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Islamaj R, Wei CH, Lai PT, Luo L, Coss C, Gokal Kochar P, Miliaras N, Rodionov O, Sekiya K, Trinh D, Whitman D, Lu Z. The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop. Database (Oxford) 2024; 2024:baae071. [PMID: 39126204 PMCID: PMC11315767 DOI: 10.1093/database/baae071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/03/2024] [Accepted: 07/09/2024] [Indexed: 08/12/2024]
Abstract
The automatic recognition of biomedical relationships is an important step in the semantic understanding of the information contained in the unstructured text of the published literature. The BioRED track at BioCreative VIII aimed to foster the development of such methods by providing the participants the BioRED-BC8 corpus, a collection of 1000 PubMed documents manually curated for diseases, gene/proteins, chemicals, cell lines, gene variants, and species, as well as pairwise relationships between them which are disease-gene, chemical-gene, disease-variant, gene-gene, chemical-disease, chemical-chemical, chemical-variant, and variant-variant. Furthermore, relationships are categorized into the following semantic categories: positive correlation, negative correlation, binding, conversion, drug interaction, comparison, cotreatment, and association. Unlike most of the previous publicly available corpora, all relationships are expressed at the document level as opposed to the sentence level, and as such, the entities are normalized to the corresponding concept identifiers of the standardized vocabularies, namely, diseases and chemicals are normalized to MeSH, genes (and proteins) to National Center for Biotechnology Information (NCBI) Gene, species to NCBI Taxonomy, cell lines to Cellosaurus, and gene/protein variants to Single Nucleotide Polymorphism Database. Finally, each annotated relationship is categorized as 'novel' depending on whether it is a novel finding or experimental verification in the publication it is expressed in. This distinction helps differentiate novel findings from other relationships in the same text that provides known facts and/or background knowledge. The BioRED-BC8 corpus uses the previous BioRED corpus of 600 PubMed articles as the training dataset and includes a set of newly published 400 articles to serve as the test data for the challenge. All test articles were manually annotated for the BioCreative VIII challenge by expert biocurators at the National Library of Medicine, using the original annotation guidelines, where each article is doubly annotated in a three-round annotation process until full agreement is reached between all curators. This manuscript details the characteristics of the BioRED-BC8 corpus as a critical resource for biomedical named entity recognition and relation extraction. Using this new resource, we have demonstrated advancements in biomedical text-mining algorithm development. Database URL: https://codalab.lisn.upsaclay.fr/competitions/16381.
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Affiliation(s)
- Rezarta Islamaj
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Chih-Hsuan Wei
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Po-Ting Lai
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Ling Luo
- School of Computer Science and Technology, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, Liaoning 116024, China
| | - Cathleen Coss
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Preeti Gokal Kochar
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Nicholas Miliaras
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Oleg Rodionov
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Keiko Sekiya
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Dorothy Trinh
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Deborah Whitman
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
| | - Zhiyong Lu
- National Library of Medicine (NLM), National Institutes of Health (NIH), 8600 Rockville Pike, Bethesda, MD 20894, United States
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Medwid S, Kim RB. Implementation of pharmacogenomics: Where are we now? Br J Clin Pharmacol 2024; 90:1763-1781. [PMID: 36366858 DOI: 10.1111/bcp.15591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/01/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
Abstract
Pharmacogenomics (PGx), examining the effect of genetic variation on interpatient variation in drug disposition and response, has been widely studied for several decades. However, as cost, as well as turnaround time associated with PGx testing, has significantly improved, the use of PGx in the clinical setting has been gaining momentum. Nevertheless, challenges have emerged in the broader clinical implementation of PGx. In this review, we will outline current models of PGx delivery and methodologies of evaluation, and discuss clinically relevant PGx tests and associated medications. Additionally, we will describe our approach for the broad implementation of pre-emptive DPYD genotyping in patients taking fluoropyrimidines in Ontario, Canada, as an example of clinically actionable PGx testing with sufficient clinical evidence of patient benefit that can become a new standard of patient care. We will highlight challenges associated with PGx testing, including a lack of diversity in PGx studies as well as general limitations that impact the broad adoption of PGx testing. Lastly, we examine the future of PGx, discussing new clinical targets, methodologies and analysis approaches.
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Affiliation(s)
- Samantha Medwid
- Department of Medicine, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
| | - Richard B Kim
- Department of Medicine, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
- London Health Sciences Centre, London, Ontario, Canada
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Yang Y, Yu K, Gao S, Yu S, Xiong D, Qin C, Chen H, Tang J, Tang N, Zhu H. Alzheimer's Disease Knowledge Graph Enhances Knowledge Discovery and Disease Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601339. [PMID: 39005357 PMCID: PMC11245034 DOI: 10.1101/2024.07.03.601339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Background Alzheimer's disease (AD), a progressive neurodegenerative disorder, continues to increase in prevalence without any effective treatments to date. In this context, knowledge graphs (KGs) have emerged as a pivotal tool in biomedical research, offering new perspectives on drug repurposing and biomarker discovery by analyzing intricate network structures. Our study seeks to build an AD-specific knowledge graph, highlighting interactions among AD, genes, variants, chemicals, drugs, and other diseases. The goal is to shed light on existing treatments, potential targets, and diagnostic methods for AD, thereby aiding in drug repurposing and the identification of biomarkers. Results We annotated 800 PubMed abstracts and leveraged GPT-4 for text augmentation to enrich our training data for named entity recognition (NER) and relation classification. A comprehensive data mining model, integrating NER and relationship classification, was trained on the annotated corpus. This model was subsequently applied to extract relation triplets from unannotated abstracts. To enhance entity linking, we utilized a suite of reference biomedical databases and refine the linking accuracy through abbreviation resolution. As a result, we successfully identified 3,199,276 entity mentions and 633,733 triplets, elucidating connections between 5,000 unique entities. These connections were pivotal in constructing a comprehensive Alzheimer's Disease Knowledge Graph (ADKG). We also integrated the ADKG constructed after entity linking with other biomedical databases. The ADKG served as a training ground for Knowledge Graph Embedding models with the high-ranking predicted triplets supported by evidence, underscoring the utility of ADKG in generating testable scientific hypotheses. Further application of ADKG in predictive modeling using the UK Biobank data revealed models based on ADKG outperforming others, as evidenced by higher values in the areas under the receiver operating characteristic (ROC) curves. Conclusion The ADKG is a valuable resource for generating hypotheses and enhancing predictive models, highlighting its potential to advance AD's disease research and treatment strategies.
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Affiliation(s)
- Yue Yang
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Kaixian Yu
- Independent Researcher, Shanghai, P.R. China
| | - Shan Gao
- Department of Mathematics and Statistics, Yunnan University
| | - Sheng Yu
- Center for Statistics Science, Tsinghua University
| | - Di Xiong
- Department of Statistics, Shanghai University
| | - Chuanyang Qin
- Department of Mathematics and Statistics, Yunnan University
| | - Huiyuan Chen
- Department of Mathematics and Statistics, Yunnan University
| | - Jiarui Tang
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Niansheng Tang
- Department of Mathematics and Statistics, Yunnan University
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill
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Sanou G, Manso T, Todorov K, Giudicelli V, Duroux P, Kossida S. IMGT/mAb-KG: the knowledge graph for therapeutic monoclonal antibodies. Front Immunol 2024; 15:1393839. [PMID: 38975336 PMCID: PMC11225432 DOI: 10.3389/fimmu.2024.1393839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/31/2024] [Indexed: 07/09/2024] Open
Abstract
Introduction Therapeutic monoclonal antibodies (mAbs) have demonstrated promising outcomes in diverse clinical indications, including but not limited to graft rejection, cancer, and autoimmune diseases lately.Recognizing the crucial need for the scientific community to quickly and easily access dependable information on monoclonal antibodies (mAbs), IMGT®, the international ImMunoGeneTics information system®, provides a unique and invaluable resource: IMGT/mAb-DB, a comprehensive database of therapeutic mAbs, accessible via a user-friendly web interface. However, this approach restricts more sophisticated queries and segregates information from other databases. Methods To connect IMGT/mAb-DB with the rest of the IMGT databases, we created IMGT/mAb-KG, a knowledge graph for therapeutic monoclonal antibodies connected to IMGT structures and genomics databases. IMGT/mAb-KG is developed using the most effective methodologies and standards of semantic web and acquires data from IMGT/mAb-DB. Concerning interoperability, IMGT/mAb-KG reuses terms from biomedical resources and is connected to related resources. Results and discussion In February 2024, IMGT/mAb-KG, encompassing a total of 139,629 triplets, provides access to 1,489 mAbs, approximately 500 targets, and over 500 clinical indications. It offers detailed insights into the mechanisms of action of mAbs, their construction, and their various products and associated studies. Linked to other resources such as Thera-SAbDab (Therapeutic Structural Antibody Database), PharmGKB (a comprehensive resource curating knowledge on the impact of genetic variation on drug response), PubMed, and HGNC (HUGO Gene Nomenclature Committee), IMGT/mAb-KG is an essential resource for mAb development. A user-friendly web interface facilitates the exploration and analyse of the content of IMGT/mAb-KG.
