Current Challenges and Advancements in Residue Analytical Methods 2019003821, 9780841234130, 9780841234161, 0841234132

Citation preview

Current Challenges and Advancements in Residue Analytical Methods

ACS SYMPOSIUM SERIES 1300

Current Challenges and Advancements in Residue Analytical Methods Elisabeth A. Schoenau, Editor Golden Pacific Laboratories, Fresno, California

Tao Geng, Editor Bayer CropScience, Chesterfield, Missouri

Ryan Hill, Editor Covance Bioanalytical Services LLC, Indianapolis, Indiana

Norma L. Houston, Editor Corteva AgriScience, Agric. Div. of DowDuPont, Johnston, Iowa

Manasi Saha, Editor BASF Corporation, Research Triangle Park, North Carolina

Xiao Zhou, Editor Corteva AgriScience, Agric. Div. of DowDuPont, Indianapolis, Indiana Sponsored by the ACS Division of Agrochemicals

American Chemical Society, Washington, DC

Library of Congress Cataloging-in-Publication Data Names: Schoenau, Elisabeth A., editor. | American Chemical Society. Division of Agrochemicals. Title: Current challenges and advancements in residue analytical methods / Elisabeth A. Schoenau, editor (Golden Pacific Laboratories, Fresno, California) [and five others] ; sponsored by the ACS Division of Agrochemicals. Description: Washington, DC : American Chemical Society, [2019] | Series: ACS symposium series ; 1300 | Includes bibliographical references and index. Identifiers: LCCN 2019003821 (print) | LCCN 2019011757 (ebook) | ISBN 9780841234130 (ebook) | ISBN 9780841234161 (print) Subjects: LCSH: Chemistry, Analytic--Technique. | Sample preparation (Chemistry) Classification: LCC QD75.22 (ebook) | LCC QD75.22 .C87 2019 (print) | DDC 543/.19--dc23 LC record available at https://lccn.loc.gov/2019003821

The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ANSI Z39.48n1984. Copyright © 2019 American Chemical Society All Rights Reserved. Reprographic copying beyond that permitted by Sections 107 or 108 of the U.S. Copyright Act is allowed for internal use only, provided that a per-chapter fee of $40.25 plus $0.75 per page is paid to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. Republication or reproduction for sale of pages in this book is permitted only under license from ACS. Direct these and other permission requests to ACS Copyright Office, Publications Division, 1155 16th Street, N.W., Washington, DC 20036. The citation of trade names and/or names of manufacturers in this publication is not to be construed as an endorsement or as approval by ACS of the commercial products or services referenced herein; nor should the mere reference herein to any drawing, specification, chemical process, or other data be regarded as a license or as a conveyance of any right or permission to the holder, reader, or any other person or corporation, to manufacture, reproduce, use, or sell any patented invention or copyrighted work that may in any way be related thereto. Registered names, trademarks, etc., used in this publication, even without specific indication thereof, are not to be considered unprotected by law. PRINTED IN THE UNITED STATES OF AMERICA

Foreword The purpose of the series is to publish timely, comprehensive books developed from the ACS sponsored symposia based on current scientific research. Occasionally, books are developed from symposia sponsored by other organizations when the topic is of keen interest to the chemistry audience. Before a book proposal is accepted, the proposed table of contents is reviewed for appropriate and comprehensive coverage and for interest to the audience. Some papers may be excluded to better focus the book; others may be added to provide comprehensiveness. When appropriate, overview or introductory chapters are added. Drafts of chapters are peer-reviewed prior to final acceptance or rejection. As a rule, only original research papers and original review papers are included in the volumes. Verbatim reproductions of previous published papers are not accepted.

ACS Books Department

Contents Preface .............................................................................................................................. ix

General Residue Method Considerations 1.

Elements of Method Design ..................................................................................... 3 Elisabeth A. Schoenau

2.

The Use of Radiolabeled Compounds for Residue Method Development ........ 17 Phillip Cassidy

3.

From Regulatory Considerations to Globalization of Residue Analytical Methods ................................................................................................................... 23 Manasi Saha

4.

Evaluation of Extraction Efficiency of Residue Analytical Methods ................ 29 James J. Stry, Xiao Zhou, and Venkat Gaddamidi

Testing of Proteins in Agricultural Biotechnology 5.

Data Trends in Protein Analysis for Safety Assessments ................................... 49 Rong Wang, Ryan C. Hill, and Norma L. Houston

6.

Endogenous Allergens from Genetically Modified Soybean: Background, Assessment, and Quantification ............................................................................ 73 Tao Geng, Yongcheng Wang, Lucy Liu, Bin Li, and Ryan C. Hill

Challenging Matrix Types 7.

Challenging Matrixes: Bee-Related Matrixes: Challenges and Techniques for Residue Analysis .............................................................................................. 97 R. Stephen Andrews

8.

Creation of a High-Throughput Method for the Analysis of Thiamethoxam in Bee Matrixes ..................................................................................................... 117 Joe Warnick

9.

A Stepwise Approach to Method Development ................................................. 127 Steven C. Moser

vii

Editors’ Biographies .................................................................................................... 135

Indexes Author Index ................................................................................................................ 139 Subject Index ................................................................................................................ 141

viii

Preface The purpose of this book is to discuss some of the most recent and difficult residue analytical challenges in the current agricultural product registration process. Knowledge of chemistry and physical science, in general, is rapidly expanding. Although a single book cannot survey the entire scope of this subject matter, we hope this text provides a modern foundation of knowledge to new agricultural analytical chemists as well as an update for those needing to refresh their knowledge in key and newly challenging areas. This book is unique because rather than focusing on academic scientists studying agricultural chemistry, it brings together a large cross-section of industry scientists, who are developing methods and concepts on a daily basis to ensure the fulfillment of the most current regulatory requirements of analytical methods. This book should prove useful to those actively conducting research in academia or industry as well as those who want to understand the subject matter to improve their decision-making processes regarding the science discussed herein. In August 2017, the ACS publishing group approached the co-organizers of the 2017 fall ACS symposium entitled “Advances in Residue Analytical Methods: Innovation, Current Status, and Future Prospects” and requested that they consider submitting a proposal for a symposium series book. This book is the result of that request. However, it is interesting to note that there were originally three symposia, which were folded into one symposium making for a discussion of a variety of diverse but similarly related topics. As a result of this combination, this book contains three sections, which loosely parallel the three separate symposium topics and discuss the material related to that presented at the fall 2017 National Meeting on August 20, 2017 in Washington, D.C. The first of the three sections, “General Residue Method Considerations” contains four chapters covering method design, radiolabeling in method development, regulatory considerations, and extraction efficiency evaluation for residue analytical methods, and serves as a broad foundation of relevant general information when preparing and executing residue analytical methods for safety assessment and regulatory registration. The second section, “Testing of Proteins in Agricultural Biotechnology”, contains two chapters focusing primarily on the analysis of endogenous proteins as well as current regulatory trends in proteins expressed from genetically modified crops. Finally, the third section, “Challenging Matrix Types” discusses specific analytical challenges, which have been encountered when addressing new ecological safety concerns. General approaches for tackling these analytical challenges are included. The layout of the book is conducive both to being read in order of publication and by reading chapters, (or sections of the book) separately. ix

For inquiries regarding the subject matter, please feel free to contact the corresponding author for the chapter of interest. The editors would like to thank all of the authors for their contributions and hard work bringing this project to fruition. In addition, we, the book editors, would like to mention that the author for the final chapter of the book is no longer with us. He passed away June 1, 2018. We have decided to honor Steven Moser’s accomplishments and contributions to pesticide analysis by publishing his chapter posthumously. A colleague of his graciously agreed to assist the chapter through the peer-review and copy-editing processes. Further edits were made by the editors of this book. As a result, the chapter, although not the originally submitted text verbatim, encompasses the ideas and concepts submitted by Steven Moser. On his professional LinkedIn profile, we found he posted the following statement: “I am always looking to invent or improve the things that surround me.” We hope by publishing his chapter we have helped him to achieve this goal; furthermore, we hope that anyone that reads his chapter may be inspired to do the same. Elisabeth A. Schoenau Senior Chemist Golden Pacific Laboratories 4720 W. Jennifer Avenue, Suite 105 Fresno, California 93722, United States Tao Geng Protein Chemist and Allergist Monsanto Company 700 Chesterfield Parkway West Chesterfield, Missouri 63017, United States Ryan Hill Senior Lead Scientist Covance Inc. 8211 SciCor Drive Indianapolis, Indiana 46214, United States Norma L. Houston Research Scientist Corteva AgriScience, Agriculture Division of DowDuPont 8325 NW 62nd Avenue Johnston, Iowa 50131, United States Manasi Saha Senior Research Scientist BASF Corporation 26 Davis Drive Research Triangle Park, North Carolina 27709, United States x

Xiao Zhou Associate Scientist Corteva AgriScience, Agriculture Division of DowDuPont 9330 Zionsville Road Indianapolis, Indiana 46268, United States

xi

General Residue Method Considerations

Chapter 1

Elements of Method Design Elisabeth A. Schoenau* Golden Pacific Laboratories, LLC, 4720 W. Jennifer Avenue, Suite 105, Fresno, California 93722, United States *E-mail [email protected].

This chapter focuses on the description and comparison of the elements of method design and development. Method development is predestined. The targeted analyte or analytes are chosen and a method that can quantitatively determine the amount of those analyte(s) present in a given matrix type is developed. Method design focuses on preparing the method for everyday use, considering factors such as ease of use, robustness, and ruggedness. Method design targets the practicality and application of a method and its subsequent validation, transfer, and everyday use. Acceptance criteria for method development as well as practicality criteria for method design are both discussed. This chapter loosely follows the outline of a presentation given at the fall 2017 National Meeting on August 20, 2017, in Washington, D.C. Additional applicable material regarding the subject matter has been added.

Pragmatism in Method Design There are many components necessary to undertaking analytical chemistry work and method design. One of the most important of these is to approach each analytical obstacle with pragmatism. Being pragmatic is “dealing with things sensibly and realistically in a way that is based on practical rather than theoretical considerations” (1). It is essential to approach method development and design with pragmatism because analytical chemists are faced with the task of solving difficult problems. Whether chemists are just starting their careers or have a considerable amount of experience, there are a large number of tools and information at their disposal. Theoretically supported decisions © 2019 American Chemical Society

are routinely made during method development and are understood through the lens of well-known chemistry concepts. Practicality, however, largely depends on the experience of the chemist surrounding the specific type of analytical challenge. Because each novel problem may require a unique approach, the amount of background practice with each problem type may be lacking. As a result, oftentimes chemists over rely on the theoretical underpinnings of their experience and use the most complex solutions to solve troublesome problems. Instead, the most elegant solution to a difficult problem is often the most concise, the most direct, the simplest.

What Is Method Design versus Development? In this chapter, method development is considered as the steps taken to generate a preliminary method. Although the discussion here focuses on method development and design through the scope of development of residue analytical methods (specifically for small molecules), these concepts are applicable to a wide scope of different types of method development. The resulting preliminary residue analytical method produced from method development may already meet many analytical acceptance criteria typically assessed during method validation. However, addressing elements of method design adds a different set of acceptance criteria, taking into consideration the practicality of the method. This addition of extra criteria shifts the focus to encompass not only a standard that defines the acceptance of the data (e.g., accuracy and precision) but also the suitability or acceptance of the method of obtaining the data (e.g., sample throughput). The generally accepted purpose of a residue analytical method is to provide a proven, documented set of instructions for the determination of target analyte(s) in specified matrix types over a particular concentration range. Method development addresses this definition. However, a more thorough (and more practical) definition of the purpose of a residue analytical method, which can be met through the process of method design, is to provide a well-documented, easily understood, set of complete instructions for the determination of target analyte(s) in (potentially) several different specified matrix types over a particular concentration range in as many samples as possible, as quickly, painlessly, and inexpensively as possible, while still producing quality data that meet the objectives of the acceptance criteria of the data being generated. One of the places this concept was first explored was with the development and validation of the “quick, easy, cheap, effective, rugged, and safe” (QuEChERS) procedure (2), highlighting several practical measures of a successful analytical method.

