptor-binding curve, although the intracellular mechaand QC-backed CALUX bioassay can do. Each instrumental nisms involved from formation of the aromatic hydrocarbon analysis derived TEQ-result that differs significantly from receptor (AhR)-ligand complex to the induction of luciferase the bioanalytical BEQ-result of the same sample strongly gene expression are multifold and complex. When plotted suggests the presence of (a) potentially hazardous novel on a semi-log scale, to accommodate a wide range of concompound(s) that activate(s) the “dioxin” signalling pathway centrations until saturation of the response, the data show a yet is not one of the 29 dioxin-like compounds included in sigmoidal relationship to concentration. Typical calibration the TEF-scheme to date [20]. curves are therefore sigmoidal in shape, for which a 4-paWithin the scope of establishing strong EU-wide standards rameter logistic (4PL) equation provides an accurate depicfor routine methods, upon request by the European Com- tion of the relationship between response (expressed in relamission we evaluated and optimized the performance of the tive light units (RLUs)) and analyte concentration and is CALUX bioassay for use within European official feed and generally considered the best suitable model. Hill’s equation food control. Previous analytical criteria in force since 2002 [22], being mathematically analogous to the logistic equawere tested, substantially revised and amended [17]. The re- tion, is commonly used to fit the response data to a line. It sulting new EU-wide criteria as adopted into EU legislation defines a minimum response (d), the maximum response

218  Originalarbeiten  « (a), the concentration required to evoke a response half-way between the minimum and maximum (c, being EC50), and a parameter that describes the steepness of the curve (b, or Hill’s slope). If the background activity of the bioassay is subtracted from the response data obtained from a calibration standard dilution series, d becomes zero, resulting in a simplified (4-1)-parameter Hill equation (Fig. 2).

relative light units x103 [RLU]

Within the working range, or dynamic range, errors of the estimated concentrations stay below a maximum acceptable value. This is also called the “reportable range” of an assay, meaning the range where the coefficient of variation (CV) in the calculated concentrations of each individual calibrator of a TCDD, or a PCB 126, standard dilution series measured in triplicate is below 15 %. An alternative option that is usually easier to fulfill is that the CV of the RLUs measured in triplicate is below 15 %. Estimated concentrations Hill’s equations at the lower and upper branches of a concentration-re4-parameter Hill equation sponse curve generally show higher relative variability. In a–d the lower branch this may be due to nonspecific background y=d+ x b 1 +  c  “noise” contributing to the measured response values. The lower end of the assay working range (the assay reporting (4-1)-parameter Hill equation (d = 0) limit) must therefore be set, at least by a factor of three, a above the procedural/method blank values. In the upper y= b  x branch, however, the effect of the decreasing slope trans1 +  c lates into high variability of estimated concentrations even from rather precise triplicate response measurements: a A concentration-response curve with a sufficient number of small variation in the response leads to a large variation in standards to define adequately the relationship between con- the estimated concentration. The upper end of the working centration and response shall be used, for which 8 to 12 con- range thus should be set at the EC70 value (70 % of the centrations of TCDD, PCB 126 or a suitable mixture of con- maximal effective concentration), but in general it is the geners is appropriate. The unknown concentration of the concentration below which triplicate-CVs of estimated conanalyte(s) may then be determined by finding the concentra- centrations, or of the RLUs, are below 15 %. The working tion on the curve that produces a comparable induction re- range should be established during validation. sponse. Before carrying out the validation of a bioanalytical The calibration curve is usually fit to concentration-response method the experimental concentration range should be data pairs by minimizing the sum of squared residuals (SSR). known. This range should correspond to the working range Model curve parameters are optimized in an iterative proof the concentration-response curve, to allow adequate esti- cess to achieve minimal bias of the calibrator concentrations mation of the BEQ-level of the analytes of interest. over the maximum usable calibration range. SSR regression, however, assumes that the variance is constant for all values of x (homoscedastic data), which is in real18 ity rarely the case for bioanalytical response data. As a result, low-qual16 ity data points exhibiting a higher a = top variation, generally found in the up14 per part of dose-response curves, b = Hill slope 12 influence the fit to the same extent as high-quality data points typically 10 located in the middle and lower y = d + (a – d)/(1+(x/c)b) parts of the curves. Although after 8 c = EC50 performing SSR regression the coefficient of determination (R2) fre6 quently suggests a perfect fit, this 4 may not be the case in the low concentration range where SSR regres2 sion is often far from ideal. The use d = bottom of the R2 parameter included in pre3 4 10 0.01 0.1 1.0 10 100 10 vious EU legislation [8,9] is therefore strongly discouraged and it was assay-concentration [pmol/L] now discarded as a quality-of-fit criFig. 2 Concentration-response curve (exemplary): 4-parameter Hill equation fitted to concen- terion. tration-response data pairs; a: upper asymptote/maximum response (top), b: Hill slope, c: If an evaluation of the behaviour of variance across the calibration range EC50, d: lower asymptote/minimum response (bottom)

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»  Originalarbeiten  219 reveals that the concentration-response data are not homoscedastic, but that the noise (variance) of the bioanalytical response data increases with the response (heteroscedastic data), the quality of the curve fit may be improved by transforming the data mathematically [23], or by placing less “weight” on responses exhibiting higher variation. Weighted sum of squared residuals (WSSR) regression reflects that the variance of the response data is a function of the magnitude of the response. As a weighing factor wi, the inverse variance wi = 1/variance (yi) of replicate response data at each concentration i may be used, yi being the observed standard response, ŷi the response predicted by the curve model, while n is the total number of concentration levels. n

WSSR =

w [y – ŷ ] Σ i=1 i

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Calculations on concentration-response data sets generated from series of standard dilutions may easily be performed e. g. in custom-tailored MS Excel® data sheets using the “Solver” add-on, or with any other suitable software solution. 2.1.2 Measuring the Cell response to Sample Extracts