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Affiliation(s)
- Gaoussou Sanou
- The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier, France
- Montpellier Laboratory for Computer Science, Robotics and Microelectronics (LIRMM), University of Montpellier, National Center for Scientific Research (CNRS), Montpellier, France
| | - Taciana Manso
- The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier, France
| | - Konstantin Todorov
- Montpellier Laboratory for Computer Science, Robotics and Microelectronics (LIRMM), University of Montpellier, National Center for Scientific Research (CNRS), Montpellier, France
| | - Véronique Giudicelli
- The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier, France
| | - Patrice Duroux
- The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier, France
| | - Sofia Kossida
- The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier, France
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Guo J, Zhou W, Ma X, Li Y, Zhang H, Wei J, Du S, Jin T. Genetic Variability of CYP4F2, CYP2D6, CYP2E1, and ACE in the Chinese Yi Population. Biochem Genet 2024:10.1007/s10528-024-10748-y. [PMID: 38850376 DOI: 10.1007/s10528-024-10748-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/17/2024] [Indexed: 06/10/2024]
Abstract
Genetic polymorphisms of very important pharmacogenes (VIP) are a significant factor contributing to inter-individual variability in drug therapy. The purpose of this study was to identify significantly different loci in the Yi population and to enrich their pharmacogenomic information. 54 VIP variants were selected from the Pharmacogenomics Knowledge Base (PharmGKB) and genotyped in 200 Yi individuals. Then, we compared their genotype distribution between the Yi population and the other 26 populations using the χ2 test. Compared with the other 26 populations, the genotype frequencies of 4 single nucleotide polymorphisms (SNPs), rs2108622 (CYP4F2), rs1065852 (CYP2D6), rs2070676 (CYP2E1), and rs4291 (ACE), had significant differences in the Yi population. For example, the TT genotype frequency of rs2108622 (8.1%) was higher than that of African populations, and the AA genotype frequency of rs1065852 (27.3%) was higher than that of other populations except East Asians. We also found that the Yi populations differed the least from East Asians and the most from Africans. Furthermore, the differences in these variants might be related to the effectiveness and toxicity risk of using warfarin, iloperidone, cisplatin cyclophosphamide, and other drugs in the Yi population. Our data complement the pharmacogenomic information of the Yi population and provide theoretical guidance for their personalized treatment.
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Affiliation(s)
- Jinping Guo
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Wenqian Zhou
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Xiaoya Ma
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Yujie Li
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Huan Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Jie Wei
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China
- College of Life Science, Northwest University, Xi'an, 710127, China
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Shuli Du
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China.
- College of Life Science, Northwest University, Xi'an, 710127, China.
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China.
| | - Tianbo Jin
- Key Laboratory of Resource Biology and Biotechnology in Western China (Northwest University), School of Life Sciences, Ministry of Education, Northwest University, #229 North TaiBai Road, Xi'an, Shaanxi, 710069, China.
- College of Life Science, Northwest University, Xi'an, 710127, China.
- Provincial Key Laboratory of Biotechnology of Shaanxi Province, Northwest University, Xi'an, Shaanxi, 710069, China.
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Abdel‐latif R, Badji R, Mohammed S, Al‐Muftah W, Mbarek H, Darwish D, Assaf D, Al‐Badriyeh D, Elewa H, Afifi N, Masoodi NA, Omar AS, Al Suwaidi J, Bujassoum S, Al Hail M, Ismail SI, Althani A. QPGx-CARES: Qatar pharmacogenetics clinical applications and research enhancement strategies. Clin Transl Sci 2024; 17:e13800. [PMID: 38818903 PMCID: PMC11140449 DOI: 10.1111/cts.13800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/03/2024] [Accepted: 04/05/2024] [Indexed: 06/01/2024] Open
Abstract
Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.
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Affiliation(s)
- Rania Abdel‐latif
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | - Radja Badji
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | | | - Wadha Al‐Muftah
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | - Hamdi Mbarek
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | - Dima Darwish
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | - Duha Assaf
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | | | - Hazem Elewa
- College of Pharmacy, QU HealthQatar UniversityDohaQatar
| | - Nahla Afifi
- Qatar Biobank for Medical ResearchQatar Foundation for Education, Science, and CommunityDohaQatar
| | | | - Amr Salah Omar
- Cardiology and Cardiovascular SurgeryDepartment Hamad Medical CorporationDohaQatar
| | - Jassim Al Suwaidi
- Cardiology and Cardiovascular SurgeryDepartment Hamad Medical CorporationDohaQatar
| | - Salha Bujassoum
- Medical Oncology, National Center for Cancer Care and ResearchDepartment Hamad Medical CorporationDohaQatar
| | - Moza Al Hail
- Pharmacy DepartmentHamad Medical CorporationDohaQatar
| | - Said I. Ismail
- Qatar Genome Program, Qatar Precision Health InstituteQatar FoundationDohaQatar
| | - Asma Althani
- Biomedical Research CenterQatar UniversityDohaQatar
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Dafniet B, Taboureau O. Prediction of adverse drug reactions due to genetic predisposition using deep neural networks. Mol Inform 2024; 43:e202400021. [PMID: 38850150 DOI: 10.1002/minf.202400021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/12/2024] [Accepted: 03/13/2024] [Indexed: 06/10/2024]
Abstract
Drug development is a long and costly process, often limited by the toxicity and adverse drug reactions (ADRs) caused by drug candidates. Even on the market, some drugs can cause strong ADRs that can vary depending on an individual polymorphism. The development of Genome-wide association studies (GWAS) allowed the discovery of genetic variants of interest that may cause these effects. In this study, the objective was to investigate a deep learning approach to predict genetic variations potentially related to ADRs. We used single nucleotide polymorphisms (SNPs) information from dbSNP to create a network based on ADR-drug-target-mutations and extracted matrixes of interaction to build deep Neural Networks (DNN) models. Considering only information about mutations known to impact drug efficacy and drug safety from PharmGKB and drug adverse reactions based on the MedDRA System Organ Classes (SOCs), these DNN models reached a balanced accuracy of 0.61 in average. Including molecular fingerprints representing structural features of the drugs did not improve the performance of the models. To our knowledge, this is the first model that exploits DNN to predict ADR-drug-target-mutations. Although some improvements are suggested, these models can be of interest to analyze multiple compounds over all of the genes and polymorphisms information accessible and thus pave the way in precision medicine.
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Affiliation(s)
- Bryan Dafniet
- INSERM U1133, CNRS UMR 8251, Université Paris Cité, 35 rue Hélène Brion, Paris, 75013, France
| | - Olivier Taboureau
- INSERM U1133, CNRS UMR 8251, Université Paris Cité, 35 rue Hélène Brion, Paris, 75013, France
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Yang J, Jiang YH, Zhou X, Yao JQ, Wang YY, Liu JQ, Zhang PC, Tang WF, Li Z. Material basis and molecular mechanisms of Chaihuang Qingyi Huoxue Granule in the treatment of acute pancreatitis based on network pharmacology and molecular docking-based strategy. Front Immunol 2024; 15:1353695. [PMID: 38765004 PMCID: PMC11099290 DOI: 10.3389/fimmu.2024.1353695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/17/2024] [Indexed: 05/21/2024] Open
Abstract
Objectives This study aimed to analyze active compounds and signaling pathways of CH applying network pharmacology methods, and to additionally verify the molecular mechanism of CH in treating AP. Materials and methods Network pharmacology and molecular docking were firstly used to identify the active components of CH and its potential targets in the treatment of AP. The pancreaticobiliary duct was retrogradely injected with sodium taurocholate (3.5%) to create an acute pancreatitis (AP) model in rats. Histological examination, enzyme-linked immunosorbent assay, Western blot and TUNEL staining were used to determine the pathway and mechanism of action of CH in AP. Results Network pharmacological analysis identified 168 active compounds and 276 target proteins. In addition, there were 2060 targets associated with AP, and CH had 177 targets in common with AP. These shared targets, including STAT3, IL6, MYC, CDKN1A, AKT1, MAPK1, MAPK3, MAPK14, HSP90AA1, HIF1A, ESR1, TP53, FOS, and RELA, were recognized as core targets. Furthermore, we filtered out 5252 entries from the Gene Ontology(GO) and 186 signaling pathways from the Kyoto Encyclopedia of Genes and Genomes(KEGG). Enrichment and network analyses of protein-protein interactions predicted that CH significantly affected the PI3K/AKT signaling pathway, which played a critical role in programmed cell death. The core components and key targets showed strong binding activity based on molecular docking results. Subsequently, experimental validation demonstrated that CH inhibited the phosphorylation of PI3K and AKT in pancreatic tissues, promoted the apoptosis of pancreatic acinar cells, and further alleviated inflammation and histopathological damage to the pancreas in AP rats. Conclusion Apoptosis of pancreatic acinar cells can be enhanced and the inflammatory response can be reduced through the modulation of the PI3K/AKT signaling pathway, resulting in the amelioration of pancreatic disease.