Method Validation Discussion The pharmaceutical industry has a well-developed set of method validation acceptance criteria. An exhaustive amount of regulatory guidelines have been put forth to ensure the generation of accurate and precise data to support the safe use of pharmaceuticals. This is both beneficial and prohibitory. From the 4

agrochemical perspective, thorough guidelines addressing each individual aspect of method validation criteria are helpful, because the goal is to produce the best data possible and support the safe use of pesticides. However, if the agrochemical industry guidelines for residue analytical methods were as exhaustive as the pharmaceutical guidelines, the agility now possible in method development and design would likely be diminished. In addition, pharmaceutical method development and design largely focus on a small subset of matrix types, whereas agrochemical methods may cover matrix types from head lettuce (3) to tobacco (4), and laminate flooring (5) to insects as food (6). Due to the wide abundance of guidelines for the pharmaceutical industry, and the resulting discussion in literature, many of the ideas discussed herein are shared from pharmaceutical industry discussions. Just as the concepts of small molecule analysis can be applied to bioanalytical method validation and protein analysis, concepts from the pharmaceutical industry can be related for use in method development and design. Regulatory Guideline Considerations

Figure 1. GLP studies hierarchy of applicable guidelines. There is a wealth of literature available on whether method validation is being conducted (7), how method validation is being conducted, and the acceptance criteria used to address if a method can be considered “validated.” As stated by M.J. Ruiz-Angel et al. in their paper “Are Analysts Doing Method Validation in Liquid Chromatography?,” an extremely important consideration to be made when beginning method development (and by association, method design) is “When validating a method, it is compulsory to indicate the followed guideline, since the validation criteria should be clearly established for each parameter” (7). This initiates the question: what are the differences between applicable sets of regulatory guidelines for validation studies? This is addressed in a different chapter in this book. However, once the applicable regulatory guideline is identified, there is a hierarchy to the evaluation of the validation 5

criteria. In studies that must adhere to the good laboratory practice standards (GLPs, consideration must be given to performing the development, design, and validation of the residue analytical method accordingly (if the validation is conducted under GLPs). A hierarchy, which applies to the guidelines relating to GLP residue study conduct, is described in Figure 1. Figure 1 shows that the GLPs are the overarching guidelines to follow. After the relevant GLP guidelines, those items not defined by the GLP guidelines are determined by the applicable regulatory guidelines. After the regulatory guidelines is the study protocol, followed by the residue analytical method (if the method validation study is complete), and finally facility standard operating procedures (SOPs). Once the applicable regulatory guideline is chosen, each validation criteria listed in the applicable regulatory guideline must be addressed in determining whether the method was successfully validated. Although many of the guidelines may list information to be addressed in the method or method report, they do not always provide a definition of how these criteria are met. Method Development Acceptance Criteria The following is a list of acceptance or performance markers that are generally present in the applicable guidelines. After each criterion is a brief definition and an example or explanation of how to assess acceptability. The following definitions for specificity, accuracy, precision, linearity, range, detection limit, quantitation limit, robustness, and ruggedness are from a review authored by Chandran and Singh (8). It is important to note that different regulatory guidelines may focus on different criteria, and it is imperative that the data, once produced, meet the acceptance criteria of the specified regulatory guideline.

Accuracy Definition: Agreement between measured and real value (8). Example/Explanation: Accuracy is often assessed by the percent recovery of a given analyte. For most residue analytical method guidelines, an acceptable recovery range is 70 to 120% (9–11) with means in between 70 to 120% (10) or 70 to 110% (11) (depending on the regulatory guideline) at each fortification level. Santé et Consommateurs (SANCO) guidelines specify different accuracy and precision criteria based on the limit of quantitation (LOQ) (11).

Precision Definition: Agreement between a series of measurements (8). Example/Explanation: For most residue analytical method guidelines, an acceptable coefficient of variance (CV, or relative standard deviation, RSD) is less than or equal to ± 20% at each fortification level (9–11). 6

Linearity Definition: Proportionality of measured value to concentration (8). Example/Explanation: Linearity is sometimes confused with the linearity over a given range. Linearity is the proportionality of the response to the analyte concentration. The measured value in relation to the concentration may be linear over limited ranges. This is assessed by examining the fit of the line of the calibration curve through least linear squares analysis or by evaluating the correlation coefficient of the calibration curve. For mass selective detectors, the relationship of the response in relation to the concentration tends to be less proportional at extremely low concentrations, whereas UV detectors may be linear over wider ranges.

Range Definition: Concentration interval where method is precise, accurate, and linear (8). Example/Explanation: The range of a residue analytical method is the range of concentrations over which the determined residues are precise, accurate, and the calibration curve is linear.

Detection Limit (Also Known as Limit of Detection [LOD]) Definition: Lowest amount of analyte that can be detected (8). More specifically, the quantity of analyte signal that can be reliably distinguished from noise. Example/Explanation: There are multiple ways to determine the LOD and may be specifically dependent on the matrix type and associated regulatory guideline. For example, from 40 CFR 136 Appendix B (12), for water matrix types, first an estimate of the method detection limit (MDL) is made based on the instrument response, resulting in a signal-to-noise ratio in the range of 2.5 to 5, the concentration of 3 times the standard deviation of replicate measurements of the analyte in reagent grade water, the region of the curve where there is a significant change in sensitivity, or instrumental limitations. Following the initial estimation of the MDL, a standard is prepared at 1 to 5 times the estimated MDL in clean reagent water or other water type (if the matrix to be tested is different from clean water). From this prepared sample, seven aliquots are taken through the entire method process. From that point, the standard deviation of the determined analyte values is calculated and an MDL is determined. However, this process is iterative, so multiple method attempts (using seven replicates) may be made for an accurate MDL. 7

Another approach sometimes used, especially in crop residue analytical methods, is to use the lowest calibration standard analyzed to back-calculate the detection limit. This is most defensible when the lowest calibration standard is dependent on a specific set criterion (i.e., in a facility SOP) for the lowest calibration standard concentration, such as setting the concentration at a level where the signal-to-noise ratio is about 3:1 or 5:1, and so forth. The LOD will always be lower than the LOQ. Some methods specify that “no recorded response” be reported as “not detected” (ND), whereas residue/response detected between the LOD and LOQ is sometimes expressed as “estimated.” Response that is recorded less than the LOD is sometimes reported as ND and sometimes as less than LOD. Reporting estimated residues between the LOD and LOQ may be advantageous to improve dissipation plotting or for use when the data are for risk assessment. Values may be reported differently in a given study, depending on the use for the data. Regardless of the process used to determine the LOD, it should be clearly described in the written residue analytical method, as it is generally not acceptable to set the LOD arbitrarily in the absence of objective criteria.

Quantitation Limit (Also Known as Limit of Quantitation [LOQ]) Definition: Lowest amount of analyte that can be reliably measured or quantitated. Example/Explanation: This is sometimes defined as the lowest level successfully validated. Finding the LOD and LOQ for a given method in a given matrix can be iterative, depending on the needs of the residue analytical method. Sometimes it is set based on a regulatory need (i.e., oftentimes it is set at a maximum of 10 ppb in crops). However, if the requested LOQ is unachievable, adaptation of the method may be undertaken to reach the necessary LOQ, and/or a higher level may need to be established. At times, instead of using the lowest level validated, a statistical approach is taken to determine if a lower LOD and LOQ are achievable. For example, a statistical procedure defined by Roy-Keith Smith (13) is as follows

t0.99 = the one-tailed statistic at the 99% confidence level for n replicates. S = the standard deviation of recovery results from n samples fortified at the estimated LOQ. However, once the statistical method is employed, a varying set of LOQs may result. At times, it is most practical to establish an LOQ and an LOD that encompass all matrix types validated to indicate to the end user of the method what is achievable for all validated matrix types rather than establishing unique levels for each matrix type and each analyte. This approach is likely preferable, unless the targeted maximum allowable LOQ was not achieved for all analytes in all matrix types. 8

Selectivity Definition: According to O. Gonzalez et al. (14), “Selectivity is the measure of the extent to which an analytical method can determine an analyte without interference from other compounds.” They go on to explain that a method is specific only when a method is perfectly selective. This is slightly in conflict with the following definition of specificity. This speaks to the disagreement in various literature sources on the definition and way to measure the different acceptance criteria.

Specificity Definition: Ability to measure desired analyte in a complex mixture (8). Example/Explanation: Due to the conflicting definition between the two previously mentioned references (8, 14), it is important to relate to the reader that, at times in agrochemistry, due to the complexity and number of different matrix types a residue analytical method may cover, although a method may be selective, it is oftentimes not specific. However, the most critical thing to recognize about the lack of specificity for a given analyte(s) or matrix (es) is that it should be documented! Method Design Practicality Criteria Other qualities that are germane to generating practical methods but that are sometimes neglected in discussions about method development and subsequent validation are as follows.

Robustness Definition: Ability to remain unaffected by small changes in parameters (8). Example/Explanation: Adjusting the pH of a buffer, a temperature, or other alterable parameter incrementally up or down and checking where the method no longer functions tests the robustness of the method steps. The upper and lower limits of the parameter where the data quality is affected are the end of the range over which the method is robust for that parameter.

Ruggedness Definition: Reproducibility under normal but variable laboratory conditions (8). Example/Explanation: Ruggedness is a measure of a method’s transferability and is routinely tested through the independent laboratory validation process (9–11). Normal but variable laboratory conditions 9

may include different lot numbers and sources of solvents as well as distinction in technique between analysts and a multitude of sources of common laboratory equipment (i.e., manufacturers, model numbers, types, etc.). For example, most laboratories will not have exactly the same centrifuge, homogenizing equipment, HPLCs, or mass spectrometers. However, as long as equivalent equipment is available at the laboratory a method is transferred to, a method that successfully produces acceptable results using that equipment is rugged.

Ease of Use Definition: Minimization of the difficulty of method steps. Example/Explanation: This parameter specifically encompasses how skilled or experienced an analyst needs to be in order to complete the method. When designing a method, care must be taken to make the method only as complex as it must be. The more sophisticated the method is, the more technically skilled the analyst needs to be. For example, during method design, the designing chemist may consider whether to employ a solid phase extraction (SPE) cleanup or a liquid-liquid extraction. For an experienced chemist, either procedure may be technically acceptable. However, for a technician, one procedure may be less challenging than the other. Regulatory guidelines may make mention of analysts’ experience. For example, see U.S. EPA OCSPP 850.6100 section (e)(1)(ii) of the Ecological Effects Test Guidelines (10) or 40 CFR 136 Appendix B (12). Another approach to reducing the complexity of a method is through automation. For example, use of an automated SPE workstation (i.e., a programmable robotic SPE unit) may eliminate the need for a chemist with experience in performing SPE. In addition, it may improve the precision between sample results and/or the sample throughput. However, the ultimate end use of the method must be evaluated against the potential cost. With this in mind, an automated SPE workstation may be considered prohibitively expensive and potentially difficult to program. As a result, the use of automation for more routine enforcement or screening purposes is more logical than its use for preregistration residue analysis. That is, the sample throughput must be weighed against cost. A compromise for reduction of complexity in relation to this example may be the use of in-line SPE when possible. Regardless, the least technical procedure that produces accurate and precise results in a robust and rugged manner should be chosen when possible.

Length of Method Definition: Complexity and time needed to complete the method. 10

Example/Explanation: When a method is long and complex, there are more chances for errors to be made. Furthermore, methods that are lengthy may cause analyst fatigue, resulting in mistakes. As a result, when a long procedure cannot be avoided, it is desirable to determine and document acceptable stopping points and storage of the samples and/or sample extracts during the break in extraction.

Cost Definition: Cost to complete sample analysis (oftentimes this is measured in cost per sample). Example/Explanation: If a method is longer (takes more time), has more method steps, requires more technical competence, or uses materials that are more expensive than the average method, the cost to complete sample analysis will be higher. When considering the downstream cost of transferring a method out to a contract research organization, or conducting the method in-house, it is important to consider all the factors affecting cost. Other practical method parameters listed here can also directly affect cost.