Sample extracts should be tested in triplicate, or at least in duplicate. The coefficient of variation (CV) in the calculated concentrations, or at least in the response data (RLUs), measured in triplicate, should be below 15 %. When using duplicates, a standard solution or a control extract tested in four to six wells distributed across the plate shall produce a response or concentration within the working range based on a CV < 15 %. 2.2 Initial Validation Before the new criteria entered into force in 2012, bioanalytical results were directly compared to the EU regulatory limits given in TEQs. However, BEQs from screening and TEQs from GC/ HRMS analysis may not always yield a one-to-one relationship, and this variation can result from physical properties of the sample, recovery losses, congener patterns, differences between TEF and REP values, and/or the properties of the sample material used for recovery control and correction. This observation underlines the need to evaluate this correspondence for each sample matrix (or matrix group) of interest during an initial validation process before applying the method in routine, to ensure that bioassay BEQs provide a reliable numerical indication of the TEQ-levels in the sample. Matrix-matched calibration and the use of external standards has proven to be an acceptable alternative where isotope dilution cannot be applied as in GC/HRMS analysis, to compensate for effects driving apparent recoveries and the differences between BEQs and TEQs. For an initial assessment of the BEQ/ TEQ-relationship, representative confirmed “blank” samples (“reference samples” [17]) previously shown not to produce a significant cell response in the CALUX with at least four different levels of contamination shall be prepared. Calibrator concentrations should, in general, be evenly spaced across the cali-


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bration range to avoid the problem of leverage. However, method performance must be demonstrated only at the level of interest (LOI), namely the ML and/or the AL. To keep the workload reasonable, deviation from good statistical practice by excluding the 1.5x ML calibrator seems acceptable provided the BEQ/TEQ relationship can be demonstrated to be linear across the calibration range.

It is therefore suggested to establish four concentration levels by spiking the homogenized sample material at 0x, 0.5x, 1x and 2x ML (or AL), each with six independent repetitions, allowing the precision to be evaluated at each level. According to Commission Regulation 2017/644 [14], repeatability of bioanalytical results (RSDr) of samples contaminated at the ML must be assessed during validation. From the same calibration data, the cut-off concentration may be assessed based on within-laboratory reproducibility conditions (RSDRw). We therefore decided to combine both procedures and to apply the somewhat stricter requirement for intermediate precision to an overall approach. During initial validation the basic method performance, in particular efficiency and reproducibility of extraction and clean-up steps, is assessed. The use of spiked “blank” samples pre-tested in the assay instead of incurred sample material ensures that the cell response is not significantly influenced by co-extracted non-regulated AhR-agonists but mainly results from PCDD/Fs and/or DL-PCBs present in the sample extract. Variability of congener patterns and matrix properties leading to enhanced scatter of bioanalytical results, as expected in routine screening, are not taken into account at this stage. The spiked samples are extracted, extracts cleaned-up and luciferase activity is measured. BEQs corrected for blank and recovery are plotted vs. TEQ values (blank sample concentrations, plus the nominal spiking concentrations). From the regression line and the 95%-prediction interval calculated on the BEQ/TEQ data pairs, various essential performance parameters can be derived, including the mean matrix blank, working range, sensitivity, repeatability (or within-lab reproducibility) at the ML and/or AL, the BEQ/TEQ-ratio and the initial cut-off concentration based on the ML and/or AL. 2.3 Cut-off Concentrations

The capability of a screening method to detect samples potentially exceeding the established legal limits is its fundamental performance characteristic and a function of the cutoff concentration. Its evaluation is based on various key requirements and should be part of each validation study. Cut-offs for checking sample compliance with an ML (or AL) must take into account the GC/HRMS measurement uncertainty (MU) and the variability of bioanalytical results (RSDRw < 25 %) at the ML (or AL), and ensure a false-compliant rate of < 5 %. After GC/HRMS analysis only those samples exceeding the ML (or AL) plus the expanded measurement uncertainty (U) will be considered noncompliant without reasonable doubt. The bioassay cut-off shall therefore be established not on the ML, but on the GC/HRMS

220  Originalarbeiten  « “decision limit” (DL = ML + U), and calculated as lower endpoint of the distribution of BEQs corresponding to the TEQ concentration equaling DL (xDL). Samples with BEQ-results below the cut-off are declared “compliant” with immediate legally binding force. We developed two alternative concepts: (1), a calibration approach, and (2), a “quick and easy” approach, both of which were adopted into EU legislation [14,15]. 2.3.1 Calibration Approach

This approach is based on the extent of correspondence between BEQ and TEQ values established by matrix-matched calibration experiments involving samples spiked around the LOI. If the samples are analysed in six consecutive series, each including levels e. g. at 0x, 0.5x, 1x and 2x ML (or AL), this results in six individual calibration lines and within-lab reproducibility conditions for the six repetitions on each level. After regression analysis on the BEQ/TEQ data, the cut-off concentration ycut-off (in BEQ) may be calculated from the prediction interval. Screening results exceeding ycut-off indicate that TEQ levels in the sample are likely to fall above the “decision limit” xDL = ML+U of the confirmatory method (e. g. U = 20 %) [24]. Mathematically, ycut-off is obtained at xDL from the lower band of the prediction interval of the dependent variable y, being the BEQ-level above which 95 % (or 99 %) of the area under the Gaussian normal distribution curve of measured BEQ values corresponding to xDL are located (Fig. 3). ycut-off = yDL – syx × tα,f=n-2 × √1⁄m + 1⁄n + (xDL – x)2⁄QXX ycut-off: cut-off concentration (BEQ)

yDL: mean BEQ result from m independent replicates of samples contaminated at xDL syx: residual standard deviation tα,f=n-2: t-multiplier (α = 5 % or 1 %, f = degrees of freedom, single-sided) m: number of replicates on each level (index j) n: total number of calibration points (index i) xDL: concentration (TEQ) representing the GC/ HRMS decision limit (xDL = ML + U) x: mean of the concentrations (TEQ) of all calibration samples n Qxx = Σi=1 (xi – x)2: sum of squared residuals (SSR) xi: sample concentration (TEQ) of calibration point i

Following this approach, the sensitivity of the bioanalytical method and the suitability of the control sample used to compensate for any bias within acceptable recovery ranges are also taken into account. The lowest BEQ level of target analytes that can be distinguished from the absence of those compounds (blank values) is called the critical value (ycrit), which is based on a selected α level of confidence (type I error). Although α = 5 % is suggested in legislation, we decided to use α = 1 % (two-tailed) as this approach is more conservative leading to slightly lower cut-off concentrations. Con-

sequently, less than 1 % false-compliant results were found in our laboratory analyses of more than 1000 contaminated samples. Separate MLs are set by the EU for PCDD/Fs, and for the sum of PCDD/Fs and DL-PCBs. Checking compliance of samples without selective clean-up requires appropriate bioassay cut-off estimates based on both MLs. For checking sample compliance with ALs, this calibration approach may also be applied. However, this is not required as exceedance of ALs does not result in drastic consequences, such as closing of farms and/or withdrawal of food from the market. Therefore, an appropriate percentage (e. g. 70 %) of the respective AL may be used as cut-off value, as well. 2.3.2 “Quick and Easy” Approach

