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Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

[Image: see text] The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it lever...

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Autores principales: Mouchahoir, Trina, Schiel, John E., Rogers, Rich, Heckert, Alan, Place, Benjamin J., Ammerman, Aaron, Li, Xiaoxiao, Robinson, Tom, Schmidt, Brian, Chumsae, Chris M., Li, Xinbi, Manuilov, Anton V., Yan, Bo, Staples, Gregory O., Ren, Da, Veach, Alexander J., Wang, Dongdong, Yared, Wael, Sosic, Zoran, Wang, Yan, Zang, Li, Leone, Anthony M., Liu, Peiran, Ludwig, Richard, Tao, Li, Wu, Wei, Cansizoglu, Ahmet, Hanneman, Andrew, Adams, Greg W., Perdivara, Irina, Walker, Hunter, Wilson, Margo, Brandenburg, Arnd, DeGraan-Weber, Nick, Gotta, Stefano, Shambaugh, Joe, Alvarez, Melissa, Yu, X. Christopher, Cao, Li, Shao, Chun, Mahan, Andrew, Nanda, Hirsh, Nields, Kristen, Nightlinger, Nancy, Niu, Ben, Wang, Jihong, Xu, Wei, Leo, Gabriella, Sepe, Nunzio, Liu, Yan-Hui, Patel, Bhumit A., Richardson, Douglas, Wang, Yi, Tizabi, Daniela, Borisov, Oleg V., Lu, Yali, Maynard, Ernest L., Gruhler, Albrecht, Haselmann, Kim F., Krogh, Thomas N., Sönksen, Carsten P., Letarte, Simon, Shen, Sean, Boggio, Kristin, Johnson, Keith, Ni, Wenqin, Patel, Himakshi, Ripley, David, Rouse, Jason C., Zhang, Ying, Daniels, Carly, Dawdy, Andrew, Friese, Olga, Powers, Thomas W., Sperry, Justin B., Woods, Josh, Carlson, Eric, Sen, K. Ilker, Skilton, St John, Busch, Michelle, Lund, Anders, Stapels, Martha, Guo, Xu, Heidelberger, Sibylle, Kaluarachchi, Harini, McCarthy, Sean, Kim, John, Zhen, Jing, Zhou, Ying, Rogstad, Sarah, Wang, Xiaoshi, Fang, Jing, Chen, Weibin, Yu, Ying Qing, Hoogerheide, John G., Scott, Rebecca, Yuan, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460773/
https://www.ncbi.nlm.nih.gov/pubmed/36018776
http://dx.doi.org/10.1021/jasms.2c00129
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author Mouchahoir, Trina
Schiel, John E.
Rogers, Rich
Heckert, Alan
Place, Benjamin J.
Ammerman, Aaron
Li, Xiaoxiao
Robinson, Tom
Schmidt, Brian
Chumsae, Chris M.
Li, Xinbi
Manuilov, Anton V.
Yan, Bo
Staples, Gregory O.
Ren, Da
Veach, Alexander J.
Wang, Dongdong
Yared, Wael
Sosic, Zoran
Wang, Yan
Zang, Li
Leone, Anthony M.
Liu, Peiran
Ludwig, Richard
Tao, Li
Wu, Wei
Cansizoglu, Ahmet
Hanneman, Andrew
Adams, Greg W.
Perdivara, Irina
Walker, Hunter
Wilson, Margo
Brandenburg, Arnd
DeGraan-Weber, Nick
Gotta, Stefano
Shambaugh, Joe
Alvarez, Melissa
Yu, X. Christopher
Cao, Li
Shao, Chun
Mahan, Andrew
Nanda, Hirsh
Nields, Kristen
Nightlinger, Nancy
Niu, Ben
Wang, Jihong
Xu, Wei
Leo, Gabriella
Sepe, Nunzio
Liu, Yan-Hui
Patel, Bhumit A.
Richardson, Douglas
Wang, Yi
Tizabi, Daniela
Borisov, Oleg V.
Lu, Yali
Maynard, Ernest L.
Gruhler, Albrecht
Haselmann, Kim F.
Krogh, Thomas N.
Sönksen, Carsten P.
Letarte, Simon
Shen, Sean
Boggio, Kristin
Johnson, Keith
Ni, Wenqin
Patel, Himakshi
Ripley, David
Rouse, Jason C.