Safety Definition: Considerations about how to make a method safe to execute. Example/Explanation: If a method is difficult to execute safely, not only will it be more expensive to conduct, it will also likely be more difficult to conduct quickly and correctly. In addition, it is imperative to mention any unusual hazards in the method. The method author should especially consider relating hazards to human or environmental health regarding unusual reagents and/or equipment.

Documentation Definition: Necessary documentation to prepare an acceptable written method. Example/Explanation: At a minimum, the regulatory guideline governing the generation of the method should be referred to for suggested report formats. Whenever possible, it is preferable to structure a report as the reference guideline suggests so that all necessary elements are included. When method documents are formatted as suggested in the guidelines, all necessary instructions and caveats are likely to be included, which helps guarantee that the method will be accepted during review to meet data requirements.

11

Matrix Diversity Definition: Validation of the method on more than one matrix type. Example/Explanation: For a water method, consideration should be given to validating the method (especially enforcement methods) on drinking water (i.e., tap, ground, bottled), surface water (i.e., irrigation, river, lake), and wastewater. For crop methods, it is important to consider validating the method on several matrixes including high and low water content matrix types. If the method is being developed for use in a residue program, the best practice is to validate the method on the most traditionally difficult matrix types to analyze. This decreases development after the method is established, thus reducing costs. However, the method developed for the most difficult matrix will likely be exhaustive. It is not necessary, nor desirable, to use this method for all matrix types. The shortest path to the goal should be used while reducing the total number of different methods for the same set of analytes in different matrixes.

Necessary Equipment Definition: Cost and availability for the equipment necessary to conduct a method. Example/Explanation: The question to ask here is, “Do most labs have this equipment?” Other relevant questions are “If the lab does not have this equipment, is it expensive or difficult to obtain?,” “Can the equipment be substituted?,” and “What is the advantage to using this equipment over other equipment?” Sometimes the uniqueness of the abilities of the equipment will outweigh cost and availability concerns.

Throughput/Time and Scalability Definition: The number of samples that can be analyzed over a given period. Reducing the sample size and extraction volume to increase sample throughput. Example/Explanation: Sample throughput directly relates to the complexity and cost of a method. High sample throughput generally relates to a less complex method and reduces analysis costs. It is typically beneficial to increase sample throughput. However, when increasing sample throughput, care should be taken not to compromise data quality. Sometimes changes to improve sample throughput (including scaling of the extraction) can be employed after a method has been validated. Depending on the scope and type of change, however, revalidation of the method may become necessary. As a result, addressing sample throughput during method design prior to validation is suggested. 12

Generation of Waste (Environmental Concerns) Definition: The amount of waste that is generated and the level of hazard from generated waste as well as cost of disposal. Example/Explanation: There are many ways to address environmental concerns stemming from residue analysis. The first is to reduce waste where possible. In the event an extraction can be changed from macroscale to even semimicroscale, a considerable amount of waste can be avoided. First, less reagent use results in less upfront cost for the chemicals and backend cost of disposal. Second, changing an extraction from macroscale to semimicroscale or microscale can increase sample throughput, further reducing costs (and potentially analyst fatigue). See Figure 2 for an example of the effect of scaling an extraction on the generation and management of process waste. Finally, avoiding solvents that are in different hazard classes may decrease cost and safety risk. For example, avoiding a chlorinated solvent when a flammable solvent is equally functional may reduce health risks.

Figure 2. Potential effect of method scaling on waste generation. 13

Documentation of Expected Difficulties (Writing a Well-Documented Method) Definition: The thoroughness and clarity with which the entire method (especially difficult method steps) and expected problems with the method is documented. Example/Explanation: A method should provide all the details necessary to execute it successfully. This is perhaps the most imperative practicality criterion to meet. It is extremely important to approach fulfilling this criterion from an outside point of view. A corollary can be drawn from an exercise that young writers learn in grade school. The exercise directions are to write out how to make a peanut butter and jelly sandwich. After the student’s descriptions are complete, a volunteer attempts to prepare the sandwich by following the instructions as they are read aloud. Most students (even most adults) participating will not have indicated that the jars need to be opened, or that a knife or other implement is needed for spreading the jelly and peanut butter, and so on. Preparing a residue analytical method should follow a similar thought process. There are some assumptions that are safe to make, namely that someone trained as at least a laboratory technician will execute the method. However, other assumptions are not acceptable. For example, a method may specify, “Reconstitute the residue in 5 mL of water” after the solvent has been evaporated to dryness. Literally, this may be read “Add 5 mL of water to the dried residues.” As a result, the analyst would add the indicated amount of water and continue to the next step. However, according to a different analyst, at another facility, following their facility SOPs, this may mean that the water should be added and then the sample container should be sonicated for 2 min. When writing an analytical method, the author cannot assume that the sonication will be performed unless it is explicitly written into the method. Similarly, assumptions can be made that an analyst should know how to complete specific tasks, but the tasks need to be completely detailed. An example of describing a critical detail may involve a variable step that is affected by the method’s ruggedness range. For example, the method step “Add 2 mL of concentrated hydrochloric acid” may be acceptable when a method is used for the analysis of drinking water. However, if the pH is critical, the buffer capacity of the water could affect whether or not the method will work as expected. As a result, a better way to state this step may be “Add 2 mL of concentrated hydrochloric acid to bring the sample to a pH of less than 5.” Writing the method step with a targeted pH or pH range lets the reader know that the pH adjustment is critical for the method to function. An even better way to write this step may be “Add 2 mL of concentrated hydrochloric acid to the sample. Check the pH of the sample. If the pH is not less than 5, add additional concentrated hydrochloric acid in 0.5-mL increments until a pH of less than 5 is reached.” When the method is complex, consideration should be given to using a graphical representation such as flowcharts for different matrix analysis. Another useful tool may be truncated flowcharts that reference specific sections of the method to better direct the analyst. 14

Well-documented methods and safety data sheets (SDS) should have a lot in common. They should have a harmonized format due to regulations. Each laboratory should have its own method template that satisfies all applicable guidelines for the applicable method type. Continuity from one method to the next aids the reader in locating important information. Example chromatograms are necessary for a well-documented method. Critical points in the method must be pointed out. When a step must be executed in a very particular way, it should be specified. There is a balance between creating a method that is clear and concise while still including enough description of details to ensure success. It is often better to err to the side of including too many details rather than too few! Intended Use Considerations When approaching method design, it is imperative to know what requirements are most important in regards to the method that is being generated. Common methods for pesticide residue analysis are screening methods, data collection methods, and enforcement methods. Each individual method type is used to generate a unique set of information. It is advantageous to adjust the design criteria to meet the goals of the method. For example, for a screening method, increasing sample throughput should be a top goal. However, it is important to recognize that it is not always feasible or necessary to meet every acceptance or practicality criteria. Regardless, it is always a good idea to consider how well a method meets each of the criteria and to improve the method fit where possible.

Conclusion In conclusion, assessing criteria of both method acceptance and practicality is essential to producing a residue analytical method that best serves its intended purpose. Method development and design are both critical processes. Both must be implemented to produce successful methods.

References 1. 2.

3.

4.

Google. Search for “Pragmatic.” www.google.com (accessed May 27, 2018). Anastassiades, M.; Lehotay, S.; Stajnbaher, D.; Schenck, F. Fast and Easy Multiresidue Method Employing Acetonitrile Extraction/Partitioning and “Dispersive Solid-Phase Extraction” for the Determination of Pesticide Residues in Produce. J. AOAC Int. 2003, 2, 412–431. Chen, M.; Chen, J.; Syu, J.; Pei, C.; Chien, H. Insecticide Residues in Head Lettuce, Cabbage, Chinese Cabbage, and Broccoli Grown in Fields. J. Agric. Food Chem. 2014, 62, 3644–3648. Mayer-Helm, B.; Hofbauer, L.; Muller, J. Method Development for the Determination of Selected Pesticides on Tobacco by HighPerformance Liquid Chromatography-Electrospray Ionisation-Tandem Mass Spectrometery. Talanta 2008, 74, 1184–1190. 15

5.

6.

7.

8. 9.

10.

11.

12.

13. 14.

Starr, J.; Gemma, A.; Graham, S.; Stout, D., II A Test House Study of Pesticides and Pesticide Degradation Products Following an Indoor Application. Indoor Air 2014, 24, 390–402. Poma, G.; Cuykx, M.; Amato, E.; Calaprice, C.; Focant, J.; Covaci, A. Evaluation of Hazardous Chemicals in Insects and Insect-Based Food Intended for Human Consumption. Food Chem. Toxicol. 2017, 100, 70–79. Ruiz-Angel, M.; Garcia-Alvarez-Coque, M.; Berthod, A.; Carda-Broch, S. Are Analysts Doing Method Validation in Liquid Chromatography? J. Chromatogr. A 2014, 1353, 2–9. Chandran, S.; Singh, S. Comparison of Various International Guidelines for Analytical Method Validation. Pharmazie 2006, 62, 4–14. U.S. EPA. OPPTS 860.1340 Residue Chemistry Test Guideline, 1996, Residue Analytical Method. https://www.epa.gov/test-guidelinespesticides-and-toxic-substances/series-860-residue-chemistry-testguidelines (accessed May 31, 2018). U.S. EPA. OCSPP 850.6100 Ecological Effects Test Guideline, Environmental Chemistry Methods and Associated ILV. https://www.epa.gov/test-guidelines-pesticides-and-toxic-substances/series850-ecological-effects-test-guidelines (accessed May 31, 2018). European Commission. European Commission (Directorate of General Health and Consumer Protection) (2010); Guidance Document on Pesticide Residue Analytical Methods Document; SANCO/825/00/rev. 8.1/16/11/2010; https://ec.europa.eu/food/sites/food/files/plant/docs/ pesticides_ppp_app-proc_guide_res_post-reg-cont-monitor.pdf (accessed May 31, 2018). Definition and Procedure for the Determination of the Method Detection Limit—Revision 2. Code of Federal Regulations; Appendix B to Part 136, Title 40; 2017. Smith, R. Handbook of Environmental Analysis, 4th ed.; AOAC International, 1999. Gonzalez, O.; Encarnacion Blanco, M.; Iriarte, G.; Bartolome, L.; Itxaso Maguregui, M.; Alonso, R. Bioanalytical Chromatographic Method Validation According to Current Regulations, with a Special Focus on the Non-Well Defined Parameters Limit of Quantitation, Robustness and Matrix Effect. J. Chromatogr. A 2014, 1353, 10–27.

16

Chapter 2

The Use of Radiolabeled Compounds for Residue Method Development Phillip Cassidy* Exponent, Washington, District of Columbia 20036, United States *E-mail: [email protected].

Radiolabeled test materials can be used as an aid in residue method development. Residue chemistry has become more difficult due to the need to detect a greater number of compounds and sometimes more challenging compounds at lower detection limits. When used in method development, radiolabeled compounds can offer time savings and can be very helpful in understanding issues that may affect recovery. Radiolabeled compounds free the developer from matrix effects and other issues that can complicate the steps needed to perform method development.