Extension to matrices not yet included in the scope of a validated bioanalytical method, new or changing regulatory limits, significant changes in congener patterns e. g. during a contamination incident, or other ad hoc scenarios may require a laboratory to establish preliminary cut-off estimates on short notice, for which purpose we present three abbreviated options. Option 1

A preliminary cut-off concentration may be estimated from bioanalytical results (corrected for blank and recovery) of at least six analyses of representative blank samples, or low contaminated samples, spiked at the decision limit xDL of the confirmatory method and analysed under within-laboratory reproducibility conditions. BEQ results from control samples contaminated at xDL (in TEQ) and analyzed together with 6 series of unknown samples may be used requiring no extra work or expenditure. Assuming a normal distribution of the BEQ data being the simplest mathematical model and usually a good first approximation, the cut-off (in BEQ) equals the lower endpoint of the BEQ distribution around the mean-BEQ value (yDL), represented by the one-sided 95 % confidence interval: ycut-off = yDL – 1.64 × SDRw yDL: mean of bioanalytical results (BEQ) from n ≥ 6 inde-

pendent analyses of samples contaminated at xDL (TEQ) SDRw: standard deviation (BEQ), (within-laboratory reproducibility conditions

Option 2 Same as option 1, but samples are spiked at 2/3 of the ML, or at the AL, and the cut-off (in BEQ) equals the mean of bioanalytical results: ycut-off = y⅔ML or ycut-off = yAL y⅔ML / yAL: mean of bioanalytical results (BEQ) from n ≥ 6 independent analyses of samples contaminated at ⅔ ML, or the AL, respectively, under within-laboratory reproducibility conditions

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In option 2, the scatter of BEQ results around yDL and the sensitivity (calibration slope) of the bioanalytical method are not taken into account. This may lead to more conservative (lower) cut-offs (see Tab. 1) and consequently to increased fractions of false-noncompliant results. Option 3 The BEQ concentration corresponding to ⅔ ML, or the AL, being a rough estimate not based on any measurements may be used as preliminary cut-off provided a false-compliant rate below 5 % and an acceptable rate for falsenoncompliant results are ensured. Its use may lead to increased fractions of false-noncompliant results. 2.3.3 Restrictions to Initial Cut-off Concentrations

bioanalytical results [pg BEQ/g fat]

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Fig. 3 Initial validation: calibration data, regression line (−), confidence interval (--), prediction interval (−−) and cut-off concentration. β = β-error, ML: maximum level for the sum of PCDD/ Fs and DL-PCBs in poultry meat, DL: GC/HRMS decision limit (ML + U, with U = 20 %); details are given in Table 1.

During initial validation, a limited number of spiked samples are used while matrix properties and congener patterns (except for the spiking concentrations) do not vary between samples. Repeatability (RSDr) and within-laboratory reproducibility (RSDRw) at the ML may therefore be well under control and clearly below 20 %, or 25 %, respectively. In some cases, this may lead to a cut-off (in BEQ) close to, or even above, the nominal value of the respective ML (yCut-off ≥ ML). In routine screening, however, a large variety of matrix properties and congener patterns may be encountered in the unknown samples, causing higher variability in BEQ results thus requiring lower cut-offs to avoid false-compliant results. A modified (artificially reduced) cutoff yCut-off, mod shall be established in such cases, based on an assumed RSDRw of 25 %. Precision may be obtained from the calibration data as relative residual standard deviation syx,rel calculated at the DL (for an example, see Tab. 1).

or even < 0.01 %) and an acceptable percentage of false-noncompliant results (around 20 %), depending on sample matrix and the level of contamination. Added concentrations of TCDD were in a range up to 4.2 pg WHO-TEQ/g fat, and up to 1.4 pg WHO-TEQ/g fat for PCB 126 (Tab. 1). Six spiked sample experiments were repeated at each of four distinct concentrations (including level “0”), yielding 24 calibration samples. Details of sample extraction, cleanup and measurement of luciferase activity will be described elsewhere [25]. Slight variations in the percentage of extractable lipids lead to variations in the actual fat-based TEQ concentrations within each of levels 1 through 3. The numerical example in Table 1 enables the interested reader to inspect the necessary calculations and resulting performance parameters in detail.

2.4 Application to Analytical Practice: A comprehensive Example

Aliquots of TCDD and PCB 126 in DMSO were added to 18 portions of 10 g each of “blank” homogenized poultry meat while 6 portions remained “blank”. The sample material was pre-analyzed with GC/HRMS and found to be contaminated with 0.33 pg WHO-PCDD/F-TEQ/g fat and 0.37 WHOPCB-TEQ/g fat (fraction of extractable lipids: 12.5 %). From our experience with more than 1000 confirmed samples of various types of EU-regulated foods it is generally not required to use complex congener mixtures for spiking. Cutoffs obtained from calibration involving just TCDD and PCB 126 at levels around the PCDD/F-ML, and the AL set for DL-PCBs, respectively, work reliably in screening of unknown samples contaminated within the calibrated range, producing a very low percentage of false-compliant (< 1 %,


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3 Quality Control 3.1 Internal Quality Control

Commission Regulation (EU) 2017/644 [14] requires a procedural (or matrix) blank and a sample contaminated at the level(s) of interest (external standard) for control of precision and apparent recovery to be included in each series of samples. A run should be rejected if assay performance or results of QC samples do not comply with defined acceptance criteria. Incurred or spiked blank “reference” samples pre-analyzed by GC/HRMS shall be used [21]. A confirmed blank material showing a low response in the bioassay shall be fortified at the desired level(s). Congener mixtures of PCDD/Fs and DL-PCBs used for sample spiking should ide-