Zhang, Ying
Daniels, Carly
Dawdy, Andrew
Friese, Olga
Powers, Thomas W.
Sperry, Justin B.
Woods, Josh
Carlson, Eric
Sen, K. Ilker
Skilton, St John
Busch, Michelle
Lund, Anders
Stapels, Martha
Guo, Xu
Heidelberger, Sibylle
Kaluarachchi, Harini
McCarthy, Sean
Kim, John
Zhen, Jing
Zhou, Ying
Rogstad, Sarah
Wang, Xiaoshi
Fang, Jing
Chen, Weibin
Yu, Ying Qing
Hoogerheide, John G.
Scott, Rebecca
Yuan, Hua
author_facet Mouchahoir, Trina
Schiel, John E.
Rogers, Rich
Heckert, Alan
Place, Benjamin J.
Ammerman, Aaron
Li, Xiaoxiao
Robinson, Tom
Schmidt, Brian
Chumsae, Chris M.
Li, Xinbi
Manuilov, Anton V.
Yan, Bo
Staples, Gregory O.
Ren, Da
Veach, Alexander J.
Wang, Dongdong
Yared, Wael
Sosic, Zoran
Wang, Yan
Zang, Li
Leone, Anthony M.
Liu, Peiran
Ludwig, Richard
Tao, Li
Wu, Wei
Cansizoglu, Ahmet
Hanneman, Andrew
Adams, Greg W.
Perdivara, Irina
Walker, Hunter
Wilson, Margo
Brandenburg, Arnd
DeGraan-Weber, Nick
Gotta, Stefano
Shambaugh, Joe
Alvarez, Melissa
Yu, X. Christopher
Cao, Li
Shao, Chun
Mahan, Andrew
Nanda, Hirsh
Nields, Kristen
Nightlinger, Nancy
Niu, Ben
Wang, Jihong
Xu, Wei
Leo, Gabriella
Sepe, Nunzio
Liu, Yan-Hui
Patel, Bhumit A.
Richardson, Douglas
Wang, Yi
Tizabi, Daniela
Borisov, Oleg V.
Lu, Yali
Maynard, Ernest L.
Gruhler, Albrecht
Haselmann, Kim F.
Krogh, Thomas N.
Sönksen, Carsten P.
Letarte, Simon
Shen, Sean
Boggio, Kristin
Johnson, Keith
Ni, Wenqin
Patel, Himakshi
Ripley, David
Rouse, Jason C.
Zhang, Ying
Daniels, Carly
Dawdy, Andrew
Friese, Olga
Powers, Thomas W.
Sperry, Justin B.
Woods, Josh
Carlson, Eric
Sen, K. Ilker
Skilton, St John
Busch, Michelle
Lund, Anders
Stapels, Martha
Guo, Xu
Heidelberger, Sibylle
Kaluarachchi, Harini
McCarthy, Sean
Kim, John
Zhen, Jing
Zhou, Ying
Rogstad, Sarah
Wang, Xiaoshi
Fang, Jing
Chen, Weibin
Yu, Ying Qing
Hoogerheide, John G.
Scott, Rebecca
Yuan, Hua
author_sort Mouchahoir, Trina
collection PubMed
description [Image: see text] The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.