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Affiliation(s)
- Jia Yang
- School of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Yu-Hong Jiang
- Department of Integrated Traditional Chinese and Western Medicine, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Zhou
- Department of Spleen and Stomach Diseases, Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, Sichuan, China
- The Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Digestive System Diseases of Luzhou city, Affiliated Traditional Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Jia-Qi Yao
- Department of Integrated Traditional Chinese and Western Medicine, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yang-Yang Wang
- School of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, Sichuan, China
| | - Jian-Qin Liu
- Department of Spleen and Stomach Diseases, Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, Sichuan, China
- The Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Digestive System Diseases of Luzhou city, Affiliated Traditional Medicine Hospital of Southwest Medical University, Luzhou, China
| | - Peng-Cheng Zhang
- Department of Integrated Traditional Chinese and Western Medicine, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Wen-Fu Tang
- Department of Integrated Traditional Chinese and Western Medicine, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhi Li
- Department of Spleen and Stomach Diseases, Chinese Medicine Hospital Affiliated to Southwest Medical University, Luzhou, Sichuan, China
- The Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Digestive System Diseases of Luzhou city, Affiliated Traditional Medicine Hospital of Southwest Medical University, Luzhou, China
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Wang D, Bolleddula J, Coenen-Stass A, Grombacher T, Dong JQ, Scheuenpflug J, Locatelli G, Feng Z. Implementation of whole-exome sequencing for pharmacogenomics profiling and exploring its potential clinical utilities. Pharmacogenomics 2024; 25:197-206. [PMID: 38511470 DOI: 10.2217/pgs-2023-0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024] Open
Abstract
Whole-exome sequencing (WES) is widely used in clinical settings; however, the exploration of its use in pharmacogenomic analysis remains limited. Our study compared the variant callings for 28 core absorption, distribution, metabolism and elimination genes by WES and array-based technology using clinical trials samples. The results revealed that WES had a positive predictive value of 0.71-0.92 and a sensitivity of single-nucleotide variants between 0.68 and 0.95, compared with array-based technology, for the variants in the commonly targeted regions of the WES and PhamacoScan™ assay. Besides the common variants detected by both assays, WES identified 200-300 exclusive variants per sample, totalling 55 annotated exclusive variants, including important modulators of metabolism such as rs2032582 (ABCB1) and rs72547527 (SULT1A1). This study highlights the potential clinical advantages of using WES to identify a wider range of genetic variations and enabling precision medicine.
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Affiliation(s)
- Danyi Wang
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | - Jayaprakasam Bolleddula
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | | | | | - Jennifer Q Dong
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
| | | | | | - Zheng Feng
- EMD Serono Research & Development Institute, Inc., Billerica, MA, USA, an affiliate of Merck KGaA USA
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10
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Dolin RH, Shenvi E, Alvarez C, Barrows RC, Boxwala A, Lee B, Nathanson BH, Kleyner Y, Hagemann R, Hongsermeier T, Kapusnik-Uner J, Lakdawala A, Shalaby J. PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service. Appl Clin Inform 2024; 15:378-387. [PMID: 38388174 PMCID: PMC11098593 DOI: 10.1055/a-2274-6763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
OBJECTIVES Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study. METHODS We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview. RESULTS Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice. CONCLUSION Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.
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Affiliation(s)
| | - Edna Shenvi
- Elimu Informatics, El Cerrito, California, United States
| | - Carla Alvarez
- Elimu Informatics, El Cerrito, California, United States
| | | | - Aziz Boxwala
- Elimu Informatics, El Cerrito, California, United States
| | - Benson Lee
- College of Pharmacy, Touro University California, Vallejo, California, United States
| | | | - Yelena Kleyner
- Elimu Informatics, El Cerrito, California, United States
| | - Rachel Hagemann
- Independent Contractor, San Francisco, California, United States
| | | | | | | | - James Shalaby
- Elimu Informatics, El Cerrito, California, United States
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11
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Niarakis A, Ostaszewski M, Mazein A, Kuperstein I, Kutmon M, Gillespie ME, Funahashi A, Acencio ML, Hemedan A, Aichem M, Klein K, Czauderna T, Burtscher F, Yamada TG, Hiki Y, Hiroi NF, Hu F, Pham N, Ehrhart F, Willighagen EL, Valdeolivas A, Dugourd A, Messina F, Esteban-Medina M, Peña-Chilet M, Rian K, Soliman S, Aghamiri SS, Puniya BL, Naldi A, Helikar T, Singh V, Fernández MF, Bermudez V, Tsirvouli E, Montagud A, Noël V, Ponce-de-Leon M, Maier D, Bauch A, Gyori BM, Bachman JA, Luna A, Piñero J, Furlong LI, Balaur I, Rougny A, Jarosz Y, Overall RW, Phair R, Perfetto L, Matthews L, Rex DAB, Orlic-Milacic M, Gomez LCM, De Meulder B, Ravel JM, Jassal B, Satagopam V, Wu G, Golebiewski M, Gawron P, Calzone L, Beckmann JS, Evelo CT, D’Eustachio P, Schreiber F, Saez-Rodriguez J, Dopazo J, Kuiper M, Valencia A, Wolkenhauer O, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Front Immunol 2024; 14:1282859. [PMID: 38414974 PMCID: PMC10897000 DOI: 10.3389/fimmu.2023.1282859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 12/22/2023] [Indexed: 02/29/2024] Open
Abstract
Introduction The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
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Affiliation(s)
- Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
| | - Marc E. Gillespie
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- St. John’s University, Queens, NY, United States
| | - Akira Funahashi
- Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ahmed Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Michael Aichem
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Karsten Klein
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
| | - Tobias Czauderna
- Faculty of Applied Computer Sciences & Biosciences, University of Applied Sciences Mittweida, Mittweida, Germany
| | - Felicia Burtscher
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Takahiro G. Yamada
- Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
| | - Yusuke Hiki
- Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, Kanagawa, Japan
| | - Noriko F. Hiroi
- Faculty of Creative Engineering, Kanagawa Institute of Technology, Kanagawa, Japan
- Keio University School of Medicine, Tokyo, Japan
| | - Finterly Hu
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Nhung Pham
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Egon L. Willighagen
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Alberto Valdeolivas
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Aurelien Dugourd
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Francesco Messina
- Department of Epidemiology, Preclinical Research and Advanced Diagnostic, National Institute for Infectious Diseases’ Lazzaro Spallanzani’ - IRCCS, Rome, Italy
| | - Marina Esteban-Medina
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Seville, Spain
| | - Kinza Rian
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France
| | - Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Aurélien Naldi
- Lifeware Group, Inria, Saclay-île de France, Palaiseau, France
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Vidisha Singh
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France
| | | | - Viviam Bermudez
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Eirini Tsirvouli
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arnau Montagud
- Barcelona Supercomputing Center (BSC.), Barcelona, Spain
| | - Vincent Noël
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | | | | | - Benjamin M. Gyori
- Harvard Medical School, Laboratory of Systems Pharmacology, Boston, MA, United States
| | - John A. Bachman
- Harvard Medical School, Laboratory of Systems Pharmacology, Boston, MA, United States
| | - Augustin Luna
- Computational Biology Branch, National Library of Medicine, Bethesda, MD, United States
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Janet Piñero
- Medbioinformatics Solutions SL, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Laura I. Furlong
- Medbioinformatics Solutions SL, Barcelona, Spain
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Dept. of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan
- Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan
| | - Yohan Jarosz
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Rupert W. Overall
- Institute for Biology, Humboldt University of Berlin, Berlin, Germany
| | - Robert Phair
- Integrative Bioinformatics, Inc., Mountain View, CA, United States
| | - Livia Perfetto
- Department of Biology and Biotechnology Charles Darwin, Sapienza University of Rome, Rome, Italy
| | - Lisa Matthews
- Department of Biochemistry & Molecular Pharmacology, NYU. Langone Medical Center, New York, NY, United States
| | | | | | - Luis Cristobal Monraz Gomez
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Jean Marie Ravel
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt am Main, Germany
| | - Guanming Wu
- Oregon Health Sciences University, Portland, OR, United States
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Laurence Calzone
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, Netherlands
| | - Peter D’Eustachio
- Department of Biochemistry & Molecular Pharmacology, NYU. Langone Medical Center, New York, NY, United States
| | - Falk Schreiber
- Department of Computer and Information Science, University of Konstanz, Konstanz, Germany
- Faculty of Information Technology, Monash University, Clayton, Victoria, VIC, Australia
