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Your search keyword '"Itkin, Zina"' showing total 3 results
Start Over Author "Itkin, Zina" Publication Type Electronic Resources
3 results on '"Itkin, Zina"'

1. Deep learning identifies synergistic drug combinations for treating COVID-19

Author

Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Synthetic Biology Center, Massachusetts Institute of Technology. Institute for Medical Engineering & Science, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Jin, Wengong, Stokes, Jonathan, Eastman, Richard T., Itkin, Zina, Zakharov, Alexey V., Collins, James J., Jaakkola, Tommi S, Barzilay, Regina, Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Synthetic Biology Center, Massachusetts Institute of Technology. Institute for Medical Engineering & Science, Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Jin, Wengong, Stokes, Jonathan, Eastman, Richard T., Itkin, Zina, Zakharov, Alexey V., Collins, James J., Jaakkola, Tommi S, and Barzilay, Regina

Abstract

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.

Published
2021

2. Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Author

Kearney, Sara E, Kearney, Sara E, Zahoránszky-Kőhalmi, Gergely, Brimacombe, Kyle R, Henderson, Mark J, Lynch, Caitlin, Zhao, Tongan, Wan, Kanny K, Itkin, Zina, Dillon, Christopher, Shen, Min, Cheff, Dorian M, Lee, Tobie D, Bougie, Danielle, Cheng, Ken, Coussens, Nathan P, Dorjsuren, Dorjbal, Eastman, Richard T, Huang, Ruili, Iannotti, Michael J, Karavadhi, Surendra, Klumpp-Thomas, Carleen, Roth, Jacob S, Sakamuru, Srilatha, Sun, Wei, Titus, Steven A, Yasgar, Adam, Zhang, Ya-Qin, Zhao, Jinghua, Andrade, Rodrigo B, Brown, M Kevin, Burns, Noah Z, Cha, Jin K, Mevers, Emily E, Clardy, Jon, Clement, Jason A, Crooks, Peter A, Cuny, Gregory D, Ganor, Jake, Moreno, Jesus, Morrill, Lucas A, Picazo, Elias, Susick, Robert B, Garg, Neil K, Goess, Brian C, Grossman, Robert B, Hughes, Chambers C, Johnston, Jeffrey N, Joullie, Madeleine M, Kinghorn, A Douglas, Kingston, David GI, Krische, Michael J, Kwon, Ohyun, Maimone, Thomas J, Majumdar, Susruta, Maloney, Katherine N, Mohamed, Enas, Murphy, Brian T, Nagorny, Pavel, Olson, David E, Overman, Larry E, Brown, Lauren E, Snyder, John K, Porco, John A, Rivas, Fatima, Ross, Samir A, Sarpong, Richmond, Sharma, Indrajeet, Shaw, Jared T, Xu, Zhengren, Shen, Ben, Shi, Wei, Stephenson, Corey RJ, Verano, Alyssa L, Tan, Derek S, Tang, Yi, Taylor, Richard E, Thomson, Regan J, Vosburg, David A, Wu, Jimmy, Wuest, William M, Zakarian, Armen, Zhang, Yufeng, Ren, Tianjing, Zuo, Zhong, Inglese, James, Michael, Sam, Simeonov, Anton, Zheng, Wei, Shinn, Paul, Jadhav, Ajit, Boxer, Matthew B, Hall, Matthew D, Xia, Menghang, Guha, Rajarshi, Rohde, Jason M, Kearney, Sara E, Kearney, Sara E, Zahoránszky-Kőhalmi, Gergely, Brimacombe, Kyle R, Henderson, Mark J, Lynch, Caitlin, Zhao, Tongan, Wan, Kanny K, Itkin, Zina, Dillon, Christopher, Shen, Min, Cheff, Dorian M, Lee, Tobie D, Bougie, Danielle, Cheng, Ken, Coussens, Nathan P, Dorjsuren, Dorjbal, Eastman, Richard T, Huang, Ruili, Iannotti, Michael J, Karavadhi, Surendra, Klumpp-Thomas, Carleen, Roth, Jacob S, Sakamuru, Srilatha, Sun, Wei, Titus, Steven A, Yasgar, Adam, Zhang, Ya-Qin, Zhao, Jinghua, Andrade, Rodrigo B, Brown, M Kevin, Burns, Noah Z, Cha, Jin K, Mevers, Emily E, Clardy, Jon, Clement, Jason A, Crooks, Peter A, Cuny, Gregory D, Ganor, Jake, Moreno, Jesus, Morrill, Lucas A, Picazo, Elias, Susick, Robert B, Garg, Neil K, Goess, Brian C, Grossman, Robert B, Hughes, Chambers C, Johnston, Jeffrey N, Joullie, Madeleine M, Kinghorn, A Douglas, Kingston, David GI, Krische, Michael J, Kwon, Ohyun, Maimone, Thomas J, Majumdar, Susruta, Maloney, Katherine N, Mohamed, Enas, Murphy, Brian T, Nagorny, Pavel, Olson, David E, Overman, Larry E, Brown, Lauren E, Snyder, John K, Porco, John A, Rivas, Fatima, Ross, Samir A, Sarpong, Richmond, Sharma, Indrajeet, Shaw, Jared T, Xu, Zhengren, Shen, Ben, Shi, Wei, Stephenson, Corey RJ, Verano, Alyssa L, Tan, Derek S, Tang, Yi, Taylor, Richard E, Thomson, Regan J, Vosburg, David A, Wu, Jimmy, Wuest, William M, Zakarian, Armen, Zhang, Yufeng, Ren, Tianjing, Zuo, Zhong, Inglese, James, Michael, Sam, Simeonov, Anton, Zheng, Wei, Shinn, Paul, Jadhav, Ajit, Boxer, Matthew B, Hall, Matthew D, Xia, Menghang, Guha, Rajarshi, and Rohde, Jason M

