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1. SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

Author

Lovenia, Holy, Mahendra, Rahmad, Akbar, Salsabil Maulana, Miranda, Lester James V., Santoso, Jennifer, Aco, Elyanah, Fadhilah, Akhdan, Mansurov, Jonibek, Imperial, Joseph Marvin, Kampman, Onno P., Moniz, Joel Ruben Antony, Habibi, Muhammad Ravi Shulthan, Hudi, Frederikus, Montalan, Railey, Ignatius, Ryan, Lopo, Joanito Agili, Nixon, William, Karlsson, Börje F., Jaya, James, Diandaru, Ryandito, Gao, Yuze, Amadeus, Patrick, Wang, Bin, Cruz, Jan Christian Blaise, Whitehouse, Chenxi, Parmonangan, Ivan Halim, Khelli, Maria, Zhang, Wenyu, Susanto, Lucky, Ryanda, Reynard Adha, Hermawan, Sonny Lazuardi, Velasco, Dan John, Kautsar, Muhammad Dehan Al, Hendria, Willy Fitra, Moslem, Yasmin, Flynn, Noah, Adilazuarda, Muhammad Farid, Li, Haochen, Lee, Johanes, Damanhuri, R., Sun, Shuo, Qorib, Muhammad Reza, Djanibekov, Amirbek, Leong, Wei Qi, Do, Quyet V., Muennighoff, Niklas, Pansuwan, Tanrada, Putra, Ilham Firdausi, Xu, Yan, Tai, Ngee Chia, Purwarianti, Ayu, Ruder, Sebastian, Tjhi, William, Limkonchotiwat, Peerat, Aji, Alham Fikri, Keh, Sedrick, Winata, Genta Indra, Zhang, Ruochen, Koto, Fajri, Yong, Zheng-Xin, and Cahyawijaya, Samuel

Subjects
Computer Science - Computation and Language
Abstract

Southeast Asia (SEA) is a region rich in linguistic diversity and cultural variety, with over 1,300 indigenous languages and a population of 671 million people. However, prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA, compromising the quality of AI models for SEA languages. Evaluating models for SEA languages is challenging due to the scarcity of high-quality datasets, compounded by the dominance of English training data, raising concerns about potential cultural misrepresentation. To address these challenges, we introduce SEACrowd, a collaborative initiative that consolidates a comprehensive resource hub that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities. Through our SEACrowd benchmarks, we assess the quality of AI models on 36 indigenous languages across 13 tasks, offering valuable insights into the current AI landscape in SEA. Furthermore, we propose strategies to facilitate greater AI advancements, maximizing potential utility and resource equity for the future of AI in SEA., Comment: https://seacrowd.github.io/ Accepted in EMNLP 2024