222  Originalarbeiten  « Tab. 1 Comprehensive example: Initial validation and calculation of cut-off concentrations following the calibration and “Quick and Easy” approaches (sample matrix: poultry meat) calibration approach

parameter

calibration data (pg TEQ/g fat, pg BEQ/g fat) level 0 concentrations (GC/HRMS): PCDD/Fs: 0.33 (ML: 1.75) pg TEQ/g fat DL-PCBs: 0.37 (AL: 0.75) pg TEQ/g fat

calibration data (pg TEQ/g fat, pg BEQ/g fat) level 1 added concentrations: TCDD: + 1.00 pg TEQ/g fat PCB 126: + 0.40 pg TEQ/g fat



numerical value

x1, y1

0.70

0.31

x2, y2

0.70

0.98

x3, y3

0.70

0.92

x4, y4

0.70

0.72

x5, y5

0.70

0.59

x6, y6

0.70

0.61

x7, y7

2.01

1.41

x8, y8

2.05

2.61

x9, y9

2.29

2.05

x10, y10

2.16

2.17

x11, y11

2.15

1.98

x12, y12

2.16

1.47

x13, y13

3.63

2.84

x14, y14

3.47

4.07

x15, y15

3.58

3.81

x16, y16

3.64

3.59

x17, y17

3.51

2.78

x18, y18

3.66

2.70

x19, y19

6.15

5.14

x20, y20

6.21

6.93

x21, y21

6.40

6.71

x22, y22

6.36

5.76

x23, y23

6.24

6.23

x24, y24

6.37

4.88

number of calibration points

n

number of replicates on each level

m

6

mean of all concentrations (pg TEQ/g fat)

x

3.18

mean of all results (pg BEQ/g fat)

y

2.97

y-intercept, method calibration line (pg BEQ/g fat)

a

–0.013

slope, method calibration line

b

0.939

coefficient of correlation

24

r

0.9626

ML

3.0

expanded uncertainty (GC/HRMS) (pg TEQ/g fat)

U

0.60

GC/HRMS decision limit DL (pg TEQ/g fat)

xDL

3.60

BEQ concentration corresponding to DL

yDL

3.37

maximum level for PCDD/Fs+DL-PCBs (pg TEQ/g fat)

residual standard deviation (pg BEQ/g fat)

syx

0.5708

relative residual standard deviation at DL (%)

syx,rel

17.0

t-multiplier (α = 1 %, f = degrees of freedom, two-tailed)

tα,f=n-2

sum of squared residuals (SSR) cut-off concentration (pg BEQ/g fat) cut-off concentration (pg BEQ/g fat), modified*

Qxx =

2.819 102.6720

n

Σ (x – x) i=1 i

2

ycut-off

2.6

ycut-off, mod

(2.3)

“Quick and Easy” approach option 1 mean of “level 2” results (pg BEQ/g fat)

yDL

3.30

relative standard deviation (%)

RSDRw

18.1

cut-off (pg BEQ/g fat)

ycut-off

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»  Originalarbeiten  223 Tab. 1 Comprehensive example: Initial validation and calculation of cut-off concentrations following the calibration and “Quick and Easy” approaches (sample matrix: poultry meat) (continued) “Quick and Easy” approach

parameter

numerical value

y⅔ML

1.95

yCut-off

2.0

⅔ML

2.0

option 2 mean of “level 1” results (pg BEQ/g fat) relative standard deviation (%)

RSDRw

cut-off (pg BEQ/g fat)

23.2

option 3 cut-off (pg BEQ/g fat) * exemplary calculation only, not to be applied, because yCut-off < ML

ally be representative for the patterns observed in respective foods. However, since a variety of patterns may be present in unknown samples this seems impractical, except e. g. during contamination incidents where relevant congener fingerprints were previously determined by GC/HRMS. As stated above, spiking with just TCDD and PCB 126 at levels of the PCDD/F-ML, and the AL set for DL-PCBs, respectively, works very reliably in routine screening. However, most important is to ensure that possible differences between TEF and REP values will not lead to an underestimation of the TEQ levels in unknown samples. Internal QC shall ensure that factors determining the extent of uncertainty do not significantly change during the routine use of the bioanalytical method over longer periods of time. Results of procedural/method blanks (or “blank” samples) and positive control samples shall be recorded in QC charts. Procedural blanks should be monitored with regard to the requested minimum difference to the reporting level. Monitoring reference samples contaminated at the level(s) of interest shall ensure that requirements for apparent recoveries of PCDD/Fs, DL-PCBs and for the sum of PCDD/Fs and DL-PCBs are met. Within-laboratory reproducibility for these groups of analytes must be shown not to exceed the tolerance value. Uncorrected results of reference samples expressed in BEQs shall also be monitored over time for evaluation of the performance of the bioanalytical method. The final extracts of one fifth of all routine samples shall be measured both without and with TCDD added at a concentration corresponding to the ML, to check for possible suppression of the AhR induction response by other compounds present in the extract. Results shall be monitored in QC charts. The BEQ value measured for the spiked extract shall be compared to the sum of the BEQ result of the unspiked extract and the spiking concentration (in TEQ). If the BEQ result of the spiked extract is more than 25 % lower than the calculated sum this may be an indication of a potential signal suppression and the respective sample must be submitted to confirmatory instrumental analysis. 3.2 Confirmation of Bioanalytical Results, QC Database Potential non-compliance with a BEQ level above the cut-off value must always be verified by a full re-analysis of the original


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sample using a confirmatory method. Depending on sample matrix and laboratory experience, 2 % to 10 % of the compliant samples should also be confirmed to demonstrate the absence of false-compliant results. This is especially important during the initial phase of a bioassay laboratory and may be reduced with growing experience over time. The importance of GC/ HRMS confirmatory analysis during preparation of QC and reference samples and for follow-up of suspected samples suggests that screening and confirmatory laboratories establish a mutually beneficial collaborative agreement.

