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1. The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models

Author

Cattaneo, Alberto, Bonner, Stephen, Martynec, Thomas, Luschi, Carlo, Barrett, Ian P, and Justus, Daniel

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods
Abstract

Knowledge Graph Completion has been increasingly adopted as a useful method for several tasks in biomedical research, like drug repurposing or drug-target identification. To that end, a variety of datasets and Knowledge Graph Embedding models has been proposed over the years. However, little is known about the properties that render a dataset useful for a given task and, even though theoretical properties of Knowledge Graph Embedding models are well understood, their practical utility in this field remains controversial. We conduct a comprehensive investigation into the topological properties of publicly available biomedical Knowledge Graphs and establish links to the accuracy observed in real-world applications. By releasing all model predictions and a new suite of analysis tools we invite the community to build upon our work and continue improving the understanding of these crucial applications.

Published
2024

2. Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

Author

Bonner, Stephen, Kirik, Ufuk, Engkvist, Ola, Tang, Jian, and Barrett, Ian P

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks, Quantitative Biology - Molecular Networks
Abstract

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modeling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely-connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modeling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition., Comment: Briefings in Bioinformatics, 2022

Published
2021
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3. A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets

Author

Ye, Cheng, Swiers, Rowan, Bonner, Stephen, and Barrett, Ian

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning
Abstract

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data driven drug discovery., Comment: 16 pages, 4 figures, IEEE/ACM Transactions on Computational Biology and Bioinformatics

Published
2021
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4. Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

Author

Bonner, Stephen, Barrett, Ian P, Ye, Cheng, Swiers, Rowan, Engkvist, Ola, Hoyt, Charles Tapley, and Hamilton, William L

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Knowledge Graphs (KG) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery and have the potential to assist in key challenges such as target identification. In the drug discovery domain, KGs can be employed as part of a process which can result in lab-based experiments being performed, or impact on other decisions, incurring significant time and financial costs and most importantly, ultimately influencing patient healthcare. For KGE models to have impact in this domain, a better understanding of not only of performance, but also the various factors which determine it, is required. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our goal is not to focus on the best overall model or configuration, instead we take a deeper look at how performance can be affected by changes in the training setup, choice of hyperparameters, model parameter initialisation seed and different splits of the datasets. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models. Indeed these factors should be reported along with model architectures to ensure complete reproducibility and fair comparisons of future work, and we argue this is critical for the acceptance of use, and impact of KGEs in a biomedical setting.

Published
2021
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5. A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Author

Bonner, Stephen, Barrett, Ian P, Ye, Cheng, Swiers, Rowan, Engkvist, Ola, Bender, Andreas, Hoyt, Charles Tapley, and Hamilton, William L

Subjects
Computer Science - Artificial Intelligence
Abstract

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, whilst relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data is required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorised according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and a evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, whilst also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

Published
2021
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6. Cultivating Innovative Pragmatic Cluster-Randomized Registry Trials Embedded in Hemodialysis Care: Workshop Proceedings From 2018

Author

Lee, Elliot J, Patel, Aakil, Acedillo, Rey R, Bachynski, Jovina C, Barrett, Ian, Basile, Erika, Battistella, Marisa, Benjamin, Derek, Berry, David, Blake, Peter G, Chan, Patricia, Bohm, Clara J, Clemens, Kristin K, Cook, Charles, Dember, Laura, Dirk, Jade S, Dixon, Stephanie, Fowler, Elisabeth, Getchell, Leah, Gholami, Nazanine, Goldstein, Cory, Hahn, Emma, Hogeterp, Betty, Huang, Susan, Hughes, Michelle, Jardine, Meg J, Kalatharan, Shasikara, Kilburn, Shane, Lacson, Eduardo, Leonard, Sean, Liberty, Channing, Lindsay, Craig, MacRae, Jennifer M, Manns, Braden J, McCallum, Janice, McIntyre, Christopher W, Molnar, Amber O, Mustafa, Reem A, Nesrallah, Gihad E, Oliver, Matthew J, Pandes, Michael, Pandeya, Sanjay, Parmar, Malvinder S, Rabin, Elijah Z, Riley, Johnathan, Silver, Samuel A, Sontrop, Jessica M, Sood, Manish M, Suri, Rita S, Tangri, Navdeep, Tascona, Daniel J, Thomas, Alison, Wald, Ron, Walsh, Michael, Weijer, Charles, Weir, Matthew A, Vorster, Hans, Zimmerman, Deborah, and Garg, Amit X

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Trials and Supportive Activities, Clinical Research, Comparative Effectiveness Research, Assistive Technology, Kidney Disease, Bioengineering, Good Health and Well Being, chronic kidney disease, cluster randomized, hemodialysis, patient-oriented research, pragmatic, randomized controlled trial, registry-based, workshop, Clinical sciences
Abstract

Hemodialysis is a life-sustaining treatment for persons with kidney failure. However, those on hemodialysis still face a poor quality of life and a short life expectancy. High-quality research evidence from large randomized controlled trials is needed to identify interventions that improve the experiences, outcomes, and health care of persons receiving hemodialysis. With the support of the Canadian Institutes of Health Research and its Strategy for Patient-Oriented Research, the Innovative Clinical Trials in Hemodialysis Centers initiative brought together Canadian and international kidney researchers, patients, health care providers, and health administrators to participate in a workshop held in Toronto, Canada, on June 2 and 3, 2018. The workshop served to increase knowledge and awareness about the conduct of innovative, pragmatic, cluster-randomized registry trials embedded into routine hemodialysis care and provided an opportunity to discuss and build support for new trial ideas. The workshop content included structured presentations, facilitated group discussions, and expert panel feedback. Partnerships and promising trial ideas borne out of the workshop will continue to be developed to support the implementation of future large-scale trials.