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Bioquant, Heidelberg, Germany
| | - Joaquin Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocío, Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), FPS, Hospital Virgen del Rocio, Seville, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Sevilla, Spain
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC.), Barcelona, Spain
- I.C.R.E.A., Pg. Lluís Companys 23, Barcelona, Spain
| | - Olaf Wolkenhauer
- Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany
- Leibniz Institute for Food Systems Biology, at the Technical University Munich, Munich, Germany
| | | | - Emmanuel Barillot
- Institut Curie, P.S.L. Research University, Paris, France
- INSERM, Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
| | | | - Rudi Balling
- Institute of Molecular Psychiatry, University of Bonn, Bonn, Germany
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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12
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Biswas M, Vanwong N, Sukasem C. Pharmacogenomics and non-genetic factors affecting drug response in autism spectrum disorder in Thai and other populations: current evidence and future implications. Front Pharmacol 2024; 14:1285967. [PMID: 38375208 PMCID: PMC10875059 DOI: 10.3389/fphar.2023.1285967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 12/26/2023] [Indexed: 02/21/2024] Open
Abstract
Autism spectrum disorder (ASD) may affect family and social life profoundly. Although there is no selective pharmacotherapy for ASD, the Food and Drug Administration (FDA) has recommended risperidone/aripiprazole to treat the associated symptoms of ASD, such as agitation/irritability. Strong associations of some pharmacokinetic/pharmacodynamic gene variants, e.g., CYP2D6 and DRD2, with risperidone-induced hyperprolactinemia have been found in children with ASD, but such strong genetic associations have not been found directly for aripiprazole in ASD. In addition to pharmacogenomic (PGx) factors, drug-drug interactions (DDIs) and possibly cumulative effects of DDIs and PGx may affect the safety or effectiveness of risperidone/aripiprazole, which should be assessed in future clinical studies in children with ASD. Reimbursement, knowledge, and education of healthcare professionals are the key obstacles preventing the successful implementation of ASD pharmacogenomics into routine clinical practice. The preparation of national and international PGx-based dosing guidelines for risperidone/aripiprazole based on robust evidence may advance precision medicine for ASD.
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Affiliation(s)
- Mohitosh Biswas
- Department of Pharmacy, University of Rajshahi, Rajshahi, Bangladesh
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, Bangkok, Thailand
| | - Natchaya Vanwong
- Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
- Cardiovascular Precision Medicine Research Group, Special Task Force of Activating Research (STAR), Chulalongkorn University, Bangkok, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, Bangkok, Thailand
- Pharmacogenomics and Precision Medicine Clinic, Bumrungrad Genomic Medicine Institute (BGMI), Bumrungrad International Hospital, Bangkok, Thailand
- Faculty of Pharmaceutical Sciences, Burapha University, Mueang, Thailand
- Department of Pharmacology and Therapeutics, MRC Centre for Drug Safety Science, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
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13
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Guan Y, Zhang Y, Zhao X, Wang Y. Comprehensive analysis revealed the immunoinflammatory targets of rheumatoid arthritis based on intestinal flora, miRNA, transcription factors, and RNA-binding proteins databases, GSEA and GSVA pathway observations, and immunoinfiltration typing. Hereditas 2024; 161:6. [PMID: 38273392 PMCID: PMC10809458 DOI: 10.1186/s41065-024-00310-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVE Rheumatoid arthritis (RA) is a chronic inflammatory arthritis. This study aimed to identify potential biomarkers and possible pathogenesis of RA using various bioinformatics analysis tools. METHODS The GMrepo database provided a visual representation of the analysis of intestinal flora. We selected the GSE55235 and GSE55457 datasets from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) separately. With the intersection of these DEGs with the target genes associated with RA found in the GeneCards database, we obtained the DEGs targeted by RA (DERATGs). Subsequently, Disease Ontology, Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes were used to analyze DERATGs functionally. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on the data from the gene expression matrix. Additionally, the protein-protein interaction network, transcription factor (TF)-targets, target-drug, microRNA (miRNA)-mRNA networks, and RNA-binding proteins (RBPs)-DERATGs correlation analyses were built. The CIBERSORT was used to evaluate the inflammatory immune state. The single-sample GSEA (ssGSEA) algorithm and differential analysis of DERATGs were used among the infiltration degree subtypes. RESULTS There were some correlations between the abundance of gut flora and the prevalence of RA. A total of 54 DERATGs were identified, mainly related to immune and inflammatory responses and immunodeficiency diseases. Through GSEA and GSVA analysis, we found pathway alterations related to metabolic regulations, autoimmune diseases, and immunodeficiency-related disorders. We obtained 20 hub genes and 2 subnetworks. Additionally, we found that 39 TFs, 174 drugs, 2310 miRNAs, and several RBPs were related to DERATGs. Mast, plasma, and naive B cells differed during immune infiltration. We discovered DERATGs' differences among subtypes using the ssGSEA algorithm and subtype grouping. CONCLUSIONS The findings of this study could help with RA diagnosis, prognosis, and targeted molecular treatment.
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Affiliation(s)
- Yin Guan
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Yue Zhang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Xiaoqian Zhao
- Department of Ethics Committee, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China
| | - Yue Wang
- Department of Rheumatism Immunity Branch, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu, China.
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14
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Zhao L, Chen J, Bai B, Song G, Zhang J, Yu H, Huang S, Wang Z, Lu G. Topical drug delivery strategies for enhancing drug effectiveness by skin barriers, drug delivery systems and individualized dosing. Front Pharmacol 2024; 14:1333986. [PMID: 38293666 PMCID: PMC10825035 DOI: 10.3389/fphar.2023.1333986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/27/2023] [Indexed: 02/01/2024] Open
Abstract
Topical drug delivery is widely used in various diseases because of the advantages of not passing through the gastrointestinal tract, avoiding gastrointestinal irritation and hepatic first-pass effect, and reaching the lesion directly to reduce unnecessary adverse reactions. The skin helps the organism to defend itself against a huge majority of external aggressions and is one of the most important lines of defense of the body. However, the skin's strong barrier ability is also a huge obstacle to the effectiveness of topical medications. Allowing the bioactive, composition in a drug to pass through the stratum corneum barrier as needed to reach the target site is the most essential need for the bioactive, composition to exert its therapeutic effect. The state of the skin barrier, the choice of delivery system for the bioactive, composition, and individualized disease detection and dosing planning influence the effectiveness of topical medications. Nowadays, enhancing transdermal absorption of topically applied drugs is the hottest research area. However, enhancing transdermal absorption of drugs is not the first choice to improve the effectiveness of all drugs. Excessive transdermal absorption enhances topical drug accumulation at non-target sites and the occurrence of adverse reactions. This paper introduces topical drug delivery strategies to improve drug effectiveness from three perspectives: skin barrier, drug delivery system and individualized drug delivery, describes the current status and shortcomings of topical drug research, and provides new directions and ideas for topical drug research.
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Affiliation(s)
- Lin Zhao
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jiamei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Bai Bai
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guili Song
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jingwen Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Han Yu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shiwei Huang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhang Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guanghua Lu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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15
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Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, Haw R, Jassal B, Matthews L, May B, Petryszak R, Ragueneau E, Rothfels K, Sevilla C, Shamovsky V, Stephan R, Tiwari K, Varusai T, Weiser J, Wright A, Wu G, Stein L, Hermjakob H, D’Eustachio P. The Reactome Pathway Knowledgebase 2024. Nucleic Acids Res 2024; 52:D672-D678. [PMID: 37941124 PMCID: PMC10767911 DOI: 10.1093/nar/gkad1025] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/14/2023] [Accepted: 10/20/2023] [Indexed: 11/10/2023] Open
Abstract
The Reactome Knowledgebase (https://reactome.org), an Elixir and GCBR core biological data resource, provides manually curated molecular details of a broad range of normal and disease-related biological processes. Processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Here we review progress towards annotation of the entire human proteome, targeted annotation of disease-causing genetic variants of proteins and of small-molecule drugs in a pathway context, and towards supporting explicit annotation of cell- and tissue-specific pathways. Finally, we briefly discuss issues involved in making Reactome more fully interoperable with other related resources such as the Gene Ontology and maintaining the resulting community resource network.
