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A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression
There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this tria...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988280/ https://www.ncbi.nlm.nih.gov/pubmed/29870556 http://dx.doi.org/10.1371/journal.pone.0198116 |
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author | Blasco, Hélène Patin, Franck Descat, Amandine Garçon, Guillaume Corcia, Philippe Gelé, Patrick Lenglet, Timothée Bede, Peter Meininger, Vincent Devos, David Gossens, Jean François Pradat, Pierre-François |
author_facet | Blasco, Hélène Patin, Franck Descat, Amandine Garçon, Guillaume Corcia, Philippe Gelé, Patrick Lenglet, Timothée Bede, Peter Meininger, Vincent Devos, David Gossens, Jean François Pradat, Pierre-François |
author_sort | Blasco, Hélène |
collection | PubMed |
description | There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10–6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials. |
format | Online Article Text |
id | pubmed-5988280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-59882802018-06-16 A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression Blasco, Hélène Patin, Franck Descat, Amandine Garçon, Guillaume Corcia, Philippe Gelé, Patrick Lenglet, Timothée Bede, Peter Meininger, Vincent Devos, David Gossens, Jean François Pradat, Pierre-François PLoS One Research Article There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10–6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials. Public Library of Science 2018-06-05 /pmc/articles/PMC5988280/ /pubmed/29870556 http://dx.doi.org/10.1371/journal.pone.0198116 Text en © 2018 Blasco et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Blasco, Hélène Patin, Franck Descat, Amandine Garçon, Guillaume Corcia, Philippe Gelé, Patrick Lenglet, Timothée Bede, Peter Meininger, Vincent Devos, David Gossens, Jean François Pradat, Pierre-François A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title | A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title_full | A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title_fullStr | A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title_full_unstemmed | A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title_short | A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression |
title_sort | pharmaco-metabolomics approach in a clinical trial of als: identification of predictive markers of progression |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5988280/ https://www.ncbi.nlm.nih.gov/pubmed/29870556 http://dx.doi.org/10.1371/journal.pone.0198116 |
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