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Unravelling the Proteomics of HLA-B*57:01+ Antigen Presenting Cells during Abacavir Medication. J Pers Med 2022; 12:jpm12010040. [PMID: 35055355 PMCID: PMC8781935 DOI: 10.3390/jpm12010040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/21/2021] [Accepted: 12/30/2021] [Indexed: 12/19/2022] Open
Abstract
Type B adverse drug reactions (ADRs) are unpredictable based on the drug’s pharmacology and represent a key challenge in pharmacovigilance. For human leukocyte antigen (HLA)-mediated type B ADRs, it is assumed that the protein/small-molecule interaction alters the biophysical and mechanistic properties of the antigen presenting cells. Sophisticated methods enabled the molecular appreciation of HLA-mediated ADRs; in several instances, the drug molecule occupies part of the HLA peptide binding groove and modifies the recruited peptide repertoire thereby causing a strong T-cell-mediated immune response that is resolved upon withdrawal of medication. The severe ADR in HLA-B*57:01+ patients treated with the antiretroviral drug abacavir (ABC) in anti-HIV therapy is an example of HLA-drug-T cell cooperation. However, the long-term damages of the HLA-B*57:01-expressing immune cells following ABC treatment remain unexplained. Utilizing full proteome sequencing following ABC treatment of HLA-B*57:01+ cells, we demonstrate stringent proteomic alteration of the HLA/drug presenting cells. The proteomic content indisputably reflects the cellular condition; this knowledge directs towards individual pharmacovigilance for the development of personalized and safe medication.
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Chan J, Wang X, Turner JA, Baldwin NE, Gu J. Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing. Bioinformatics 2020; 35:2818-2826. [PMID: 30624606 PMCID: PMC6691331 DOI: 10.1093/bioinformatics/btz006] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/13/2018] [Accepted: 01/04/2019] [Indexed: 02/07/2023] Open
Abstract
Motivation Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. Availability and implementation Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinyan Chan
- Baylor Scott & White Research Institute, Dallas, TX, USA.,Institute of Biomedical Studies, Baylor University, Waco, TX, USA
| | - Xuan Wang
- Baylor Scott & White Research Institute, Dallas, TX, USA
| | - Jacob A Turner
- Department of Mathematics and Statistics, Stephen F. Austin State University, Nacogdoches, TX, USA
| | | | - Jinghua Gu
- Baylor Scott & White Research Institute, Dallas, TX, USA
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Xue R, Liao J, Shao X, Han K, Long J, Shao L, Ai N, Fan X. Prediction of Adverse Drug Reactions by Combining Biomedical Tripartite Network and Graph Representation Model. Chem Res Toxicol 2019; 33:202-210. [PMID: 31777246 DOI: 10.1021/acs.chemrestox.9b00238] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
As one of the primary contributors to high clinical attrition rates of drugs, toxicity evaluation is of critical significance to new drug discovery. Unsurprisingly, a vast number of computational methods have been developed at various stages of development pipeline to evaluate potential adverse drug reactions (ADRs). Despite previous success of these methods on individual ADR or certain drug family, there are great challenges to toxicity evaluation. In this study, a novel strategy was developed to predict the drug-ADR associations by combining deep learning and the biomedical tripartite network. This heterogeneous network contains biomedical linked data of three entities, for example, drugs, targets, and ADRs. For the first time, GraRep, a deep learning method for distributed representations, is introduced to learn graph representations and identify hidden features from the tripartite network which are further used for ADR prediction. Through this approach, drug-ADR associations could possibly be discovered from a systemic perspective. The accuracy of our method is 0.95 based on internal resource validation and 0.88 based on external resource validation. Moreover, our results show the prediction accuracy using the tripartite network is better than the one with bipartite network, suggesting the model performance can be improved with further enrichment on information. According to the result of 10-fold cross validation, the deep learning model outperforms two traditional methods (topology-based measures and chemical structure-based measures). Additionally, predictive models are also constructed using other deep learning methods, and comparable results are achieved. In summary, the biomedical tripartite network-based deep learning model proposed here proves to offer a promising solution for prediction of ADRs.
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Affiliation(s)
- Rui Xue
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Ke Han
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Jingbo Long
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Li Shao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine , Zhejiang University , 79 Qingchun Road , Hangzhou , 310003 , China
| | - Ni Ai
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences , Zhejiang University , Hangzhou 310058 , China
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Choi YH, Han CY, Kim KS, Kim SG. Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases. Toxicol Res 2019; 35:319-330. [PMID: 31636843 PMCID: PMC6791658 DOI: 10.5487/tr.2019.35.4.319] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 04/25/2019] [Accepted: 05/08/2019] [Indexed: 12/11/2022] Open
Abstract
Adverse drug reactions (ADRs) constitute key factors in determining successful medication therapy in clinical situations. Integrative analysis of electronic medical record (EMR) data and use of proper analytical tools are requisite to conduct retrospective surveillance of clinical decisions on medications. Thus, we suggest that electronic medical recording and human genetic databases are considered together in future directions of pharmacovigilance. We analyzed EMR-based ADR studies indexed on PubMed during the period from 2005 to 2017 and retrospectively acquired 1161 (29.6%) articles describing drug-induced adverse reactions (e.g., liver, kidney, nervous system, immune system, and inflammatory responses). Of them, only 102 (8.79%) articles contained useful information to detect or predict ADRs in the context of clinical medication alerts. Since insufficiency of EMR datasets and their improper analyses may provide false warnings on clinical decision, efforts should be made to overcome possible problems on data-mining, analysis, statistics, and standardization. Thus, we address the characteristics and limitations on retrospective EMR database studies in hospital settings. Since gene expression and genetic variations among individuals impact ADRs, pharmacokinetics, and pharmacodynamics, appropriate paths for pharmacovigilance may be optimized using suitable databases available in public domain (e.g., genome-wide association studies (GWAS), non-coding RNAs, microRNAs, proteomics, and genetic variations), novel targets, and biomarkers. These efforts with new validated biomarker analyses would be of help to repurpose clinical and translational research infrastructure and ultimately future personalized therapy considering ADRs.
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Affiliation(s)
- Young Hee Choi
- College of Pharmacy, Dongguk University_Seoul, Goyang,
Korea
| | - Chang Yeob Han
- Department of Pharmacology, School of Medicine, Wonkwang University, Iksan,
Korea
| | - Kwi Suk Kim
- Department of Pharmacy, Seoul National University Hospital, Seoul,
Korea
| | - Sang Geon Kim
- Department of Pharmacy, Seoul National University Hospital, Seoul,
Korea
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul,
Korea
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