BEQ/TEQ data pairs of confirmed noncompliant and compliant samples shall be collected for each sample matrix/matrix group of interest in a QC database. Results from successful participation in inter-laboratory studies may also be included. Bioassay performance shall be re-evaluated after collection of 20 results per matrix, or matrix group. The BEQ/TEQ-ratio should be re-established and compared to that obtained from initial validation. Actual false-compliant and false-noncompliant rates must be calculated. Consequently, initial cut-off concentrations may require adjustment. The rate of false-noncompliant results (α-error) is the fraction of results potentially noncompliant in previous screening but confirmed to be compliant, compared to all results suspected to be noncompliant. Evaluation of the advantageousness of the screening method, however, shall be based on comparison of false-noncompliant samples with the total number of screened samples. This rate must be low enough to render the bioanalytical screening upstream to the GC/HRMS technology beneficial and economically worthwhile. Bioassay performance shall be re-evaluated whenever sufficient new BEQ/TEQ-results for a sample matrix were collected in the database, or for any other relevant reason, e. g. if new results with unusual congener patterns were included, being an on-going process. 3.3 Participation in Interlaboratory Comparison Studies Laboratories can objectively assess their performance and demonstrate reliability of their results through frequent successful participation in proficiency testing (PT) or inter-laboratory comparison (ILC) programs [26], thus demonstrating competency for their analytical discipline and validating their technical training of personnel and the traceability of standards. Continuous successful participation in PT or ILC schemes covering

224  Originalarbeiten  « Tab. 2 Proficiency testing studies organized by the EU-RL for Dioxins and PCBs in Feed and Food (time period: 2008–2017), including 32 relevant sample matrices year study 1 study 2 2008 guar gum fish oil 2009 canned sausage (pig‘s meat) – 2010 mineral clay, compound feed, fish oil, fish meal milk fat, pig‘s fat 2011 grass meal fish, fish oil 2012 pork, lard whole egg, egg powder 2013 feed fat milk powder, milk fat 2014 sepiolite cod liver, fish liver oil 2015 compound feed, sugar beet pulp olive oil, palm oil halibut filet, fish oil 2016 grass meal, dried basil 2017 palm fatty acid distillate bovine liver

the relevant matrices, analytes and concentration ranges is therefore a fundamental requirement for laboratory accreditation [26], and of Commission Regulation (EU) 2017/644 [14] in particular. PT and ILC samples shall cover the most frequent congener patterns representing various sources. To ensure availability within the network of the EU-RL and NRLs for Dioxins and PCBs in Feed and Food, the EU-RL has in fulfilment of its duties following Article 32 of Council Regulation 882/2004 [27] organized and evaluated 19 PT studies including 32 relevant sample matrices since 2008 (Tab. 2). Samples were prepared from regular market food, contamination incidents and other sources and contained at least the 17 PCDD/Fs and 12 DL-PCBs included in the TEF scheme and selected non-dioxin-like (NDL) PCBs 28, 52, 101, 138, 153 and 180, in concentrations around their respective levels of interest. Homogeneity was tested following the provisions in ISO 13528:2015-08 [28] and in The International Harmonized Protocol for the Proficiency Testing of Analytical Chemistry Laboratories [29] by analysing in duplicate 10 portions of each material for individual congeners and WHO-PCDD/F-PCB-TEQ, WHO-PCDD/F-TEQ, WHO-PCB-TEQ, and the sum of NDLPCBs 28, 52, 101, 138, 153 and 180. Participants used their own reference standards and the bioanalytical, physico-chemical and/or confirmatory methods of their choice.

Assigned values were assessed as Huber robust means [30] only of participants’ results obtained from GC/HRMS or GC/MS-MS methods, after excluding extreme outliers and investigating the distribution of the remaining results using histogram for fast visualisation and kernel density estimation. z-Scores providing an appropriate scaling of the difference between a participant’s result and the assigned value for the analyte concentration were calculated as z = (x – xa)/σp from the participant’s result x, the assigned value xa, and σp, the standard deviation for proficiency testing (target standard deviation). σp was defined as 10 % for results expressed in WHO-PCDD/F-TEQ, WHO-PCB-TEQ and WHOPCDD/F-PCB-TEQ, as 15 % for the sum of six NDL-PCBs (PCB 28, 52, 101, 138, 153, 180) and for individual PCDD/F and PCB congeners as 20 %. Z-scores outside the range of ±2 classify the participant’s analytical performance as “questionable” and outside the range of ±3 as “actionable”.

The main criterion for assessment of bioanalytical PT results is the screening method’s ability to reliably identify samples compliant with established legal limits and those samples which are suspected to be noncompliant. However, we introduced “bioassay-scores” to facilitate advanced performance evaluation. Reported BEQ values x are compared with the assigned WHOTEQ values xa calculated from the results of physical-chemical methods.

(x – xa)

Bioassay-score = σ bioassay

For PCDD/F-BEQ, PCB-BEQ and PCDD/F-PCB-BEQ results, the bioassay target standard deviation for proficiency testing σBioassay was set to 20 % equalling the maximum tolerable repeatability (RSDr). A participant’s bioanalytical performance is classified as “questionable” if bioassay-scores are outside the range ±2 and “actionable” outside the range ±3. It should be emphasized here that a bioassay-score within a range of ±2 based on σBioassay = 20 % is a considerable methodological achievement, given the various reasons for possible differences between TEQ and BEQ results as discussed above. Because bioassays cannot identify and quantify individual congeners, direct comparison of bioassay-scores and z-scores may lead to misunderstandings. However, bioassay-scores may serve as a valuable tool to assess and demonstrate the bioanalytical performance characteristics of a particular lab.

4 Reporting Bioanalytical Results

Samples with BEQ results for PCDD/Fs, DL-PCBs, or for the sum of PCDD/Fs and DL-PCBs below the respective level of reporting shall be expressed as “lower than the reporting level”, and the reporting level shall be given. The lipid content of a sample shall be determined and reported if expected to be in a range of 0–2 % and the ML is expressed on fat basis. For other samples, reporting of the lipid content is optional. Samples with a BEQ-result below the respective cut-off value are declared “compliant” with immediate legally binding force; the result is directly reported to the competent authorities. A numerical result may also be given, expressed as PCDD/F-

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»  Originalarbeiten  225 BEQ, PCB-BEQ and/or PCDD/F-PCB-BEQ. Samples with a BEQ-result at or exceeding the respective cut-off are declared “suspected to be noncompliant” and reported together with the measured BEQ-level to the laboratory running the confirmatory method. Those samples must be investigated by follow-up confirmatory analysis producing the required numerical information in TEQ. If the TEQ-result indeed indicates noncompliance, full confirmatory analysis must be repeated. If the result from the second analysis proves noncompliance, the duplicate mean TEQ-value shall be reported.