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Affiliation(s)
- Marija Milacic
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Deidre Beavers
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Patrick Conley
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Chuqiao Gong
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Marc Gillespie
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Johannes Griss
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Dermatology, Medical University of Vienna, 1090 Vienna, Austria
| | - Robin Haw
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Bijay Jassal
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Lisa Matthews
- NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Bruce May
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | | | - Eliot Ragueneau
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Karen Rothfels
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Cristoffer Sevilla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | | | - Ralf Stephan
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Institute for Globally Distributed Open Research and Education (IGDORE)
| | - Krishna Tiwari
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Thawfeek Varusai
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Joel Weiser
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Adam Wright
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Guanming Wu
- Oregon Health and Science University, Portland, OR 97239, USA
| | - Lincoln Stein
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
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Austin BK, Firooz A, Valafar H, Blenda AV. An Updated Overview of Existing Cancer Databases and Identified Needs. BIOLOGY 2023; 12:1152. [PMID: 37627037 PMCID: PMC10452211 DOI: 10.3390/biology12081152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.
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Affiliation(s)
- Brittany K. Austin
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
| | - Ali Firooz
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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Tafazoli A, Mikros J, Khaghani F, Alimardani M, Rafigh M, Hemmati M, Siamoglou S, Golińska AK, Kamiński KA, Niemira M, Miltyk W, Patrinos GP. Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics. Hum Genomics 2023; 17:62. [PMID: 37452347 PMCID: PMC10347842 DOI: 10.1186/s40246-023-00508-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes. METHODS Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype-phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging. RESULTS Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99). CONCLUSION While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.
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Affiliation(s)
- Alireza Tafazoli
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland
- Laboratory of Pharmacogenomics, Department of Molecular Neuropharmacology, Maj Institute of Pharmacology Polish Academy of Sciences, Kraków, Poland
| | - John Mikros
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Faeze Khaghani
- Department of Pharmaceutical Biotechnology, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran
| | - Maliheh Alimardani
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Student Research Committee, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Rafigh
- Medical Genetics Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahboobeh Hemmati
- Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Stavroula Siamoglou
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | | | - Karol A Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
- Department of Cardiology, Medical University of Bialystok, Białystok, Poland
| | - Magdalena Niemira
- Clinical Research Centre, Medical University of Bialystok, Białystok, Poland
| | - Wojciech Miltyk
- Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland.
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.
- Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
- Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.
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Nunez-Torres R, Pita G, Peña-Chilet M, López-López D, Zamora J, Roldán G, Herráez B, Álvarez N, Alonso MR, Dopazo J, Gonzalez-Neira A. A Comprehensive Analysis of 21 Actionable Pharmacogenes in the Spanish Population: From Genetic Characterisation to Clinical Impact. Pharmaceutics 2023; 15:pharmaceutics15041286. [PMID: 37111771 PMCID: PMC10140932 DOI: 10.3390/pharmaceutics15041286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/03/2023] [Accepted: 04/16/2023] [Indexed: 04/29/2023] Open
Abstract
The implementation of pharmacogenetics (PGx) is a main milestones of precision medicine nowadays in order to achieve safer and more effective therapies. Nevertheless, the implementation of PGx diagnostics is extremely slow and unequal worldwide, in part due to a lack of ethnic PGx information. We analysed genetic data from 3006 Spanish individuals obtained by different high-throughput (HT) techniques. Allele frequencies were determined in our population for the main 21 actionable PGx genes associated with therapeutical changes. We found that 98% of the Spanish population harbours at least one allele associated with a therapeutical change and, thus, there would be a need for a therapeutical change in a mean of 3.31 of the 64 associated drugs. We also identified 326 putative deleterious variants that were not previously related with PGx in 18 out of the 21 main PGx genes evaluated and a total of 7122 putative deleterious variants for the 1045 PGx genes described. Additionally, we performed a comparison of the main HT diagnostic techniques, revealing that after whole genome sequencing, genotyping with the PGx HT array is the most suitable solution for PGx diagnostics. Finally, all this information was integrated in the Collaborative Spanish Variant Server to be available to and updated by the scientific community.
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Affiliation(s)
- Rocio Nunez-Torres
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Guillermo Pita
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
| | - Daniel López-López
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
| | - Jorge Zamora
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Gema Roldán
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
| | - Belén Herráez
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Nuria Álvarez
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - María Rosario Alonso
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
| | - Joaquín Dopazo
- Computational Medicine Platform, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, 41013 Sevilla, Spain
- Bioinformatics in Rare Diseases (BiER), Centre for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, 41013 Sevilla, Spain
- Computational Systems Medicine Group, Institute of Biomedicine of Seville, IBiS, University Hospital Virgen del Rocío/CSIC/University of Sevilla, 41013 Seville, Spain
- Functional Genomics Node, FPS/ELIXIR-ES, Hospital Virgen del Rocío, 41013 Sevilla, Spain
| | - Anna Gonzalez-Neira
- Human Genotyping Unit (CEGEN), Cancer Genetics Program, National Cancer Research Center (CNIO), 28029 Madrid, Spain
- Centre for Biomedical Network Research on Rare Diseases (CIBERER-U706), ISCIII, 28029 Madrid, Spain
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Ramsey LB, Gong L, Lee SB, Wagner JB, Zhou X, Sangkuhl K, Adams SM, Straka RJ, Empey PE, Boone EC, Klein TE, Niemi M, Gaedigk A. PharmVar GeneFocus: SLCO1B1. Clin Pharmacol Ther 2023; 113:782-793. [PMID: 35797228 PMCID: PMC10900141 DOI: 10.1002/cpt.2705] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/24/2022] [Indexed: 11/06/2022]
Abstract
The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms. To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system that was first developed for cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps, and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.
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Affiliation(s)
- Laura B Ramsey
- Divisions of Clinical Pharmacology and Research in Patient Services, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Li Gong
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Seung-Been Lee
- Precision Medicine Institute, Macrogen Inc., Seoul, Korea
| | - Jonathan B Wagner
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
| | - Xujia Zhou
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Solomon M Adams
- School of Pharmacy, Shenandoah University, Fairfax, Virginia, USA
| | - Robert J Straka
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA
| | - Philip E Empey
- School of Pharmacy and Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Erin C Boone
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
- Department of Medicine (BMIR), Stanford University, Stanford, California, USA
| | - Mikko Niemi
- Department of Clinical Pharmacology, University of Helsinki, Helsinki, Finland
- Individualized Drug Therapy Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Clinical Pharmacology, HUS Diagnostic Center, Helsinki University Hospital, Helsinki, Finland
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri, USA
- School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, USA
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The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery. Int J Mol Sci 2023; 24:ijms24032946. [PMID: 36769269 PMCID: PMC9917784 DOI: 10.3390/ijms24032946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/23/2023] [Accepted: 01/25/2023] [Indexed: 02/05/2023] Open
Abstract
Complex disorders, such as depression, remain a mystery for scientists. Although genetic factors are considered important for the prediction of one's vulnerability, it is hard to estimate the exact risk for a patient to develop depression, based only on one category of vulnerability criteria. Genetic factors also regulate drug metabolism, and when they are identified in a specific combination, may result in increased drug resistance. A proper understanding of the genetic basis of depression assists in the development of novel promising medications and effective disorder management schemes. This review aims to analyze the recent literature focusing on the correlation between specific genes and the occurrence of depression. Moreover, certain aspects targeting a high drug resistance identified among patients suffering from major depressive disorder were highlighted in this manuscript. An expected direction of future drug discovery campaigns was also discussed.
