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McCune JS, Navarro SL, Risler LJ, Phillips BR, Ren S, Schoch HG, Baker KS. The presence of busulfan metabolites and pharmacometabolomics in plasma drawn immediately before allograft infusion in hematopoietic cell transplant recipients. Clin Transl Sci 2023; 16:2577-2590. [PMID: 37749994 PMCID: PMC10719475 DOI: 10.1111/cts.13651] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/07/2023] [Accepted: 09/10/2023] [Indexed: 09/27/2023] Open
Abstract
Busulfan is hepatically metabolized through glutathione (GSH) conjugation; in vitro, this process depletes hepatocyte GSH stores and generates the cytotoxic metabolite γ-glutamyldehydroalanylglycine, which is too unstable to be quantitated in vivo. We sought to evaluate if pre-graft (i.e., immediately before allograft infusion) concentrations of busulfan metabolites' and of endogenous metabolomic compounds (EMCs) representing the glutathione pathway were associated with clinical outcomes in hematopoietic cell transplant (HCT) recipients receiving busulfan. The clinical outcomes evaluated were relapse, acute graft versus host disease (GVHD), chronic GVHD, non-relapse mortality, and neutrophil nadir. In pre-graft samples obtained from patients immediately before allograft infusion, our objectives were to evaluate for: (1) the presence of busulfan and its metabolites tetrahydrothiophenium ion (THT+), tetrahydrothiophene 1-oxide, sulfolane, and 3-hydroxysulfolane (N = 124); (2) EMCs using a global metabolomics assay (N = 77); and (3) the association of the busulfan metabolites and the EMCs with clinical outcomes. In the pre-graft samples, busulfan and THT+ could not be detected. THT 1-oxide, sulfolane, and 3-hydroxysulfolane were quantitated in 9.6%, 26%, and 58% of pre-graft samples; their concentrations were not associated with clinical outcomes. Four pre-graft EMCs were statistically significantly associated with the neutrophil nadir. The pre-graft EMCs were not associated with the other clinical outcomes. In conclusion, busulfan's metabolites are present in patients' plasma immediately before allograft infusion; the neutrophil nadir is associated with pre-graft EMCs. Future research should investigate the association of clinical outcomes with the concentrations of busulfan's metabolites and EMCs in the pre-graft plasma from allogeneic HCT recipients.
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Affiliation(s)
- Jeannine S. McCune
- Department of Hematologic Malignancies Translational SciencesCity of HopeDuarteCaliforniaUSA
| | - Sandi L. Navarro
- Division of Public Health SciencesFred Hutchinson Cancer CenterSeattleWashingtonUSA
| | - Linda J. Risler
- Department of PharmaceuticsUniversity of WashingtonSeattleWashingtonUSA
| | - Brian R. Phillips
- Department of PharmaceuticsUniversity of WashingtonSeattleWashingtonUSA
| | - Suping Ren
- Department of Hematologic Malignancies Translational SciencesCity of HopeDuarteCaliforniaUSA
| | - H. Gary Schoch
- Clinical Research DivisionFred Hutchinson Cancer CenterSeattleWashingtonUSA
| | - K. Scott Baker
- Clinical Research DivisionFred Hutchinson Cancer CenterSeattleWashingtonUSA
- Department of PediatricsUniversity of WashingtonSeattleWashingtonUSA
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Jian J, He D, Gao S, Tao X, Dong X. Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication. Pharmaceuticals (Basel) 2023; 16:1568. [PMID: 38004434 PMCID: PMC10675232 DOI: 10.3390/ph16111568] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 10/19/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.
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Affiliation(s)
- Jingai Jian
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
| | - Donglin He
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
| | - Songyan Gao
- Institute of Translational Medicine, Shanghai University, Shanghai 200444, China;
| | - Xia Tao
- Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China
| | - Xin Dong
- School of Medicine, Shanghai University, Shanghai 200444, China; (J.J.); (D.H.)
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3
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Shen G, Moua KTY, Perkins K, Johnson D, Li A, Curtin P, Gao W, McCune JS. Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring. Front Pharmacol 2023; 14:1126981. [PMID: 37021042 PMCID: PMC10069443 DOI: 10.3389/fphar.2023.1126981] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/14/2023] [Indexed: 04/07/2023] Open
Abstract
The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.