Published
2019

8. Drug Screening in Human PSC-Cardiac Organoids Identifies Pro-proliferative Compounds Acting via the Mevalonate Pathway

Author

Mills, Richard J., Parker, Benjamin L., Quaife-Ryan, Gregory A., Voges, Holly K., Needham, Elise J., Bornot, Aurelie, Ding, Mei, Andersson, Henrik, Polla, Magnus, Elliott, David A., Drowley, Lauren, Clausen, Maryam, Plowright, Alleyn T., Barrett, Ian P., Wang, Qing-Dong, James, David E., Porrello, Enzo R., and Hudson, James E.

Published
2019
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9. Supplementary Figures from Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma

Author

Egan, Donagh, primary, Kreileder, Martina, primary, Nabhan, Myriam, primary, Iglesias-Martinez, Luis F., primary, Dovedi, Simon J., primary, Valge-Archer, Viia, primary, Grover, Amit, primary, Wilkinson, Robert W., primary, Slidel, Timothy, primary, Bendtsen, Claus, primary, Barrett, Ian P., primary, Brennan, Donal J., primary, Kolch, Walter, primary, and Zhernovkov, Vadim, primary

Published
2023
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10. Supplementary Tables from Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma

Author

Egan, Donagh, primary, Kreileder, Martina, primary, Nabhan, Myriam, primary, Iglesias-Martinez, Luis F., primary, Dovedi, Simon J., primary, Valge-Archer, Viia, primary, Grover, Amit, primary, Wilkinson, Robert W., primary, Slidel, Timothy, primary, Bendtsen, Claus, primary, Barrett, Ian P., primary, Brennan, Donal J., primary, Kolch, Walter, primary, and Zhernovkov, Vadim, primary

Published
2023
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11. Data from Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma

Author

Egan, Donagh, primary, Kreileder, Martina, primary, Nabhan, Myriam, primary, Iglesias-Martinez, Luis F., primary, Dovedi, Simon J., primary, Valge-Archer, Viia, primary, Grover, Amit, primary, Wilkinson, Robert W., primary, Slidel, Timothy, primary, Bendtsen, Claus, primary, Barrett, Ian P., primary, Brennan, Donal J., primary, Kolch, Walter, primary, and Zhernovkov, Vadim, primary

Published
2023
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12. Small gene networks delineate immune cell states and characterize immunotherapy response in melanoma

Author

Egan, Donagh, primary, Kreileder, Martina, additional, Nabhan, Myriam, additional, Iglesias-Martinez, Luis F., additional, Dovedi, Simon J., additional, Valge-Archer, Viia, additional, Grover, Amit, additional, Wilkinson, Robert W., additional, Slidel, Timothy, additional, Bendtsen, Claus, additional, Barrett, Ian P., additional, Brennan, Donal J., additional, Kolch, Walter, additional, and Zhernovkov, Vadim, additional

Published
2023
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13. Video 1 - ATMi increases the rate of mitotic catastrophe in glioma cells when p53 is knocked down. from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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14. Video 4 - ATMi increases the rate of mitotic catastrophe in glioma cells when p53 is knocked down. from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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15. Data from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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16. Supplementary Information from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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17. Video 2 - ATMi increases the rate of mitotic catastrophe in glioma cells when p53 is knocked down. from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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18. Video 3 - ATMi increases the rate of mitotic catastrophe in glioma cells when p53 is knocked down. from Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, primary, Ahmad, Syed F., primary, Hughes, Gareth, primary, Sheridan, Victoria, primary, Odedra, Rajesh, primary, Farrington, Paul, primary, Cadogan, Elaine B., primary, Riches, Lucy C., primary, Garcia-Trinidad, Antonio, primary, Thomason, Andrew G., primary, Patel, Bhavika, primary, Vincent, Jennifer, primary, Lau, Alan, primary, Pike, Kurt G., primary, Hunt, Thomas A., primary, Sule, Amrita, primary, Valerie, Nicholas C.K., primary, Biddlestone-Thorpe, Laura, primary, Kahn, Jenna, primary, Beckta, Jason M., primary, Mukhopadhyay, Nitai, primary, Barlaam, Bernard, primary, Degorce, Sebastien L., primary, Kettle, Jason, primary, Colclough, Nicola, primary, Wilson, Joanne, primary, Smith, Aaron, primary, Barrett, Ian P., primary, Zheng, Li, primary, Zhang, Tianwei, primary, Wang, Yingchun, primary, Chen, Kan, primary, Pass, Martin, primary, Durant, Stephen T., primary, and Valerie, Kristoffer, primary

Published
2023
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21. 11th German Conference on Chemoinformatics (GCC 2015): Fulda, Germany. 8–10 November 2015

