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Axelsson MAB, Tukukino C, Parodi López N, Wallerstedt SM. Bleeding in patients on concurrent treatment with a selective serotonin reuptake inhibitor (SSRI) and low-dose acetylsalicylic acid (ASA) compared with SSRI or low-dose ASA alone-A systematic review and meta-analysis. Br J Clin Pharmacol 2024; 90:916-932. [PMID: 38351575 DOI: 10.1111/bcp.16000] [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: 09/12/2023] [Revised: 11/12/2023] [Accepted: 12/06/2023] [Indexed: 04/05/2024] Open
Abstract
AIMS The aim of this study was to systematically review whether concurrent treatment with an SSRI and low-dose ASA increases the risk of bleeding compared with treatment with an SSRI alone or ASA alone. METHODS Medline, Embase, the Cochrane Library, PsycINFO and Web of Science (from database inception to January 2023) were searched according to PICO: P = patients on treatment with an SSRI and/or low-dose ASA; I = intervention: SSRI + ASA; C = comparison: ASA or SSRI alone; O = outcomes: bleeding/major bleeding. The included articles were assessed using checklists. Studies without major risk of bias formed the basis for the conclusions. Extracted data were pooled using random-effects meta-analyses. Certainty of evidence was assessed according to GRADE. RESULTS Twenty-four studies met the PICO and were included. One randomized and six nonrandomized studies were assessed not to have major risk of bias. Regarding SSRI + ASA vs. ASA only, the pooled hazard ratio of three nonrandomized studies (n = 38 467) was 1.37 (95% confidence interval: 1.10; 1.70; I2 = 0%), and the pooled odds ratio of two nonrandomized studies (n = 28 296) was 0.95 (0.77; 1.19; I2 = 0%). Regarding SSRI + ASA vs. SSRI only, the randomized controlled trial (n = 1048) reported a hazard ratio of 1.82 (0.66; 5.02), the hazard ratio being 1.60 (1.24; 2.06) for ASA vs. placebo in patients without SSRI treatment; and one nonrandomized controlled study (n = 18 920) reported an incidence rate ratio of 1.03 (0.96; 1.12). CONCLUSIONS The compiled evidence was too uncertain to support an interaction when an SSRI is added to low-dose ASA. Low-dose ASA added to an SSRI may imply an increased risk of bleeding primarily attributable to the initiation of ASA.
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Affiliation(s)
- Magnus A B Axelsson
- Department of Clinical Chemistry, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Carina Tukukino
- Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Pharmacology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Naldy Parodi López
- Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Pharmacology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Susanna M Wallerstedt
- Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- HTA-centrum, Sahlgrenska University Hospital, Gothenburg, Sweden
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Kietaibl S, Ahmed A, Afshari A, Albaladejo P, Aldecoa C, Barauskas G, De Robertis E, Faraoni D, Filipescu DC, Fries D, Godier A, Haas T, Jacob M, Lancé MD, Llau JV, Meier J, Molnar Z, Mora L, Rahe-Meyer N, Samama CM, Scarlatescu E, Schlimp C, Wikkelsø AJ, Zacharowski K. Management of severe peri-operative bleeding: Guidelines from the European Society of Anaesthesiology and Intensive Care: Second update 2022. Eur J Anaesthesiol 2023; 40:226-304. [PMID: 36855941 DOI: 10.1097/eja.0000000000001803] [Citation(s) in RCA: 60] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
BACKGROUND Management of peri-operative bleeding is complex and involves multiple assessment tools and strategies to ensure optimal patient care with the goal of reducing morbidity and mortality. These updated guidelines from the European Society of Anaesthesiology and Intensive Care (ESAIC) aim to provide an evidence-based set of recommendations for healthcare professionals to help ensure improved clinical management. DESIGN A systematic literature search from 2015 to 2021 of several electronic databases was performed without language restrictions. Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was used to assess the methodological quality of the included studies and to formulate recommendations. A Delphi methodology was used to prepare a clinical practice guideline. RESULTS These searches identified 137 999 articles. All articles were assessed, and the existing 2017 guidelines were revised to incorporate new evidence. Sixteen recommendations derived from the systematic literature search, and four clinical guidances retained from previous ESAIC guidelines were formulated. Using the Delphi process on 253 sentences of guidance, strong consensus (>90% agreement) was achieved in 97% and consensus (75 to 90% agreement) in 3%. DISCUSSION Peri-operative bleeding management encompasses the patient's journey from the pre-operative state through the postoperative period. Along this journey, many features of the patient's pre-operative coagulation status, underlying comorbidities, general health and the procedures that they are undergoing need to be taken into account. Due to the many important aspects in peri-operative nontrauma bleeding management, guidance as to how best approach and treat each individual patient are key. Understanding which therapeutic approaches are most valuable at each timepoint can only enhance patient care, ensuring the best outcomes by reducing blood loss and, therefore, overall morbidity and mortality. CONCLUSION All healthcare professionals involved in the management of patients at risk for surgical bleeding should be aware of the current therapeutic options and approaches that are available to them. These guidelines aim to provide specific guidance for bleeding management in a variety of clinical situations.
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Affiliation(s)
- Sibylle Kietaibl
- From the Department of Anaesthesiology & Intensive Care, Evangelical Hospital Vienna and Sigmund Freud Private University Vienna, Austria (SK), Department of Anaesthesia and Critical Care, University Hospitals of Leicester NHS Trust (AAh), Department of Cardiovascular Sciences, University of Leicester, UK (AAh), Department of Paediatric and Obstetric Anaesthesia, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark (AAf), Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (AAf), Department of Anaesthesiology & Critical Care, CNRS/TIMC-IMAG UMR 5525/Themas, Grenoble-Alpes University Hospital, Grenoble, France (PA), Department of Anaesthesiology & Intensive Care, Hospital Universitario Rio Hortega, Valladolid, Spain (CA), Department of Surgery, Lithuanian University of Health Sciences, Kaunas, Lithuania (GB), Division of Anaesthesia, Analgesia, and Intensive Care - Department of Medicine and Surgery, University of Perugia, Italy (EDR), Department of Anesthesiology, Perioperative and Pain Medicine, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA (DFa), University of Medicine and Pharmacy Carol Davila, Department of Anaesthesiology & Intensive Care, Emergency Institute for Cardiovascular Disease, Bucharest, Romania (DCF), Department of Anaesthesia and Critical Care Medicine, Medical University Innsbruck, Innsbruck, Austria (DFr), Department of Anaesthesiology & Critical Care, APHP, Université Paris Cité, Paris, France (AG), Department of Anesthesiology, University of Florida, College of Medicine, Gainesville, Florida, USA (TH), Department of Anaesthesiology, Intensive Care and Pain Medicine, St.-Elisabeth-Hospital Straubing, Straubing, Germany (MJ), Department of Anaesthesiology, Medical College East Africa, The Aga Khan University, Nairobi, Kenya (MDL), Department of Anaesthesiology & Post-Surgical Intensive Care, University Hospital Doctor Peset, Valencia, Spain (JVL), Department of Anaesthesiology & Intensive Care, Johannes Kepler University, Linz, Austria (JM), Department of Anesthesiology & Intensive Care, Semmelweis University, Budapest, Hungary (ZM), Department of Anaesthesiology & Post-Surgical Intensive Care, University Trauma Hospital Vall d'Hebron, Barcelona, Spain (LM), Department of Anaesthesiology & Intensive Care, Franziskus Hospital, Bielefeld, Germany (NRM), Department of Anaesthesia, Intensive Care and Perioperative Medicine, GHU AP-HP. Centre - Université Paris Cité - Cochin Hospital, Paris, France (CMS), Department of Anaesthesiology and Intensive Care, Fundeni Clinical Institute, Bucharest and University of Medicine and Pharmacy Carol Davila, Bucharest, Romania (ES), Department of Anaesthesiology and Intensive Care Medicine, AUVA Trauma Centre Linz and Ludwig Boltzmann-Institute for Traumatology, The Research Centre in Co-operation with AUVA, Vienna, Austria (CS), Department of Anaesthesia and Intensive Care Medicine, Zealand University Hospital, Roskilde, Denmark (AW) and Department of Anaesthesiology, Intensive Care Medicine & Pain Therapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany (KZ)
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Shnayder NA, Grechkina VV, Khasanova AK, Bochanova EN, Dontceva EA, Petrova MM, Asadullin AR, Shipulin GA, Altynbekov KS, Al-Zamil M, Nasyrova RF. Therapeutic and Toxic Effects of Valproic Acid Metabolites. Metabolites 2023; 13:metabo13010134. [PMID: 36677060 PMCID: PMC9862929 DOI: 10.3390/metabo13010134] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/11/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Valproic acid (VPA) and its salts are psychotropic drugs that are widely used in neurological diseases (epilepsy, neuropathic pain, migraine, etc.) and psychiatric disorders (schizophrenia, bipolar affective disorder, addiction diseases, etc.). In addition, the indications for the appointment of valproate have been expanding in recent years in connection with the study of new mechanisms of action of therapeutic and toxic metabolites of VPA in the human body. Thus, VPA is considered a component of disease-modifying therapy for multiple tumors, neurodegenerative diseases (Huntington's disease, Parkinson's disease, Duchenne progressive dystrophy, etc.), and human immunodeficiency syndrome. The metabolism of VPA is complex and continues to be studied. Known pathways of VPA metabolism include: β-oxidation in the tricarboxylic acid cycle (acetylation); oxidation with the participation of cytochrome P-450 isoenzymes (P-oxidation); and glucuronidation. The complex metabolism of VPA explains the diversity of its active and inactive metabolites, which have therapeutic, neutral, or toxic effects. It is known that some active metabolites of VPA may have a stronger clinical effect than VPA itself. These reasons explain the relevance of this narrative review, which summarizes the results of studies of blood (serum, plasma) and urinary metabolites of VPA from the standpoint of the pharmacogenomics and pharmacometabolomics. In addition, a new personalized approach to assessing the cumulative risk of developing VPA-induced adverse reactions is presented and ways for their correction are proposed depending on the patient's pharmacogenetic profile and the level of therapeutic and toxic VPA metabolites in the human body fluids (blood, urine).