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Affiliation(s)
- Guofang Shen
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Kao Tang Ying Moua
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Kathryn Perkins
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Deron Johnson
- Clinical Informatics, City of Hope Medical Center, Duarte, CA, United States
| | - Arthur Li
- Division of Biostatistics, City of Hope, Duarte, CA, United States
| | - Peter Curtin
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Wei Gao
- Division of Engineering and Applied Science, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Jeannine S. McCune
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
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McCune JS, Navarro SL, Baker KS, Risler LJ, Phillips BR, Randolph TW, Shireman L, Schoch G, Deeg HJ, Zhang Y, Men A, Maton L, Huitema ADR. Prediction of Busulfan Clearance by Predose Plasma Metabolomic Profiling. Clin Pharmacol Ther 2023; 113:370-379. [PMID: 36369996 PMCID: PMC9888309 DOI: 10.1002/cpt.2794] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022]
Abstract
Intravenous busulfan doses are often personalized to a target plasma exposure (targeted busulfan) using an individual's busulfan clearance (BuCL). We evaluated whether BuCL could be predicted by a predose plasma panel of 841 endogenous metabolomic compounds (EMCs). In this prospective cohort of 132 hematopoietic cell transplantation (HCT) patients, all had samples collected immediately before busulfan administration (preBU) and 96 had samples collected 2 weeks before busulfan (2-week-preBU). BuCL was significantly associated with 37 EMCs after univariate linear regression analysis and controlling for false discovery (< 0.05) in the 132 preBU samples. In parallel, with preBU samples, we included all 841 EMCs in a least absolute shrinkage and selection operator-penalized regression which selected 13 EMCs as predominantly associated with BuCL. Then, we constructed a prediction model by estimating coefficients for these 13 EMCs, along with sex, using ordinary least-squares. When the resulting linear prediction model was applied to the 2-week-preBU samples, it explained 40% of the variation in BuCL (adjusted R2 = 0.40). Pathway enrichment analysis revealed 18 pathways associated with BuCL. Lysine degradation followed by steroid biosynthesis, which aligned with the univariate analysis, were the top two pathways. BuCL can be predicted before busulfan administration with a linear regression model of 13 EMCs. This pharmacometabolomics method should be prioritized over use of a busulfan test dose or pharmacogenomics to guide busulfan dosing. These results highlight the potential of pharmacometabolomics as a precision medicine tool to improve or replace pharmacokinetics to personalize busulfan doses.
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Affiliation(s)
- Jeannine S. McCune
- City of Hope, Department of Hematologic Malignancies Translational Sciences, Duarte, California (CA), 91010, United States of America (USA)
| | - Sandi L. Navarro
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA
| | - K. Scott Baker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA,Department of Pediatrics, University of Washington, Seattle, WA, 98195, USA
| | - Linda J. Risler
- Department of Pharmaceutics, University of Washington, Seattle, WA, 98195, USA
| | - Brian R. Phillips
- Department of Pharmaceutics, University of Washington, Seattle, WA, 98195, USA
| | - Timothy W. Randolph
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA
| | - Laura Shireman
- Department of Pharmaceutics, University of Washington, Seattle, WA, 98195, USA
| | - Gary Schoch
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA
| | - H. Joachim Deeg
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA,Department of Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Yuzheng Zhang
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (WA), 98109, USA
| | - Alex Men
- Department of Pharmaceutics, University of Washington, Seattle, WA, 98195, USA
| | - Loes Maton
- Department of Pharmacy & Pharmacology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Alwin D. R. Huitema
- Department of Pharmacy & Pharmacology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands,Department of Pharmacology, Princes Maxima & Pharmacology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands,Department of Clinical Pharmacy, University Medical Center Utrecht, The Netherlands
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5
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McCune JS, Nakamura R, O'Meally D, Randolph TW, Sandmaier BM, Karolak A, Hockenbery D, Navarro SL. Pharmacometabonomic Association of Cyclophosphamide 4-hydroxylation in Hematopoietic Cell Transplant Recipients. Clin Transl Sci 2022; 15:1215-1224. [PMID: 35106927 PMCID: PMC9099130 DOI: 10.1111/cts.13239] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 11/26/2022] Open
Abstract