Published
2024

6. Novel advances in biotransformation and bioactivation research – 2020 year in review

Author

Khojasteh, S Cyrus, Argikar, Upendra A, Driscoll, James P, Heck, Carley JS, King, Lloyd, Jackson, Klarissa D, Jian, Wenying, Kalgutkar, Amit S, Miller, Grover P, Kramlinger, Valerie, Rietjens, Ivonne MCM, Teitelbaum, Aaron M, Wang, Kai, Wei, Cong, Johnson, Benjamin M, Shu, Yue-Zhong, Zhuo, Xiaoliang, Meanwell, Nicholas A, Cerny, Matthew A, Obach, R Scott, Sharma, Raman, Spracklin, Douglas K, Walker, Gregory S, Goracci, Laura, Desantis, Jenny, Valeri, Aurora, Castellani, Beatrice, Eleuteri, Michela, Cruciani, Gabriele, Lall, Manjinder S, Bassyouni, Asser, Bradow, James, Brown, Maria, Bundesmann, Mark, Chen, Jinshan, Ciszewski, Gregory, Hagen, Anne E, Hyek, Dennis, Jenkinson, Stephen, Liu, Bo, Pan, Senliang, Reilly, Usa, Sach, Neal, Smaltz, Daniel J, Starr, Jeremy, Wagenaar, Melissa, de Bruyn Kops, Christina, Sicho, Martin, Mazzolari, Angelica, Kirchmair, Johannes, Korprasertthaworn, Porntipa, Chau, Nuy, Nair, Pramod C, Rowland, Andrew, Miners, John O, Wrobleski, Stephen T, Moslin, Ryan, Lin, Shuqun, Zhang, Yanlei, Spergel, Steven, Kempson, James, Tokarski, John S, Strnad, Joann, Zupa-Fernandez, Adriana, Cheng, Lihong, Shuster, David, Gillooly, Kathleen, Yang, Xiaoxia, Heimrich, Elizabeth, McIntyre, Kim W, Chaudhry, Charu, Khan, Javed, Ruzanov, Max, Tredup, Jeffrey, Mulligan, Dawn, Xie, Dianlin, Sun, Huadong, Huang, Christine, D'Arienzo, Celia, Aranibar, Nelly, Chiney, Manoj, Chimalakonda, Anjaneya, Pitts, William J, Lombardo, Louis, Carter, Percy H, Burke, James R, Weinstein, David S, Li, Jing, Liu, Ju, Enders, Jennifer, Arciprete, Michael, Tran, Chris, Aluri, Krishna, Guan, Li-Hua, O'Shea, Jonathan, Bisbe, Anna, Charisse, Klaus, Zlatev, Ivan, Najarian, Diana, Xu, Yuanxin, Kim, Jaeah, El Zahar, Noha M, Bartlett, Michael G, Katyayan, Kishore, Yi, Ping, Monk, Scott, Cassidy, Kenneth, Takahashi, Ryan H, Grandner, Jessica M, Bobba, Sudheer, Liu, Yanzhou, Beroza, Paul, Zhang, Donglu, Ma, Shuguang, Post, Noah, Yu, Rosie, Greenlee, Sarah, Gaus, Hans, Hurh, Eunju, Matson, John, Wang, Yanfeng, Tajima, Yuya, Toyoda, Takeshi, Hirayama, Yuichiro, Matsushita, Kohei, Yamada, Takanori, Ogawa, Kumiko, Watanabe, Kenji, Takamura-Enya, Takeji, Totsuka, Yukari, Wakabayashi, Keiji, Miyoshi, Noriyuki, Zhang, Jiayin, Chan, Chi-Kong, Ham, Yat-Hing, Chan, Wan, Schleiff, Mary Alexandra, Flynn, Noah R, Payakachat, Sasin, Schleiff, Benjamin Mark, Pinson, Anna O, Province, Dennis W, Swamidass, S Joshua, Boysen, Gunnar, Nardone-White, Dasean T, Bissada, Jennifer E, Abouda, Arsany A, Zhang, Zhuming, Connolly, Peter J, Lim, Heng Keang, Pande, Vineet, Meerpoel, Lieven, Teleha, Christopher, Branch, Jonathan R, Ondrus, Janine, Hickson, Ian, Bush, Tammy, Luistro, Leopoldo, Packman, Kathryn, Bischoff, James R, Ibrahim, Salam, Parrett, Christopher, Chong, Yolanda, Gottardis, Marco M, Bignan, Gilles, Mulder, Teresa, Bobba, Sudder, Johnson, Kevin M, Wang, Wei, Zhang, Chenghong, Cai, Jingwei, Choo, Edna F, Crawford, James J, Landry, Matthew L, Chen, Huifen, Kenny, Jane R, Lee, Wendy, Young, Wendy B, Geib, Timon, Thulasingam, Madhuranayaki, Haeggstrom, Jesper Z, Sleno, Lekha, Monroe, James J, Tanis, Keith Q, Podtelezhnikov, Alexei A, Nguyen, Truyen, Machotka, Sam V, Lynch, Donna, Evers, Raymond, Palamanda, Jairam, Miller, Randy R, Pippert, Todd, Cabalu, Tamara D, Johnson, Timothy E, Aslamkhan, Amy G, Kang, Wen, Tamburino, Alex M, Mitra, Kaushik, Agrawal, Nancy GB, and Sistare, Frank D

Subjects
METABOLISM, Toxicology, 030226 pharmacology & pharmacy, 03 medical and health sciences, nonp450 enzymes, 0302 clinical medicine, p450 enzymes, Biotransformation, INTRINSIC CLEARANCE, Political science, MASS-BALANCE, Humans, Pharmacology (medical), Pharmacology & Pharmacy, P450 Enzymes, General Pharmacology, Toxicology and Pharmaceutics, ANTISENSE OLIGONUCLEOTIDE, Toxicologie, VLAG, ARISTOLOCHIC ACID, Drug metabolism, Science & Technology, WIMEK, bioactivation, IDENTIFICATION, Year in review, IN-VITRO, NONHEPATOTOXIC DRUGS, Drug metabolizing enzymes, Drug development, 030220 oncology & carcinogenesis, COVALENT BINDING DATA, Engineering ethics, biotransformation, LIVER-MICROSOMES, Life Sciences & Biomedicine
Abstract