Author

Fechner, Uli, de Graaf, Chris, Torda, Andrew E., Güssregen, Stefan, Evers, Andreas, Matter, Hans, Hessler, Gerhard, Richmond, Nicola J., Schmidtke, Peter, Segler, Marwin H. S., Waller, Mark P., Pleik, Stefanie, Shea, Joan-Emma, Levine, Zachary, Mullen, Ryan, van den Broek, Karina, Epple, Matthias, Kuhn, Hubert, Truszkowski, Andreas, Zielesny, Achim, Fraaije, Johannes (Hans), Gracia, Ruben Serral, Kast, Stefan M., Bulusu, Krishna C., Bender, Andreas, Yosipof, Abraham, Nahum, Oren, Senderowitz, Hanoch, Krotzky, Timo, Schulz, Robert, Wolber, Gerhard, Bietz, Stefan, Rarey, Matthias, Zimmermann, Markus O., Lange, Andreas, Ruff, Manuel, Heidrich, Johannes, Onlia, Ionut, Exner, Thomas E., Boeckler, Frank M., Bermudez, Marcel, Firaha, Dzmitry S., Hollóczki, Oldamur, Kirchner, Barbara, Tautermann, Christofer S., Volkamer, Andrea, Eid, Sameh, Turk, Samo, Rippmann, Friedrich, Fulle, Simone, Saleh, Noureldin, Saladino, Giorgio, Gervasio, Francesco L., Haensele, Elke, Banting, Lee, Whitley, David C., Oliveira Santos, Jana Sopkova-de, Bureau, Ronan, Clark, Timothy, Sandmann, Achim, Lanig, Harald, Kibies, Patrick, Heil, Jochen, Hoffgaard, Franziska, Frach, Roland, Engel, Julian, Smith, Steven, Basu, Debjit, Rauh, Daniel, Kohlbacher, Oliver, Boeckler, Frank M., Essex, Jonathan W., Bodnarchuk, Michael S., Ross, Gregory A., Finkelmann, Arndt R., Göller, Andreas H., Schneider, Gisbert, Husch, Tamara, Schütter, Christoph, Balducci, Andrea, Korth, Martin, Ntie-Kang, Fidele, Günther, Stefan, Sippl, Wolfgang, Mbaze, Luc Meva’a, Ntie-Kang, Fidele, Simoben, Conrad V., Lifongo, Lydia L., Ntie-Kang, Fidele, Judson, Philip, Barilla, Jiří, Lokajíček, Miloš V., Pisaková, Hana, Simr, Pavel, Kireeva, Natalia, Petrov, Alexandre, Ostroumov, Denis, Solovev, Vitaly P., Pervov, Vladislav S., Friedrich, Nils-Ole, Sommer, Kai, Rarey, Matthias, Kirchmair, Johannes, Proschak, Eugen, Weber, Julia, Moser, Daniel, Kalinowski, Lena, Achenbach, Janosch, Mackey, Mark, Cheeseright, Tim, Renner, Gerrit, Renner, Gerrit, Schmidt, Torsten C., Schram, Jürgen, Egelkraut-Holtus, Marion, van Oeyen, Albert, Kalliokoski, Tuomo, Fourches, Denis, Ibezim, Akachukwu, Mbah, Chika J., Adikwu, Umale M., Nwodo, Ngozi J., Steudle, Alexander, Masek, Brian B., Nagy, Stephan, Baker, David, Soltanshahi, Fred, Dorfman, Roman, Dubrucq, Karen, Patel, Hitesh, Koch, Oliver, Mrugalla, Florian, Kast, Stefan M., Ain, Qurrat U., Fuchs, Julian E., Owen, Robert M., Omoto, Kiyoyuki, Torella, Rubben, Pryde, David C., Glen, Robert, Bender, Andreas, Hošek, Petr, Spiwok, Vojtěch, Mervin, Lewis H., Barrett, Ian, Firth, Mike, Murray, David C., McWilliams, Lisa, Cao, Qing, Engkvist, Ola, Warszycki, Dawid, Śmieja, Marek, Bojarski, Andrzej J., Aniceto, Natalia, Freitas, Alex, Ghafourian, Taravat, Herrmann, Guido, Eigner-Pitto, Valentina, Naß, Alexandra, Kurczab, Rafał, Bojarski, Andrzej J., Lange, Andreas, Günther, Marcel B., Hennig, Susanne, Büttner, Felix M., Schall, Christoph, Sievers-Engler, Adrian, Ansideri, Francesco, Koch, Pierre, Stehle, Thilo, Laufer, Stefan, Böckler, Frank M., Zdrazil, Barbara, Montanari, Floriane, Ecker, Gerhard F., Grebner, Christoph, Hogner, Anders, Ulander, Johan, Edman, Karl, Guallar, Victor, Tyrchan, Christian, Ulander, Johan, Tyrchan, Christian, Klute, Wolfgang, Bergström, Fredrik, Kramer, Christian, Nguyen, Quoc Dat, Frach, Roland, Kibies, Patrick, Strohfeldt, Steven, Böttcher, Saraphina, Pongratz, Tim, Horinek, Dominik, Kast, Stefan M., Rupp, Bernd, Al-Yamori, Raed, Lisurek, Michael, Kühne, Ronald, Furtado, Filipe, van den Broek, Karina, Wessjohann, Ludger, Mathea, Miriam, Baumann, Knut, Mohamad-Zobir, Siti Zuraidah, Fu, Xianjun, Fan, Tai-Ping, Bender, Andreas, Kuhn, Maximilian A., Sotriffer, Christoph A., Zoufir, Azedine, Li, Xitong, Mervin, Lewis, Berg, Ellen, Polokoff, Mark, Ihlenfeldt, Wolf D., Ihlenfeldt, Wolf D., Pretzel, Jette, Alhalabi, Zayan, Fraczkiewicz, Robert, Waldman, Marvin, Clark, Robert D., Shaikh, Neem, Garg, Prabha, Kos, Alexander, Himmler, Hans-Jürgen, Sandmann, Achim, Jardin, Christophe, Sticht, Heinrich, Steinbrecher, Thomas B., Dahlgren, Markus, Cappel, Daniel, Lin, Teng, Wang, Lingle, Krilov, Goran, Abel, Robert, Friesner, Richard, Sherman, Woody, Pöhner, Ina A., Panecka, Joanna, Wade, Rebecca C., Bietz, Stefan, Schomburg, Karen T., Hilbig, Matthias, Rarey, Matthias, Jäger, Christian, Wieczorek, Vivien, Westerhoff, Lance M., Borbulevych, Oleg Y., Demuth, Hans-Ulrich, Buchholz, Mirko, Schmidt, Denis, Rickmeyer, Thomas, Krotzky, Timo, Kolb, Peter, Mittal, Sumit, Sánchez-García, Elsa, Nogueira, Mauro S., Oliveira, Tiago B., da Costa, Fernando B., and Schmidt, Thomas J.