5 Setting up a Bioassay Lab for Dioxin Screening

When setting up a bioanalytical laboratory several factors may be worth considering for streamlining operations and ongoing success. State-of-the-art equipment, instrumentation, utilities, consumables, computers, and information technology are important prerequisites; however, technical expertise is the most vital resource. Skilled, dedicated staff constitutes what a lab ultimately is or will become. Technicians should be able to perform certain critical tasks (e. g. solvent transfer, raw data processing) that are essential for high quality results. Laboratory facilities must, to the greatest possible extent, be free of Ah-receptor active compounds potentially emitted from walls, floors, surfaces, plastic or rubber parts, packing materials, also from clothing, cremes, perfume etc., or present in solvents, chemicals and materials, affecting bioanalytical results beyond a tolerable extent. Accreditation is non-negotiable, involves demonstrating expertise with specific analytes from relevant matrices and to undergo inspections by accreditation bodies. 5.1 Laboratory Safety Depending on national workplace safety regulations the supervisor of a bioanalytical laboratory to be set up may have to register with the competent authorities before using recombinant rat or mouse hepatoma cells. It is her/his responsibility to make sure that her/his laboratory is in compliance with the requirements of biosafety level 1 (BL1) [31]. Strict adherence to standard microbiological practices and techniques is emphasized. The biological safety cabinet (BSC) is the principal device to be used to provide personnel, environmental, and product (sample extracts, cells) protection from exposure to toxic chemicals (dioxins, PCBs) or contamination (bacteria). Laboratory waste containing recombinant nucleic acid molecules must be inactivated prior to leaving the facility and access to an autoclave in the building to decontaminate such waste must thus be ensured. Additionally, organizational/institutional approved standard operating procedures for handling and disposal of TCDD and related chemicals (PCDD/Fs and PCBs) must be in place.

Before starting any work, German national health and safety regulations in accordance with Article 6 of the Hazardous Substances Ordinance [32] require a workplace-related assessment of hazards and potential risks associated with the chemicals and laboratory operations to be used [33] and that


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the necessary protective measures have been taken [34,35]. Working alone in the lab – outside of earshot and sight of other people – with hazardous substances such as concentrated acids, flammable solvents or toxic compounds such as dioxins and PCBs or solutions thereof containing relevant concentrations is strongly discouraged. In contrast to confirmatory methods, the generation of concentration-response curves requires preparation of TCDD working solutions in DMSO (an effective skin penetration enhancer) over a wide concentration range covering several orders of magnitude. The working solution for the highest calibrator contains 325 ng TCDD in 1 mL DMSO, being up to 10.000 times the concentration found in food sample extracts. As in any research laboratory, if working alone cannot be avoided, additional suitable technical or organisational personal protection measures must have been put in place [36,37] and it is imperative that others are actively aware of the activities being carried out [38–41]. At the very least, a “laboratory buddy system” must be in place and reliably working in practice. 5.2 Everything you need for your Bioassay Laboratory 5.2.1 Apparatus and Instruments Analytical equipment and instrumentation of our lab as listed in Table 3 reflects what will be required for a bioanalytical laboratory that is being started up. Compressed nitrogen gas supply and standard laboratory equipment such as glassware, microliter pipettes, bottle top dispensers and vortex mixers, must also be available. 5.2.2 Cell lines, Consumables, Chemicals and Solvents Recombinant rat or mouse hepatoma cell lines such as H4L1.1c4 rat, H1L6.1c2 mouse or “3rd generation” H4L7.5c3 rat hepatoma cells are suitable for performing CALUX bioassays and are available from the sources given in part 1 of this paper [17]. A complete list of consumables, chemicals and solvents with suppliers, required amounts and per-sample costs (around 35 €, based on current market prices) is available from the authors upon request. This Excel®-based index can be used as a supply inventory to support strategic purchasing management. 5.2.3 Protocols and Spreadsheet for Evaluation of Bioanalytical Results Protocols for extraction of all EU-regulated sample matrices, clean-up of extracts, cell culturing, seeding and exposure of cells, and measurement of luciferase activity, together with a comprehensive Excel® spreadsheet for establishing dose-response curves and evaluation of results based on current legal requirements [14,15] will be available from the authors after publication [25] upon request. 5.3 Getting Started After an initial training phase of cell culturing and establishing concentration-response curves, it might be a good idea to check back e. g. with the cell line supplier on a number of performance parameters such as unspecific assay background, fold induction

226  Originalarbeiten  « Tab. 3 Instrumentation at the EU-RL Bioassay Research Lab device analytical balance (4 decimal places) centrifuge (4000 rpm) CO2-incubator concentration evaporator workstation (e. g. TuboVap II, Biotage) cooler/freezer combination drying oven freezer –25°C (sample storage) freezer –80°C (intermediate storage of cells) high-temperature drying oven (decontamination of glass ware at 435 °C) horizontal shaker inverse microscope (phase contrast) laboratory fume hood liquid nitrogen container (storage of cells, working stock) luminometer (e. g. Centro LB 960, Berthold) metal block thermostat for micro vials (e. g. Evaporator, Liebisch) pH meter powder mixer (e. g. Junior powder mixer 0.5 L, Biomation) safety cabinet shaking water bath ultra turrax dispersing instrument (with 8 dispersing elements) ultrasonic water bath vacuum system (e. g. biovac 104, Ilmvac)

at low concentrations, slope and EC50, before preparing one’s own working stock for storage in a liquid nitrogen container. As a first step in sample analysis, extraction and clean-up of “blank” samples pre-analysed by GC/HRMS is suggested. Each sample extract should be analysed in duplicate, both unspiked and spiked with TCDD and PCB 126 at relevant concentrations e. g. prior to the final reduction of the solvent, to check for matrix background and assay response. Valuable information on recovery and reproducibility in the final step of the analytical procedure can also be gathered. As an intermediate step, spiking after sample extraction may be helpful to gather relevant performance information. Subsequently, full analyses of “blank” and spiked “blank” samples should be performed. For availability of “reference” samples, a sample archive should be set up containing “blank” and incurred samples contaminated in the relevant concentration ranges and covering the EU-regulated matrices of interest. These samples may originate from routine monitoring, from contamination incidents and/or other sources. 5.4 Turn-around Times and Sample Throughput Turn-around times for separate analysis of food samples for PCDD/Fs and DL-PCB-Bs at the Bioassay Research Lab are 1.5

amount 1 1 2 2 3 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1

days for pure oils and fats, and 2.5 days for all other samples (Tab. 4), based on 10 to 12 samples per series, accumulating to approx. 400 samples annually per lab assistant. This was achieved within the scope of method optimization and validation studies, in addition to frequent strategy meetings, discussion of intermediate results, experimenting and testing, and given that the facility was never designed or optimized for highthroughput screening. In a routine bioassay laboratory, however, 20 samples can easily be analysed per week, or 800 samples annually, per lab assistant. In two “high-throughput” NRLs (FASFC, BE and RIKILT, NL) up to 3.500 samples are analysed annually by three to four lab assistants (1.000 samples per assistant). The resulting financial savings when compared to the costs of pure GC/HRMS analysis, are significant [19].