The widely used alkylating agent cyclophosphamide (CY) has substantive interpatient variability in the area under the curve (AUC) of it and its metabolites. Numerous factors may influence the drug‐metabolizing enzymes that metabolize CY to 4‐hydroxycyclophosphamide (4HCY), the principal precursor to CY’s cytotoxic metabolite. We sought to identify endogenous metabolomics compounds (EMCs) associated with 4HCY formation clearance (ratio of 4HCY/CY AUC) using global metabolomics. Patients who undergo hematopoietic cell transplantation receiving post‐transplant CY (PT‐CY) were enrolled, cohort 1 (n = 26) and cohort 2 (n = 25) donating longitudinal blood samples before they started HCT (pre‐HCT), before infusion of the donor allograft (pre‐graft), before the first dose of PT‐CY (pre‐CY), and 24 h after the first dose of PT‐CY (24‐h post‐CY), which is also immediately before the second dose of CY. A total of 512 and 498 EMCs were quantitated in two cohorts, respectively. Both univariate linear regression with false discovery rate (FDR), and pathway enrichment analyses using a global association test were performed. At the pre‐CY time point, no EMCs were associated at FDR less than 0.1. At pre‐HCT, cohort 1 had one EMC (levoglucosan) survive the FDR threshold. At pre‐graft, cohort 1 and cohort 2 had 20 and 13 EMCs, respectively, exhibiting unadjusted p values less than 0.05, with the only EMCs having an FDR less than 0.1 being two unknown EMCs. At 24‐h post‐CY, there were three EMCs, two ketones, and threitol, at FDR less than 0.1 in cohort 2. These results demonstrate the potential of pharmacometabonomics, but future studies in larger samples are needed to optimize CY.
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Affiliation(s)
- Jeannine S McCune
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, USA
| | - Ryotaro Nakamura
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, USA
| | - Denis O'Meally
- Center for Gene Therapy, Hematologic Malignancies Research Institute, City of Hope, Duarte, CA, USA
| | - Timothy W Randolph
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Brenda M Sandmaier
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Aleksandra Karolak
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, USA.,Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, City of Hope, Duarte, CA, USA
| | - David Hockenbery
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.,Department of Medicine, University of Washington, Seattle, WA, USA
| | - Sandi L Navarro
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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Hu C, Jia W. Multi-omics profiling: the way towards precision medicine in metabolic diseases. J Mol Cell Biol 2021; 13:mjab051. [PMID: 34406397 PMCID: PMC8697344 DOI: 10.1093/jmcb/mjab051] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/19/2021] [Accepted: 06/21/2021] [Indexed: 12/12/2022] Open
Abstract
Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.
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Affiliation(s)
- Cheng Hu
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus,
Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth
People's Hospital, Shanghai 200233, China
- Institute for Metabolic Disease, Fengxian Central Hospital, The Third School of
Clinical Medicine, Southern Medical University, Shanghai 201499, China
| | - Weiping Jia
- Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus,
Shanghai Clinical Center for Diabetes, Shanghai Jiao Tong University Affiliated Sixth
People's Hospital, Shanghai 200233, China
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7
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Feasibility of pharmacometabolomics to identify potential predictors of paclitaxel pharmacokinetic variability. Cancer Chemother Pharmacol 2021; 88:475-483. [PMID: 34089352 DOI: 10.1007/s00280-021-04300-7] [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: 03/15/2021] [Accepted: 05/18/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Paclitaxel is a commonly used chemotherapy drug with substantial variability in pharmacokinetics (PK) that affects treatment efficacy and toxicity. Pharmacometabolomic signatures that explain PK variability could be used to individualize dosing to improve therapeutic outcomes. The objective of this study was to identify pretreatment metabolites or metabolomic signatures that explain variability in paclitaxel PK. METHODS This analysis was conducted using data previously collected on a prospective observational study of 48 patients with breast cancer receiving weekly 80 mg/m2 paclitaxel infusions. Paclitaxel plasma concentrations were measured during the first infusion to estimate paclitaxel time above threshold (Tc>0.05) and maximum concentration (Cmax). Metabolites measured in pretreatment whole blood by nuclear magnetic resonance spectrometry were analyzed for an association with Tc>0.05 and Cmax using Pearson correlation followed by stepwise linear regression. RESULTS Pretreatment creatinine, glucose, and lysine concentrations were positively correlated with Tc>0.05, while pretreatment betaine was negatively correlated and lactate was positively correlated with Cmax (all uncorrected p < 0.05). After stepwise elimination, creatinine was associated with Tc>0.05, while betaine and lactate were associated with Cmax (all p < 0.05). CONCLUSION This study identified pretreatment metabolites that may be associated with paclitaxel PK variability demonstrating feasibility of a pharmacometabolomics approach for understanding paclitaxel PK. However, identification of more robust pharmacometabolomic predictors will be required for broad and routine application for the clinical dosing of paclitaxel.