This annual review is the sixth of its kind since 2016 (see references). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation and bioactivation. These fields are constantly evolving with new molecular structures and discoveries of corresponding pathways for metabolism that impact relevant drug development with respect to efficacy and safety. Based on the selected articles, we created three sections: (1) drug design, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation and safety (Table 1). Unlike in years past, more biotransformation experts have joined and contributed to this effort while striving to maintain a balance of authors from academic and industry settings.[Table: see text]. ispartof: DRUG METABOLISM REVIEWS vol:53 issue:3 pages:384-433 ispartof: location:England status: published

Published
2021

18. Recent advances in computational metabolite structure predictions and altered metabolic pathways assessment to inform drug development processes.

Author

Schleiff, Mary Alexandra, Dhaware, Deepika, Sodhi, Jasleen K., de Bruyn Kops, Christina, Stork, Conrad, Šícho, Martin, Kochev, Nikolay, Svozil, Daniel, Jeliazkova, Nina, Kirchmair, Johannes, Djoumbou-Feunang, Yannick, Fiamoncini, Jarlei, Gil-de-la-Fuente, Alberto, Greiner, Russell, Manach, Claudine, Wishart, David S., Hughes, Tyler B., Le Dang, Na, Kumar, Ayush, and Flynn, Noah R.

Subjects
DRUG development, DRUG side effects, DRUG interactions, DRUG metabolism, DRUG efficacy, GENETIC polymorphisms
Abstract

Many drug candidates fail during preclinical and clinical trials due to variable or unexpected metabolism which may lead to variability in drug efficacy or adverse drug reactions. The drug metabolism field aims to address this important issue from many angles which range from the study of drug–drug interactions, pharmacogenomics, computational metabolic modeling, and others. This manuscript aims to provide brief but comprehensive manuscript summaries highlighting the conclusions and scientific importance of seven exceptional manuscripts published in recent years within the field of drug metabolism. Two main topics within the field are reviewed: novel computational metabolic modeling approaches which provide complex outputs beyond site of metabolism predictions, and experimental approaches designed to discern the impacts of interindividual variability and species differences on drug metabolism. The computational approaches discussed provide novel outputs in metabolite structure and formation likelihood and/or extend beyond the saturated field of drug phase I metabolism, while the experimental metabolic pathways assessments aim to highlight the impacts of genetic polymorphisms and clinical animal model metabolic differences on human metabolism and subsequent health outcomes. [ABSTRACT FROM AUTHOR]

Published
2021
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20. Significance of Multiple Bioactivation Pathways for Meclofenamate as Revealed through Modeling and Reaction Kinetics

Author

Schleiff, Mary Alexandra, Flynn, Noah R., Payakachat, Sasin, Schleiff, Benjamin Mark, Pinson, Anna O., Province, Dennis W., Swamidass, S. Joshua, Boysen, Gunnar, and Miller, Grover P.

Abstract

Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spectrometry and quantitate yields during reactions. Of the four quinone adducts, we were able to characterize structures for three of them. Based on kinetics, the most efficient bioactivation pathway led to the monohydroxy para-quinone-imine followed by the dechloro-ortho-quinone-imine. Two very inefficient pathways led to the dihydroxy ortho-quinone and a likely multiply adducted quinone. When taken together, bioactivation pathways for meclofenamate accounted for approximately 13% of total metabolism. In sum, XenoNet facilitated prediction of reactive metabolite structures, whereas quantitative experimental studies provided a tractable approach to validate actual bioactivation pathways for meclofenamate. Our results provide a foundation for assessing reactive metabolite load more accurately for future comparative studies with other NSAIDs and drugs in general.Significance StatementMeclofenamate bioactivation may initiate hepatotoxicity, yet common risk assessment approaches are often cumbersome and inefficient and yield qualitative insights that do not scale relative bioactivation risks. We developed and applied innovative computational modeling and quantitative kinetics to identify and validate meclofenamate bioactivation pathways and relevance as a function of time and concentration. This strategy yielded novel insights on meclofenamate bioactivation and provides a tractable approach to more accurately and efficiently assess other drug bioactivations and correlate risks to toxicological outcomes.

Published
2021
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