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Hamza GM, Raghunathan R, Ashenden S, Zhang B, Miele E, Jarnuczak AF. Proteomics of prostate cancer serum and plasma using low and high throughput approaches. Clin Proteomics 2024; 21:21. [PMID: 38475692 DOI: 10.1186/s12014-024-09461-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 02/12/2024] [Indexed: 03/14/2024] Open
Abstract
Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.
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Affiliation(s)
| | - Rekha Raghunathan
- Bioanalytical and Biomarker, Prevail Therapeutics, Wholly Owned Subsidiary of Eli Lilly and Company, New York, NY, 10016, USA
| | | | - Bairu Zhang
- Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Eric Miele
- Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
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Sokolov A, Ashenden S, Sahin N, Lewis R, Erdem N, Ozaltan E, Bender A, Roth FP, Cokol M. Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background. Front Pharmacol 2019; 10:448. [PMID: 31105571 PMCID: PMC6494965 DOI: 10.3389/fphar.2019.00448] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 04/08/2019] [Indexed: 12/02/2022] Open
Abstract
Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug’s chemical structure and ABC-transporter substrate-likeness. We represented a drug’s chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug’s efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.
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Affiliation(s)
- Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Stephanie Ashenden
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.,Discovery Sciences, IMed Biotech Unit, AstraZeneca R&D, Cambridge, United Kingdom
| | - Nil Sahin
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.,Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Richard Lewis
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Nurdan Erdem
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Elif Ozaltan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Department of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.,Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Canadian Institute for Advanced Research, Toronto, ON, Canada
| | - Murat Cokol
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States.,Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.,Donnelly Centre, University of Toronto, Toronto, ON, Canada.,Axcella Health, Cambridge, MA, United States
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Mason DJ, Stott I, Ashenden S, Weinstein ZB, Karakoc I, Meral S, Kuru N, Bender A, Cokol M. Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. J Med Chem 2017; 60:3902-3912. [PMID: 28383902 DOI: 10.1021/acs.jmedchem.7b00204] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs. Of the 12 pairs predicted to be synergistic, 10 were experimentally validated, corresponding to a 2.8-fold enrichment. Having thus validated our methodology, we produced a compendium of interaction predictions for all pairwise combinations among 100 antibiotics. Our methodology can make reliable antibiotic interaction predictions for any antibiotic pair within the applicability domain of the model since it solely requires chemical structures as an input.
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Affiliation(s)
- Daniel J Mason
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Ian Stott
- Unilever Research and Development , Port Sunlight, Wirral CH63 3JW, United Kingdom
| | - Stephanie Ashenden
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Zohar B Weinstein
- Boston University School of Medicine , Boston, Massachusetts 02118, United States
| | - Idil Karakoc
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Selin Meral
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Nurdan Kuru
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge , Cambridge CB2 1EW, United Kingdom
| | - Murat Cokol
- Faculty of Engineering and Natural Sciences, Sabanci University , Tuzla, Istanbul 34956, Turkey.,Department of Molecular Biology and Microbiology, Tufts University School of Medicine , Boston, Massachusetts 02111, United States.,Laboratory of Systems Pharmacology, Harvard Medical School , Boston, Massachusetts 02115, United States
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