Published
2016
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24. Biculturalism--Outsiders Within: The Career Development Experiences of Black Human Resource Developers.

Author

Barrett, Ian C., Cervero, Ronald M., and Johnson-Bailey, Juanita

Abstract

Interviews with 10 black human resource developers exploring their career experiences indicated that they faced similar discrimination challenges as other black workers. They developed bicultural strategies for handling racial issues, such as support systems at work and outside the workplace. (Contains 35 references.) (SK)

Published
2003

25. review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Author

Bonner, Stephen, Barrett, Ian P, Ye, Cheng, Swiers, Rowan, Engkvist, Ola, Bender, Andreas, Hoyt, Charles Tapley, and Hamilton, William L

Subjects
DRUG discovery, KNOWLEDGE graphs, DRUG repositioning, DRUG toxicity, MACHINE learning
Abstract

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene–disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain. [ABSTRACT FROM AUTHOR]

Published
2022
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28. Additional file 1 of Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty

Author

Mervin, Lewis H., Trapotsi, Maria-Anna, Afzal, Avid M., Barrett, Ian P., Bender, Andreas, and Engkvist, Ola

Abstract

Additional file 1: Figure S1. a) Number of Active and Inactive compounds across the 559 models and across the three different pXC50 Thresholds (5, 6, and 7). Figure S2. Ideal probabilities as a function of the delta of PRF versus RF error margins across emulated train-test standard deviations. Figure S3. Ideal probabilities as a function of the delta of PRF versus RF error margins across emulated train-test standard deviations. Figure S4. Ideal probabilities as a function of the delta of PRF versus RF error margins across emulated train-test standard deviations. Figure S5. Comparison between RF scikit-learn implementation and PRF (when σ = 0). Figure S6. Ideal probabilities as a function of the delta of PRF versus RF error margins across emulated train-test standard deviations for models trained with a min of 80% putative inactives. Figure S7. Ideal probabilities as a function of the delta of PRF versus RF error margins across emulated train-test standard deviations for models trained without putative inactives. Figure S8. Correlation analysis of model sizes (when pChEMBL threshold is 5 [10 µM]) as a function of PRF improvement.

Published
2021
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30. The DNA sequence of the human X chromosome