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Affiliation(s)
- Natalia A. Shnayder
- Institute of Personalized Psychiatry and Neurology, Shared Core Facilities, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology, 192019 Saint Petersburg, Russia
- Shared Core Facilities “Molecular and Cell Technologies”, V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia
- Correspondence: (N.A.S.); (R.F.N.); Tel.: +7-(812)-620-0222 (N.A.S. & R.F.N.)
| | - Violetta V. Grechkina
- Institute of Personalized Psychiatry and Neurology, Shared Core Facilities, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology, 192019 Saint Petersburg, Russia
| | - Aiperi K. Khasanova
- Department of Psychiatry, Russian Medical Academy for Continual Professional Education, 125993 Moscow, Russia
| | - Elena N. Bochanova
- Shared Core Facilities “Molecular and Cell Technologies”, V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia
| | - Evgenia A. Dontceva
- Shared Core Facilities “Molecular and Cell Technologies”, V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia
| | - Marina M. Petrova
- Shared Core Facilities “Molecular and Cell Technologies”, V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 660022 Krasnoyarsk, Russia
| | - Azat R. Asadullin
- Department of Psychiatry and Addiction, Bashkir State Medical University, 45000 Ufa, Russia
| | - German A. Shipulin
- Centre for Strategic Planning and Management of Biomedical Health Risks, 119121 Moscow, Russia
| | - Kuanysh S. Altynbekov
- Republican Scientific and Practical Center of Mental Health, Almaty 050022, Kazakhstan
- Department of Psychiatry and Narcology, S.D. Asfendiarov Kazakh National Medical University, Almaty 050022, Kazakhstan
| | - Mustafa Al-Zamil
- Department of Physiotherapy, Faculty of Continuing Medical Education, Peoples’ Friendship University of Russia, 11798 Moscow, Russia
| | - Regina F. Nasyrova
- Institute of Personalized Psychiatry and Neurology, Shared Core Facilities, V.M. Bekhterev National Medical Research Centre for Psychiatry and Neurology, 192019 Saint Petersburg, Russia
- Correspondence: (N.A.S.); (R.F.N.); Tel.: +7-(812)-620-0222 (N.A.S. & R.F.N.)
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Barry EL, Fedirko V, Jin Y, Lui K, Mott LA, Peacock JL, Passarelli MN, Baron JA, Jones DP. Plasma Metabolomics Analysis of Aspirin Treatment and Risk of Colorectal Adenomas. Cancer Prev Res (Phila) 2022; 15:521-531. [PMID: 35653338 PMCID: PMC9357068 DOI: 10.1158/1940-6207.capr-21-0555] [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: 11/07/2021] [Revised: 03/10/2022] [Accepted: 05/26/2022] [Indexed: 02/03/2023]
Abstract
Despite substantial observational and experimental evidence that aspirin use can provide protection against the development of colorectal neoplasia, our understanding of the molecular mechanisms involved is inadequate and limits our ability to use this drug effectively and safely for chemoprevention. We employed an untargeted plasma metabolomics approach using liquid chromatography with high-resolution mass spectroscopy to explore novel metabolites that may contribute to the chemopreventive effects of aspirin. Associations between levels of metabolic features in plasma and aspirin treatment were investigated among 523 participants in a randomized placebo-controlled clinical trial of two doses of aspirin (81 or 325 mg/day) and were linked to risk of colorectal adenoma occurrence over 3 years of follow-up. Metabolic pathways that were altered with aspirin treatment included linoleate and glycerophospholipid metabolism for the 81-mg dose and carnitine shuttle for both doses. Metabolites whose levels increased with 81 mg/day aspirin treatment and were also associated with decreased risk of adenomas during follow-up included certain forms of lysophosphatidylcholine and lysophosphatidylethanolamine as well as trihydroxyoctadecenoic acid, which is a derivative of linoleic acid and is upstream of cyclooxygenase inhibition by aspirin in the linoleate and arachidonic acid metabolism pathways. In conclusion, our findings regarding lysophospholipids and metabolites in the linoleate metabolism pathway may provide novel insights into the chemopreventive effects of aspirin in the colorectum, although they should be considered hypothesis-generating at this time. PREVENTION RELEVANCE This research used metabolomics, an innovative discovery-based approach, to identify molecular changes in human blood that may help to explain how aspirin use reduces the risk of colorectal neoplasia in some individuals. Ultimately, this work could have important implications for optimizing aspirin use in the prevention of colorectal cancer.
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Affiliation(s)
- Elizabeth L. Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | - Veronika Fedirko
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX; Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Yutong Jin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Ken Lui
- Department of Medicine, Emory University, Atlanta, GA
| | - Leila A. Mott
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | - Janet L. Peacock
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | | | - John A. Baron
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH
- Department of Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC
| | - Dean P. Jones
- Department of Medicine, Emory University, Atlanta, GA
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Badamasi IM, Maulidiani M, Lye MS, Ibrahim N, Shaari K, Stanslas J. A Preliminary Nuclear Magnetic Resonance Metabolomics Study Identifies Metabolites that Could Serve as Diagnostic Markers of Major Depressive Disorder. Curr Neuropharmacol 2022; 20:965-982. [PMID: 34126904 PMCID: PMC9881106 DOI: 10.2174/1570159x19666210611095320] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/17/2021] [Accepted: 05/28/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The evaluation of metabolites that are directly involved in the physiological process, few steps short of phenotypical manifestation, remains vital for unravelling the biological moieties involved in the development of the (MDD) and in predicting its treatment outcome. METHODOLOGY Eight (8) urine and serum samples each obtained from consenting healthy controls (HC), twenty-five (25) urine and serum samples each from first episode treatment naïve MDD (TNMDD) patients, and twenty (22) urine and serum samples each s from treatment naïve MDD patients 2 weeks after SSRI treatment (TWMDD) were analysed for metabolites using proton nuclear magnetic resonance (1HNMR) spectroscopy. The evaluation of patients' samples was carried out using Partial Least Squares Discriminant Analysis (PLS-DA) and Orthogonal Partial Least Square- Discriminant Analysis (OPLSDA) models. RESULTS In the serum, decreased levels of lactate, glucose, glutamine, creatinine, acetate, valine, alanine, and fatty acid and an increased level of acetone and choline in TNMDD or TWMDD irrespective of whether an OPLSDA or PLSDA evaluation was used were identified. A test for statistical validations of these models was successful. CONCLUSION Only some changes in serum metabolite levels between HC and TNMDD identified in this study have potential values in the diagnosis of MDD. These changes included decreased levels of lactate, glutamine, creatinine, valine, alanine, and fatty acid, as well as an increased level of acetone and choline in TNMDD. The diagnostic value of these changes in metabolites was maintained in samples from TWMDD patients, thus reaffirming the diagnostic nature of these metabolites for MDD.