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8
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Carter KA, Simpson CD, Raftery D, Baker MG. Short Report: Using Targeted Urine Metabolomics to Distinguish Between Manganese Exposed and Unexposed Workers in a Small Occupational Cohort. Front Public Health 2021; 9:666787. [PMID: 34095069 PMCID: PMC8172780 DOI: 10.3389/fpubh.2021.666787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives: Despite the widespread use of manganese (Mn) in industrial settings and its association with adverse neurological outcomes, a validated and reliable biomarker for Mn exposure is still elusive. Here, we utilize targeted metabolomics to investigate metabolic differences between Mn-exposed and -unexposed workers, which could inform a putative biomarker for Mn and lead to increased understanding of Mn toxicity. Methods: End of shift spot urine samples collected from Mn exposed (n = 17) and unexposed (n = 15) workers underwent a targeted assay of 362 metabolites using LC-MS/MS; 224 were quantified and retained for analysis. Differences in metabolite abundances between exposed and unexposed workers were tested with a Benjamini-Hochberg adjusted Wilcoxon Rank-Sum test. We explored perturbed pathways related to exposure using a pathway analysis. Results: Seven metabolites were significantly differentially abundant between exposed and unexposed workers (FDR ≤ 0.1), including n-isobutyrylglycine, cholic acid, anserine, beta-alanine, methionine, n-isovalerylglycine, and threonine. Three pathways were significantly perturbed in exposed workers and had an impact score >0.5: beta-alanine metabolism, histidine metabolism, and glycine, serine, and threonine metabolism. Conclusion: This is one of few studies utilizing targeted metabolomics to explore differences between Mn-exposed and -unexposed workers. Metabolite and pathway analysis showed amino acid metabolism was perturbed in these Mn-exposed workers. Amino acids have also been shown to be perturbed in other occupational cohorts exposed to Mn. Additional research is needed to characterize the biological importance of amino acids in the Mn exposure-disease continuum, and to determine how to appropriately utilize and interpret metabolomics data collected from occupational cohorts.
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Affiliation(s)
- Kayla A Carter
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Christopher D Simpson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States
| | - Daniel Raftery
- Northwest Metabolomics Research Center, University of Washington, Seattle, WA, United States
| | - Marissa G Baker
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States
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Fu J, Zhang Y, Liu J, Lian X, Tang J, Zhu F. Pharmacometabonomics: data processing and statistical analysis. Brief Bioinform 2021; 22:6236068. [PMID: 33866355 DOI: 10.1093/bib/bbab138] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/09/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jin Liu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Xichen Lian
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jing Tang
- Department of Bioinformatics in Chongqing Medical University, China
| | - Feng Zhu
- College of Pharmaceutical Sciences in Zhejiang University, China
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10
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Horn DL, Bettcher LF, Navarro SL, Pascua V, Neto FC, Cuschieri J, Raftery D, O'Keefe GE. Persistent metabolomic alterations characterize chronic critical illness after severe trauma. J Trauma Acute Care Surg 2021; 90:35-45. [PMID: 33017357 PMCID: PMC8011937 DOI: 10.1097/ta.0000000000002952] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Following trauma, persistent inflammation, immunosuppression, and catabolism may characterize delayed recovery or failure to recover. Understanding the metabolic response associated with these adverse outcomes may facilitate earlier identification and intervention. We characterized the metabolic profiles of trauma victims who died or developed chronic critical illness (CCI) and hypothesized that differences would be evident within 1-week postinjury. METHODS Venous blood samples from trauma victims with shock who survived at least 7 days were analyzed using mass spectrometry. Subjects who died or developed CCI (intensive care unit length of stay of ≥14 days with persistent organ dysfunction) were compared with subjects who recovered rapidly (intensive care unit length of stay, ≤7 days) and uninjured controls. We used partial least squares discriminant analysis, t tests, linear mixed effects regression, and pathway enrichment analyses to make broad comparisons and identify differences in metabolite concentrations and pathways. RESULTS We identified 27 patients who died or developed CCI and 33 who recovered rapidly. Subjects were predominantly male (65%) with a median age of 53 years and Injury Severity Score of 36. Healthy controls (n = 48) had similar age and sex distributions. Overall, from the 163 metabolites detected in the samples, 56 metabolites and 21 pathways differed between injury outcome groups, and partial least squares discriminant analysis models distinguished injury outcome groups as early as 1-day postinjury. Differences were observed in tryptophan, phenylalanine, and tyrosine metabolism; metabolites associated with oxidative stress via methionine metabolism; inflammatory mediators including kynurenine, arachidonate, and glucuronic acid; and products of the gut microbiome including indole-3-propionate. CONCLUSIONS The metabolic profiles in subjects who ultimately die or develop CCI differ from those who have recovered. In particular, we have identified differences in markers of inflammation, oxidative stress, amino acid metabolism, and alterations in the gut microbiome. Targeted metabolomics has the potential to identify important metabolic changes postinjury to improve early diagnosis and targeted intervention. LEVEL OF EVIDENCE Prognostic/epidemiologic, level III.