© 2019 American Chemical Society

The importance of the job of the residue chemist is always being challenged. Years ago it was assumed that advancements in technology would reduce the time needed for residue method development. While advancing technology has improved residue testing, it has also required extensive method development to convert many of the traditional methods to new methods of instrumentation. Often, the ability to detect pesticides at lower levels with new advancements in instrumentation has in turn driven regulators to demand lower detection limits. The following summarize many of the other potential reasons for continued method development: • • • • •

Robustness Analysis for additional metabolites Conversion of older methods to faster, more precise methods of analysis Methods for new matrix types Conversion of methods to meet the demand of smaller sample sizes (e.g., bee testing)

Residue testing is expected to grow at a rate of 7% per year from 2016 to 2022 (1). This increase is due to the implementation of more stringent food safety regulations, increasing chemical contamination outbreaks, and a significant jump in international trade of food materials, which then increases the need for establishing import tolerances for those crops. In addition, there has been a rise in food detection for contaminants other than pesticide residues that can also result from horticulture and animal husbandry. Method development with radiolabeled molecules can provide faster and more efficient means of developing new methods. Before elaborating on the reasons radiolabeled compounds can be advantageous, it is important to describe what is meant by the term radiolabeled in the context of this chapter. Radiolabeling refers to the incorporation of a radioisotope as a replacement for an atom within the molecular formula. Most common, and exclusively described in this chapter, is the replacement of a carbon-12 atom with the radioactive carbon-14 atom. The carbon-14 atom is preferable for several reasons including its long half-life, 5,730 years. Because of this long half-life, the amount of radioactivity associated with the molecule is not likely to change over time. This means that analysts will see relatively the same amount of radioactivity during the course of their experimentation. Furthermore, the carbon-14 isotope is easily shielded with glass and the threat of adverse exposure to laboratory personnel is minimal as compared to other radiolabeled isotopes; therefore, the use of carbon-14 is relatively safe. Carbon-14 has a specific activity of 62.5 mCi per mmole. A molecule containing one carbon-14 will be expected to have a specific activity in the range of 50–60 mCi per mmole if all the molecules contain this same atomic substitution. Using rather inexpensive equipment, this level of specific activity is sufficient for detecting low levels that are achievable with liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS) or other highly sensitive equipment. As an example of a radiolabeled molecule, Figure 1 shows the incorporation of the radioisotope within the structure of a molecule. 18

Figure 1. Radiolabeled cyprodinil. In the molecular structure to the left, each of the carbons in the benzene has a radioisotope incorporation. In the molecular structure to the right, the radioisotope incorporation only occurs at one specific carbon atom location. Each approach is acceptable, but the more carbons labeled, the greater the specific activity and the better the ability to detect the molecule. For each molecule, though, there is a point at which the specific activity can be too great and radiolytic decay of the molecule is possible. This point is where degradation of the molecule is enhanced by the isotopic activity of the molecule. For each molecule, the point at which radiolytic decay occurs is different, but as a general rule a specific activity of 40–60 mCi/mmol is considered safe and radiolytic decay is avoided. Incorporation of one radiolabeled carbon generates a specific activity of 50–60 mCi per mmole. However, incorporation of the radiolabeled atom is not inclusive to every molecule. When radiolabeling is attempted, a mixture of labeled and unlabeled molecules is produced. Furthermore, for benzene labeled molecules, not every carbon within the ring is labeled. As a result, multiple carbons (up to 6) can contain a 14C atom, but the overall specific activity can still be within the range mentioned (40–60 mCi/mmol). In terms of regulatory assessment, radiolabeled molecules are needed for parent active compounds for plant, animal, and soil metabolism studies. Therefore, radiolabeled compound inventories are often already available to assist with residue method development. For the residue chemist, lower quantities of the radiolabeled material are required relative to the metabolism studies, resulting in minimal impact to inventories needed for other studies. Labs using radioisotopes need a Nuclear Regulatory Commission (NRC) license issued by the state where they reside or by the federal government. Because the policies regarding nuclear materials vary from state to state, an explanation of what is needed is outside the scope of this chapter. The primary reason for having radiolabeled standards for residue method development is that they provide a determination of mass balance, or that the accounting of the radioactivity is mathematically possible. Using a liquid scintillation counter for determining the radioactivity in extraction solutions (liquids) or an oxidizer for determining the radioactivity in solids can quickly provide information regarding the ratio of the amount of radioactivity contained in the solid (unextracted) and liquid (extracted) phases. These forms 19

of instrumentation are much less expensive than other, more sophisticated instruments yet show very sensitive detection. Unlike nonradiolabeled experiments, the radioactivity associated with the tagged molecule resides can be determined. This knowledge enables the method developer to understand whether the radioactivity is being extracted. Furthermore, at each step in an extraction sequence, the developer can determine where losses occur and where the radioactivity may have been lost to waste solvent fractions or unextracted residue, or retained by glassware or other solid surfaces. By following the radioactivity added by fortification, an understanding of where that radioactivity resides following extractions, purification or other procedures can be determined with extremely high accuracy. In other words, fortification can occur prior to each step of an extraction to optimize elution from a solid phase extraction cartridge or other ensuing cleanup steps. Because the determination of radioactivity is unique to the radiolabel, no matrix effects, including enhancement or suppression, are possible. Naturally occurring 14C abundance is on the order of 1 or 1.5 atoms per 1012 carbon atoms, and therefore naturally occurring 14C abundance produces negligible error. Furthermore, high-performance liquid chromatography detectors that are specific to radioisotopes provide a clean and easy-to-understand picture devoid of matrix. As an example, see Figure 2. When radiolabeled detection is further paired with LC-MS/MS or gas chromatography—mass spectrometry/mass spectrometry analysis, the combination becomes a powerful tool for the residue method developer. In the Q1 detector, a single 14C atom results in mass additions of +2 (enrichment from 12C to 14C). A characteristic pattern develops between the ratio of the molecule without the radioisotope enrichment and those at +2 amu with the radioisotope enriched peak. Ring-labeled molecules will show a characteristic peak pattern of the non-isotope enriched mass and then masses at +2, +4, +6, +8, +10, and +12 amu, because each specific molecule has different populations of the radioisotope enrichment at different positions within the ring. This sequence of peaks (based on the amu populations) and their relative ratios to each other and the nonradiolabeled molecule provide a fingerprint for identification. This LC-MS/MS pattern will allow the method developer to determine if a compound is degrading (hydrolyzed or oxidized) during the extraction process. For example, if all of the radioactivity is carried through the extraction process but recovery values are still low, it may be that the compound has been chemically altered by the sequence of extractions and purifications. The mass trace allows an understanding of what degradation molecule (and the relative abundance) is generated. The radiolabeled pattern of enrichment that was evident in the parent molecule can be used to identify those compounds generated from the parent molecule. Similarly, relying on the characteristic enrichment pattern that will be present, the formation of cysteine or glutathione conjugates from animal matrixes or glucoside conjugates from plant matrixes during method development can be determined. It also allows discovery of the formation of adduct complexes (usually Na or Ca) during the extraction process. Such information on degradation, conjugation, or adduct formation is difficult to gather from nonradiolabeled method development. Typically, method development 20

without radiolabeled materials would simply show low recoveries and would involve a significant amount of effort in trying to elucidate the reasons for the low recovery. This task may be as arduous as trial and error to determine which steps of the extraction resulted in low recovery and by pouring over the mass spectra to see if any of the Q1 masses might constitute molecules formed from the parent molecule.

Figure 2. Typical radio chromatogram versus an ultraviolet (UV) trace chromatogram. However, there are issues that can obscure the information gathered during method development with radioisotopes. For one, chlorine atoms in the molecule can complicate the mass patterns observed in the mass detector. Chlorine with a mass of 35 has a natural abundance of 24.23% as the chlorine-37 atom. Therefore, molecules with one or more chlorine atoms will show the enrichment of +2 (or multiples of 2 depending on the number of chlorines present) because of the natural 21

abundance of the 37Cl. As chlorine is fairly common in molecular structures, it frequently complicates the mass pattern the analyst would hope to interpret. Other issues can be identified through the use of radioisotopes, but radioisotopes may provide little information on solutions to the issues. As an example, some compounds, such as the molecule bromoxynil, become bound to soil very quickly and complicate the methods needed to optimize the extraction. In field studies, approximately 76% of the molecule can become irreversibly bound to the soil after approximately 70 days from application (2). While method development with a radiolabeled compound would allow the analyst to quickly understand where the compound resides, it does little to suggest how to release this bound residue. The use of 14C molecules will likely help analysts in method development in the future. New compounds composed of complex natural products present challenges to the method developer because of their sheer size and the many possibilities for chemical reactivity on the molecule. Likewise, development of RNA interference insecticides, peptides, and proteins for crop protection makes determination of the specific compound difficult without a radiolabeled tag. Lastly, very small amounts of food adulterants (nitrates, acrylamide, aflatoxins, and mycotoxins) present many issues to the developer, because there are no readily available methods for analyzing these compounds. As mentioned at the beginning of this chapter, monitoring of these food contaminants will likely accelerate in response to new food inspection regulations. This chapter has attempted to explain radiolabeled compounds as they relate to residue method development. It has also described the potential advantages if method development utilizes an isotope-enriched compound. Method developers can use this information to cope under the current residue landscape in which molecules are being advanced with many challenges, the least of which is detection at ever-increasing smaller levels.

References 1.

2.

Markets and Markets. Pesticide Residue Testing Market Worth 1.63 Billion USD by 2022. https://www.marketsandmarkets.com/PressReleases/ pesticide-residue-testing.asp (accessed May 31, 2018). Rosenbrock, P.; Munch, J. C.; Scheunert, I.; Dörfler, U. Biodegradation of the Herbicide Bromoxynil and Its Plant Cell Wall Bound Residues in an Agricultural Soil. Pestic. Biochem. Physiol. 2004, 78 (1), 49–57.

22

Chapter 3

From Regulatory Considerations to Globalization of Residue Analytical Methods Manasi Saha* BASF Agricultural Solution Center, BASF Corporation, 26 Davis Drive, Research Triangle Park, North Carolina 27709, United States *E-mail: [email protected].

The focus of this chapter is to discuss trends, requirements, and challenges associated with the development of analytical methods for residue analysis that can be used globally. As regulatory environments for crop protection chemicals are changing continuously, the requirements for analytical methods for residue analysis are also changing globally, especially for those in the United States and European Union. The crop protection industries are continuously searching for new ways to meet guideline requirements while in parallel, trying to develop efficient, cost effective residue analytical methods. Given the disparity in global requirements, it is a challenging task for the industry, both from a time and technical perspective, to develop methods for global use. This chapter will describe in detail some of the differences in regulatory guidelines.

Discussion Residue analytical methods are developed and validated to be used globally, covering many regional guidelines. Therefore, there are tremendous demands for residue analytical methods to provide high sample throughput for both data generation and enforcement purposes. The bigger challenge is to develop and validate methodologies that fulfill guidelines for several countries. The global analytical method requirements, including low limits of quantitation (i.e., 30 ppt for water methods), extraction efficiency, matrix effects, isomeric separation, and enforcement compatibility, must be considered when developing methods to be used globally. © 2019 American Chemical Society

In general, residue analytical methods are developed for both data generation and enforcement purposes. Data generation methods are usually used for risk assessments to estimate human exposure (i.e., dietary and occupational) and ecotoxicology exposure (i.e., groundwater, soil, and sediment). Enforcement methods are used by monitoring laboratories for screening residues in food and feed stuffs. Selection Criteria for Analyte Inclusion in Residue Analytical Methods The analytes included in the residue analytical methods for plant and animal matrixes are the major metabolites present at and above 10% of the total radioactive residue (TRR) observed in plant and animal metabolism studies using 14C-labeled active ingredients. Similarly, analytes to be included in soil methods are the major degradates that have toxicological and/or ecotoxicological significance. These major degradates should be present at all time during the study conduct period at and above 5% and 10% of the total applied radioactivity (TAR) using the parent compound in environmental fate metabolism studies conducted with 14C-labeled active ingredients for the European Union and United States, respectively. Generally, the most relevant studies used for the selection of the major degradates are the aerobic soil metabolism, hydrolysis, and photo degradation studies. Other studies such as anaerobic soil metabolism or water sediment studies may also produce degradates of interest depending on the target matrix for analysis. The analytes included in the enforcement method are generally those included in the residue definition. Guidelines for Residue Analytical Method Validation Methods for Plant and Animal Sample Analysis Data generation methods (pre-registration) are developed to analyze samples collected from several raw agricultural commodities (RAC) studies, which are conducted depending on the use pattern of the active ingredient of interest. The residue values from the RACs are then used for dietary risk assessments to estimate human exposure (i.e., dietary and occupational). Enforcement methods (postregistration) are also provided by the registrants for each active ingredient and are used to routinely monitor food and feed products for pesticide residues. The monitoring is generally done by regulatory bodies to check for compliance with established maximum residue limits (MRLs) and to assess consumer exposure to pesticides. Validation for enforcement and data generation methods is conducted to cover global guidelines (i.e., in the United States (1) and European Union (2)). Enforcement methods are used by monitoring laboratories and conducted under multiple guidelines in both the United States (1), and European Union (3). There are some differences between U.S. and EU guidelines for method validation in both study design and requirements, as presented in Table 1. Most 24

of the global methods use the EU guideline study design under which U.S. study design is covered.