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Affiliation(s)
- Ibrahim Mohammed Badamasi
- Pharmacotherapeutics Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia;
| | - Maulidiani Maulidiani
- Laboratory of Natural Products Institute of Bioscience, Universiti Putra Malaysia, Selangor, Malaysia; ,Present address of this author: Faculty of Science and Marine Environment, Universiti Malaysia Terengganu
| | - Munn Sann Lye
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia;
| | | | - Khozirah Shaari
- Laboratory of Natural Products Institute of Bioscience, Universiti Putra Malaysia, Selangor, Malaysia;
| | - Johnson Stanslas
- Pharmacotherapeutics Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia; ,Address correspondence to this author at the Pharmacotherapeutics Unit, Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia; E-mails: ,
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Gonzalez-Covarrubias V, Martínez-Martínez E, del Bosque-Plata L. The Potential of Metabolomics in Biomedical Applications. Metabolites 2022; 12:metabo12020194. [PMID: 35208267 PMCID: PMC8880031 DOI: 10.3390/metabo12020194] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/28/2022] [Accepted: 01/30/2022] [Indexed: 12/12/2022] Open
Abstract
The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.
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Affiliation(s)
| | - Eduardo Martínez-Martínez
- Laboratory of Cell Communication and Extracellular Vesicles, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico;
| | - Laura del Bosque-Plata
- Laboratory of Nutrigenetics and Nutrigenomics, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City 14610, Mexico
- Correspondence: ; Tel.: +52-55-53-50-1974
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Gómez-Cebrián N, Vázquez Ferreiro P, Carrera Hueso FJ, Poveda Andrés JL, Puchades-Carrasco L, Pineda-Lucena A. Pharmacometabolomics by NMR in Oncology: A Systematic Review. Pharmaceuticals (Basel) 2021; 14:ph14101015. [PMID: 34681239 PMCID: PMC8539252 DOI: 10.3390/ph14101015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 12/14/2022] Open
Abstract
Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.
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Affiliation(s)
- Nuria Gómez-Cebrián
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
| | | | | | | | - Leonor Puchades-Carrasco
- Drug Discovery Unit, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain;
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
| | - Antonio Pineda-Lucena
- Molecular Therapeutics Program, Centro de Investigación Médica Aplicada, 31008 Navarra, Spain
- Correspondence: (L.P.-C.); (A.P.-L.); Tel.: +34-963246713 (L.P.-C.)
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Huang Q, Cao L, Luo N, Qian H, Wei M, Xue L, Zhou Q, Zou B, Tan L, Chu Y, Ma X, Wang C, Wu H, Zhang L, Sun L, Li D, Fan X, Miao L, Zhou G. Predicting Range of Initial Warfarin Dose Based on Pharmacometabolomic and Genetic Inputs. Clin Pharmacol Ther 2021; 110:1585-1594. [PMID: 34460938 DOI: 10.1002/cpt.2407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 08/22/2021] [Indexed: 12/29/2022]
Abstract
Anticoagulation response to warfarin during the initial stage of therapy varies among individuals. In this study, we aimed to combine pharmacometabolomic and pharmacogenetic data to predict interindividual variation in warfarin response, and, on this basis, suggest an initial daily dose range. The baseline metabolic profiles, genotypes, and clinical information of 160 patients with heart valve disease served as the variables of the function of the last international normalized ratio measured before a patient's discharge (INRday7 ) to screen for potential biomarkers. The partial least-squares model showed that two baseline metabolites (uridine and guanosine), one single-nucleotide variation (VKORC1), and four clinical parameters (weight, creatinine level, amiodarone usage, and initial daily dose) had good predictive power for INRday7 (R2 = 0.753 for the training set, 0.643 for the test set). With these biomarkers, a machine learning algorithm (two-dimensional linear discriminant analysis-multinomial logit model) was used to predict the subgroups with extremely warfarin-sensitive or less warfarin-sensitive patients with a prediction accuracy of 91% for the training set and 90% for the test set, indicating that individual responses to warfarin could be effectively predicted. Based on this model, we have successfully designed an algorithm,"IniWarD," for predicting an effective dose range in the initial 7-day warfarin therapy. The results indicate that the daily dose range suggested by the IniWarD system is more appropriate than that of the conventional genotype-based method, and the risk of bleeding or thrombus due to warfarin could thus be avoided.
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Affiliation(s)
- Qing Huang
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Ling Cao
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Nan Luo
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Hanyu Qian
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Meng Wei
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Ling Xue
- Department of Clinical Pharmacology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Qiang Zhou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Bingjie Zou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Li Tan
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Yanan Chu
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Xueping Ma
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Changtian Wang
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Haiwei Wu
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lei Zhang
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Lei Sun
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Demin Li
- Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
| | - Xialei Fan
- National Medical Products Administration, Key Laboratory for Impurity Profile of Chemical Drugs, Jiangsu Institute for Food and Drug Control, Nanjing, China
| | - Liyan Miao
- Department of Clinical Pharmacology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Zhou
- Department of Clinical Pharmacy, Jinling Hospital, State Key Laboratory of Analytical Chemistry for Life Science & Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Department of Cardio-Thoracic Surgery, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, China
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9
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Li R, Yang L, Guan S, Lin M, Lai H, Liu K, Liu Z, Zhang X. UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females. J Proteome Res 2021; 20:4578-4588. [PMID: 34384217 DOI: 10.1021/acs.jproteome.1c00554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Although previous studies have shown that certain factors interfere with the sensitivity of propofol, the mechanisms for interindividual variability in response to propofol remain unclear. This study aimed to screen the metabolites to predict patients' sensitivity to propofol and to identify metabolic pathways to explore possible mechanisms associated with propofol resistance. Sera from 40 female patients undergoing elective hysteroscopic surgery in a prospective cohort propofol study were obtained before the administration of propofol. The patients' responsiveness to propofol was differentiated based on propofol effect-site concentration. Serum samples from two sets, a discovery set (n = 24) and an independent validation set (n = 16), were analyzed using ultraperformance liquid chromatography coupled with mass spectrometry based untargeted metabolomics. In the discovery set, 494 differential metabolites were screened out, and then 391 potential candidate biomarkers with the area under receiver operating characteristic curve >0.80 were selected. Pathway analysis showed that the pathway of glycerophospholipid metabolism was the most influential pathway. In the independent validation set, six potential biomarkers enabled the discrimination of poor responders from good and intermediate responders, which might be applied to predict propofol sensitivity. The mass spectrometry data are available via MetaboLights (http://www.ebi.ac.uk/metabolights/login) with the identifier MTBLS2311.
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Affiliation(s)
- Ruiyun Li
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Lu Yang
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Su Guan
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Ming Lin
- Department of Anesthesiology, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, China
| | - Hanjin Lai
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Kun Liu
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Zimeng Liu
- Department of Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Xuyu Zhang
- Department of Anesthesiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
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10
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Stockard B, Bhise N, Shin M, Guingab-Cagmat J, Garrett TJ, Pounds S, Lamba JK. Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML. Front Oncol 2021; 11:678008. [PMID: 34178663 PMCID: PMC8222790 DOI: 10.3389/fonc.2021.678008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/04/2021] [Indexed: 01/03/2023] Open
Abstract
Background Acute myeloid leukemia (AML) is a hematological malignancy with a dismal prognosis. For over four decades, AML has primarily been treated by cytarabine combined with an anthracycline. Although a significant proportion of patients achieve remission with this regimen, roughly 40% of children and 70% of adults relapse. Over 90% of patients with resistant or relapsed AML die within 3 years. Thus, relapsed and resistant disease following treatment with standard therapy are the most common clinical failures that occur in treating this disease. In this study, we evaluated the relationship between AML cell line global metabolomes and variation in chemosensitivity. Methods We performed global metabolomics on seven AML cell lines with varying chemosensitivity to cytarabine and the anthracycline doxorubicin (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, THP-1) using ultra-high performance liquid chromatography - mass spectrometry (UHPLC-MS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from UHPLC-MS using MetaboAnalyst to identify cellular metabolites associated with drug chemosensitivity. Results A total of 1,624 metabolic features were detected across the leukemic cell lines. Of these, 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly greater abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis found enrichment of several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, nine annotated metabolites were significantly different in resistance vs. sensitive cell lines, including D-raffinose, guanosine, inosine, guanine, aldopentose, two xenobiotics (allopurinol and 4-hydroxy-L-phenylglycine) and glucosamine/mannosamine. Pathway analysis associated these metabolites with the purine metabolic pathway. Conclusion Overall, our results demonstrate that metabolomics differences contribute toward drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response.