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Zhong Y, De T, Mishra M, Avitia J, Alarcon C, Perera MA. Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans. Am J Hum Genet 2023; 110:58-70. [PMID: 36608685 PMCID: PMC9892765 DOI: 10.1016/j.ajhg.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023] Open
Abstract
Expression quantitative locus (eQTL) studies have paved the way in identifying genetic variation impacting gene expression levels. African Americans (AAs) are disproportionately underrepresented in eQTL studies, resulting in a lack of power to identify population-specific regulatory variants especially related to drug response. Specific drugs are known to affect the biosynthesis of drug metabolism enzymes as well as other genes. We used drug perturbation in cultured primary hepatocytes derived from AAs to determine the effect of drug treatment on eQTL mapping and to identify the drug response eQTLs (reQTLs) that show altered effect size following drug treatment. Whole-genome genotyping (Illumina MEGA array) and RNA sequencing were performed on 60 primary hepatocyte cultures after treatment with six drugs (Rifampin, Phenytoin, Carbamazepine, Dexamethasone, Phenobarbital, and Omeprazole) and at baseline (no treatment). eQTLs were mapped by treatment and jointly with Meta-Tissue. We found varying transcriptional changes across different drug treatments and identified Nrf2 as a potential general transcriptional regulator. We jointly mapped eQTLs with gene expression data across all drug treatments and baseline, which increased our power to detect eQTLs by 2.7-fold. We also identified 2,988 reQTLs (eQTLs with altered effect size after drug treatment). reQTLs were more likely to overlap transcription factor binding sites, and we uncovered reQTLs for drug metabolizing genes such as CYP3A5. Our results provide insights into the genetic regulation of gene expression in hepatocytes through drug perturbation and provide insight into SNPs that effect the liver's ability to respond to transcription upregulation.
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Affiliation(s)
- Yizhen Zhong
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Tanima De
- Integrative Translational Genetic, Regeneron Genetic Center, Tarrytown, NY 10591, USA
| | - Mrinal Mishra
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Juan Avitia
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Cristina Alarcon
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Minoli A Perera
- Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
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22
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Biswas M, Jinda P, Sukasem C. Pharmacogenomics in Asians: Differences and similarities with other human populations. Expert Opin Drug Metab Toxicol 2023; 19:27-41. [PMID: 36755439 DOI: 10.1080/17425255.2023.2178895] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/07/2023] [Indexed: 02/10/2023]
Abstract
INTRODUCTION Various pharmacogenomic (PGx) variants differ widely in different ethnicities. and clinical outcomes associated with these variants may also be substantially varied. Literature was searched in different databases, i.e. PubMed, ScienceDirect, Web of Science, and PharmGKB, from inception to 30 June 2022 for this review. AREAS COVERED Certain PGx variants were distinctly varied in Asian populations compared to the other human populations, e.g. CYP2C19*2,*3,*17; CYP2C9*2,*3; CYP2D6*4,*5,*10,*41; UGT1A1*6,*28; HLA-B*15:02, HLA-B*15:21, HLA-B*58:01, and HLA-A*31:01. However, certain other variants do not vary greatly between Asian and other ethnicities, e.g. CYP3A5*3; ABCB1, and SLCO1B1*5. As evident in this review, the risk of major adverse cardiovascular events (MACE) was much stronger in Asian patients taking clopidogrel and who inherited the CYP2C19 loss-of-function alleles, e.g. CYP2C19*2 and*3, when compared to the western/Caucasian patients. Additionally, the risk of carbamazepine-induced severe cutaneous adverse drug reactions (SCARs) for the patients inheriting HLA-B*15:02 and HLA-B*15:21 alleles varied significantly between Asian and other ethnicities. In contrast, both Caucasian and Asian patients inheriting the SLCO1B1*5 variant possessed a similar magnitude of muscle toxicity, i.e. myopathy. EXPERT OPINION Asian countries should take measures toward expanding PGx research, as well as initiatives for the purposes of obtaining clinical benefits from this newly evolving and economically viable treatment model.
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Affiliation(s)
- Mohitosh Biswas
- Department of Pharmacy, University of Rajshahi, 6205, Rajshahi, Bangladesh
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
| | - Pimonpan Jinda
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
| | - Chonlaphat Sukasem
- Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 10400, Bangkok, Thailand
- Laboratory for Pharmacogenomics, Ramathibodi Hospital, Somdech Phra Debaratana Medical Center SDMC, 10400, Bangkok, Thailand
- Pharmacogenomics and Precision Medicine Clinic, Bumrungrad Genomic Medicine Institute (BGMI), Bumrungrad International Hospital, 10110, Bangkok, Thailand
- MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 3GL, Liverpool, UK
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Pharmacology Mechanism of Polygonum Bistorta in Treating Ulcerative Colitis Based on Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022. [DOI: 10.1155/2022/6461560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Aim. Ulcerative colitis (UC) is a refractory gastrointestinal disease. The study aimed to expound the mechanism of Polygonum bistorta (PB) in treating UC by network pharmacology, molecular docking, and experiment verification. Methods. The compositions and targets of PB and UC-associated targets were obtained by searching the websites and the literature. The potential mechanism of PB in the treatment of UC was predicted by protein-protein interaction network construction, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecule docking was performed by AutoDock. In vitro experiments explored the mechanism of quercetin (Que), the main active composition of PB, in treating UC. Results. Six compositions, 139 PB targets, and 934 UC-associated targets were obtained. 93 overlapping targets between PB and UC were identified, and 18 of them were the core targets. 467 biological processes, 10 cell components, and 30 molecular functions were obtained by GO analysis. 102 pathways were enriched through KEGG analysis. Among them, the IL-17 signaling pathway had high importance. The core targets FOS, JUN, IL-1β, CCL2, CXCL8, and MMP9 could dock with Que successfully. Act1, TRAF6, FOS, and JUN were identified by KEGG as the key proteins of the IL-17 signaling pathway. The expressions of the abovementioned proteins were increased in Caco-2 cells stimulated by Dextran sulfate sodium and decreased after being treated by Que. Conclusion. PB might treat UC by downregulating the IL-17 signaling pathway. It is worth doing further research on PB treating UC in vivo.
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Verma SS, Keat K, Li B, Hoffecker G, Risman M, Sangkuhl K, Whirl-Carrillo M, Dudek S, Verma A, Klein TE, Ritchie MD, Tuteja S. Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population. J Transl Med 2022; 20:550. [PMID: 36443877 PMCID: PMC9703665 DOI: 10.1186/s12967-022-03745-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/30/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population. METHODS A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles. RESULTS We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68). CONCLUSIONS Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.
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Affiliation(s)
- Shefali S. Verma
- grid.25879.310000 0004 1936 8972Department of Pathology & Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Karl Keat
- grid.25879.310000 0004 1936 8972Genomics & Computational Biology PhD Program, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Binglan Li
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Glenda Hoffecker
- grid.25879.310000 0004 1936 8972Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Marjorie Risman
- grid.25879.310000 0004 1936 8972Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | | | - Katrin Sangkuhl
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Michelle Whirl-Carrillo
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Scott Dudek
- grid.25879.310000 0004 1936 8972Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Anurag Verma
- grid.25879.310000 0004 1936 8972Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Teri E. Klein
- grid.168010.e0000000419368956Department of Biomedical Data Science, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956Department of Biomedical Data Science and Medicine (BMIR), Stanford University, Stanford, CA USA
| | - Marylyn D. Ritchie
- grid.25879.310000 0004 1936 8972Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
| | - Sony Tuteja
- grid.25879.310000 0004 1936 8972Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA USA
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Zhou K, Lu D, You J, Liu T, Sun J, Lu Y, Pan J, Li Y, Liu C. Integrated plasma pharmacochemistry and network pharmacology to explore the mechanism of Gerberae Piloselloidis Herba in treatment of allergic asthma. JOURNAL OF ETHNOPHARMACOLOGY 2022; 298:115624. [PMID: 35970314 DOI: 10.1016/j.jep.2022.115624] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/21/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Gerberae Piloselloidis Herba (GPH), a commonly used traditional medicine in China, is derived from Gerbera piloselloides (Linn.) Cass. It is featured by its special bioactivities as antitussive, expectorant, anti-asthma, anti-bacterial, anti-tumor, uterine analgesia, and immunity-enhancing. With a long history of medication in ethnic minority areas in China, it is often used as an effective treatment for cough and sore throat as well as allergic asthma. Although our previous investigation also has discovered GPH performed effective treatment on allergic asthma, its underlying mechanism remains unclear. AIM OF THE STUDY This research aims to reveal the pharmacological mechanism of GPH in the treatment for allergic asthma through combination of plasma pharmacology and network pharmacology. MATERIALS AND METHODS Firstly, the components of GPH in blood samples were identified using UHPLC- Q-Orbitrap HRMS. An interaction network of "compound-target-disease" was constructed based on the compounds confirmed in blood and on their corresponding targets of allergic asthma acquired from disease gene databases, predicting the possible biological targets and potential signal pathways of GPH with the network pharmacology analysis. Then, a molecular docking between the blood ingredients and the core targets was carried out using the Autodock Vina software. Subsequently, after establishing a mouse model with allergic asthma induced by ovalbumin (OVA), the effect of GPH on allergic asthma was evaluated by analyzing a series of indicators including behavior, lung pathological changes, inflammatory factors in serum and bronchoalveolar lavage fluid (BALF). Finally, the key pathway and targets predicted by network pharmacology and molecular docking were further verified using Western blot analysis. RESULTS Eleven chemical constituents (such as arbutin, neochlorogenic acid, chlorogenic acid, etc.) were identified through the analysis of plasma samples, on which basis a total of 142 genes intersecting GPH and allergic asthma were collected by network pharmacology. After performing enrichment analysis of these genes in gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG), it was found that arbutin-related targets mainly focused on phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt) signal pathway, while luteolin and marmesin -related targets tended to locate at Interleukin-17 (IL-17) signal pathway. Meanwhile, the findings of molecular docking suggested that such components as arbutin, luteolin and marmesin entering into blood had good binding with the core targets related to PI3K/Akt and IL-17 pathways. In addition, GPH improved the OVA-induced asthma symptoms, the alveolar septa thickening and the infiltration of inflammatory cell around bronchi and bronchioles as well as reduced the levels of IgE, IL-8 and TNF-α in serum or BALF. Furthermore, GPH could inhibit the phosphorylation level of Akt and the expression of PI3K, an efficacy supported by the findings by way of Western blot which suggests that GPH in the treatment of allergic asthma was linked to PI3K/Akt signal pathway. CONCLUSION In this study, a comprehensive strategy to combine the UPLC-Q-Orbitrap HRMS with network pharmacology was employed to clarify the mechanism of GPH against allergic asthma, a finding where GPH may inhibit PI3K/Akt signal pathway to protect mice from OVA-induced allergic asthma. This study provides a deeper understanding of the pharmacological mechanism of GPH in treatment of asthma, offering a scientific reference for further research and clinical application of GPH in terms of allergic asthma.