6 Resumé and Outlook Reliable and cost-effective bioanalytical screening methods with a throughput of up to 1 000 samples annually per technical assistant and turnaround times of less than three days implemented upstream of GC/HRMS confirmatory methods are au-

Tab. 4 Turn-around times at the EU-RL Bioassay Research Lab, based on 10 to 12 samples per series and separate analysis for PCDD/F-BEQ and DL-PCB-BEQ (20 samples can be analysed by a lab assistant within a week) analytical steps day seeding of cells, sample homogenization, fast extraction, evaporation to dryness (automated, overnight) 1 determination of lipid content, clean-up on acidic silica and fractionated elution from carbon/celite, reduction of solvents 2 (automated), solvent exchange, exposure of cells lysis of cells, measurement of luciferase activity, evaluation of results (half-day) 3

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»  Originalarbeiten  227 thorized by European legislation. Upon request by the European Commission we evaluated and optimized the performance of the CALUX bioassay for use within European official feed and food control. Previous analytical criteria in force since 2002 were tested, substantially revised and amended. In part 1 of our paper, we introduced the reader to the tasks of the EU-RL Bioassay Research Lab and the CALUX bioassay. New EU-wide standards as regards the nature of bioanalytical results and sample-related analytical requirements were described in part 2. This third and final part focused on requirements laid down in Commission Regulation (EU) 2017/644 of 5 April 2017 relative to assay performance, method validation, quality control, and the reporting of bioanalytical results. A new statistical approach to cut-off concentrations is presented in detail together with two short-cut approaches to be used during certain ad hoc scenarios, and illustrated with a practical example and the necessary mathematical formulae. Analysts interested in applying bioanalytical methods for screening food and feed samples for elevated levels of dioxins and DL-PCBs should be aware of how easily the capability can be integrated into an existing analytical laboratory. Some guidance is given on how to implement a bioassay laboratory for dioxin screening. Details of the related bioanalytical procedures applicable to all EU-regulated food samples, and results from validation studies and quality control will consecutively be published, as well.

Acknowledgements We are grateful to Mrs. Renate Tritschler (routine dioxin GC/ HRMS-lab at CVUA Freiburg) for providing a wealth of EUregulated food samples together with the results from GC/ HRMS analyses. Development of the AhR-based CALUX bioassay was supported by a United States National Institutes of Environmental Health Sciences Superfund Research Grant (P42ES04699). The European Commission’s financial support of the work of the Bioassay Research Laboratory at the EU-RL for Dioxins and PCBs in Feed and Food is gratefully acknowledged.

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reduction of the presence of dioxins, furans and PCBs in feed and food. OJ L 272, 13.09.2014, p 17–18 (2014). [6] EC: Commission Regulation (EU) No 1259/2011 of 2 December 2011 amending Regulation (EC) No 1881/2006 as regards maximum levels for dioxins, dioxin-like PCBs and non dioxin-like PCBs in foodstuffs. OJ L 320, 03.12.2011, p 18–23 (2011). [7] German Environment Agency (UBA): POP-Dioxins Database of the German Federation and Laender. Time trend of dioxin levels in human milk. Available at: www.dioxindb.de/f_umwelt_mensch2. Accessed on 31.03.2018. [8] EC: Commission Directive 2002/69/EC laying down sampling methods and methods of analysis for official control of dioxins and dioxin-like PCBs in foodstuffs. OJ L 209, 06.08.2002, p 5–14 (2002). [9] EC: Commission Directive 2002/70/EC establishing requirements for the determination of levels of dioxins and dioxin-like PCBs in feedingstuffs. OJ L 209, 6.8.2002, p 15–21 (2002). [10] EC: Commission Regulation (EU) No 252/2012 of 21 March 2012 laying down methods of sampling and analysis for the official control of levels of dioxins, dioxin-like PCBs and non dioxin-like PCBs in certain foodstuffs and repealing Regulation (EC) 1883/2006. OJ L 84, 23.03.2012, p 1-22 (2012). [11] EC: Commission Regulation (EU) No 278/2012 of 28 March 2012 amending Regulation (EC) No 152/2009 as regards the determination of the levels of dioxins and polychlorinated biphenyls, OJ L 91, 29.3.2012, p 8–22 (2012). [12] EC: Commission Regulation (EU) No 589/2014 of 2 June 2014 laying down methods of sampling and analysis for the control of levels of dioxins, dioxin-like PCBs and non-dioxin-like PCBs in certain foodstuffs and repealing Regulation (EU) No 252/2012, OJ L 164, 03.06.2014, p 18–40 (2014). [13] EC: Commission Regulation (EU) No 709/2014 of 20 June 2014 amending Regulation (EC) No 152/2009 as regards the determination of the levels of dioxins and polychlorinated biphenyls. OJ L 188, 27.06.2014, p 1–18 (2014). [14] EC: Commission Regulation (EU) 2017/644 of 5 April 2017 laying down methods of sampling and analysis for the control of levels of dioxins, dioxin-like PCBs and non-dioxin-like PCBs in certain foodstuffs and repealing Regulation (EU) No 589/2014, OJ L 92, 06.04.2017, p 9–34 (2017). [15] EC: Commission Regulation (EU) 2017/771 of 3 May 2017 amending Regulation (EC) No 152/2009 as regards the methods for the determination of the levels of dioxins and polychlorinated biphenyls. OJ L 115, 04.05.2017, p 22–42 (2017). [16] German Environment Agency (UBA): POP-Dioxins Database of the German Federation and Laender (2018). Available at: www.dioxindb.de. Accessed on 31.03.2018. [17] Haedrich J, Stumpf C, Denison MS: Bioanalytical Screening for Dioxins and PCBs in EU-regulated Foods: New Analytical Criteria adopted by the European Union in Commission Regulation (EU) 2017/644. Part 1: Introduction. Deut Lebensm Rundsch 114 (3), 99–108 (2018). [18] Hoogenboom LAP et al.: The CALUX bioassay: current status of its application to screening food and feed. Trend Anal Chem 25, 410–420 (2006). [19] Detailed calculations are available from the authors upon request. [20] Goeyens L et al.: Discrepancies between bio-analytical and chemo-analytical results have a non-negligible message. Organohal Compd 72, 964–967 (2010). [21] Haedrich J, Stumpf C, Denison MS: Bioanalytical screening for Dioxins and PCBs in EU-regulated foods: New analytical criteria adopted by the European Union in Commission Regulation (EU) 2017/644. Part 2: sample analysis criteria and the nature of bioanalytical results. Deut Lebensm Rundsch 114 (4), 167–175 (2018). [22] De Lean AP, Munson J, Rodbard D: Simultaneous analysis of families of sigmoidal curves: application to bioassay, radioligand assay, and physiological dose-response curves. Am J Physiol 235, E97-E102 (1978).