Table 1. Guidelines and Study Design Differences for Method Validation in Plant and Animal Matrixes for the United States and European Union Country

U.S.

Guideline

Study Design (Per matrix type)

data generation and enforcement

OPPTS 860.1340 (1)

1 reagent blank 2 controls 2 replicates at limit of quantitation (LOQ) or proposed tolerance 2 replicates at least ten times LOQ or higher fortifications to encompass the highest residue

data generation

SANCO/3029/99 rev. 4 (11/07/00) (2)

Method/Purpose

EU enforcement

1 reagent blank 5 controls 5 replicates at LOQ 5 replicates at SANCO/825/00 rev. 8.1 ten times LOQ or higher (Nov 16, 2010) (3) fortifications to encompass the highest residue

Methods for Environmental Sample Analysis A soil method is required to determine the residue from soil samples collected from a terrestrial field dissipation study. This type of study typically measures the dissipation of active ingredients, as well as the rise and fall of its relevant degradation products. A water method is required for ground water monitoring, enforcement of the drinking water limit, and post-registration control of surface water. For methods used for the analysis of environmental samples (soil and water), one residue analytical method is usually developed for both data generation and enforcement purposes. There are not significant differences in the study design requirements between the United States and European Union for environmental matrices. In the United States, the guideline OCSPP 850.6100 (4) is used for both data generation and enforcement methods. On the other hand, in the European Union, separate guidelines are used for data generation and enforcement methods. Guideline differences between the United States and European Union are shown below in Table 2.

25

Table 2. Guidelines and Study Design Differences for Method Validation in Environmental Matrixes for the United States and European Union Country

U.S.

Guideline

Study Design (Per matrix type)

data generation and enforcement

OCSPP 850.6100 (4)

1 reagent blank 5 controls 5 replicates at limit of quantitation (LOQ) 5 replicates at ten times LOQ

data generation

SANCO/3029/99 rev. 4 (11/07/00) (2)

enforcement (for water only)

SANCO/825/00 rev. 8.1 (Nov 16, 2010) (3)

Method/Purpose

EU

1 reagent blank 5 controls 5 replicates at limit of quantitation (LOQ) 5 replicates at ten times LOQ

Methods for Air Sample Analysis An analytical method for air is required both in the European Union and United States if the active ingredient is volatile. The air method is used for monitoring the exposure of operators, workers, and/or bystanders. An analytical method for air is developed primarily for the parent compound. Additional analytes are included in the air method if they are volatile, and if spray drift and particles associated with the active substance and/or relevant metabolites would cause harmful exposure. For conducting the air method validation, SANCO/3029/99 rev. 4 (11/07/00) (2) is mainly used globally as a specific guideline from the United States is not available. Method validation is required only for the data generation method.

Guidelines for Residue Analytical Method Independent Laboratory Validations Independent laboratory validations (ILV) are mainly conducted for the methods used for enforcement (post-registration). In the European Union, an ILV is mandated for all enforcement methods for plant, animal, and water matrices. A soil method ILV is not required. In the United States, all enforcement methods for plant and animal matrices require an ILV. For soil and water, both data generation and enforcement methods require an ILV according to the OCSPP 850.6100 (4) guideline. An air method ILV is not required by United States and European Union guidelines.

26

Conclusions In conclusion, it is challenging for registrants to develop and validate methods to satisfy requirements for global use. The industries and monitoring laboratories are continuously looking for ways to improve and streamline processes to fulfill the required guidelines. The ideal situation would be to have standardized global guidelines for method validation and residue analysis.

References 1.

2.

3.

4.

U.S. EPA, National Service Center for Environmental Publications (NSCEP). Residue Chemistry Test Guideline, 19961, Residue Analytical Method; 1996; https://nepis.epa.gov/ (accessed 05/15/2018). European Commission (Directorate of General Health and Consumer Protection). Guidance Document on Pesticide Residue Analytical Methods Document; SANCO/3029/99 rev. 4 (11/07/00); 2000; https://ec.europa.eu/ food/sites/food/files/plant/docs/pesticides_ppp_app-proc_guide_res_prereg-cont-monitor.pdf (accessed 05/15/2018). European Commission. European Commission (Directorate of General Health and Consumer Protection). Guidance Document on Pesticide Residue Analytical Methods Document; SANCO/825/00/rev. 8.1/16/11/2010; 2010; https://ec.europa.eu/food/sites/food/files/plant/docs/pesticides_ppp_appproc_guide_res_post-reg-cont-monitor.pdf (accessed 05/15/2018). U.S. EPA National Service Center for Environmental Publications (NSCEP). Ecological Effects Test Guideline, OCSPP 850.6100 Environmental Chemistry Methods and Associated ILV; 2012; https://nepis.epa.gov/ (accessed 05/15/2018).

27

Chapter 4

Evaluation of Extraction Efficiency of Residue Analytical Methods James J. Stry,1 Xiao Zhou,*,2 and Venkat Gaddamidi1 1FMC

Agricultural Solutions, Stine Research Center, Newark, Delaware 19711, United States 2Corteva Agrisciences, Agriculture Division of DowDuPont, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States *E-mail: [email protected].

The validation of residue analytical methods is critical for agrochemical risk assessment and enforcement purposes. In addition to utilizing fortified control samples to demonstrate analytical performance, the efficiency of an extraction procedure needs to be evaluated using incurred residue samples. Using incurred residues in these studies establishes that the residues can be adequately released from the plant during the extraction procedure. This chapter explores three approaches for determining if a method sufficiently extracts incurred residues. The approaches discussed are: the analysis of crop samples treated with radiolabeled compounds, bridging experiments using field residue samples, and using excised plants and hydroponics to generate incurred residue samples when other options are not available.

Introduction The validation of residue analytical methods is critical for agrochemical risk assessment and enforcement purposes, and is also required for a product’s registration (1–4). The method validation typically includes the analysis of fortified control samples to demonstrate analytical performance as well as an assessment of the extraction method’s efficiency using incurred residues. The analytical validation and evaluation of concurrent recoveries is accomplished © 2019 American Chemical Society

using experiments where a known amount of the analyte is added to a control sample. The sample is then extracted, and the extract is purified as needed. Finally, the cleaned-up sample is analyzed using highly selective techniques such as LC/MS/MS. This experiment demonstrates whether or not the concentration of the analytes in an extract can be accurately and precisely determined. However, it does not demonstrate that the residues available in the incurred tissue samples can be quantitatively released into the extract. Pesticide residue could be left unextracted in the sample and can result in an underestimation of the residue level. Therefore, it is critical to validate the extraction procedure’s efficiency to demonstrate that the residue incurred by the sample tissues can be accurately determined. The extraction efficiency validation has gained more and more attention by regulatory authorities and has become a hot topic in the residue analytical validation area (4, 5). This chapter discusses how to determine if the extraction method used in a residue analytical method can efficiently extract the available incurred residues. The discussions and examples presented are focused on crop samples as representative matrices. It is important to note that extraction efficiency testing is also required for animal tissue and environmental samples (soil and sediment) (4, 5). Much of what we discuss in this chapter can be applied to the analysis of animal tissue and environmental samples. There are multiple ways to demonstrate that an extraction method adequately transfers the compounds of interest from the crop into the solvent. For new development compounds, plant metabolism studies are conducted using 14C labeled active ingredients. The main purposes of the metabolism study include identification of major components of the terminal residues and elucidation of the chemical’s metabolic pathway in the treated plants (3, 6, 7). The radiolabeled active ingredient is applied to the plants at a rate and growth stage close to the proposed label. The raw agricultural commodities (RAC) from the plants treated with the radiolabeled test compound are collected, the residue extracted, and the radiolabel used to trace and characterize/identify the metabolites using radiotracer techniques. The goal of the metabolism study is to characterize and/or identify greater than 90% of the total radioactive residue (TRR) in the tissues of interest when possible (6, 7). It is commonly accepted that it may not be possible to achieve this goal in many circumstances (5). For example, some components of the residue can become bound to the crop matrices and then cannot be extracted without the destruction of the analyte(s) of interest (5). Nonetheless, with this challenging goal, exhaustive efforts are usually made in the metabolism study to extract as much residue as possible. These often involve the use of rigorous, solvent intensive, and time-consuming extraction methods. Conducting these extractions is not an issue for a metabolism study where a limited number of samples are analyzed. If the extraction procedure in the residue analytical method is the same as the extraction procedure used in the radiolabeled metabolism studies, an extraction efficiency study is not required. However, the metabolism extraction method is usually not suitable for residue data collection where numerous samples are analyzed to support multiple field trials, decline trials, and crop rotation studies. This issue is amplified for enforcement methods where 30

monitoring laboratories are required to screen multiple samples for many active ingredients in a single day (8, 9). To analyze field samples or to monitor residues in food, a less time-consuming and more environmentally friendly extraction procedure must be developed. Ideally, the extraction should remove the entire available residue, be completed in less than one hour, use less than 50 mL of solvent per sample, and minimize the opportunity for sample contamination. Since the metabolism extraction has been developed and demonstrated to extract the entire available residue, it is used as a measuring stick to which the residue extractions are compared. Demonstrating that the residue and the metabolism extraction methods remove similar amounts of the total radioactivity is not sufficient because it does not show that all of the compounds of interest are extracted at a comparable level (4). For residue data collection methods, the comparison must be made for each analyte included in the risk assessment residue definition. For enforcement methods, the comparison must be made for each analyte included in the enforcement residue definition. Therefore, the extracts must be analyzed using a radiochemical HPLC (High-Performance Liquid Chromatography) method to separate and quantify the individual components of each residue definition (4). Metabolism studies are expensive and require large amounts of radiolabeled material. For registered products, viable radiolabeled metabolism samples may no longer be available. In this case, field residue samples can be used to bridge the residue extraction method to a previously radiovalidated extraction method (10). The residue extraction procedure may be compared to the metabolism extraction method or to a radiovalidated residue extraction method. This approach requires treated field residue samples with measurable (greater than 0.01 mg/kg) incurred residues. The incurred field residue samples need to be homogenized and extracted using the metabolism, and the residue extraction methods and the results compared. The extracts will be analyzed using a selective detection method, such as LC/MS/MS, and the concentration of the residues of interest determined. Since the objective of the study is to demonstrate extraction efficiency and not to validate the residue method, extracts may be analyzed without conducting any purification steps. Analyzing the extracts without any purification may necessitate the use of matrix matched standards or internal standards to correct for any ionization effects in the LC/MS/MS detector (11–13). Using samples with significant residues (greater than 0.1 mg/kg) is preferred because small losses, or small variations in the detector response, will not have a large impact on the result of the analysis. These samples also afford the potential for a greater dilution factor, and therefore minimize potential ionization effects (11–13). To compensate for variability in the sample homogenization, extract handling, and the analyte detection, the comparison is done using five replicates for each extraction method. If field residue samples with incurred residues are not available, another option is to generate incurred residue samples using excised plants or hydroponically grown plants. This approach is more cost effective than conducting field residue trials or using large amounts of radiolabeled material to grow a full plant. Hydroponically grown plants are especially useful when demonstrating extraction efficiency in multiple crop groups, which is currently required in the European 31

Union (EU) (1). Depending on availability, radiolabeled or non-radiolabeled material can be used. When radiolabeled material is used, the extracted residue can be analyzed using the metabolism study approach previously outlined. When non-radiolabeled material is used, a bridging approach may be employed. In this chapter, we will provide examples of each approach and discuss their advantages and disadvantages. The examples discussed are intended to be representative studies, and will be illustrated by the following molecules: oxathiapiprolin, indoxacarb, and chlorsulfuron. Depending on the chemistry of the active ingredient tested, modifications to these approaches may be necessary.