Author

Ross, Mark T., Grafham, Darren V., Coffey, Alison J., Scherer, Steven, McLay, Kirsten, Muzny, Donna, Platzer, Matthias, Howell, Gareth R., Burrows, Christine, Bird, Christine P., Frankish, Adam, Lovell, Frances L., Howe, Kevin L., Ashurst, Jennifer L., Fulton, Robert S., Sudbrak, Ralf, Wen, Gaiping, Jones, Matthew C., Hurles, Matthew E., Andrews, T. Daniel, Scott, Carol E., Searle, Stephen, Ramser, Juliane, Whittaker, Adam, Deadman, Rebecca, Carter, Nigel P., Hunt, Sarah E., Chen, Rui, Cree, Andrew, Gunaratne, Preethi, Havlak, Paul, Hodgson, Anne, Metzker, Michael L., Richards, Stephen, Scott, Graham, Steffen, David, Sodergren, Erica, Wheeler, David A., Worley, Kim C., Ainscough, Rachael, Ambrose, Kerrie D., Ansari-Lari, M. Ali, Aradhya, Swaroop, Ashwell, Robert I. S., Babbage, Anne K., Bagguley, Claire L., Ballabio, Andrea, Banerjee, Ruby, Barker, Gary E., Barlow, Karen F., Barrett, Ian P., Bates, Karen N., Beare, David M., Beasley, Helen, Beasley, Oliver, Beck, Alfred, Bethel, Graeme, Blechschmidt, Karin, Brady, Nicola, Bray-Allen, Sarah, Bridgeman, Anne M., Brown, Andrew J., Brown, Mary J., Bonnin, David, Bruford, Elspeth A., Buhay, Christian, Burch, Paula, Burford, Deborah, Burgess, Joanne, Burrill, Wayne, Burton, John, Bye, Jackie M., Carder, Carol, Carrel, Laura, Chako, Joseph, Chapman, Joanne C., Chavez, Dean, Chen, Ellson, Chen, Guan, Chen, Yuan, Chen, Zhijian, Chinault, Craig, Ciccodicola, Alfredo, Clark, Sue Y., Clarke, Graham, Clee, Chris M., Clegg, Sheila, Clerc-Blankenburg, Kerstin, Clifford, Karen, Cobley, Vicky, Cole, Charlotte G., Conquer, Jen S., Corby, Nicole, Connor, Richard E., David, Robert, Davies, Joy, Davis, Clay, Davis, John, Delgado, Oliver, DeShazo, Denise, Dhami, Pawandeep, Ding, Yan, Dinh, Huyen, Dodsworth, Steve, Draper, Heather, Dugan-Rocha, Shannon, Dunham, Andrew, Dunn, Matthew, Durbin, K. James, Dutta, Ireena, Eades, Tamsin, Ellwood, Matthew, Emery-Cohen, Alexandra, Errington, Helen, Evans, Kathryn L., Faulkner, Louisa, Francis, Fiona, Frankland, John, Fraser, Audrey E., Galgoczy, Petra, Gilbert, James, Gill, Rachel, Glockner, Gernot, Gregory, Simon G., Gribble, Susan, Griffiths, Coline, Grocock, Russell, Gu, Yanghong, Gwilliam, Rhian, Hamilton, Cerissa, Hart, Elizabeth A., Hawes, Alicia, Heath, Paul D., Heitmann, Katja, Hennig, Steffen, Hernandez, Judith, Hinzmann, Bernd, Ho, Sarah, Hoffs, Michael, Howden, Phillip J., Huckle, Elizabeth J., Hume, Jennifer, Hunt, Paul J., Hunt, Adrienne R., Isherwood, Judith, Jacob, Leni, Johnson, David, Jones, Sally, de Jong, Pieter J., Joseph, Shirin S., Keenan, Stephen, Kelly, Susan, Kershaw, Joanne K., Khan, Ziad, Kioschis, Petra, Klages, Sven, Knights, Andrew J., Kosiura, Anna, Kovar-Smith, Christie, Laird, Gavin K., Langford, Cordelia, Lawlor, Stephanie, Leversha, Margaret, Lewis, Lora, Liu, Wen, Lloyd, Christine, Lloyd, David M., Loulseged, Hermela, Loveland, Jane E., Lovell, Jamieson D., Lozado, Ryan, Lu, Jing, Lyne, Rachael, Ma, Jie, Maheshwari, Manjula, Matthews, Lucy H., McDowall, Jennifer, McLaren, Stuart, McMurray, Amanda, Meidl, Patrick, Meitinger, Thomas, Milne, Sarah, Miner, George, Mistry, Shailesh L., Morgan, Margaret, Morris, Sidney, Muller, Ines, Mullikin, James C., Nguyen, Ngoc, Nordsiek, Gabriele, Nyakatura, Gerald, O'Dell, Christopher N., Okwuonu, Geoffery, Palmer, Sophie, Pandian, Richard, Parker, David, Parrish, Julia, Pasternak, Shiran, Patel, Dina, Pearce, Alex V., Pearson, Danita M., Pelan, Sarah E., Perez, Lesette, Porter, Keith M., Ramsey, Yvonne, Reichwald, Kathrin, Rhodes, Susan, Ridler, Kerry A., Schlessinger, David, Schueler, Mary G., Sehra, Harminder K., Shaw-Smith, Charles, Shen, Hua, Sheridan, Elizabeth M., Shownkeen, Ratna, Skuce, Carl D., Smith, Michelle L., Sotheran, Elizabeth C., Steingruber, Helen E., Steward, Charles A., Storey, Roy, Swann, R. Mark, Swarbreck, David, Tabor, Paul E., Taudien, Stefan, Taylor, Tineace, Teague, Brian, Thomas, Karen, Thorpe, Andrea, Timms, Kirsten, Tracey, Alan, Trevanion, Steve, Tromans, Anthony C., d'Urso, Michele, Verduzco, Daniel, Villasana, Donna, Waldron, Lenee, Wall, Melanie, Wang, Qiaoyan, Warren, James, Warry, Georgina L., Wei, Xuehong, West, Anthony, Whitehead, Siobhan L., Whiteley, Mathew N., Wilkinson, Jane E., Willey, David L., Williams, Gabrielle, Williams, Leanne, Williamson, Angela, Williamson, Helen, Wilming, Laurens, Woodmansey, Rebecca L., Wray, Paul W., Yen, Jennifer, Zhang, Jingkun, Zhou, Jianling, Zoghbi, Huda, Zorilla, Sara, Buck, David, Reinhardt, Richard, Poustka, Annemarie, Rosenthal, Andre, Lehrach, Hans, Meindl, Alfons, Minx, Patrick J., Hillier, LaDeana W., Willard, Huntington F., Wilson, Richard K., Waterston, Robert H., Rice, Catherine M., Vaudin, Mark, Coulson, Alan, Nelson, David L., Weinstock, George, Sulston, John E., Durbin, Richard, Hubbard, Tim, Gibbs, Richard A., Beck, Stephan, Rogers, Jane, and Bentley, David R.

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Mark T. Ross (corresponding author) [1]; Darren V. Grafham [1]; Alison J. Coffey [1]; Steven Scherer [2]; Kirsten McLay [1]; Donna Muzny [2]; Matthias Platzer [3]; Gareth R. Howell [...]