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Affiliation(s)
- Kun Zhou
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China; School of Pharmacy, Guizhou Medical University, Guiyang, 550025, China
| | - Dingyan Lu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China
| | - Jingrui You
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China; School of Pharmacy, Guizhou Medical University, Guiyang, 550025, China
| | - Ting Liu
- Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang, 550004, China
| | - Jia Sun
- Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang, 550004, China
| | - Yuan Lu
- Guizhou Provincial Key Laboratory of Pharmaceutics, Guizhou Medical University, Guiyang, 550004, China
| | - Jie Pan
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China
| | - Yongjun Li
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China.
| | - Chunhua Liu
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and TCM (Ministry of Education), Guizhou Medical University, Guiyang, 550004, China.
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Pan J, Yang H, Zhu L, Lou Y, Jin B. Qingfei Jiedu decoction inhibits PD-L1 expression in lung adenocarcinoma based on network pharmacology analysis, molecular docking and experimental verification. Front Pharmacol 2022; 13:897966. [PMID: 36091822 PMCID: PMC9454399 DOI: 10.3389/fphar.2022.897966] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/25/2022] [Indexed: 11/25/2022] Open
Abstract
Objective: We aim at investigating the molecular mechanisms through which the Qingfei Jiedu decoction (QFJDD) regulates PD-L1 expression in lung adenocarcinoma (LUAD). Methods: Bioactive compounds and targets of QFJDD were screened from TCMSP, BATMAN-TCM, and literature. Then, GeneCard, OMIM, PharmGKB, Therapeutic Target, and DrugBank databases were used to identify LUAD-related genes. The protein-protein interaction (PPI) network was constructed using overlapping targets of bioactive compounds in LUAD with the Cytoscape software and STRING database. The potential functions and pathways in which the hub genes were enriched by GO, KEGG, and DAVID pathway analyses. Molecular docking of bioactive compounds and key genes was executed via AutoDock Vina. Qualitative and quantitative analyses of QFJDD were performed using UPLC-Q-TOF-MS and UPLC. Expressions of key genes were determined by qRT-PCR, immunoreactivity score (IRS) of PD-L1 was assessed by immunohistochemistry (IHC), while the CD8+PD-1+T% derived from spleen tissues of Lewis lung cancer (LLC) bearing-mice was calculated using flow cytometry (FCM). Results: A total of 53 bioactive compounds and 288 targets of QFJDD as well as 8151 LUAD associated genes were obtained. Further, six bioactive compounds, including quercetin, luteolin, kaempferol, wogonin, baicalein, and acacetin, and 22 hub genes were identified. The GO analysis showed that the hub genes were mainly enriched in DNA or RNA transcription. KEGG and DAVID pathway analyses revealed that 20 hub genes were primarily enriched in virus, cancer, immune, endocrine, and cardiovascular pathways. The EGFR, JUN, RELA, HIF1A, NFKBIA, AKT1, MAPK1, and MAPK14 hub genes were identified as key genes in PD-L1 expression and PD-1 checkpoint pathway. Moreover, ideal affinity and regions were identified between core compounds and key genes. Notably, QFJDD downregulated EGFR, JUN, RELA, HIF1A, NFKBIA, and CD274 expressions (p < 0.05), while it upregulated AKT1 and MAPK1 (p < 0.05) levels in A549 cells. The PD-L1 IRS of LLC tissue in the QFJDD high dose (Hd) group was lower than model group (p < 0.01). CD8+PD-1+T% was higher in the QFJDD Hd group than in normal and model groups (p < 0.05). Conclusion: QFJDD downregulates PD-L1 expression and increases CD8+PD-1+T% via regulating HIF-1, EGFR, JUN and NFκB signaling pathways. Therefore, QFJDD is a potential treatment option for LUAD.
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Affiliation(s)
- Junjie Pan
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine (Dingqiao District), Hangzhou, Zhejiang, China
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
| | - Hongkuan Yang
- Respiratory Intensive Care Unit, The People’s Hospital of Gaozhou, Maoming, Guangdong, China
| | - Lihong Zhu
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine (Dingqiao District), Hangzhou, Zhejiang, China
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
| | - Yafang Lou
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine (Dingqiao District), Hangzhou, Zhejiang, China
- Department of Pulmonary and Critical Care Medicine, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou, Zhejiang, China
- *Correspondence: Yafang Lou, ; Bo Jin,
| | - Bo Jin
- College of Life Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
- *Correspondence: Yafang Lou, ; Bo Jin,
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Genetic polymorphisms of pharmacogenomic VIP variants in the Lahu population from Yunnan Province. Gene 2022; 844:146825. [PMID: 35995116 DOI: 10.1016/j.gene.2022.146825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/29/2022] [Accepted: 08/16/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Pharmacogenomics has been widely used to study the very important pharmacogenetic (VIP) variants among populations, but information on pharmacogenomics in the Lahu population is limited. The purpose of this study was to determine the differences in the distribution of VIP variants between the Lahu and the other 26 populations. METHODS We genotyped 55 VIP variants of 27 genes in the Lahu population from the PharmGKB database. χ2 test was used to compare the genotype and allele frequencies between the Lahu and the other 26 populations from the 1000 Genomes Project. RESULTS The genotype and allele frequencies of single nucleotide polymorphisms (SNPs) on rs20417 (PTGS2), rs776746 (CYP3A5), rs2115819 (ALOX5), and rs3093105 (CYP4F2) were considerably different in the Lahu population compared with those in the other 26 populations. Besides, based on the PharmGKB database, we identified several VIP variants that may alter the drug metabolism of aspirin (PTGS2), tacrolimus (CYP3A5), montelukast (ALOX5), and vitamin E (CYP4F2). CONCLUSION The results show that there are significant differences in the genotype frequency distribution between the Lahu and the other 26 populations. Our study supplements the pharmacogenomics information of the Lahu population and provides a theoretical basis for individualized medicine in Lahu.
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Meng W, Zhang W, Yang S, Dou X, Liu Y, Li H, Liu J, Jin T, Li B. Analysis of pharmacogenomic very important pharmacogenomic variants: CYP3A5, ACE, PTGS2 and NAT2 genes in Chinese Bai population. Per Med 2022; 19:403-410. [PMID: 35801384 DOI: 10.2217/pme-2021-0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Aim: Our study aimed to screen the genotype frequencies of very important pharmacogenomic (VIP) mutations and identify their differences between Bai and other populations. Materials & methods: We selected 66 VIP variants from PharmGKB (www.pharmgkb.org/) for genotyping. χ2 test was used to identify differences in loci between these populations and Fst values of Bai and the other 26 populations were analyzed. Results: Our study showed that the frequencies of SNPs of CYP3A5, ACE, PTGS2 and NAT2 differed significantly from those of the other 26 populations. At the same time, we found that some VIP variants may affect the metabolism of drugs and the genetic relationship between the Bai population and East Asian populations was found to be the closest. Conclusion: By comparing the genotype frequencies of different populations, the loci with significant differences were identified and discussed, providing a theoretical basis for individualized drug use in the Bai ethnic population.