Demonstrating Extraction Efficiency Using Radiolabeled Incurred Residue Metabolism Samples The active ingredient in the fungicide Zorvec™ is oxathiapiprolin (13). Zorvec™ is registered for use on many crops, including vegetables, leafy greens, and tobacco. Radiolabeled metabolism studies in plants indicate that oxathiapiprolin can produce the metabolites coded RZB20, E8S72, and WR791. The structures for oxathiapiprolin and the metabolites are provided in Figure 1. Radiolabeled metabolism studies were conducted using lettuce, grapes, and wheat plants. The plant samples generated during the radiolabeled metabolism study were used to demonstrate extraction efficiency of oxathiapiprolin (QGU42) and the metabolites RZB20, E8S72, and WR791. The plant fractions selected for the extraction efficiency study were lettuce forage (leaves), grape berries, wheat grain, and wheat straw. Lettuce, grape berries, wheat grain, and wheat straw were selected because they contained quantifiable residues of the active ingredient or the metabolites. They also represent three of the four EU crop groups, lettuce (high water content), grape berries (high acid content), and wheat grain and straw (dry) (1). When radiolabeled metabolism samples are available, this is the preferred approach to demonstrate extraction efficiency (3). The objective of this work was to demonstrate extraction efficiency for the method used to generate quantitative field residue data for risk assessment. An additional goal was to test the extraction efficiency of the two commonly used multi-residue methods, the DFG S 19 (L00.00-34) method and the QuEChERS method (14, 15). Updates to the EU guidelines now require extraction efficiency data for the proposed enforcement methods. The DFG S 19 method and the QuEChERS methods were tested because multi-residue methods are preferred as enforcement methods and validation studies indicate that oxathiapiprolin and the metabolites can be analyzed using these methods. The extraction procedures used by each method are summarized in Table 1 (14, 15). As discussed, the metabolism study demonstrated that the extraction method removes all unbound residue from the samples; therefore, it is the standard to which the other extraction procedures were compared.

32

Figure 1. Chemical structures of oxathiapiprolin and the metabolites RZB20, E8S72, and WR791.

Table 1. Extraction Method Summaries Method

Apparatus/ Equipment

Sample Size

Metabolism

Homogenizing Probe

25 g

2 × 125 mL acetonitrile 2 × 125 mL 3:1 acetonitrile:water

Residue

Geno/Grinder

5g

1 × 16 mL 3:1 acetonitrile:water 2 × 12 mL 3:1 acetonitrile:water

QuEChERS

Wrist Action Shaker

5g

10 mL water for straw only 1 × 5 mL acetonitrile

DFG S 19

Homogenizing Probe

25 g

Up to 100 mL water 1 × 200 mL acetone

33

Solvent System

Table 2. Percent Radioactivity Extracted from the Crop Samples Percent Total Radioactive Residue Extracted

Percent Extracted Compared to the Metabolism Extractiona

Commodity

Metabolism Methodb

Residue Method

QuEChERS Method

DFG S 19 Method

Residue Method

QuEChERS Method

DFG S 19 Method

Lettuce Foliage

89.2/88.4

87.7

71.8

91.3

98

81.2

103.3

Grape Berries

91.7/93.0

94.8

72.1

91.1

103

77.5

98.0

Wheat Grain/ Strawc

71.3/72.3

80.3

12.2

58.8

113

16.9

81.3

34

a Calculation: [(residue method)/(metabolism method)] *100. b Two values are presented in the table because the samples were extracted twice. An initial study (first value) compared the metabolism method to the residue method. A second study was conducted (second value) which compared the metabolism method to the QuEChERS and DFG S 19 methods. c Note that wheat grain was used for the residue method comparison and wheat straw was used for the QuEChERS and DFG S19 comparison. Different samples needed to be used because of sample availability.

Prior to extraction, an aliquot of the homogenized crop samples was combusted and the total radioactivity in the sample was determined. After the extraction procedure was completed, aliquots from each extract were removed and the total radioactivity extracted was determined using a scintillation counter. Knowing the total radioactivity in both the sample and the extract allowed for the calculation of the percent of radioactivity extracted. These results are provided in Table 2. A comparison between the amount extracted by the metabolism method and the amount extracted using the residue and multi-residue methods was also calculated and are presented in Table 2. Based on the total radioactivity removed from the samples, the residue data collection method and the DFG S 19 method are comparable to the metabolism method in all tested crop matrices. The QuEChERS method was comparable for the lettuce and grape berry samples but significantly lower for the wheat straw sample. It is worthwhile to note that the QuEChERS method was developed for the analysis of crops with high moisture content such as fruits and vegetables. Caution needs to be taken when applying any extraction method to matrices for which it was not intended. Since incurred residue samples could be composed of many low-level metabolites, which are not included in the residue definition, the extracts need to be further characterized using a radiochemical HPLC instrument (3, 10). The extracts were transferred into a solvent compatible with HPLC analysis. A typical radio-chromatogram from the grape berry metabolism extract is presented in Figure 2.

Figure 2. Radio-chromatogram of a grape extract using the metabolism extraction. 35

The grape berry extracts have a measurable amount of the metabolites RZB20, E8S72, and WR791, and only a small amount of oxathiapiprolin (QGU42). The area under each peak was determined and the concentration of each analyte of interest in the extract was calculated. Considering the sample size, extract volume, and analyte concentration in the extract, the concentration of each analyte in the plant tissue sample was calculated in mg/kg. The results are presented in Table 3.

Table 3. Calculated Concentration of Oxathiapiprolin and Metabolites in Each Crop Sample Analyte Concentration (mg/kg) Determined from Each Extraction Method Analyte

Metabolisma

Residue

QuEChERS

DFG S19

Grapes QGU42

0.006/0.009

0.014

0.007

0.005

RZB20

0.023/0.032

0.021

0.032

0.010

E8S72

0.042/0.040

0.039

0.028

0.042

WR791

0.073/0.094

0.090

0.060

0.086

Lettuce QGU42

0.234/0.262

0.222

0.191

0.216

RZB20

ND/ND

ND

ND

0.020

E8S72

0.023/0.013

0.016

0.007

0.016

WR791

ND/0.008

ND

0.004

0.018

Wheat Grain QGU42

ND

ND

X

X

RZB20

0.032

0.033

X

X

E8S72

0.033

0.020

X

X

WR791

0.079

0.069

X

X

Wheat Straw QGU42

ND

X

ND

ND

RZB20

0.070

X

0.037

0.063

E8S72

0.033

X

0.009

0.028

WR791

0.018

X

0.005

0.008

ND: Not detected. X: Sample not tested. a Two values are presented in the table because the samples were extracted twice. An initial study (first value) compared the metabolism method to the residue method. A second study was conducted (second value), which compared the metabolism method to the QuEChERS and DFG S 19 methods.

36

When comparing the extraction methods, it is important to use well homogenized incurred residue samples that have quantifiable residues. Poor sample homogenization will introduce variability into the results. Using samples with residues at or below the method LOQ can result in wide variations in the data and possibly result in inaccurate conclusions due to poor method performance. These studies are usually done using two replicates for each extraction. If unexpected results are generated, additional replicates may be helpful to determine if homogenization or method variability is responsible. The residue and multi-residue extraction methods are required to extract the compounds included in the residue definition. Comparing the percentage of total radioactivity extracted may not provide the complete picture. Some compounds may produce many minor metabolites that are below the method LOQ and are therefore not included in the residue definition. The presence of these compounds in the treated crop samples does not allow the extraction efficiency to be calculated using only the TRR extracted. Determining the results for each analyte as the percentage extracted of the metabolism extraction allows for an overview of the extraction methods performance. Since these results are based on a small number of replicates, the overall trend needs to be evaluated. For an analyte, the difference between 85% of the residue and 70% of the residue extracted is negligible given the uncertainty in the measurement. Before an extraction method is considered inadequate, a significant difference must be observed between the metabolism and residue extraction methods. This variability is acknowledged in the EPA 860 guidance document, which requires “most of the total toxic residue” to be extracted (3, 5).

Demonstrating Extraction Efficiency Using Field Residue Samples as a Bridging Approach

Figure 3. Chemical structures of indoxacarb. Indoxacarb is the active ingredient in the insecticides Avaunt® and Steward® (16). Indoxacarb is registered for use on alfalfa, dried beans, cotton, peanuts, and various other crops (16). Indoxacarb is not metabolized in plants, therefore the 37

residue definition in plants is parent only. The structure of indoxacarb is presented in Figure 3. The DFG S 19 multi-residue method has been validated for the analysis of indoxacarb residues in all four EU crop groups (1, 17). As previously discussed, the European Union recently updated the analytical method guidelines to require extraction efficiency data for all data collection and enforcement methods (1). Metabolism studies for indoxacarb were conducted in the early 1990s and the samples are no longer available or would not be viable due to the storage time. To demonstrate that the DFG S19 method is suitable to monitor and enforce residues, a bridging extraction efficiency study was required to maintain the product registration. Prior to this study, a successful radiolabeled extraction efficiency study had been conducted for the residue data collection method. Rather than comparing the metabolism extraction method to the DFG S 19 enforcement method, the previously radiovalidated residue method was compared. This method was preferred over the metabolism method because the extraction solvent used was compatible with the LC/MS/MS analysis. The QuEChERS multi-residue method was also tested, since it uses less solvent and is more widely implemented than the DFG S 19 multi-residue method. Samples from apple, grape, and rapeseed magnitude of residue trials were analyzed by all three residue methods. Apple, grape, and oil seed rape samples were selected because they represent multiple European Union crop groups: apples (high water content), grape (high acid content), and oil seed (high oil content) (1). The extraction methods used for this study are summarized in Table 4.

Table 4. Indoxacarb Extraction Methods Method

Equipment

Sample Size

Solvent

Radiovalidated Residue Method

Homogenizing Probe

10 g

2 × 100 mL acetonitrile 50 mL hexane

QuEChERS

Wrist Action Shaker

5g

10 mL water for straw only 1 × 5 mL acetonitrile

DFG S 19

Homogenizing Probe

25 g

Up to 100 mL water 1 × 200 mL acetone

The crop magnitude of residue samples were homogenized in the presence of dry ice so that they could accurately be subsampled and analyzed multiple times. Prior to analyzing the field samples, each method was validated to demonstrate the method performance. The method validation consisted of fortifying untreated control samples at a level greater than and at a level less than the anticipated residue value. Field residue samples with high incurred residues (greater than 0.1 mg/kg) were used for this study. Using high residue field samples eliminated the need to purify or concentrate the extracts prior to analysis. The extracts could simply be diluted and analyzed. Each homogenized field sample was analyzed in five replicates. For each extraction method, the relative standard deviation must be less 38

than 10% for the analysis to be considered accurate. If a relative standard deviation of less than 10% cannot be reached the sample should be re-homogenized and re-analyzed. The mean residue value was calculated for each extraction method. The results from the analysis are presented in Table 5.

Table 5. Incurred Indoxacarb Residue Mean Analyte Concentration in mg/kg (±RSD) Determined from Each Extraction Method Crop

Radiovalidated Residue Method

DFG S 19 Method

QuEChERS Method

Apples

0.17 (0.69%)

0.19 (5.2%)

0.17 (5.7%)

Grape Berries

0.66 (9.1%)

0.74 (6.1%)

0.64 (5.7%)

Oil Seed Forage

0.16 (5.3%)

0.19 (5.5%)

0.18 (3.4%)

The percent extracted of the radiovalidated residue extraction method was calculated as follows

The same calculation was performed for the QuEChERS extraction method. The results for each crop are presented in Figure 4.

Figure 4. Percent of radiovalidated method extracted. 39

The QuEChERS method and the DFG S 19 method extract close to, or greater than, 100% of the residue determined by the radiovalidated method. Based on these data, both methods adequately extract indoxacarb from crops with high water content, high acid content, and high oil content. Enforcement data collected using these multi-residue methods accurately represent the residue in these commodities.

Use of Excised and Hydroponically Grown Samples To Demonstrate Extraction Efficiency Chlorsulfuron is the active ingredient in the herbicides Telar® and Glean® (18). Chlorsulfuron is used to control broad leaf weeds during the production of wheat (18). Metabolism studies indicate that in wheat samples, chlorsulfuron will form the metabolite A4097. The structures of chlorsulfuron and A4097 are presented in Figure 5.

Figure 5. Structure of chlorsulfuron and A4097.

