A pharmaco-metabolomics approach in a clinical trial of ALS: Identification of predictive markers of progression

Abstract

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.

Fig 1. Global strategy of the analysis illustrating the times of sample collection, the parameters collected and the three main objectives of the study.

Fig 2

Multivariate model from blood metabolome of ALS patients at V1, A) Score scatter plot from OPLS-DA model discriminating patients in Group P (black) from patients in Group O (red), B) Loading scatter plot from OPLS-DA model showing the best discriminating metabolites. The position of the metabolites in the loading plot characterises the subjects represented in the score plot; variables near each other are positively correlated; variables opposite to each other are negatively correlated. Amino acids are represented in blue, complex lipids in orange, fatty acids in yellow and other molecules in green.

Fig 3. Venn diagram representing the 15 best discriminating metabolites between patients in Group P and patients in Group O at V1, V12 and over one year (V12-V1) in OPLS-DA models built from blood metabolomic profiles.

Fig 4. Venn diagram representing the 15 most discriminating metabolites in PLS-DA models which are associated with disease evolution (variation of ALSFRS-r, BMI, MMT, SVC over 1 year) in patients in Group P.

Fig 5. Venn diagram showing the 15 most discriminating metabolites in PLS-DA models which are associated with disease evolution (variation of ALSFRS-r, BMI, MMT, SVC over 1 year) in patients in Group O.