Published
2005
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31. A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets

Author

Ye, Cheng, Swiers, Rowan, Bonner, Stephen, and Barrett, Ian

Abstract

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage – identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Published
2022
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35. Orally Bioavailable and Blood–Brain Barrier-Penetrating ATM Inhibitor (AZ32) Radiosensitizes Intracranial Gliomas in Mice

Author

Karlin, Jeremy, primary, Allen, Jasmine, additional, Ahmad, Syed F., additional, Hughes, Gareth, additional, Sheridan, Victoria, additional, Odedra, Rajesh, additional, Farrington, Paul, additional, Cadogan, Elaine B., additional, Riches, Lucy C., additional, Garcia-Trinidad, Antonio, additional, Thomason, Andrew G., additional, Patel, Bhavika, additional, Vincent, Jennifer, additional, Lau, Alan, additional, Pike, Kurt G., additional, Hunt, Thomas A., additional, Sule, Amrita, additional, Valerie, Nicholas C.K., additional, Biddlestone-Thorpe, Laura, additional, Kahn, Jenna, additional, Beckta, Jason M., additional, Mukhopadhyay, Nitai, additional, Barlaam, Bernard, additional, Degorce, Sebastien L., additional, Kettle, Jason, additional, Colclough, Nicola, additional, Wilson, Joanne, additional, Smith, Aaron, additional, Barrett, Ian P., additional, Zheng, Li, additional, Zhang, Tianwei, additional, Wang, Yingchun, additional, Chen, Kan, additional, Pass, Martin, additional, Durant, Stephen T., additional, and Valerie, Kristoffer, additional

Published
2018
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36. Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection

Author

Mervin, Lewis H, Cao, Qing, Barrett, Ian P, Firth, Mike A, Murray, David, McWilliams, Lisa, Haddrick, Malcolm, Wigglesworth, Mark, Engkvist, Ola, Bender, Andreas, Mervin, Lewis [0000-0002-7271-0824], Bender, Andreas [0000-0002-6683-7546], and Apollo - University of Cambridge Repository

Subjects
Cell Survival, Cell Cycle, Humans, Cell Line, High-Throughput Screening Assays
Abstract

While mechanisms of cytotoxicity and cytostaticity have been studied extensively from the biological side, relatively little is currently understood regarding areas of chemical space leading to cytotoxicity and cytostasis in large compound collections. Predicting and rationalizing potential adverse mechanism-of-actions (MoAs) of small molecules is however crucial for screening library design, given the link of even low level cytotoxicity and adverse events observed in man. In this study, we analyzed results from a cell-based cytotoxicity screening cascade, comprising 296 970 nontoxic, 5784 cytotoxic and cytostatic, and 2327 cytostatic-only compounds evaluated on the THP-1 cell-line. We employed an in silico MoA analysis protocol, utilizing 9.5 million active and 602 million inactive bioactivity points to generate target predictions, annotate predicted targets with pathways, and calculate enrichment metrics to highlight targets and pathways. Predictions identify known mechanisms for the top ranking targets and pathways for both phenotypes after review and indicate that while processes involved in cytotoxicity versus cytostaticity seem to overlap, differences between both phenotypes seem to exist to some extent. Cytotoxic predictions highlight many kinases, including the potentially novel cytotoxicity-related target STK32C, while cytostatic predictions outline targets linked with response to DNA damage, metabolism, and cytoskeletal machinery. Fragment analysis was also employed to generate a library of toxicophores to improve general understanding of the chemical features driving toxicity. We highlight substructures with potential kinase-dependent and kinase-independent mechanisms of toxicity. We also trained a cytotoxic classification model on proprietary and public compound readouts, and prospectively validated these on 988 novel compounds comprising difficult and trivial testing instances, to establish the applicability domain of models. The proprietary model performed with precision and recall scores of 77.9% and 83.8%, respectively. The MoA results and top ranking substructures with accompanying MoA predictions are available as a platform to assess screening collections.

Published
2016

39. Discovery of retinoic acid receptor agonists as proliferators of cardiac progenitor cells through a phenotypic screening approach

Author

Drowley, Lauren, McPheat, Jane, Nordqvist, Anneli, Peel, Samantha, Karlsson, Ulla, Martinsson, Sofia, Müllers, Erik, Dellsén, Anita, Knight, Sinead, Barrett, Ian, Sánchez, José, Magnusson, Björn, Greber, Boris, Wang, Qing‐Dong, and Plowright, Alleyn T.

Abstract

Identification of small molecules with the potential to selectively proliferate cardiac progenitor cells (CPCs) will aid our understanding of the signaling pathways and mechanisms involved and could ultimately provide tools for regenerative therapies for the treatment of post‐MI cardiac dysfunction. We have used an in vitro human induced pluripotent stem cell‐derived CPC model to screen a 10,000‐compound library containing molecules representing different target classes and compounds reported to modulate the phenotype of stem or primary cells. The primary readout of this phenotypic screen was proliferation as measured by nuclear count. We identified retinoic acid receptor (RAR) agonists as potent proliferators of CPCs. The CPCs retained their progenitor phenotype following proliferation and the identified RAR agonists did not proliferate human cardiac fibroblasts, the major cell type in the heart. In addition, the RAR agonists were able to proliferate an independent source of CPCs, HuES6. The RAR agonists had a time‐of‐differentiation‐dependent effect on the HuES6‐derived CPCs. At 4 days of differentiation, treatment with retinoic acid induced differentiation of the CPCs to atrial cells. However, after 5 days of differentiation treatment with RAR agonists led to an inhibition of terminal differentiation to cardiomyocytes and enhanced the proliferation of the cells. RAR agonists, at least transiently, enhance the proliferation of human CPCs, at the expense of terminal cardiac differentiation. How this mechanism translates in vivo to activate endogenous CPCs and whether enhancing proliferation of these rare progenitor cells is sufficient to enhance cardiac repair remains to be investigated. An image‐based phenotypic screen, using a readout of nuclear count to measure proliferation, identified retinoic acid receptor agonists as potent proliferators of induced pluripotent stem cell‐derived cardiac progenitor cells. The cardiac progenitor cells retained their progenitor phenotype following proliferation and the retinoic acid receptor agonists did not proliferate human cardiac fibroblasts, the major cell type in the heart.