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Affiliation(s)
- Wenting Meng
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Wenjie Zhang
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Shuangyu Yang
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Xia Dou
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Yuanwei Liu
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Haiyue Li
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Jianfeng Liu
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Tianbo Jin
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
| | - Bin Li
- Key Laboratory of Resource Biology & Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, 229 TaiBai North Road, Xi'an, 710069, China.,Biomedicine Key Laboratory of Shaanxi Province, Northwest University, Xi'an, 710069, China
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Pop C, Cristina A, Iaru I, Popa SL, Mogoșan C. Nation-Wide Survey Assessing the Knowledge and Attitudes of Romanian Pharmacists Concerning Pharmacogenetics. Front Pharmacol 2022; 13:952562. [PMID: 35847030 PMCID: PMC9284104 DOI: 10.3389/fphar.2022.952562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/16/2022] [Indexed: 01/13/2023] Open
Abstract
Background: Pharmacogenetics (PGx) is an important component of personalized medicine that has the potential to improve medicines’ effectiveness and safety. However, despite progress in technology and availability, PGx testing application into patient-care in Eastern Europe countries, has been slow.Objectives: Our aim was to describe knowledge and attitudes of Romanian pharmacists concerning PGx, and identify potential factors limiting PGx implementation.Method: An anonymous, web-based questionnaire was distributed to Romanian pharmacists registered in the National Pharmacists’ Association (NPA) via an official e-mail sent by NPA representatives.Results: A total of 1,058 pharmacists completed the questionnaires, resulting in a response rate of 7.6%. Pharmacists were predominantly female (90.1%), younger than 49 years (87.5%) and mostly worked in community pharmacies (80.2%). Most pharmacists (64.8%) had a knowledge score between 30 and 49 points out of 60, and (75.4%) had attitude scores between 9 and 7 out of 10. Attitude and knowledge scores positively correlated.Conclusion: Despite performing fairly well on general questions regarding PGx, Romanian pharmacists may lack in-depth knowledge, which can affect their readiness to discuss PGx information with patients or other healthcare professionals. High pricing was considered an important impediment in PGx implementation.
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Affiliation(s)
- Cristina Pop
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Anamaria Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Irina Iaru
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- *Correspondence: Irina Iaru,
| | - Stefan L. Popa
- 2nd Medical Department, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Cristina Mogoșan
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
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Richtlinie der Gendiagnostik-Kommission (GEKO) für die Beurteilung genetischer Eigenschaften hinsichtlich ihrer Bedeutung für die Wirkung eines Arzneimittels bei einer Behandlung gemäß § 23 Abs. 2 Nr. 1b GenDG. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2022; 65:958-962. [PMID: 35776150 DOI: 10.1007/s00103-022-03559-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Drug Metabolism for the Identification of Clinical Biomarkers in Breast Cancer. Int J Mol Sci 2022; 23:ijms23063181. [PMID: 35328602 PMCID: PMC8951384 DOI: 10.3390/ijms23063181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/09/2022] [Accepted: 03/15/2022] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is classified into four major molecular subtypes, and is considered a heterogenous disease. The risk profiles and treatment of breast cancer differ according to these subtypes. Early detection dramatically improves the prospects of successful treatment, resulting in a reduction in overall mortality rates. However, almost 30% of women primarily diagnosed with the early-stage disease will eventually develop metastasis or resistance to chemotherapies. Immunotherapies are among the most promising cancer treatment options; however, long-term clinical benefit has only been observed in a small subset of responding patients. The current strategies for diagnosis and treatment rely heavily on histopathological examination and molecular diagnosis, disregarding the tumor microenvironment and microbiome involving cancer cells. In this review, we aim to praise the use of pharmacogenomics and pharmacomicrobiomics as a strategy to identify potential biomarkers for guiding and monitoring therapy in real-time. The finding of these biomarkers can be performed by studying the metabolism of drugs, more specifically, immunometabolism, and its relationship with the microbiome, without neglecting the information provided by genetics. A larger understanding of cancer biology has the potential to improve patient care, enable clinical decisions, and deliver personalized medicine.
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Sales RR, Nogueira BL, Tosatti JAG, Gomes KB, Luizon MR. Do Genetic Polymorphisms Affect Fetal Hemoglobin (HbF) Levels in Patients With Sickle Cell Anemia Treated With Hydroxyurea? A Systematic Review and Pathway Analysis. Front Pharmacol 2022; 12:779497. [PMID: 35126118 PMCID: PMC8814522 DOI: 10.3389/fphar.2021.779497] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/31/2021] [Indexed: 01/23/2023] Open
Abstract
Hydroxyurea has long been used for the treatment of sickle cell anemia (SCA), and its clinical effectiveness is related to the induction of fetal hemoglobin (HbF), a major modifier of SCA phenotypes. However, there is substantial variability in response to hydroxyurea among patients with SCA. While some patients show an increase in HbF levels and an ameliorated clinical condition under low doses of hydroxyurea, other patients present a poor effect or even develop toxicity. However, the effects of genetic polymorphisms on increasing HbF levels in response to hydroxyurea in patients with SCA (Hb SS) have been less explored. Therefore, we performed a systematic review to assess whether single-nucleotide polymorphisms (SNPs) affect HbF levels in patients with SCA treated with hydroxyurea. Moreover, we performed pathway analysis using the set of genes with SNPs found to be associated with changes in HbF levels in response to hydroxyurea among the included studies. The systematic literature search was conducted on Medline/PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, and Web of Science. Seven cohort studies were included following our inclusion and exclusion criteria. From the 728 genetic polymorphisms examined in the included studies, 50 different SNPs of 17 genes were found to be associated with HbF changes in patients with SCA treated with hydroxyurea, which are known to affect baseline HbF but are not restricted to them. Enrichment analysis of this gene set revealed reactome pathways with the lowest adjusted p-values and highest combined scores related to VEGF ligand–receptor interactions (R-HSA-194313; R-HSA-195399) and the urea cycle (R-HSA-70635). Pharmacogenetic studies of response to hydroxyurea therapy in patients with SCA are still scarce and markedly heterogeneous regarding candidate genes and SNPs examined for association with HbF changes and outcomes, suggesting that further studies are needed. The reviewed findings highlighted that similar to baseline HbF, changes in HbF levels upon hydroxyurea therapy are likely to be regulated by multiple loci. There is evidence that SNPs in intron 2 of BCL11A affect HbF changes in response to hydroxyurea therapy, a potential application that might improve the clinical management of SCA. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=208790).
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Affiliation(s)
- Rahyssa Rodrigues Sales
- Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
- *Correspondence: Rahyssa Rodrigues Sales, ; Marcelo Rizzatti Luizon,
| | - Bárbara Lisboa Nogueira
- Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Jéssica Abdo Gonçalves Tosatti
- Department of Clinical and Toxicological Analyzes, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Karina Braga Gomes
- Department of Clinical and Toxicological Analyzes, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Marcelo Rizzatti Luizon
- Graduate Program in Genetics, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Genetics, Ecology and Evolution, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil
- *Correspondence: Rahyssa Rodrigues Sales, ; Marcelo Rizzatti Luizon,
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Zhang X, Xiao H, Fu S, Yu J, Cheng Y, Jiang Y. Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis. Front Endocrinol (Lausanne) 2022; 13:1081747. [PMID: 36726458 PMCID: PMC9884696 DOI: 10.3389/fendo.2022.1081747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Patients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis. METHODS The disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein-protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD. RESULTS This study identified 495 risk genes for DKD complicated with IBD. After constructing a protein-protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C-C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product-receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway. CONCLUSION There is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.
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Affiliation(s)
- Xiaoyu Zhang
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Huijie Xiao
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shaojie Fu
- Department of Nephrology, The First Hospital of Jilin University, Changchun, China
| | - Jinyu Yu
- Department of Urology, The First Hospital of Jilin University, Changchun, China
| | - Yanli Cheng
- Department of Nephrology, The First Hospital of Jilin University, Changchun, China
- *Correspondence: Yanli Cheng, ; Yang Jiang,
| | - Yang Jiang
- Department of Gastrointestinal and Colorectal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Yanli Cheng, ; Yang Jiang,
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Realities of Pharmacogenomic and Minimizing Misconceptions and Medication Misadventures. Dela J Public Health 2021; 7:12-15. [PMID: 35619975 PMCID: PMC9124564 DOI: 10.32481/djph.2021.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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