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Affiliation(s)
- Bradley Stockard
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Neha Bhise
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Miyoung Shin
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Joy Guingab-Cagmat
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, United States
| | - Timothy J Garrett
- Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, United States
| | - Stanley Pounds
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN, United States
| | - Jatinder K Lamba
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.,University of Florida Health Cancer Center, Gainesville, FL, United States.,Center for Pharmacogenetics, University of Florida, Gainesville, FL, United States
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11
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de Abreu MS, Giacomini ACVV, Demin KA, Galstyan DS, Zabegalov KN, Kolesnikova TO, Amstislavskaya TG, Strekalova T, Petersen EV, Kalueff AV. Unconventional anxiety pharmacology in zebrafish: Drugs beyond traditional anxiogenic and anxiolytic spectra. Pharmacol Biochem Behav 2021; 207:173205. [PMID: 33991579 DOI: 10.1016/j.pbb.2021.173205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 12/14/2022]
Abstract
Anxiety is the most prevalent brain disorder and a common cause of human disability. Animal models are critical for understanding anxiety pathogenesis and its pharmacotherapy. The zebrafish (Danio rerio) is increasingly utilized as a powerful model organism in anxiety research and anxiolytic drug screening. High similarity between human, rodent and zebrafish molecular targets implies shared signaling pathways involved in anxiety pathogenesis. However, mounting evidence shows that zebrafish behavior can be modulated by drugs beyond conventional anxiolytics or anxiogenics. Furthermore, these effects may differ from human and/or rodent responses, as such 'unconventional' drugs may affect zebrafish behavior despite having no such profiles (or exerting opposite effects) in humans or rodents. Here, we discuss the effects of several putative unconventional anxiotropic drugs (aspirin, lysergic acid diethylamide (LSD), nicotine, naloxone and naltrexone) and their potential mechanisms of action in zebrafish. Emphasizing the growing utility of zebrafish models in CNS drug discovery, such unconventional anxiety pharmacology may provide important, evolutionarily relevant insights into complex regulation of anxiety in biological systems. Albeit seemingly complicating direct translation from zebrafish into clinical phenotypes, this knowledge may instead foster the development of novel CNS drugs, eventually facilitating innovative treatment of patients based on novel 'unconventional' targets identified in fish models.
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Affiliation(s)
- Murilo S de Abreu
- Bioscience Institute, University of Passo Fundo, Passo Fundo, Brazil; Laboratory of Cell and Molecular Biology and Neurobiology, Moscow Institute of Physics and Technology, Moscow, Russia; The International Zebrafish Neuroscience Research Consortium (ZNRC), Slidell, LA, USA.
| | - Ana C V V Giacomini
- Bioscience Institute, University of Passo Fundo, Passo Fundo, Brazil; Postgraduate Program in Environmental Sciences, University of Passo Fundo, Passo Fundo, Brazil
| | - Konstantin A Demin
- Institute of Experimental Medicine, Almazov Medical Research Center, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia; Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia
| | - David S Galstyan
- Institute of Experimental Medicine, Almazov Medical Research Center, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia; Granov Scientific Research Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russian Federation, St. Petersburg, Russia
| | - Konstantin N Zabegalov
- Ural Federal University, Ekaterinburg, Russia; Neurobiology Program, Sirius University, Sochi, Russia
| | - Tatyana O Kolesnikova
- Institute of Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia; School of Chemistry, Ural Federal University, Ekaterinburg, Russia; Neurobiology Program, Sirius University, Sochi, Russia
| | - Tamara G Amstislavskaya
- Scientific Research Institute of Neuroscience and Medicine, Novosibirsk, Russia; Novosibirsk State University, Novosibirsk, Russia
| | - Tatyana Strekalova
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine and Department of Normal Physiology, Sechenov 1st Moscow State Medical University, Moscow, Russia; Institute of General Pathology and Pathophysiology, Moscow, Russia; Department of Preventive Medicine, Maastricht Medical Center Annadal, Maastricht, Netherlands
| | - Elena V Petersen
- Laboratory of Cell and Molecular Biology and Neurobiology, Moscow Institute of Physics and Technology, Moscow, Russia
| | - Allan V Kalueff
- School of Pharmacy, Southwest University, Chongqing, China; School of Chemistry, Ural Federal University, Ekaterinburg, Russia; Neurobiology Program, Sirius University, Sochi, Russia.
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12
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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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13
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Jeong HC, Park JE, Seo Y, Kim MG, Shin KH. Urinary Metabolomic Profiling after Administration of Corydalis Tuber and Pharbitis Seed Extract in Healthy Korean Volunteers. Pharmaceutics 2021; 13:522. [PMID: 33918785 PMCID: PMC8069993 DOI: 10.3390/pharmaceutics13040522] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 03/26/2021] [Accepted: 04/07/2021] [Indexed: 11/17/2022] Open
Abstract
Pharmacometabolomics is a useful tool to identify biomarkers that can assess and predict response after drug administration. The primary purpose of pharmacometabolomics is to better understand the mechanisms and pathways of a drug by searching endogenous metabolites that have significantly changed after drug administration. DA-9701, a prokinetic agent, consists of Pharbitis seed and Corydalis tube extract and it is known to improve the gastrointestinal motility. Although the overall mechanism of action of DA-9701 remains unclear, its active ingredients, corydaline and chlorogenic acid, act as a 5-HT3 and D2 receptor antagonist and 5-HT4 receptor agonist. To determine the significant metabolites after the administration of DA-9701, a qualitative analysis was carried out using ultra-high performance liquid chromatography coupled with orbitrap mass spectrometer followed by a multivariate analysis. Seven candidates were selected and a statistical analysis of fold change was performed over time. Our study concluded that all the seven selected metabolites were commonly involved in lipid metabolism and purine metabolism.
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Affiliation(s)
- Hyeon-Cheol Jeong
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu 41566, Korea; (H.-C.J.); (Y.S.)
| | | | - Yohan Seo
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu 41566, Korea; (H.-C.J.); (Y.S.)
| | - Min-Gul Kim
- Center for Clinical Pharmacology and Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Korea
- Department of Pharmacology, School of Medicine, Jeonbuk National University, Jeonju 54907, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University, Jeonju 54907, Korea
| | - Kwang-Hee Shin
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Kyungpook National University, Daegu 41566, Korea; (H.-C.J.); (Y.S.)
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14
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Saigusa D, Matsukawa N, Hishinuma E, Koshiba S. Identification of biomarkers to diagnose diseases and find adverse drug reactions by metabolomics. Drug Metab Pharmacokinet 2020; 37:100373. [PMID: 33631535 DOI: 10.1016/j.dmpk.2020.11.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/12/2022]
Abstract
Metabolomics has been widely used for investigating the biological functions of disease expression and has the potential to discover biomarkers in circulating biofluids or tissue extracts that reflect in phenotypic changes. Metabolic profiling has advantages because of the use of unbiased techniques, including multivariate analysis, and has been applied in pharmacological studies to predict therapeutic and adverse reactions of drugs, which is called pharmacometabolomics (PMx). Nuclear magnetic resonance (NMR)- and mass spectrometry (MS)-based metabolomics has contributed to the discovery of recent disease biomarkers; however, the optimal strategy for the study purpose must be selected from many established protocols, methodologies and analytical platforms. Additionally, information on molecular localization in tissue is essential for further functional analyses related to therapeutic and adverse effects of drugs in the process of drug development. MS imaging (MSI) is a promising technology that can visualize molecules on tissue surfaces without labeling and thus provide localized information. This review summarizes recent uses of MS-based global and wide-targeted metabolomics technologies and the advantages of the MSI approach for PMx and highlights the PMx technique for the biomarker discovery of adverse drug effects.
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Affiliation(s)
- Daisuke Saigusa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Naomi Matsukawa
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan.
| | - Eiji Hishinuma
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
| | - Seizo Koshiba
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan; Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan; Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, 980-8573, Japan.
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15
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Abstract
Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.
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Affiliation(s)
- Arwa M Amin
- Department of Clinical and Hospital Pharmacy, College of Pharmacy, Taibah University, Medina, Saudi Arabia.