40

Samples from the chlorsulfuron metabolism studies are no longer available and field residue trials have not been conducted in many years. For products that no longer have viable metabolism samples or field residue samples, new incurred residue samples will need to be generated to demonstrate the extraction efficiency of the methods before a compound can be reregistered in the EU (1, 10). A novel low-cost alternative to greenhouse foliar applications of radiolabeled material or conducting additional field residue trials is using excised and hydroponically grown plants. Various approaches can be taken depending on the plant species being tested. For this study, wheat plants were grown in soil until stage BBCH 37 (pre-blooming forage). The plants were removed from the soil and the roots were washed in deionized water and cut under water. The excised wheat plants were immediately transferred to a 1 L beaker containing 500 mL of dose solution, which contained 14C-chlorsulfuron (20 µCi/mg) and 14C-A4097 (20µCi/mg) in pH 7 phosphate buffer. The concentration of 14C-chlorsulfuron and 14C-A4097 in the solution was approximately 5 µg/mL for each. Some of the wheat plants were placed in the 10 mM pH=7 phosphate buffer solution as control samples. The plants were kept in the solution to incubate for approximately 48 hours to allow for uptake of 14C-chlorsulfuron and 14C-A4097 from the buffer solutions. This approach is especially useful in demonstrating extraction efficiency in all four EU crop groups. Working on this scale minimizes the amount of radiolabeled material needed and allows the plant to take up and metabolize the active ingredient and form the metabolites needed to adequately address the extraction efficiency requirements. The residue generated using this technique reside in the plant cell structure, as compared to foliar applications where a large percentage of the residue can remain on the leaf surface. The incorporation of the residue into the plant makes this technique ideal for extraction efficiency testing. Following incubation, the wheat plants were homogenized in the presence of dry ice and aliquots were combusted to determine the total radioactivity taken up into the plant. The extraction methods used are summarized in Table 6. Additional aliquots of the homogenized plant were extracted, in duplicate, using the residue method and the metabolism method. The amount of the total radioactive residue extracted is presented in Table 7.

Table 6. Chlorsulfuron Extraction Methods Method

Equipment

Sample Size

Solvent

Metabolism

Homogenizing Probe

5g

1 × 40 mL 2:1 Methylene Chloride:Methanol 2 × 20 mL 2:1 Methylene Chloride:Methanol

Residue

Geno/Grinder Bead Mill

5g

3 × 15 mL 1:1 Acetone:0.1 M aqueous Ammonium Carbonate

41

Table 7. Percent Total Radioactive Residue Extracted Residue Method

Metabolism Method

Sample ID

Replicate 1

Replicate 2

Replicate 1

Replicate 2

Percent Total Radioactive Residue

96.9%

96.7%

90.0%

92.7%

Table 8. Residue of Chlorsulfuron and Metabolite A4097 (mg/kg) Mean Residue Determined (mg/kg) Sample ID

Metabolism Method

Residue Method

Chlorsulfuron

0.265

0.363

A4097

1.10

1.23

The wheat extracts were analyzed using radiochemical HPLC detection. From the radio-chromatograms, the average (n=2) concentration in the extraction solution was determined. Taking into consideration the sample mass, extraction volume, and the analyte concentration in final LC sample, the concentration of chlorsulfuron and A4097 in the wheat plants as mg/kg was calculated. The average (n=2) calculated concentration is presented in Table 8. In the excised study, the polar metabolite A4097 is observed at three to four times higher than that of the parent chlorsulfuron due to differential uptake by plants. Water solubility of A4097 is much higher and is readily taken up by the wheat plants. In the environment, the concentration of chlorsulfuron available for uptake will be significantly larger than the concentration of A4097. When using this technique, it is important not to extrapolate the concentration observed in the excised and hydroponically grown plants into what might be expected for a sample generated in the field. It is important to note that field residue data collected during GLP magnitude of residue studies indicates that chlorsulfuron and A4097 are not detected in wheat plants when used at the highest label rate and shortest pre-harvest interval (19). Chlorsulfuron has been detected in whole plants at low levels (approximately 0.01 mg/kg) immediately following applications during residue decline studies (20). The metabolite A4097 was not detected at any time point during these decline studies. Using this excised plant and hydroponic exposure allows for the uptake of chlorsulfuron and A4097 into the plant at levels much higher than would be observed in residue or metabolism samples. Working at these higher concentrations reduces the number of steps between extracting the sample and analyzing the extract on the radiochemical HPLC detector. The signal detected is significantly above the detector noise, resulting in a more accurate measurement. Although this approach increases the accuracy of the analysis, the samples being analyzed may not reflect the residues that would be observed in a field. In this example, approximately 1.0 mg/kg of the metabolite A4097 was taken up by the 42

wheat plant but in field studies less than 0.003 mg/kg of A4097 (below the limit of detection) was observed. The percent extracted of the metabolism extraction method was calculated as follows

The results are presented graphically in Figure 6.

Figure 6. Percent of chlorsulfuron and A4097 extracted from excised wheat foliage samples. The residue extraction method removed greater than 100% of the radioactivity extracted using the metabolism extraction. It can be concluded that the residue method is fit for the analysis of chlorsulfuron and the metabolite A4097 in wheat foliage.

Conclusion The assessment of a plant residue is dependent on the ability to extract the unbound residue from the commodity. Mild extraction methods are sometimes preferred because they generate clean extracts that are readily analyzed. Before residue data can be considered valid and accurate, the extraction method used must be tested to determine if it adequately removes the incurred residue of interest from the plants analyzed. Demonstrating the extraction efficiency of a residue analysis is not as straightforward as validating a method. We have discussed three approaches to demonstrate if an extraction procedure sufficiently extracts an incurred residue. An appropriate approach can be selected based on the availability of incurred residue samples and radiolabeled test material. Although time-consuming 43

and expensive, extraction efficiency must be demonstrated before a method is determined to be fit for purpose.

Acknowledgments The authors would like to thank Joseph Klems for his critical review and insightful discussions. The authors acknowledge Shayira B. Habeeb, Jiří Čermák, Simon Chapleo, and Lucy Inns for their experimental contributions. The authors also thank Tony Trullinger, Ted Carski, Mark Krieger, Ramnath Subramanian, and Tomas Fuesler for their support of this work.

References European Commission. Guidance Document on Pesticide Residue Analytical Methods; SANCO 825/2000 Revision 8.1; 2010. 2. European Commission. Residues: Guidance for Generating and Reporting Methods of Analysis in Support of Pre-Registration Data Requirements for Annex II (part A, Section 4) and Annex III (part A, Section 5) of Directive 91/ 414; Guidance Document on Pesticide Residue Analytical Methods, SANCO 3029/1999 Revision 4; 11/07/2000. 3. United States EPA Guidelines. Residue Chemistry Test Guidelines, OPPTS 860.1340 Residue Analytical Methods, 1996. 4. Organization for Economic Co-operation and Development. Guidance Document on Pesticide Residue Analytical Methods; ENV/JM/MONO (2007)17; 13 August 2007. 5. United States EPA Guidelines. Residue Chemistry Test Guidelines, OPPTS 860.1300 Nature of the Residue – Plants, Livestock, 1996. 6. Organization for Economic Co-operation and Development. 501 OECD Guideline for the Testing of Chemicals, Metabolism in Crops, 8 January 2007. 7. Organization for Economic Co-operation and Development. 501 OECD Guideline for the Testing of Chemicals, Metabolism in Livestock, 8 January 2007. 8. Alder, L.; Greulich, K.; Kempe, G.; Vieth, B. Residue Analysis of 500 High Priority Pesticides: BETTER BY GC–MS OR LC–MS/MS? Mass Spectrometry Reviews 2006, 25, 838–865. 9. Hiemstra, M.; de Kok, A. Comprehensive multi-residue method for the target analysis of pesticides in crops using liquid chromatography-tandem mass spectrometry. J. Chromatogr. A 2007, 1154 (1-2), 3–25. 10. European Commission. Technical Guideline on the Evaluation of Extraction Efficiency of Residue Analytical Methods; SANTE/2017/10632 Revision 2; 11/09/2017. 11. Henion, J.; Brewer, E.; Rule, G. Sample preparation for LC/MS/MS: analyzing biological and environmental samples. Anal. Chem. 1998, 70, 650A–656A. 1.

44

12. Kebarle, P.; Tang, L. From ions in solution to ions in the gas phase-the mechanism of electrospray mass spectrometry. Anal. Chem. 1993, 65, 972A–986A. 13. Pasteris, R. J.; Hanagan, M. A.; Bisaha, J. J.; Finkelstein, B. L.; Hoffman, L. E.; Gregory, V.; Shepherd, C. P.; Andreassi, J. L.; Sweigard, J. A.; Klyashchitsky, B. A.; Henry, Y. T.; Berger, R. A. The Discovery of Oxathiapiprolin: A New, Highly-Active Oomycete Fungicide with a Novel Site of Action. Discovery and Synthesis of Crop Protection Products; ACS Symposium Series; 2015; Vol. 1204, pp 149–161; Chapter 11. 14. Specht, W.; Pelz, S.; Gilsbach, W. Gaschromatographic determination of pesticide residues after clean-up by gel permeation chromatography and mini-silica gel-column chromatography. 6. Comm.: Replacement of dichloromethane by ethyl acetate/cyclohexane in liquid-liquid partition and simplified conditions for extraction and liquid liquid partition. Fresenius J. Anal. Chem. 1995, 353, 183–190. 15. Anastassiades, M.; Lehotay, S. J.; Stajnbaher, D.; Schenck, F. J. Fast and easy multiresidue method employing acetonitrile extraction/partitioning and “dispersive solid-phase extraction” for the determination of pesticide residues in produce. JAOAC Int. 2003, 86 (2), 412–431. 16. United States EPA. Office of Prevention, Pesticides and Toxic Substances (7505C). Pesticide Fact Sheet. Name of Chemical: Indoxacarb. Reason for Issuance: Conditional Registration; Date Issued: October 30, 2000. 17. Indoxacarb, SANCO/1408/2001 – rev.3; 23 September 2005; Review report for the active substance indoxacarb. Finalised in the Standing Committee on the Food Chain and Animal Health at its meeting on 23 September 2005 in view of the inclusion of indoxacarb in Annex I of Directive 91/414/EEC. 18. Chlorsulfuron, SANCO/198/08 – final; 28 September 2010; Review report for the active substance Chlorsulfuron. Finalised in the Standing Committee on the Food Chain and Animal Health at its meeting on 26 February 2009 in view of the inclusion of Chlorsulfuron in Annex I of Directive 91/414/EEC. 19. Conclusion regarding the peer review of the pesticide risk assessment of the active substance chlorsulfuron; EFSA Scientific Report; 2008, 201, 1–107. 20. Cairns, S.; Doig, A. Magnitude and Decline of Chlorsulfuron and IN-A4097 Residues in Barley and Wheat (Cereals) Following Application of DPX-W4189 75WG – Europe – 2010; DuPont-29874; 2011; Unpublished Results.

45

Testing of Proteins in Agricultural Biotechnology

Chapter 5

Data Trends in Protein Analysis for Safety Assessments Rong Wang,*,1 Ryan C. Hill,2 and Norma L. Houston3 1Bayer

Crop Science, Regulatory Science, 700 Chesterfield Parkway, Chesterfield, Missouri 63017, United States 2Corteva Agriscience, Agriculture Division of DowDupont, 9330 Zionsville Road, Indianapolis, Indiana 46268, United States 3Corteva Agriscience, Agriculture Division of DowDupont, 7300 NW 62nd Avenue, Johnston, Iowa 50131, United States *E-mail: [email protected].

Genetically modified (GM) crops go through a rigorous safety assessment process prior to commercialization. A number of regulatory requirements and technological innovations have contributed to the weight-of-evidence and tiered approach used to evaluate the safety of GM crops for food and feed consumption. Over the past 30 years, global regulatory agencies have gained significant experience and knowledge regarding safety evaluations in assessing the risks of GM crops, and to date, all products reviewed have been found to pose no risks to agriculture, human health, or the environment. One component of the safety assessment includes the characterization and evaluation of newly expressed proteins. In this chapter, we highlight the current data trends of this component including the process, technologies used, and unique challenges for plant-expressed proteins in GM crops.