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Affiliation(s)
- Dara L Horn
- From the Department of Surgery (D.L.H.), and Department of Anesthesiology and Pain Medicine (L.F.B., V.P., F.C.N., D.R.), University of Washington; Fred Hutchinson Cancer Research Center (S.L.N., D.R.); and Division of Trauma and Critical Care, Department of Surgery (J.C., G.E.O.), Harborview Medical Center, Seattle, Washington
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11
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Hu T, An Z, Sun Y, Wang X, Du P, Li P, Chi Y, Liu L. Longitudinal Pharmacometabonomics for Predicting Malignant Tumor Patient Responses to Anlotinib Therapy: Phenotype, Efficacy, and Toxicity. Front Oncol 2020; 10:548300. [PMID: 33282726 PMCID: PMC7689013 DOI: 10.3389/fonc.2020.548300] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/06/2020] [Indexed: 12/11/2022] Open
Abstract
Anlotinib is an oral small molecule inhibitor of multiple receptor tyrosine kinases (RTKs), which was approved by the National Medical Products Administration (NMPA) of China in 2018 for the third-line treatment of non-small cell lung cancer (NSCLC). Here, for the first time, the longitudinal pharmacometabonomics was explored for predicting malignant tumor patient responses to anlotinib, including the metabolic phenotype variation, drug efficacy, and toxicity. A total of 393 plasma samples from 16 subjects collected from a phase I additional study of anlotinib (NCT02752516) were submitted to targeted metabolomics analysis. The orthogonal partial least-squares discriminant analysis (OPLS-DA) models were constructed for the predication of anlotinib efficacy and toxicity based on the longitudinal pharmacometabonomics data. Statistical results showed that 38 metabolites, mainly involved in aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and steroid hormone biosynthesis, were all significantly upregulated attributing to anlotinib treatment. The anti-tumor efficacy and occurrence of proteinuria after anlotinib administration can be predicted with 100% accuracy using the established OPLS-DA models. Glycodeoxycholic acid and glycocholic acid possessed the most excellent sensitivity and specificity in predicting the efficacy of anlotinib, with area under the receiver operating characteristic curve (AUC of ROC curve) 0.847 and 0.828, respectively. NG, NG-dimethylarginine was the most promising biomarker for the prediction of proteinuria occurrence after anlotinib administration, with AUC of ROC curve 0.814. In conclusion, this work developed efficient and convenient discriminant models that can accurately predict the efficacy and toxicity of anlotinib based on longitudinal pharmacometabonomics study.