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16
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Barry EL, Fedirko V, Uppal K, Ma C, Liu K, Mott LA, Peacock JL, Passarelli MN, Baron JA, Jones DP. Metabolomics Analysis of Aspirin's Effects in Human Colon Tissue and Associations with Adenoma Risk. Cancer Prev Res (Phila) 2020; 13:863-876. [PMID: 32655007 DOI: 10.1158/1940-6207.capr-20-0014] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/28/2020] [Accepted: 07/06/2020] [Indexed: 12/11/2022]
Abstract
Although substantial evidence supports aspirin's efficacy in colorectal cancer chemoprevention, key molecular mechanisms are uncertain. An untargeted metabolomics approach with high-resolution mass spectrometry was used to elucidate metabolic effects of aspirin treatment in human colon tissue. We measured 10,269 metabolic features in normal mucosal biopsies collected at colonoscopy after approximately 3 years of randomized treatment with placebo, 81 or 325 mg/day aspirin from 325 participants in the Aspirin/Folate Polyp Prevention Study. Linear regression was used to identify aspirin-associated metabolic features and network analysis was used to identify pathways and predict metabolite identities. Poisson regression was used to examine metabolic features associations with colorectal adenoma risk. We detected 471 aspirin-associated metabolic features. Aside from the carnitine shuttle, aspirin-associated metabolic pathways were largely distinct for 81 mg aspirin (e.g., pyrimidine metabolism) and 325 mg (e.g., arachidonic acid metabolism). Among aspirin-associated metabolic features, we discovered three that were associated with adenoma risk and could contribute to the chemopreventive effect of aspirin treatment, and which have also previously been associated with colorectal cancer: creatinine, glycerol 3-phosphate, and linoleate. The last two of these are in the glycerophospholipid metabolism pathway, which was associated with 81 mg aspirin treatment and provides precursors for the synthesis of eicosanoids from arachidonic acid upstream of cyclooxygenase inhibition by aspirin. Conversely, carnitine shuttle metabolites were increased with aspirin treatment and associated with increased adenoma risk. Thus, our untargeted metabolomics approach has identified novel metabolites and pathways that may underlie the effects of aspirin during early colorectal carcinogenesis.
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Affiliation(s)
- Elizabeth L Barry
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
| | - Veronika Fedirko
- Department of Epidemiology, Rollins School of Public Health, Emory University and Winship Cancer Institute, Atlanta, Georgia
| | - Karan Uppal
- Department of Medicine, Emory University, Atlanta, Georgia
| | - Chunyu Ma
- Department of Medicine, Emory University, Atlanta, Georgia
| | - Ken Liu
- Department of Medicine, Emory University, Atlanta, Georgia
| | - Leila A Mott
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Janet L Peacock
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
- School of Population Health and Environmental Sciences, King's College, London, UK
| | - Michael N Passarelli
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - John A Baron
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
- Department of Medicine, University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, North Carolina
| | - Dean P Jones
- Department of Medicine, Emory University, Atlanta, Georgia
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17
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Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine. Metabolites 2020; 10:metabo10040129. [PMID: 32230776 PMCID: PMC7241083 DOI: 10.3390/metabo10040129] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 03/19/2020] [Accepted: 03/20/2020] [Indexed: 02/07/2023] Open
Abstract
Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.
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18
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Yuet WC, Derasari D, Sivoravong J, Mason D, Jann M. Selective Serotonin Reuptake Inhibitor Use and Risk of Gastrointestinal and Intracranial Bleeding. J Osteopath Med 2019; 119:102-111. [DOI: 10.7556/jaoa.2019.016] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are among the most commonly prescribed medications in the United States. Although SSRIs are highly tolerable relative to other antidepressants, they are associated with a number of adverse effects, including increased gastrointestinal tract bleeding and intracranial bleeding. Mechanisms include increased gastric acid secretion and inhibition of serotonin entrance into platelets. Patients with other bleeding risk factors, such as warfarin, clopidogrel, or aspirin use, may be at heightened risk of these adverse effects. The purpose of this article is to review the incidence of gastrointestinal tract bleeding or intracranial bleeding associated with concomitant SSRI use, the proposed mechanisms of, and the potential pharmacokinetic/pharmacodynamic interactions with anticoagulants and antiplatelets. Given the prevalence of SSRI use in the ambulatory setting, osteopathic physicians should be aware of potential drug-drug interactions and the clinical implications of SSRI-associated bleeding risk.
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19
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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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20
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van den Brink WJ, Hankemeier T, van der Graaf PH, de Lange ECM. Bundling arrows: improving translational CNS drug development by integrated PK/PD-metabolomics. Expert Opin Drug Discov 2018. [DOI: 10.1080/17460441.2018.1446935] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- W. J. van den Brink
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - T. Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - P. H. van der Graaf
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Certara QSP, Canterbury Innovation House, Canterbury, United Kingdom
| | - E. C. M. de Lange
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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21
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Augmented reality for personalized nanomedicines. Biotechnol Adv 2017; 36:335-343. [PMID: 29248686 DOI: 10.1016/j.biotechadv.2017.12.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 12/12/2017] [Accepted: 12/13/2017] [Indexed: 12/26/2022]
Abstract
As our understanding of onset and progress of diseases at the genetic and molecular level rapidly progresses, the potential of advanced technologies, such as 3D-printing, Socially-Assistive Robots (SARs) or augmented reality (AR), that are applied to personalized nanomedicines (PNMs) to alleviate pathological conditions, has become more prominent. Among advanced technologies, AR in particular has the greatest potential to address those challenges and facilitate the translation of PNMs into formidable clinical application of personalized therapy. As AR is about to adapt additional new methods, such as speech, voice recognition, eye tracing and motion tracking, to enable interaction with host response or biological systems in 3-D space, a combination of multiple approaches to accommodate varying environmental conditions, such as public noise and atmosphere brightness, will be explored to improve its therapeutic outcomes in clinical applications. For instance, AR glasses still being developed by Facebook or Microsoft will serve as new platform that can provide people with the health information they are interested in or various measures through which they can interact with medical services. This review has addressed the current progress and impact of AR on PNMs and its application to the biomedical field. Special emphasis is placed on the application of AR based PNMs to the treatment strategies against senior care, drug addiction and medication adherence.
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Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review. Molecules 2017; 22:molecules22071173. [PMID: 28703775 PMCID: PMC6152045 DOI: 10.3390/molecules22071173] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 12/20/2022] Open
Abstract
Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.
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Kim B, Lee JW, Hong KT, Yu KS, Jang IJ, Park KD, Shin HY, Ahn HS, Cho JY, Kang HJ. Pharmacometabolomics for predicting variable busulfan exposure in paediatric haematopoietic stem cell transplantation patients. Sci Rep 2017; 7:1711. [PMID: 28490733 PMCID: PMC5431879 DOI: 10.1038/s41598-017-01861-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2016] [Accepted: 04/05/2017] [Indexed: 12/31/2022] Open
Abstract
Owing to its narrow therapeutic range and high pharmacokinetic variability, optimal dosing for busulfan is important to minimise overexposure-related systemic toxicity and underexposure-related graft failure. Using global metabolomics, we investigated biomarkers for predicting busulfan exposure. We analysed urine samples obtained before busulfan administration from 59 paediatric patients divided into 3 groups classified by area under the busulfan concentration-time curve (AUC), i.e., low-, medium-, and high-AUC groups. In the high-AUC group, deferoxamine metabolites were detected. Phenylacetylglutamine and two acylcarnitines were significantly lower in the high-AUC group than in the low-AUC group. Deferoxamine, an iron-chelating agent that lowers serum ferritin levels, was detected in the high-AUC group, indicating that those patients had high ferritin levels. Therefore, in a retrospective study of 130 paediatric patients, we confirmed our hypothesis that busulfan clearance (dose/AUC) and serum ferritin level has a negative correlation (r = −0.205, P = 0.019). Ferritin, acylcarnitine, and phenylacetylglutamine are associated with liver damage, including free radical formation, deregulation of hepatic mitochondrial β-oxidation, and hyperammonaemia. Our findings reveal potential biomarkers predictive of busulfan exposure and suggest that liver function may affect busulfan exposure.
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Affiliation(s)
- Bora Kim
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Ji Won Lee
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea.,Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Kyung Taek Hong
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Kyung-Sang Yu
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - In-Jin Jang
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Kyung Duk Park
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Hee Young Shin
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Hyo Seop Ahn
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea
| | - Joo-Youn Cho
- Department of Clinical Pharmacology and Therapeutics, Seoul National University College of Medicine and Hospital, Seoul, Korea.
| | - Hyoung Jin Kang
- Department of Pediatrics, Cancer Research Institute, Seoul National University College of Medicine and Hospital, Seoul, Korea.