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spelling pubmed-94607732022-09-10 Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study Mouchahoir, Trina Schiel, John E. Rogers, Rich Heckert, Alan Place, Benjamin J. Ammerman, Aaron Li, Xiaoxiao Robinson, Tom Schmidt, Brian Chumsae, Chris M. Li, Xinbi Manuilov, Anton V. Yan, Bo Staples, Gregory O. Ren, Da Veach, Alexander J. Wang, Dongdong Yared, Wael Sosic, Zoran Wang, Yan Zang, Li Leone, Anthony M. Liu, Peiran Ludwig, Richard Tao, Li Wu, Wei Cansizoglu, Ahmet Hanneman, Andrew Adams, Greg W. Perdivara, Irina Walker, Hunter Wilson, Margo Brandenburg, Arnd DeGraan-Weber, Nick Gotta, Stefano Shambaugh, Joe Alvarez, Melissa Yu, X. Christopher Cao, Li Shao, Chun Mahan, Andrew Nanda, Hirsh Nields, Kristen Nightlinger, Nancy Niu, Ben Wang, Jihong Xu, Wei Leo, Gabriella Sepe, Nunzio Liu, Yan-Hui Patel, Bhumit A. Richardson, Douglas Wang, Yi Tizabi, Daniela Borisov, Oleg V. Lu, Yali Maynard, Ernest L. Gruhler, Albrecht Haselmann, Kim F. Krogh, Thomas N. Sönksen, Carsten P. Letarte, Simon Shen, Sean Boggio, Kristin Johnson, Keith Ni, Wenqin Patel, Himakshi Ripley, David Rouse, Jason C. Zhang, Ying Daniels, Carly Dawdy, Andrew Friese, Olga Powers, Thomas W. Sperry, Justin B. Woods, Josh Carlson, Eric Sen, K. Ilker Skilton, St John Busch, Michelle Lund, Anders Stapels, Martha Guo, Xu Heidelberger, Sibylle Kaluarachchi, Harini McCarthy, Sean Kim, John Zhen, Jing Zhou, Ying Rogstad, Sarah Wang, Xiaoshi Fang, Jing Chen, Weibin Yu, Ying Qing Hoogerheide, John G. Scott, Rebecca Yuan, Hua J Am Soc Mass Spectrom [Image: see text] The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment. American Chemical Society 2022-08-26 2022-09-07 /pmc/articles/PMC9460773/ /pubmed/36018776 http://dx.doi.org/10.1021/jasms.2c00129 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Mouchahoir, Trina
Schiel, John E.
Rogers, Rich
Heckert, Alan
Place, Benjamin J.
Ammerman, Aaron
Li, Xiaoxiao
Robinson, Tom
Schmidt, Brian
Chumsae, Chris M.
Li, Xinbi
Manuilov, Anton V.
Yan, Bo
Staples, Gregory O.
Ren, Da
Veach, Alexander J.
Wang, Dongdong
Yared, Wael
Sosic, Zoran
Wang, Yan
Zang, Li
Leone, Anthony M.
Liu, Peiran
Ludwig, Richard
Tao, Li
Wu, Wei
Cansizoglu, Ahmet
Hanneman, Andrew
Adams, Greg W.
Perdivara, Irina
Walker, Hunter
Wilson, Margo
Brandenburg, Arnd
DeGraan-Weber, Nick
Gotta, Stefano
Shambaugh, Joe
Alvarez, Melissa
Yu, X. Christopher
Cao, Li
Shao, Chun
Mahan, Andrew
Nanda, Hirsh
Nields, Kristen
Nightlinger, Nancy
Niu, Ben
Wang, Jihong
Xu, Wei
Leo, Gabriella
Sepe, Nunzio
Liu, Yan-Hui
Patel, Bhumit A.
Richardson, Douglas
Wang, Yi
Tizabi, Daniela
Borisov, Oleg V.
Lu, Yali
Maynard, Ernest L.
Gruhler, Albrecht
Haselmann, Kim F.
Krogh, Thomas N.
Sönksen, Carsten P.
Letarte, Simon
Shen, Sean
Boggio, Kristin
Johnson, Keith
Ni, Wenqin
Patel, Himakshi
Ripley, David
Rouse, Jason C.
Zhang, Ying
Daniels, Carly
Dawdy, Andrew
Friese, Olga
Powers, Thomas W.
Sperry, Justin B.
Woods, Josh
Carlson, Eric
Sen, K. Ilker
Skilton, St John
Busch, Michelle
Lund, Anders
Stapels, Martha
Guo, Xu
Heidelberger, Sibylle
Kaluarachchi, Harini
McCarthy, Sean
Kim, John
Zhen, Jing
Zhou, Ying
Rogstad, Sarah
Wang, Xiaoshi
Fang, Jing
Chen, Weibin
Yu, Ying Qing
Hoogerheide, John G.
Scott, Rebecca
Yuan, Hua
Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title_full Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title_fullStr Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title_full_unstemmed Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title_short Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study
title_sort attribute analytics performance metrics from the mam consortium interlaboratory study
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460773/
https://www.ncbi.nlm.nih.gov/pubmed/36018776
http://dx.doi.org/10.1021/jasms.2c00129
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