© 2019 American Chemical Society

Introduction Since the introduction of genetically modified (GM) crops in the 1990s, regulatory processes and risk-assessment frameworks have been developed and used to assess safety for humans, animals, and the environment (1–3) GM crops that express transgenic proteins are produced with recombinant DNA techniques that target insertions into a plant’s genome to confer a new desirable trait. To date, all GM crops have undergone extensive assessments for food, feed, and environmental safety before commercialization (2, 4). The safety assessment of GM crops follows the Organisation for Economic Co-operation and Development comparative principles, according to which conventional crops with a history of safe use serve as the baseline for evaluating the safety of GM crops (3). The regulatory assessment of a GM crop determines whether the GM crop is “as safe as” a conventional crop (5). From 1992 to 2017, nearly 100 petitions for deregulation in the United States were approved by the U.S. Department of Agriculture (USDA) (6). Over the past 30 years, global regulatory agencies have reviewed safety assessments to evaluate GM crops for safety, and to date, all products reviewed have been found to pose no risks to agriculture, human health, or the environment (7). Further, the European Commission (8) stated, “The main conclusion to be drawn from the efforts of more than 130 research projects, covering a period of more than 25 years of research, and involving more than 500 independent research groups, is that biotechnology, and in particular GMOs [genetically modified organisms], are not per se more risky than e.g. conventional plant breeding technologies”. The assessment includes gauging the potential food and feed safety of the GM crop to prevent the introduction of a potential allergen or toxin into the food supply. On a molecular level, proteins are one of nature’s most complex and versatile polymers. Other biological polymers, such as nucleic acids and carbohydrates, use less diverse monomers and have limited chemical properties and structures. Proteins are composed of an assortment of 20 amino acids with a range of three-dimensional structures that enable proteins to serve as enzymes, structural materials for tissues, mechanisms for chemical transport, and storage aids that underlie biological functions (9). The widely diverse chemistries and biological functions of proteins can make them challenging to analytically detect, quantify, and characterize to meet the demands of increasing regulatory scrutiny. A collection of analytical techniques is used to identify, quantify, and characterize the safety of the newly expressed protein(s) present in GM crops, and the results are analyzed to assess their safety. Use of advanced technology helps to generate accurate data that answer key questions to assess risks. As a result, it is important to understand the advantages and limitations of each technology to provide clear, purposeful, transparent, and thorough data to meet the requirements of global regulatory agencies. As long as regulatory requirements are met, the adoption of faster, more reliable, and affordable technologies can promote innovation and support sustainable agriculture. In this chapter, our discussion of analytical techniques used to assess the safety of newly expressed protein is organized into two major sections, “Protein Safety Assessment” and “Protein Characterization and Equivalence”. In the former section, we review and discuss 50

the current state of protein analysis and the methodologies used in the safety assessment of GM crops, including relevant applications that assess protein abundance, structure, function, and stability. In the latter section, we examine analytical techniques used to characterize purified proteins prior to use in safety studies. In-depth details of the methods are not provided in this chapter; however, the focus is on the basic principles of each technology, intended applications, potential problems for plant-expressed proteins, and the contributions of analytical methods to protein safety evaluations.

Protein Safety Assessment The thorough safety assessment of newly expressed proteins in GM crops is outlined by the joint Food and Agriculture Organization of United Nation (FAO) and World Health Organization (WHO) Food Standard Program and summarized in the Codex Alimentarius titled Foods Derived from Modern Biotechnology (4). The following sections provide a high-level overview of the safety assessment components recommended by the Codex Alimentarius, including history of safe use, sequence homology to known toxins and allergens, expression levels, heat stability, in vitro digestibility, and toxicity studies (4). The multicomponent assessment is consistent with the tiered testing strategy advocated by the International Life Science Institute (ILSI) International Food Biotechnology Committee Task Force on Protein Safety, which consists of tier I (protein hazard identification) and tier II (hazard characterization), which is conducted on a case-by-case basis (10). Technologies presented in this chapter can be used to draw conclusions regarding potential toxicological or allergenic properties of the protein(s) expressed in GM crops. Examples of each selected component are provided below. History of Safe Use The vast majority of proteins, as macronutrients, have a long history of safe use (HOSU) and are safe for consumption (11). However, a limited number of well-known dietary proteins are toxic, act as antinutrients, or are allergenic to humans, such as botulinum neurotoxin (12), some lectins (13), and the peanut allergen Ara h 2 (14), respectively. HOSU is often evaluated prior to the selection of candidate proteins (15, 16). For example, strains of Bacillus thuringiensis (Bt) bacteria that contain insecticidal crystal (Cry) proteins have more than 50 years of demonstrated safe use as biological pesticides in spray applications, and several of these Cry proteins, such as Cry1F and Cry1Ac, were introduced into GM crops and have been in use for more than 20 years (17). In addition, 5-enolpyruvylshikimate3-phosphate synthase (EPSPS) from Agrobacterium strain CP4, a key enzyme in the shikimate pathway that is involved in aromatic amino acid biosynthesis and confers glyphosate tolerance, also has more than 20 years of demonstrated safe use (18). Proteins that do not have a HOSU can be considered novel from a consumption point of view, but homologues of those proteins found in food can be used to assess the history of use of the protein. Those with limited homology 51

to commonly consumed proteins should be subjected to a multicomponent risk assessment for potential allergenicity or toxicity. To date, all proteins expressed by transgenes in GM crops have been subjected to this evaluation regardless of demonstrated HOSU; however, hazard identification and characterization should only be conducted on a stepwise and case-by-case basis (10). Sequence Homology Bioinformatic analysis takes into account the structure and function of the protein and focuses on the similarity or identity of the amino acid sequenceof the protein of interest to a collection of known protein allergens, toxins, and antinutrients. A bioinformatic investigation is typically the first step toward identifying the potential homology of a novel protein to known allergens or toxins (19, 20). Allergenic proteins are processed by antigen-presenting cells into small peptides that are part of the immune response. Evaluating regions of intact proteins for similarity to regions of known allergens makes sense biologically. For the assessment of GM crops, the Codex Alimentarius provides guidelines to support this strategy (2, 4). Under these guidelines, the amino acid sequence is considered to have potential allergenic cross-reactivity if greater than 35% sequence identity over a window of 80 or more amino acids is identified compared with known allergens; other criteria have been found to be both more selective and sensitive for detecting true cross-reactivity (21–23). One such criterion is the E score, which is a metric that describes the number of hits (exact or similar matches) expected by chance. The lower the E score, the higher the similarity between sequences. E scores provide a more robust method, with a lower false-positive rate, than the simple identity criterion (24) and appropriate accuracy and sensitivity for sequence similarity of a query protein to known allergens (21–23). An E score threshold of 1 × 10–5–1 × 10–6 has recently been reported to be useful for identifying cross-reactive immunoglobulin E- (IgE) binding epitopes (25). A second query is often performed with an eight-amino-acid sliding window that seeks to identify exact eight-amino-acid matches between a query protein and allergen proteins (Figure 1), but this criterion has been found to add little value to the allergenicity risk assessment (21, 26). Ensuring that introduced proteins are not homologous to non-IgE-mediated allergens, mainly referring to celiac disease (CD) peptides and specifically human leukocyte antigens HLA-DQ2 or -DQ8, is of concern to some regulators (27). CD is caused by the consumption of wheat, barley, rye, and sometimes oats by susceptible individuals (28). Avoidance of food products made with these grains is an effective, albeit inconvenient, means of preventing CD symptoms. A set of newly implemented regulations extend beyond protein families and require searching against a gluten-derived nine-amino acid peptide motif and its degenerate sequences consisting of Q/E-X1-P-X2 (where X1 is amino acid L, Q, F, S, or E and X2 is amino acid Y, F, A, V or Q), resulting in 50 unique four-amino-acid peptide combinations (Figure 2) (27). Although the scientific basis for this new requirement is limited, new bioinformatic tools have been developed to align the known and putative four-amino-acid stretches with current regulatory guidance (27). An unintended 52

consequence of this new requirement is that there is a high probability of finding random irrelevant matches with the 50 four-amino-acid combinations of known CD peptides and putative peptides.

Figure 1. Illustration of a static alignment using CLC Bio (QIAGEN): Eight-amino-acid stretch (solid box) against a pool of proteins in an allergen database, sliding one amino acid to the right for each search, giving a total of 147 consecutive eight-amino-acid stretches for Pru av 1 in the illustrated sequence length. 35% identity over 80 amino acids (dashed box).

Figure 2. 50 amino acid peptide combinations resulting from the Q/E-X1-P-X2 motif. 53

With the advances that have been made in genome sequencing, an enormous number of predicted protein sequences have been collected; however, the translated gene sequences do not provide a complete view of proteins or their interactions in plant biological systems. As a result, data management has become very important for allergen research. Proteins entered annually into the National Center for Biotechnology Information database may not be characterized or have clinical data to support or negate claims of allergenicity or toxicity. However, the COMPARE database (http://comparedatabase.org, accessed November 16, 2018), established by the Health and Environmental Sciences Institute’s Protein Allergen, Toxins, and Bioinformatics Committee, is an actively curated and continuously updated allergen database that includes new entries that have clinical or immune response evidence to enable meaningful allergy assessment. Other databases, including the International Union of Immunological Societies’s Structural Database of Allergenic Proteins, Allergen Database for Food Safety, AllergenOnline, AllFam – The Database of Allergen Families, AllergenPro, and AllerBase, are curated with different scopes and frequencies by their responsible organizations (29). Unlike allergenicity, about which conclusions are drawn from the weight of evidence, protein toxicity can be directly tested with established animal models for acute oral toxicity. Recent investigations of in vitro methods with human intestinal epithelial cell monolayers have shown experimentally the potential for alternative tests to predict protein toxicity (30–37). Protein toxins display target-organism specificity and dosing constraints. Toxic proteins elicit their adverse effects through specific structural features of the intact (or nearly intact) protein. When toxic proteins are denatured by heat or digested, they lose their toxicity (unlike allergenic proteins, which elicit allergic responses as major histocompatibility complex-displayed fragments). For instance, snake venoms are toxic if injected into the blood stream or soft tissues, but they are nontoxic if ingested orally (10). Consequently, evaluation of toxicity using bioinformatics is better suited to determining whether a protein is from a family that has toxic members. The bioinformatic assessments of the allergenic and toxic potentials of an introduced protein are different (15, 38). However, only the latter requires relative similarity or identity comparison and manual inspection to determine the potential risk.

Expression Protein expression is used to characterize the exposure component of risk, where risk is a function of both hazard and exposure (39). Dietary exposure to an introduced protein can be estimated from the protein expression level in edible parts of a GM crop. The enzyme-linked immunosorbent assay is the most common technique used to determine protein expression levels; however, liquid chromatography coupled with tandem mass spectrometry is also capable of protein quantification (40–42). These protein quantification methods are discussed further in Chapter 6. 54

Table 1. Expression Levels of Introduced Proteins from a Subset of GM Crops Event namea

Crop

Phenotype

Company

Year Deregulated by USDA

MZHG0JG

Maize

Glufosinate- and glyphosate-tolerant

Syngenta

2015

MON-87419-8

Maize

Diacamba- and glufosinate-tolerant

Monsanto

2016

MON-87411-9

Maize

Rootworm-resistant, glyphosate-tolerant

Monsanto

2015

55 DP-ØØ4114-3

Maize

Insect-resistant and glufosinate-tolerant

Pioneer

2013

MON-87751

Soybean

Lepidopteran-resistant

Monsanto

DAS-81419-2

Soybean

Insect-resistant

Dow AgroSciences

2014

SYHT0H2

Soybean

4-Hydroxyphenylpyruvate dioxygenase- and glufosinate-tolerant

Syngenta

2014

2014

Expression level in seed (ppm)

Protein name mEPSPS

36.89 ± 10.06

PAT

b

DMO

0.19 ± 0.048

PAT

0.93 ± 0.27

Cry3Bb1

4 ± 0.56

CP4 EPSPS

1.9 ± 0.31

Cry1F

3.3

Cry34Ab1

24

Cry35Ab1

1.1

PAT