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Amin AM, Sheau Chin L, Azri Mohamed Noor D, SK Abdul Kader MA, Kah Hay Y, Ibrahim B. The Personalization of Clopidogrel Antiplatelet Therapy: The Role of Integrative Pharmacogenetics and Pharmacometabolomics. Cardiol Res Pract 2017; 2017:8062796. [PMID: 28421156 PMCID: PMC5379098 DOI: 10.1155/2017/8062796] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 02/14/2017] [Indexed: 12/12/2022] Open
Abstract
Dual antiplatelet therapy of aspirin and clopidogrel is pivotal for patients undergoing percutaneous coronary intervention. However, the variable platelets reactivity response to clopidogrel may lead to outcome failure and recurrence of cardiovascular events. Although many genetic and nongenetic factors are known, great portion of clopidogrel variable platelets reactivity remain unexplained which challenges the personalization of clopidogrel therapy. Current methods for clopidogrel personalization include CYP2C19 genotyping, pharmacokinetics, and platelets function testing. However, these methods lack precise prediction of clopidogrel outcome, often leading to insufficient prediction. Pharmacometabolomics which is an approach to identify novel biomarkers of drug response or toxicity in biofluids has been investigated to predict drug response. The advantage of pharmacometabolomics is that it does not only predict the response but also provide extensive information on the metabolic pathways implicated with the response. Integrating pharmacogenetics with pharmacometabolomics can give insight on unknown genetic and nongenetic factors associated with the response. This review aimed to review the literature on factors associated with the variable platelets reactivity response to clopidogrel, as well as appraising current methods for the personalization of clopidogrel therapy. We also aimed to review the literature on using pharmacometabolomics approach to predict drug response, as well as discussing the plausibility of using it to predict clopidogrel outcome.
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Affiliation(s)
- Arwa M. Amin
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
| | - Lim Sheau Chin
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
| | | | | | - Yuen Kah Hay
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
| | - Baharudin Ibrahim
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Malaysia
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Lam SM, Wang Y, Li B, Du J, Shui G. Metabolomics through the lens of precision cardiovascular medicine. J Genet Genomics 2017; 44:127-138. [PMID: 28325553 DOI: 10.1016/j.jgg.2017.02.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 02/21/2017] [Accepted: 02/27/2017] [Indexed: 12/14/2022]
Abstract
Metabolomics, which targets at the extensive characterization and quantitation of global metabolites from both endogenous and exogenous sources, has emerged as a novel technological avenue to advance the field of precision medicine principally driven by genomics-oriented approaches. In particular, metabolomics has revealed the cardinal roles that the environment exerts in driving the progression of major diseases threatening public health. Herein, the existent and potential applications of metabolomics in two key areas of precision cardiovascular medicine will be critically discussed: 1) the use of metabolomics in unveiling novel disease biomarkers and pathological pathways; 2) the contribution of metabolomics in cardiovascular drug development. Major issues concerning the statistical handling of big data generated by metabolomics, as well as its interpretation, will be briefly addressed. Finally, the need for integration of various omics branches and adopting a multi-omics approach to precision medicine will be discussed.
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Affiliation(s)
- Sin Man Lam
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuan Wang
- Beijing Anzhen Hospital, Capital Medical University, The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Collaborative Innovation Center for Cardiovascular Disorders, Beijing Institute of Heart, Lung & Blood Vessel Disease, Beijing 100029, China
| | - Bowen Li
- Lipidall Technologies Company Limited, Changzhou 213000, China
| | - Jie Du
- Beijing Anzhen Hospital, Capital Medical University, The Key Laboratory of Remodeling-Related Cardiovascular Diseases, Ministry of Education, Beijing Collaborative Innovation Center for Cardiovascular Disorders, Beijing Institute of Heart, Lung & Blood Vessel Disease, Beijing 100029, China
| | - Guanghou Shui
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
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26
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Kantae V, Krekels EHJ, Esdonk MJV, Lindenburg P, Harms AC, Knibbe CAJ, Van der Graaf PH, Hankemeier T. Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy. Metabolomics 2016; 13:9. [PMID: 28058041 PMCID: PMC5165030 DOI: 10.1007/s11306-016-1143-1] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Accepted: 11/26/2016] [Indexed: 02/05/2023]
Abstract
Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.
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Affiliation(s)
- Vasudev Kantae
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Elke H. J. Krekels
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Michiel J. Van Esdonk
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Peter Lindenburg
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Amy C. Harms
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Catherijne A. J. Knibbe
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Piet H. Van der Graaf
- Division of Pharmacology, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
- Certara QSP, Canterbury Innovation Centre, Canterbury, UK
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Systems Pharmacology Cluster, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
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27
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Gupta M, Neavin D, Liu D, Biernacka J, Hall-Flavin D, Bobo WV, Frye MA, Skime M, Jenkins GD, Batzler A, Kalari K, Matson W, Bhasin SS, Zhu H, Mushiroda T, Nakamura Y, Kubo M, Wang L, Kaddurah-Daouk R, Weinshilboum RM. TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry 2016; 21:1717-1725. [PMID: 26903268 PMCID: PMC5003027 DOI: 10.1038/mp.2016.6] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2015] [Revised: 12/07/2015] [Accepted: 01/07/2016] [Indexed: 01/01/2023]
Abstract
Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P<0.0001). Baseline and plasma serotonin concentration changes were associated with clinical outcomes (P<0.05). Therefore, baseline and serotonin concentration changes were used as phenotypes for genome-wide association studies (GWAS). GWAS for baseline plasma serotonin concentrations revealed a genome-wide significant (P=7.84E-09) SNP cluster on chromosome four 5' of TSPAN5 and a cluster across ERICH3 on chromosome one (P=9.28E-08) that were also observed during GWAS for change in serotonin at 4 (P=5.6E-08 and P=7.54E-07, respectively) and 8 weeks (P=1.25E-06 and P=3.99E-07, respectively). The SNPs on chromosome four were expression quantitative trait loci for TSPAN5. Knockdown (KD) and overexpression (OE) of TSPAN5 in a neuroblastoma cell line significantly altered the expression of serotonin pathway genes (TPH1, TPH2, DDC and MAOA). Chromosome one SNPs included two ERICH3 nonsynonymous SNPs that resulted in accelerated proteasome-mediated degradation. In addition, ERICH3 and TSPAN5 KD and OE altered media serotonin concentrations. Application of a pharmacometabolomics-informed pharmacogenomic research strategy, followed by functional validation, indicated that TSPAN5 and ERICH3 are associated with plasma serotonin concentrations and may have a role in SSRI treatment outcomes.
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Affiliation(s)
- M Gupta
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - D Neavin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - D Liu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - J Biernacka
- Department of Biomedical Statistics and Bioinformatics – Genetics and Bioinformatics, Mayo Clinic, Rochester, MN, USA
| | - D Hall-Flavin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - W V Bobo
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - M A Frye
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - M Skime
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - G D Jenkins
- Department of Biomedical Statistics and Bioinformatics – Genetics and Bioinformatics, Mayo Clinic, Rochester, MN, USA
| | - A Batzler
- Department of Biomedical Statistics and Bioinformatics – Genetics and Bioinformatics, Mayo Clinic, Rochester, MN, USA
| | - K Kalari
- Department of Biomedical Statistics and Bioinformatics – Genetics and Bioinformatics, Mayo Clinic, Rochester, MN, USA
| | - W Matson
- Bedford VA Medical Center, Bedford, MA, USA
| | - S S Bhasin
- Bedford VA Medical Center, Bedford, MA, USA
| | - H Zhu
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - T Mushiroda
- RIKEN Center for Genomic Medicine, Yokohama, Japan
| | - Y Nakamura
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - M Kubo
- RIKEN Center for Genomic Medicine, Yokohama, Japan
| | - L Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - R Kaddurah-Daouk
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - R M Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA,Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA. E-mail:
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28
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Rankin NJ, Preiss D, Welsh P, Sattar N. Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future. Int J Epidemiol 2016; 45:1351-1371. [PMID: 27789671 PMCID: PMC5100629 DOI: 10.1093/ije/dyw271] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2016] [Indexed: 12/22/2022] Open
Abstract
Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.
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Affiliation(s)
- Naomi J Rankin
- BHF Glasgow Cardiovascular Research Centre
- Glasgow Polyomics, University of Glasgow, Glasgow, UK
| | - David Preiss
- Clinical Trials Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Paul Welsh
- BHF Glasgow Cardiovascular Research Centre
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Everett JR. From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine. Front Pharmacol 2016; 7:297. [PMID: 27660611 PMCID: PMC5014868 DOI: 10.3389/fphar.2016.00297] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 08/23/2016] [Indexed: 01/08/2023] Open
Abstract
Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.