Abstract

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Published
2018

3. Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Author

Kearney, Sara E, Kearney, Sara E, Zahoránszky-Kőhalmi, Gergely, Brimacombe, Kyle R, Henderson, Mark J, Lynch, Caitlin, Zhao, Tongan, Wan, Kanny K, Itkin, Zina, Dillon, Christopher, Shen, Min, Cheff, Dorian M, Lee, Tobie D, Bougie, Danielle, Cheng, Ken, Coussens, Nathan P, Dorjsuren, Dorjbal, Eastman, Richard T, Huang, Ruili, Iannotti, Michael J, Karavadhi, Surendra, Klumpp-Thomas, Carleen, Roth, Jacob S, Sakamuru, Srilatha, Sun, Wei, Titus, Steven A, Yasgar, Adam, Zhang, Ya-Qin, Zhao, Jinghua, Andrade, Rodrigo B, Brown, M Kevin, Burns, Noah Z, Cha, Jin K, Mevers, Emily E, Clardy, Jon, Clement, Jason A, Crooks, Peter A, Cuny, Gregory D, Ganor, Jake, Moreno, Jesus, Morrill, Lucas A, Picazo, Elias, Susick, Robert B, Garg, Neil K, Goess, Brian C, Grossman, Robert B, Hughes, Chambers C, Johnston, Jeffrey N, Joullie, Madeleine M, Kinghorn, A Douglas, Kingston, David GI, Krische, Michael J, Kwon, Ohyun, Maimone, Thomas J, Majumdar, Susruta, Maloney, Katherine N, Mohamed, Enas, Murphy, Brian T, Nagorny, Pavel, Olson, David E, Overman, Larry E, Brown, Lauren E, Snyder, John K, Porco, John A, Rivas, Fatima, Ross, Samir A, Sarpong, Richmond, Sharma, Indrajeet, Shaw, Jared T, Xu, Zhengren, Shen, Ben, Shi, Wei, Stephenson, Corey RJ, Verano, Alyssa L, Tan, Derek S, Tang, Yi, Taylor, Richard E, Thomson, Regan J, Vosburg, David A, Wu, Jimmy, Wuest, William M, Zakarian, Armen, Zhang, Yufeng, Ren, Tianjing, Zuo, Zhong, Inglese, James, Michael, Sam, Simeonov, Anton, Zheng, Wei, Shinn, Paul, Jadhav, Ajit, Boxer, Matthew B, Hall, Matthew D, Xia, Menghang, Guha, Rajarshi, Rohde, Jason M, Kearney, Sara E, Kearney, Sara E, Zahoránszky-Kőhalmi, Gergely, Brimacombe, Kyle R, Henderson, Mark J, Lynch, Caitlin, Zhao, Tongan, Wan, Kanny K, Itkin, Zina, Dillon, Christopher, Shen, Min, Cheff, Dorian M, Lee, Tobie D, Bougie, Danielle, Cheng, Ken, Coussens, Nathan P, Dorjsuren, Dorjbal, Eastman, Richard T, Huang, Ruili, Iannotti, Michael J, Karavadhi, Surendra, Klumpp-Thomas, Carleen, Roth, Jacob S, Sakamuru, Srilatha, Sun, Wei, Titus, Steven A, Yasgar, Adam, Zhang, Ya-Qin, Zhao, Jinghua, Andrade, Rodrigo B, Brown, M Kevin, Burns, Noah Z, Cha, Jin K, Mevers, Emily E, Clardy, Jon, Clement, Jason A, Crooks, Peter A, Cuny, Gregory D, Ganor, Jake, Moreno, Jesus, Morrill, Lucas A, Picazo, Elias, Susick, Robert B, Garg, Neil K, Goess, Brian C, Grossman, Robert B, Hughes, Chambers C, Johnston, Jeffrey N, Joullie, Madeleine M, Kinghorn, A Douglas, Kingston, David GI, Krische, Michael J, Kwon, Ohyun, Maimone, Thomas J, Majumdar, Susruta, Maloney, Katherine N, Mohamed, Enas, Murphy, Brian T, Nagorny, Pavel, Olson, David E, Overman, Larry E, Brown, Lauren E, Snyder, John K, Porco, John A, Rivas, Fatima, Ross, Samir A, Sarpong, Richmond, Sharma, Indrajeet, Shaw, Jared T, Xu, Zhengren, Shen, Ben, Shi, Wei, Stephenson, Corey RJ, Verano, Alyssa L, Tan, Derek S, Tang, Yi, Taylor, Richard E, Thomson, Regan J, Vosburg, David A, Wu, Jimmy, Wuest, William M, Zakarian, Armen, Zhang, Yufeng, Ren, Tianjing, Zuo, Zhong, Inglese, James, Michael, Sam, Simeonov, Anton, Zheng, Wei, Shinn, Paul, Jadhav, Ajit, Boxer, Matthew B, Hall, Matthew D, Xia, Menghang, Guha, Rajarshi, and Rohde, Jason M

Abstract

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Published
2018

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