228  Originalarbeiten  « [23] Elskens M et al.: CALUX measurements: statistical inferences for the dose-response curve. Talanta 85, 1966–1973 (2011). [24] The GC/HRMS routine dioxin lab at CVUA Freiburg (DE) frequently estimates the expanded measurement uncertainty U = k × u to be 15 to 20 % (k = 2). [25] Haedrich J, Stumpf C, Denison M: Publication in preparation (2018). All necessary protocols and a spreadsheet for evaluation of results will be available from the authors upon request after the relevant procedures have been published. [26] EN ISO/IEC 17025:2018-03: General requirements for the competence of testing and calibration laboratories. International Organization for Standardization, Genève, Switzerland (2018). [27] EC: Regulation (EC) No 882/2004 of the European Parliament and of the Council of 29 April 2004 on official controls performed to ensure the verification of compliance with feed and food law, animal health and animal welfare rules. OJ L 165, 30.04.2004, p 1–141 (2004). [28] ISO 13528:2015-08: Statistical methods for use in proficiency testing by interlaboratory comparison. International Organization for Standardization, Genève, Switzerland (2015). [29] Thompson M, Ellison SLR, Wood R: The international harmonized protocol for the proficiency testing of analytical chemistry laboratories (IUPAC Technical Report). Pure Appl Chem 78(1), 145-196 (2006). [30] Huber PJ: Robust statistics. John Wiley & Sons, Inc., New York. Republished in paperback, 2004. 2nd ed., Wiley (2009). [31] EC: Directive 2000/54/EC of the European Parliament and of the Council of 18 September 2000 on the protection of workers from risks related to exposure to biological agents at work. OJ L 262, 17.10.2000, p 21 (2000). [32] Federal Institute for Occupational Safety and Health (BAuA): Hazardous Substances Ordinance of 26 November 2010. BGBl I p 1643 (2010). Available at: www.baua.de. Accessed on 31.03.2018. [33] Deutsche Gesetzliche Unfallversicherung e. V. (DGUV): DGUV Regulation 1, accident prevention regulation – principles of prevention, section 3: assessment of working conditions, documentation requirements and duty to provide information (2014). Available at: www.dguv.de. Accessed on 31.03.2018. [34] Federal Institute for Occupational Safety and Health (BAuA): Ausschuss für Gefahrstoffe; Technische Regeln für Gefahrstoffe (TRGS) 526, Gefährdungsbeurteilung und Substitutionsprüfung (2008). Available at: www.baua.de. Accessed on 31.03.2018. [35] European Agency for Safety and Health at Work: The practical prevention of risks from dangerous substances at work. (2003). Available at: osha.europa.eu/en/publications/reports/106/view. Accessed on 24.04.2018. [36] Deutsche Gesetzliche Unfallversicherung e. V. (DGUV): DGUV Information 213-850/-851, Working safely in Laboratories – Basic Principles

Der Autor dieses kompakten Fachbuchs schildert Bedeutung, Vorkommen und Klinik der häufigsten Nahrungsmittelunverträglichkeiten sowie deren Diagnostik und Therapie. Praxistipps, Fallbeispiele, Differenzialdiagnosen und weitere Zusatzinformationen liefern dem Leser das Rüstzeug für die kompetente Beratung seiner Patienten. Gratis-Download des Anamnesefragebogens für alle Interessierten unter: www.Online-PlusBase.de

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Claudia Stumpf Chemical-technical Assistant; Bioassay Research Laboratory at the EU-RL for Dioxins and PCBs in Feed and Food, Freiburg (2006 to 2017). Optimization and validation of efficient bioanalytical screening methods, contributions to the new EU-wide bioanalytical standards, training of scientists from National Reference Laboratories and research institutions, internships at the Free University of Brussels (BE) and at the University of California Davis (US).

and Guidelines, section 4.3.3: Working alone (2015). Available at: bgi850-0.vur.jedermann.de/index.jsp. Accessed on 31.03.2018. [37] Deutsche Gesetzliche Unfallversicherung e. V. (DGUV): DGUV Regulation 1, accident prevention regulation – principles of prevention, section 8: hazardous tasks (2014). Available at: www.dguv.de. Accessed on 31.03.2018. [38] The American Chemical Society, ACS Committee on Chemical Safety: Safety in academic chemistry laboratories - 8th Edition (2017). Available at: www.acs.org. Accessed on 31.03.2018. [39] National Academy of Sciences, Committee on Prudent Practices in the Laboratory, Board on Chemical Sciences and Technology, Division on Earth and Life Studies: Prudent practices in the laboratory - handling and management of chemical hazards. National Academies Press, Washington, DC (US) (2011). Available at: www.nap.edu/read/12654/chapter/1. Accessed on 31.03.2018. [40] American Chemical Society: Alaimo RJ (ed.): ACS Handbook of Chemical Health and Safety. Oxford University Press, New York, NY (US) (2001). Available at: www.acs.org. Accessed on 31.03.2018. [41] United States Department of Labor, Occupational Safety and Health Administration: Occupational Safety and Health Standards, Standard number 1910.1450 App A – Toxic and Hazardous Substances (2017). Available at: www.osha.gov. Accessed on 31.03.2018.

Von Axel Vogelreuter. 2012. XII, 230 Seiten. 41 farbige Abbildungen. 34 farbige Tabellen. Mit Anamnesefragebogen. Gebunden. € 42,- [D]. ISBN 978-3-8047-2938-4 E-Book PDF. € 42,- [D]. ISBN 978-3-8047-3102-8 E-Book E-PUB. € 42,- [D]. ISBN 978-3-8047-3116-5 E-Books sind erhältlich unter www.dav-medien.de

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