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Affiliation(s)
- Jeremy R Everett
- Medway Metabonomics Research Group, University of Greenwich Kent, UK
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30
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Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, Brennan L, Wishart DS, Oresic M, Hankemeier T, Broadhurst DI, Lane AN, Suhre K, Kastenmüller G, Sumner SJ, Thiele I, Fiehn O, Kaddurah-Daouk R. Metabolomics enables precision medicine: "A White Paper, Community Perspective". Metabolomics 2016; 12:149. [PMID: 27642271 PMCID: PMC5009152 DOI: 10.1007/s11306-016-1094-6] [Citation(s) in RCA: 368] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 08/08/2016] [Indexed: 01/12/2023]
Abstract
INTRODUCTION BACKGROUND TO METABOLOMICS Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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Affiliation(s)
- Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079 USA
| | - Warwick Dunn
- School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Michael A. Schmidt
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO 80521 USA
| | - Steven S. Gross
- Department of Pharmacology, Weill Cornell Medical College, New York, NY 10021 USA
| | - Jennifer A. Kirwan
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain
| | | | - David S. Wishart
- Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB Canada
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
| | - Thomas Hankemeier
- Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands
| | | | - Andrew N. Lane
- Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, KY USA
| | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany
| | - Susan J. Sumner
- Discovery Sciences, RTI International, Research Triangle Park, Durham, NC USA
| | - Ines Thiele
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis, Davis, CA USA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Rima Kaddurah-Daouk
- Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Box 3903, Durham, NC 27710 USA
| | - for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079 USA
- School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
- Advanced Pattern Analysis and Countermeasures Group, Research Innovation Center, Colorado State University, Fort Collins, CO 80521 USA
- Department of Pharmacology, Weill Cornell Medical College, New York, NY 10021 USA
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK
- Department of Biochemistry and Molecular Biomedicine, Faculty of Biology, Universitat de Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
- Institute of Biomedicine of Universitat de Barcelona (IBUB) and CSIC-Associated Unit, Barcelona, Spain
- UCD Institute of Food and Health, UCD, Belfield, Dublin Ireland
- Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, AB Canada
- Turku Centre for Biotechnology, University of Turku, Turku, Finland
- Division of Analytical Biosciences and Cluster Systems Pharmacology, Leiden Academic Centre for Drug Research, Leiden University & Netherlands Metabolomics Centre, Leiden, The Netherlands
- School of Science, Edith Cowan University, Perth, Australia
- Center for Environmental Systems Biochemistry, Department Toxicology and Cancer Biology, Markey Cancer Center, Lexington, KY USA
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Doha, Qatar
- Institute of Bioinformatics and Systems Biology, Helmholtz Center Munich, Oberschleißheim, Germany
- Discovery Sciences, RTI International, Research Triangle Park, Durham, NC USA
- University of Luxembourg, Luxembourg Centre for Systems Biomedicine, Campus Belval, Esch-Sur-Alzette, Luxembourg
- West Coast Metabolomics Center, UC Davis, Davis, CA USA
- Biochemistry Department, King Abdulaziz University, Jeddah, Saudi Arabia
- Psychiatry and Behavioral Sciences, Duke Internal Medicine and Duke Institute for Brain Sciences and Center for Applied Genomics and Precision Medicine, Duke University Medical Center, Box 3903, Durham, NC 27710 USA
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Shipkova M, Svinarov D. LC–MS/MS as a tool for TDM services: Where are we? Clin Biochem 2016; 49:1009-23. [DOI: 10.1016/j.clinbiochem.2016.05.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2016] [Revised: 04/23/2016] [Accepted: 05/01/2016] [Indexed: 12/23/2022]
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Abstract
INTRODUCTION The initial decades of the 21st century have witnessed striking technical advances that have made it possible to detect, identify and quantitatively measure large numbers of plasma or tissue metabolites. In parallel, similar advances have taken place in our ability to sequence DNA and RNA. Those advances have moved us beyond studies of single metabolites and single genetic polymorphisms to the study of hundreds or thousands of metabolites and millions of genomic variants in a single cell or subject. It is now possible to merge and integrate large data sets generated by the use of different "-omics" techniques to increase our understanding of the molecular basis for variation in disease risk and/or drug response phenotypes. OBJECTIVES This "Brief Review" will outline some of the challenges and opportunities associated with studies in which metabolomic data have been merged with genomics in an attempt to gain novel insight into mechanisms associated with variation in drug response phenotypes, with an emphasis on the application of a pharmacometabolomics-informed pharmacogenomic research strategy and with selected examples of the application of that strategy. METHODS Studies that used pharmacometabolomics to inform and guide pharmacogenomics were reviewed. Clinical studies that were used as the basis for pharmacometabolomics-informed pharmacogenomic studies, published in five independent manuscripts, are described briefly. RESULTS Within these five manuscripts, both pharmacokinetic and pharmacodynamic metabolomics approaches were used. Candidate gene and genome-wide approaches that were used in concert with these metabolomic data identified novel metabolite-gene relationships that were associated with drug response phenotypes in these pharmacometabolomics-informed pharmacogenomics studies. CONCLUSION This "Brief Review" outlines the emerging discipline of pharmacometabolomics-informed pharmacogenomics in which metabolic profiles are associated with both clinical phenotypes and genetic variants to identify novel genetic variants associated with drug response phenotypes based on metabolic profiles.
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Affiliation(s)
- Drew Neavin
- Department of Molecular Pharmacology and Experimental Therapeutics, 200 First Street SW, Mayo Clinic, Rochester, MN 55905
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, 3552, Blue Zone, Duke South, Durham, NC 27710
| | - Richard Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, 200 First Street SW, Mayo Clinic, Rochester, MN 55905
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NMR-based metabonomic analysis of normal rat urine and faeces in response to (±)-venlafaxine treatment. J Pharm Biomed Anal 2016; 123:82-92. [DOI: 10.1016/j.jpba.2016.01.044] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Revised: 01/17/2016] [Accepted: 01/19/2016] [Indexed: 11/24/2022]
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Burt T, Nandal S. Pharmacometabolomics in Early-Phase Clinical Development. Clin Transl Sci 2016; 9:128-38. [PMID: 27127917 PMCID: PMC5351331 DOI: 10.1111/cts.12396] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Accepted: 04/01/2016] [Indexed: 12/28/2022] Open
Affiliation(s)
- T Burt
- Burt Consultancy, Durham, North Carolina, USA
| | - S Nandal
- Department of Medical Oncology Novartis (Singapore) Pte Ltd, Singapore
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Wishart DS. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016; 15:473-84. [PMID: 26965202 DOI: 10.1038/nrd.2016.32] [Citation(s) in RCA: 850] [Impact Index Per Article: 106.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, CW 405, Biological Sciences Building, University of Alberta, Edmonton, Alberta, Canada T6G 2E9.,Department of Computing Science, 2-21 Athabasca Hall University of Alberta, Edmonton, Alberta, Canada T6G 2E8.,National Institute of Nanotechnology, National Research Council, Edmonton, Alberta, Canada T6G 2M9
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Pharmacokinetic and pharmacometabolomic study of pirfenidone in normal mouse tissues using high mass resolution MALDI-FTICR-mass spectrometry imaging. Histochem Cell Biol 2015; 145:201-11. [PMID: 26645566 DOI: 10.1007/s00418-015-1382-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2015] [Indexed: 10/22/2022]
Abstract
Given the importance of pirfenidone as the first worldwide-approved drug for idiopathic pulmonary fibrosis treatment, its pharmacodynamic properties and the metabolic response to pirfenidone treatment have not been fully elucidated. The aim of the present study was to get molecular insights of pirfenidone-related pharmacometabolomic response using MALDI-FTICR-MSI. Quantitative MALDI-FTICR-MSI was carried out for determining the pharmacokinetic properties of pirfenidone and its related metabolites 5-hydroxymethyl pirfenidone and 5-carboxy pirfenidone in lung, liver and kidney. To monitor the effect of pirfenidone administration on endogenous cell metabolism, additional in situ endogenous metabolite imaging was performed in lung tissue sections. While pirfenidone is highly abundant and delocalized across the whole micro-regions of lung, kidney and liver, 5-hydroxymethyl pirfenidone and 5-carboxy pirfenidone demonstrate heterogeneous distribution patterns in lung and kidney. In situ endogenous metabolite imaging study of lung tissue indicates no significant effects of pirfenidone on metabolic pathways. Remarkably, we found 129 discriminative m/z values which represent clear differences between control and treated lungs, the majority of which are currently unknown. PCA analysis and heatmap view can accurately distinguish control and treated groups. This is the first pharmacokinetic study to investigate the tissue distribution of orally administered pirfenidone and its related metabolites simultaneously in organs without labeling. The combination of pharmametabolome with histological features provides detailed mapping of drug effects on metabolism as response of healthy lung tissue to pirfenidone treatment.
