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3 results on '"Andrew Dawdy"'

1. Cysteine metabolic engineering and selective disulfide reduction produce superior antibody-drug-conjugates

Author

Renée Procopio-Melino, Frank W. Kotch, Amar S. Prashad, Jose M. Gomes, Wenge Wang, Bo Arve, Andrew Dawdy, Lawrence Chen, Justin Sperry, Christine Hosselet, Tao He, Ronald Kriz, Laura Lin, Kimberly Marquette, Lioudmila Tchistiakova, Will Somers, Jason C. Rouse, and Xiaotian Zhong

Subjects
Medicine, Science
Abstract

Abstract Next-generation site-specific cysteine-based antibody–drug-conjugates (ADCs) broaden therapeutic index by precise drug-antibody attachments. However, manufacturing such ADCs for clinical validation requires complex full reduction and reoxidation processes, impacting product quality. To overcome this technical challenge, we developed a novel antibody manufacturing process through cysteine (Cys) metabolic engineering in Chinese hamster ovary cells implementing a unique cysteine-capping technology. This development enabled a direct conjugation of drugs after chemoselective-reduction with mild reductant tris(3-sulfonatophenyl)phosphine. This innovative platform produces clinical ADC products with superior quality through a simplified manufacturing process. This technology also has the potential to integrate Cys-based site-specific conjugation with other site-specific conjugation methodologies to develop multi-drug ADCs and exploit multi-mechanisms of action for effective cancer treatments.

Published
2022
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2. Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

Author

Trina Mouchahoir, John E. Schiel, Rich Rogers, Alan Heckert, Benjamin J. Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, and Hua Yuan

Subjects
Quality Control, Benchmarking, Structural Biology, Proteins, Peptide Mapping, Mass Spectrometry, Spectroscopy
Abstract

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.

Published
2022
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3. New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study

Author

Irina Perdivara, Ben Niu, Kim F. Haselmann, St John Skilton, Anthony Leone, Gregory O. Staples, Carsten P. Sønksen, Helena Maria Barysz, Andrew Hanneman, Chun Shao, Rebecca Scott, Anders Lund, Carly Daniels, Michael Jahn, Da Ren, Nunzio Sepe, K. Ilker Sen, Zoran Sosic, David Ripley, Jing Zhen, Margo Wilson, Melissa Alvarez, John G Hoogerheide, Xinbi Li, Harini Kaluarachchi, Josh Woods, Wenqin Ni, Albrecht Gruhler, Keith A. Johnson, Arnd Brandenburg, Kristen Nields, Michelle Busch, Douglas D. Richardson, Yan Wang, Ahmet Cansizoglu, Xiaoxiao Li, Greg W Adams, Simon Letarte, Joe Shambaugh, Hua Yuan, Trina Mouchahoir, Tom Robinson, Xiaoshi Wang, Nancy S. Nightlinger, Alexander Julian Veach, Chris Chumsae, Eric Carlson, Dongdong Wang, Sean Shen, Jing Fang, Wei Wu, Stefano Gotta, Justin B. Sperry, Hirsh Nanda, X. Christopher Yu, Sibylle Heidelberger, Bhumit A. Patel, Jihong Wang, Sean McCarthy, Himakshi Patel, Thomas N. Krogh, Hunter Walker, Olga V. Friese, Daniela Tizabi, Yali Lu, Kristin Boggio, Ernest L. Maynard, Rich Rogers, Ying Zhou, Nick DeGraan-Weber, John E. Schiel, Weibin Chen, Jason C. Rouse, Li Tao, Thomas W. Powers, John Kim, Xu Guo, Bo Yan, Gabriella Leo, Ying Zhang, Oleg V. Borisov, Ying Qing Yu, Martha Stapels, Wael Yared, Yan-Hui Liu, Alan Heckert, Sarah Rogstad, Li Zang, Aaron Ammerman, Li Cao, Benjamin J. Place, Richard Ludwig, Anton V. Manuilov, Andrew Mahan, Andrew Dawdy, Yi Wang, Brian Schmidt, and Peiran Liu

Subjects
business.industry, Chemistry, Process (engineering), media_common.quotation_subject, 010401 analytical chemistry, 010402 general chemistry, 01 natural sciences, 0104 chemical sciences, Peak detection, Structural Biology, Process engineering, business, Function (engineering), Spectroscopy, media_common
Abstract

The Multi-Attribute Method (MAM) Consortium was initially formed as a venue to harmonize best practices, share experiences, and generate innovative methodologies to facilitate widespread integration of the MAM platform, which is an emerging ultra-high-performance liquid chromatography-mass spectrometry application. Successful implementation of MAM as a purity-indicating assay requires new peak detection (NPD) of potential process- and/or product-related impurities. The NPD interlaboratory study described herein was carried out by the MAM Consortium to report on the industry-wide performance of NPD using predigested samples of the NISTmAb Reference Material 8671. Results from 28 participating laboratories show that the NPD parameters being utilized across the industry are representative of high-resolution MS performance capabilities. Certain elements of NPD, including common sources of variability in the number of new peaks detected, that are critical to the performance of the purity function of MAM were identified in this study and are reported here as a means to further refine the methodology and accelerate adoption into manufacturer-specific protein therapeutic product life cycles.

Published
2021
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