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Affiliation(s)
- Ting Hu
- Pharmaceutical Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Zhuoling An
- Pharmaceutical Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yongkun Sun
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xunqiang Wang
- Research and Development Department, Chia Tai Tianqing Pharmaceutical Group Co., Nanjing, China
| | - Ping Du
- Pharmaceutical Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Pengfei Li
- Pharmaceutical Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yihebali Chi
- National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lihong Liu
- Pharmaceutical Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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12
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McCune JS, McKiernan JS, van Maarseveen E, Huitema ADR, Randolph TW, Deeg HJ, Nakamura R, Baker KS. Prediction of Acute Graft versus Host Disease and Relapse by Endogenous Metabolomic Compounds in Patients Receiving Personalized Busulfan-Based Conditioning. J Proteome Res 2020; 20:684-694. [PMID: 33064008 DOI: 10.1021/acs.jproteome.0c00599] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Busulfan-based conditioning is the most commonly used high-dose conditioning regimen for allogeneic hematopoietic cell transplant (HCT). The alkylating agent busulfan has a narrow therapeutic index, with busulfan doses personalized to a target plasma exposure (targeted busulfan). Using a global pharmacometabonomics approach, we sought to identify novel biomarkers of relapse or acute graft versus host disease (GVHD) in a cohort of 84 patients receiving targeted busulfan before allogeneic HCT. A total of 763 endogenous metabolomic compounds (EMCs) were quantitated in 230 longitudinal blood samples before, during, and shortly after intravenous busulfan administration. We performed both univariate linear regression and pathway enrichment analyses using global testing. The cysteine/methionine pathway and the glycine, serine, and threonine metabolism pathway were most associated with relapse. The latter be explained by the fact that glutathione S-transferases conjugate both busulfan and glutathione, which contains glycine as a component. The d-arginine and d-ornithine metabolism pathway and arginine and proline metabolism pathway were most associated with acute GVHD. None of these associations were significant after correcting for false discovery rate (FDR) with a strict cutoff of FDR-adjusted p < 0.1. Although larger studies are needed to substantiate these findings, the results show that EMCs may be used as predictive biomarkers in HCT patients.
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Affiliation(s)
- Jeannine S McCune
- City of Hope, Department of Population Sciences, Duarte, California 91010, United States.,City of Hope, Department of Hematology & HCT, Duarte, California 91010, United States
| | - Jožefa S McKiernan
- City of Hope, Department of Population Sciences, Duarte, California 91010, United States
| | - Erik van Maarseveen
- Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, 3584 CS Utrecht, The Netherlands
| | - Alwin D R Huitema
- Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht University, 3584 CS Utrecht, The Netherlands.,Department of Pharmacy & Pharmacology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Timothy W Randolph
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - H Joachim Deeg
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States.,Department of Medicine, University of Washington, Seattle, Washington 98195, United States
| | - Ryotaro Nakamura
- City of Hope, Department of Hematology & HCT, Duarte, California 91010, United States
| | - K Scott Baker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States.,Department of Pediatrics, University of Washington, Seattle, Washington 98195, United States
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13
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Langenhorst JB, Boss J, van Kesteren C, Lalmohamed A, Kuball J, Egberts ACG, Boelens JJ, Huitema ADR, van Maarseveen EM. A semi-mechanistic model based on glutathione depletion to describe intra-individual reduction in busulfan clearance. Br J Clin Pharmacol 2020; 86:1499-1509. [PMID: 32067250 PMCID: PMC7373715 DOI: 10.1111/bcp.14256] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/04/2020] [Accepted: 02/05/2020] [Indexed: 12/17/2022] Open
Abstract
Aim To develop a semi‐mechanistic model, based on glutathione depletion and predict a previously identified intra‐individual reduction in busulfan clearance to aid in more precise dosing. Methods Busulfan concentration data, measured as part of regular care for allogeneic hematopoietic cell transplantation (HCT) patients, were used to develop a semi‐mechanistic model and compare it to a previously developed empirical model. The latter included an empirically estimated time effect, where the semi‐mechanistic model included theoretical glutathione depletion. As older age has been related to lower glutathione levels, this was tested as a covariate in the semi‐mechanistic model. Lastly, a therapeutic drug monitoring (TDM) simulation was performed comparing the two models in target attainment. Results In both models, a similar clearance decrease of 7% (range −82% to 44%), with a proportionality to busulfan metabolism, was found. After 40 years of age, the time effect increased with 4% per year of age (0.6–8%, P = 0.009), causing the effect to increase more than a 2‐fold over the observed age‐range (0–73 years). Compared to the empirical model, the final semi‐mechanistic model increased target attainment from 74% to 76%, mainly through better predictions for adult patients. Conclusion These results suggest that the time‐dependent decrease in busulfan clearance may be related to gluthathione depletion. This effect increased with older age (>40 years) and was proportional to busulfan metabolism. The newly constructed semi‐mechanistic model could be used to further improve TDM‐guided exposure target attainment of busulfan in patients undergoing HCT.