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Ellero-Simatos S, Beitelshees AL, Lewis JP, Yerges-Armstrong LM, Georgiades A, Dane A, Harms AC, Strassburg K, Guled F, Hendriks MMWB, Horenstein RB, Shuldiner AR, Hankemeier T, Kaddurah-Daouk R. Oxylipid Profile of Low-Dose Aspirin Exposure: A Pharmacometabolomics Study. J Am Heart Assoc 2015; 4:e002203. [PMID: 26504148 PMCID: PMC4845113 DOI: 10.1161/jaha.115.002203] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Background While aspirin is a well‐established and generally effective anti‐platelet agent, considerable inter‐individual variation in drug response exists, for which mechanisms are not completely understood. Metabolomics allows for extensive measurement of small molecules in biological samples, enabling detailed mapping of pathways involved in drug response. Methods and Results We used a mass‐spectrometry‐based metabolomics platform to investigate the changes in the serum oxylipid metabolome induced by an aspirin intervention (14 days, 81 mg/day) in healthy subjects (n=156). We observed a global decrease in serum oxylipids in response to aspirin (25 metabolites decreased out of 30 measured) regardless of sex. This decrease was concomitant with a significant decrease in serum linoleic acid levels (−19%, P=1.3×10−5), one of the main precursors for oxylipid synthesis. Interestingly, several linoleic acid‐derived oxylipids were not significantly associated with arachidonic‐induced ex vivo platelet aggregation, a widely accepted marker of aspirin response, but were significantly correlated with platelet reactivity in response to collagen. Conclusions Together, these results suggest that linoleic acid‐derived oxylipids may contribute to the non‐COX1 mediated variability in response to aspirin. Pharmacometabolomics allowed for more comprehensive interrogation of mechanisms of action of low dose aspirin and of variation in aspirin response.
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Affiliation(s)
- Sandrine Ellero-Simatos
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Amber L Beitelshees
- Program Personalized and Genomic Medicine, Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (A.L.B., J.P.L., L.M.Y.A., R.B.H., A.R.S.)
| | - Joshua P Lewis
- Program Personalized and Genomic Medicine, Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (A.L.B., J.P.L., L.M.Y.A., R.B.H., A.R.S.)
| | - Laura M Yerges-Armstrong
- Program Personalized and Genomic Medicine, Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (A.L.B., J.P.L., L.M.Y.A., R.B.H., A.R.S.)
| | | | - Adrie Dane
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Amy C Harms
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Katrin Strassburg
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Faisa Guled
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Margriet M W B Hendriks
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Richard B Horenstein
- Program Personalized and Genomic Medicine, Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (A.L.B., J.P.L., L.M.Y.A., R.B.H., A.R.S.)
| | - Alan R Shuldiner
- Program Personalized and Genomic Medicine, Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD (A.L.B., J.P.L., L.M.Y.A., R.B.H., A.R.S.)
| | - Thomas Hankemeier
- Analytical Biosciences Division, Leiden Academic Centre for Drug Research, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.) Netherlands Metabolomics Centre, Leiden, The Netherlands (S.E.S., A.D., A.C.H., K.S., F.G., M.B.H., T.H.)
| | - Rima Kaddurah-Daouk
- Duke University Medical Center, Durham, NC (A.G., R.K.D.) Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC (R.K.D.) Duke Institute for Brain Sciences, Duke University, Durham, NC (R.K.D.) Institute of Genome Science and Policy, Durham, NC (R.K.D.)
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Richter T, Alusik S, Paluch Z, Burianova I, Cybulja A, Sadilkova L. Suppressive effect of citalopram on plasma concentrations of thromboxane B2. Scandinavian Journal of Clinical and Laboratory Investigation 2015. [DOI: 10.3109/00365513.2015.1066848] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Yang Y, Lewis JP, Hulot JS, Scott SA. The pharmacogenetic control of antiplatelet response: candidate genes and CYP2C19. Expert Opin Drug Metab Toxicol 2015; 11:1599-617. [PMID: 26173871 DOI: 10.1517/17425255.2015.1068757] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
INTRODUCTION Aspirin, clopidogrel, prasugrel and ticagrelor are antiplatelet agents for the prevention of ischemic events in patients with acute coronary syndromes (ACS), percutaneous coronary intervention (PCI) and other indications. Variability in response is observed to different degrees with these agents, which can translate to increased risks for adverse cardiovascular events. As such, potential pharmacogenetic determinants of antiplatelet pharmacokinetics, pharmacodynamics and clinical outcomes have been actively studied. AREAS COVERED This article provides an overview of the available antiplatelet pharmacogenetics literature. Evidence supporting the significance of candidate genes and their potential influence on antiplatelet response and clinical outcomes are summarized and evaluated. Additional focus is directed at CYP2C19 and clopidogrel response, including the availability of clinical testing and genotype-directed antiplatelet therapy. EXPERT OPINION The reported aspirin response candidate genes have not been adequately replicated and few candidate genes have thus far been implicated in prasugrel or ticagrelor response. However, abundant data support the clinical validity of CYP2C19 and clopidogrel response variability among ACS/PCI patients. Although limited prospective trial data are available to support the utility of routine CYP2C19 testing, the increased risks for reduced clopidogrel efficacy among ACS/PCI patients that carry CYP2C19 loss-of-function alleles should be considered when genotype results are available.
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Affiliation(s)
- Yao Yang
- a 1 Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences , New York, NY, USA +1 212 241 3780 ; +1 212 241 0139 ;
| | - Joshua P Lewis
- b 2 University of Maryland School of Medicine, Division of Endocrinology, Diabetes and Nutrition, and Program for Personalized and Genomic Medicine , Baltimore, MD, USA
| | - Jean-Sébastien Hulot
- c 3 Icahn School of Medicine at Mount Sinai, Cardiovascular Research Center , New York, NY, USA.,d 4 Sorbonne Universités, UPMC Univ Paris 06, INSERM , UMR_S 1166 ICAN, F-75005 Paris, France
| | - Stuart A Scott
- a 1 Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences , New York, NY, USA +1 212 241 3780 ; +1 212 241 0139 ;
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Kaddurah-Daouk R, Weinshilboum R. Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine. Clin Pharmacol Ther 2015; 98:71-5. [PMID: 25871646 DOI: 10.1002/cpt.134] [Citation(s) in RCA: 123] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 03/31/2015] [Accepted: 03/31/2015] [Indexed: 12/15/2022]
Abstract
The scaling up of data in clinical pharmacology and the merger of systems biology and pharmacology has led to the emergence of a new discipline of Quantitative and Systems Pharmacology (QSP). This new research direction might significantly advance the discovery, development, and clinical use of therapeutic drugs. Research communities from computational biology, systems biology, and biological engineering--working collaboratively with pharmacologists, geneticists, biochemists, and analytical chemists--are creating and modeling large data on drug effects that is transforming our understanding of how these drugs work at a network level. In this review, we highlight developments in a new and rapidly growing field--pharmacometabolomics--in which large biochemical data-capturing effects of genome, gut microbiome, and environment exposures is revealing information about metabotypes and treatment outcomes, and creating metabolic signatures as new potential biomarkers. Pharmacometabolomics informs and complements pharmacogenomics and together they provide building blocks for QSP.
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Affiliation(s)
- R Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina, USA.,Duke Institute for Brain Sciences, Duke University, Durham, North Carolina, USA
| | - R Weinshilboum
- Mayo Clinic, Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Everett JR. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 2015; 16:737-54. [PMID: 25929853 DOI: 10.2217/pgs.15.20] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.
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Stimpfle F, Geisler T. Impact of tailored anti-P2Y12 therapies in acute coronary syndromes. Pharmacogenomics 2015; 16:493-9. [DOI: 10.2217/pgs.15.19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Acute coronary syndromes are a major disease burden and the prognosis has improved over the last decades due to improvement of medical and interventional treatments. Novel P2Y12-ADP-receptor antagonists have been introduced into clinical treatment offering more potent and rapid onset of action with the downside of increased bleeding risk. This special report will focus on interindividual variability of antiplatelet drugs in the setting of acute coronary syndromes and the current impact and potential future of point-of-care testing to personalize therapy aiming to improve prognosis in acute coronary syndrome patients.
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Affiliation(s)
- Fabian Stimpfle
- University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
| | - Tobias Geisler
- University Hospital Tübingen, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany
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