Published
2020
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41. Safety and Efficacy of Intraoperative Computer-Navigated Versus Non-Navigated Shoulder Arthroplasty at a Tertiary Referral

Author

Barrett, Ian, Ramakrishnan, Anna, and Cheung, Emilie

Abstract

Emerging technologies in shoulder arthroplasty, such as 3-dimensional planning software and real-time intraoperative navigation, are now available for surgeons to perform more accurate placement of the glenoid component without malposition or perforation. Using these tools, the surgeon can visualize the version, inclination, and containment of the implant and determine whether augmented components would be necessary. This review provides an updated investigation of the present literature to elucidate the role of computer navigation in modern shoulder arthroplasty.

Published
2019
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42. Preoperative Radiographic Evaluation of Patients With Pelvic Discontinuity.

Author

Martin, J. Ryan, Barrett, Ian J., Sierra, Rafael J., Lewallen, David G., and Berry, Daniel J.

Abstract

Background: Pelvic discontinuity (PD) is a rare but devastating mechanism of failure in total hip arthroplasty. Radiographic findings have been described for the identification of PD. However, no study has specifically examined radiographic parameters and the utility of specific views in the preoperative identification of PD. Methods: We performed a retrospective review of 133 patients who underwent acetabular revision for PD. Preoperative radiographic studies were reviewed including anteroposterior pelvis (AP; n = 133), true lateral hip (n = 132), Judet (n = 47), false profile (n = 4), and computed tomography scans (n = 14). Radiographs were read by the senior authors to identify the following parameters suggestive of PD: visible fracture line, medial migration of the inferior hemipelvis, and obturator ring asymmetry. Results: Using only the AP view, the fracture line was visible in 116 (87%), medial migration of the inferior hemipelvis in 126 (95%), and obturator ring asymmetry in 114 (86%). A fracture line was visualized in 65 of 132 hips (49%) evaluated with laterals, 36 of 47 hips (77%) evaluated with Judet views, 3 of 4 (75%) evaluated with a false profile view, and 10 of 14 (71%) evaluated with computed tomography. Conclusion: Preoperative evaluation with a combination of an AP pelvis radiograph, plus a true lateral radiograph of the hip, plus Judet films in combination with the criteria for discontinuity defined in this article, allowed for identification of PD in a 100% of patients. [ABSTRACT FROM AUTHOR]

Published
2016
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43. Soft Tissue Neoplasms Causing Apparent Venous Thoracic Outlet Syndrome

Author

Mirza, Aleem K., Barrett, Ian J., Rathore, Animesh, Elhassan, Bassem T., Rose, Peter S., Shives, Thomas, and Bower, Thomas C.

Abstract

Venous thoracic outlet syndrome (vTOS) usually results from compression of the subclavian vein classically as a result of narrowing of the costoclavicular space. We report 2 rare cases of soft tissue neoplasms resulting in apparent vTOS. The first case is a 46-year-old female with a 2-year history of intermittent unilateral shoulder pain, who was initially diagnosed with intervertebral disk herniation. Cervical fusion was performed; however, her symptoms progressed and she additionally developed paresthesias and venous congestion. Computed tomography (CT) angiogram demonstrated a 13-cm-encapsulated mass within the subscapularis muscle compressing the axillary vein. Radiological findings suggested lipoma. She subsequently underwent complete resection via a transaxillary approach with extension along the lateral border of the latissimus. Final pathology confirmed an intramuscular lipoma. The second case is a 21-year-old female who presented with acute onset of unilateral chest wall pain, palpable nodularity, and venous congestion. CT chest showed pulmonary embolism and an anterior chest wall mass. An initial attempt at resection was aborted due to proximity of the mass to the subclavian vein. The mass enlarged on serial imaging, measuring 3.8 cm in greatest dimension. Additionally, tumor thrombus was seen, and a subsequent ultrasound-guided biopsy was positive for high-grade synovial sarcoma. Positron emission tomography scan showed a pulmonary nodule that was resected thoracoscopically with pathology confirming metastatic synovial sarcoma. Subsequently, she underwent neoadjuvant chemoradiation followed by successful resection of the chest wall mass. An extended infraclavicular approach with a secondary transaxillary incision was utilized to achieve adequate exposure and margins. Final pathology was consistent with preoperative biopsy. Venous reconstruction was not needed. Although rare, an extrinsic mass as a cause of apparent TOS should be in the differential diagnosis. Surgical approach is based on tumor type, location, and proximity to the neurovascular bundle.

Published
2017
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44. Internal Fixation of Unstable Osteochondritis Dissecans in the Skeletally Mature Knee with Metal Screws

Author

Barrett, Ian, King, Alexander H., Riester, Scott, van Wijnen, Andre, Levy, Bruce A., Stuart, Michael J., and Krych, Aaron J.