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Affiliation(s)
- Jurgen B Langenhorst
- Laboratory of Translational Immunology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Model-informed Drug Development Consultant, Pharmetheus AB, Uppsala, Sweden
| | - Jill Boss
- Hospital Pharmacy, St Jansdal Hospital, Harderwijk, The Netherlands
| | | | - Arief Lalmohamed
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Jürgen Kuball
- Laboratory of Translational Immunology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Hematology, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Antoine C G Egberts
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Jaap Jan Boelens
- Stem Cell Transplant and Cellular Therapies; Pediatrics, Memorial Sloan Kettering Cancer Centre, New York City, New York, USA
| | - Alwin D R Huitema
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands.,Department of Pharmacy & Pharmacology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Erik M van Maarseveen
- Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
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14
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Everett JR. Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling. Handb Exp Pharmacol 2019; 260:263-299. [PMID: 31823071 DOI: 10.1007/164_2019_316] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.
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Affiliation(s)
- Jeremy R Everett
- Medway Metabonomics Research Group, University of Greenwich, Kent, UK.
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15
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Everett JR. NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2017; 102-103:1-14. [PMID: 29157489 DOI: 10.1016/j.pnmrs.2017.04.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 04/23/2017] [Accepted: 04/24/2017] [Indexed: 06/07/2023]
Abstract
Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.
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Affiliation(s)
- Jeremy R Everett
- Medway Metabonomics Group, University of Greenwich, Chatham Maritime, Kent ME4 4TB, UK.
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16
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Tan G, Zhao B, Li Y, Liu X, Zou Z, Wan J, Yao Y, Xiong H, Wang Y. Pharmacometabolomics identifies dodecanamide and leukotriene B4 dimethylamide as a predictor of chemosensitivity for patients with acute myeloid leukemia treated with cytarabine and anthracycline. Oncotarget 2017; 8:88697-88707. [PMID: 29179468 PMCID: PMC5687638 DOI: 10.18632/oncotarget.20733] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 08/04/2017] [Indexed: 11/25/2022] Open
Abstract
Clinical responses to standard cytarabine plus anthracycline regimen in acute myeloid leukemia (AML) are heterogeneous and there is an unmet need for biological predictors of response to this regimen. Here, we applied a pharmacometabolomics approach to identify potential biomarkers associated with response to this regimen in AML patients. Based on clinical response the enrolled 82 patients were subdivided into two groups: complete remission(CR) responders (n=42) and non-responders (n=40). Metabolic profiles of pre-treatment serum from patients were analyzed by ultra-high performance liquid chromatography coupled with mass spectrometry and the metabolic differences between the two groups were investigated by multivariate statistical analysis. A metabolite panel containing dodecanamide and leukotriene B4 dimethylamide (LTB4-DMA) had the power capacity to differentiate the two groups of patients, yielding an area under the receiver operating characteristic of 0.945 (85.2% sensitivity and 88.9% specificity) in the training set and 0.944(84.6% sensitivity and 80.0% specificity) in the test set. The patients with high levels of LTB4-DMA and low amounts of dodecanamide had good sensitivity to chemotherapeutic agents. The possible reasons were that dodecanamide was produced by leukemic cells as a lipolytic factor to fuel their growth with a potential role in drug resistance and LTB4-DMA was a potent leukotriene B4 antagonist that could be applicable in the treatment of AML. These preliminary results demonstrates the feasibility of relating chemotherapy responses with pre-treatment metabolic profiles of AML patients, and pharmacometabolomics may be a useful tool to select patients that are more likely to benefit from cytarabine plus anthracycline chemotherapy.
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Affiliation(s)
- Guangguo Tan
- School of Pharmacy, Fourth Military Medical University, Xi'an, 710032, China
| | - Bingbing Zhao
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Yanqing Li
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Xi Liu
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Zhilan Zou
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Jun Wan
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Ye Yao
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Hong Xiong
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
| | - Yanyu Wang
- Department of Hematology, The Central Hospital of Xuhui District, Shanghai, 20031, China
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17
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Lu Y, Chen C. Metabolomics: Bridging Chemistry and Biology in Drug Discovery and Development. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s40495-017-0083-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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