Abstract

Purpose Several bioabsorbable and metal options are available for internal fixation of an unstable osteochondritis dissecans (OCD) lesion, but currently there are little data on outcomes with metal headless compression screws in the adult knee. The purpose of this study was to determine (1) the radiographic healing rates, (2) midterm clinical outcomes, and (3) comparison between healed and unhealed OCD fragments after use of headless metal compression screws for the treatment of unstable OCD lesions in the knees of skeletally mature patients.Methods Retrospective chart review for all skeletally mature patients who presented with unstable femoral condyle OCD lesions of the knee was conducted. All patients underwent open or arthroscopic reduction and internal fixation using headless metal compression screws. Preoperative and postoperative radiographs were reviewed with healing defined as radiographic evidence of union of the OCD progeny fragment with the condyle. Clinical outcome data were collected retrospectively using 3 validated outcome scores: International Knee Documentation Committee (IKDC), Knee Injury and Osteoarthritis Outcome Score (KOOS), and Marx.Results Twenty-two knees in 22 patients with a mean age of 21 years (range= 14-37 years) were followed for an average of 8.7 years (range = 2-22 years). Metal, headless, cannulated compression screws were used in all 22 cases. At a mean of 31 months postoperatively (range = 2-262), fragment union was observed in 18 knees (82%). The remaining 4 knees (18%) required loose fragment excision and hardware removal at a mean of 9 months (range = 2-16 months) postoperatively. Mean postoperative Marx score was 7 (range = 0-16), the mean postoperative IKDC score was 85 (range = 62-100), and mean KOOS scores included KOOS Pain (93; range = 69-100), KOOS Symptoms (86; range = 71-100), KOOS ADL (98; range = 90-100), KOOS Sports (82; range = 50-100), and KOOS QOL (76; range = 50-100).Conclusion Headless metal compression screws provide a satisfactory union rate for treatment of unstable OCD lesions of the femoral condyles in skeletally mature patients. Patients achieving union have good knee function, maintain satisfactory activity levels, and have superior knee outcomes compared with those that failed to heal after fixation at mid to long-term follow-up.

Published
2016
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45. Extracorporeal irradiation for malignant bone tumors

Author

Hong, Angela, M.Med., Stevens, Graham, M.D., FRANZCR, Stalley, Paul, FRACS, Pendlebury, Susan, FRANZCR, Ahern, Verity, FRANZCR, Ralston, Anna, M.Sc., Estoesta, Edgar, M.Sc., Barrett, Ian, and FRACS

Abstract

Purpose: Extracorporeal irradiation (ECI) has been used selectively in the management of primary malignant bone tumors since 1996. We report our techniques for ECI and the short-term oncologic and orthopedic outcomes. Methods and Materials: Sixteen patients with primary malignant bone tumors were treated with ECI from 1996 to 2000. The median age was 14 years. The histologic diagnoses were Ewing's sarcoma (11), osteosarcoma (4) and chondrosarcoma (1). The treated sites were femur (7), tibia (4), humerus (2), ilium (2), and sacrum (1). Following induction chemotherapy in Ewing's sarcomas and osteosarcoma, en bloc resection of the tumor and tumor-bearing bone was performed. A single dose of 50 Gy was delivered to the bone extracorporeally using either a linear accelerator (9 cases) or a blood product irradiator (7 cases). The orthopedic outcome was recorded using a standard functional scale. Results: At a median follow-up of 19.5 months, there were no cases of local recurrence or graft failure. One patient required amputation due to chronic osteomyelitis. For the 10 patients with follow-up greater than 18 months, the functional outcomes were graded good to excellent. Conclusion: The short-term oncologic and orthopedic results are encouraging and suggest that ECI provides a good alternative for reconstruction in limb conservative surgery in selected patients. This technique should only be used in a multidisciplinary setting, where careful follow-up is available to assess the long-term outcomes.

Published
2001
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46. Skeletal Disorders in Children with Renal Failure

Author

Barrett, Ian R. and Papadimitriou*, Dimitri G.

Abstract

There are relatively few reports concerning management of the musculoskeletal problems of children with renal failure. From a population group of 124 children with renal failure treated at The Children's Hospital, Camperdown, 16 were referred for management of skeletal problems. These problems included genu valgum, slipped capital femoral epiphysis, ankle valrus, procurvatum of the tibia, osteonecrosis, osteochondritis dissecans, “brown tumors,” gout, and pathological fracture. The methods of management of these problems are discussed.

Published
1996

47. The high-pressure S NAr reaction of N-p-Fluorobenzyl-2-chlorobenzimidazole with amines; an approach to norastemizole and analogues

Author

Barrett, Ian C. and Kerr, Michael A.

Abstract

N- p-fluorobenzyl-2-chlorobenzimidazole was treated under hyperbaric conditions with a variety of primary and secondary amines. The resulting S NAr reaction proceeded smoothly to produce excellent yields of adducts in most cases. This methodology provides rapid access to astemizole, norastemizole and related analogues.

Published
1999
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48. Slipped Capital Femoral Epiphysis Following Radiotherapy

Author

Barrett, Ian R.

Abstract

Two children with rhabdomyosarcoma who developed slipped capital femoral epiphyses following pelvic irradiation are described. Previous case reports are summarised, and the relevant orthopedic implications of the disorder discussed.

Published
1985

50. Partial Growth Plate Closure

Author

Howman-Giles, Robert, Trochei, Maurice, Yeates, Kim, Middleton, Roland, Barrett, Ian, Scougall, James, and Whiteway, Donald

Abstract

A new technique of using 99mTc bone scan to assess partial closure of the growth plate is described. The site and degree of osseous fusion can be obtained by using the apex view. The technique has the potential of assessing serially the growth of a plate before and after surgery.

Published
1985

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