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.

In Spino-Cerebellar Tissue, Ataxin-2 Expansion Affects Ceramide-Sphingomyelin Metabolism

“…The lipid profiling efforts in a SCA2 patient cerebellum and in spinocerebellar tissues from an authentic SCA2 mouse model showed deficits for very-long-chain C24 sphingomyelin as the main consistent finding in both organisms. A reduction of very-long-chain sphingomyelin was also observed in the CSF and blood of ALS patients, and this deficit correlated with lowest survival [128,129]. C24 sphingomyelin interacts with cholesterol in lipid bilayers, as important stabilizing elements for the plasma membrane particularly in myelinating glia cells [130,131].…”

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

“…We have listed several examples of how AI has boosted therapeutic development in RDs. These entail the identification of disease biomarkers [70][71][72], the increase of patient recruitment for CTs [67], and the discovery of drugs for repurposing [63]. However, much is still to be done to overturn the low rate of R&D for RDs.…”

“…In an olesoxime trial for ALS, the application of the Biosigner algorithm (using partial least square discriminant analysis, RF, and SVM) to a pharmacometabolic approach discerned differences in the metabolic profiles between the control and treatment arms. It also recognized sphingomyelins as the most relevant disease progression marker [70]. Moreover, in West syndrome (ORPHA:3451) patients, the employment of this algorithm identified serine and an unknown metabolite (X363) as potential disease biomarkers [71].…”

Existing and Emerging Metabolomic Tools for ALS Research

“…Furthermore, metabolomics could play a crucial role in the search for biomarkers in ALS. Indeed, metabolomics was used to determine the presence of possible biomarkers in cerebrospinal fluid or plasma from ALS patients [52,53,[100][101][102][103][104]. These studies did not identify a single outstanding biomarker but were able to find distinctive metabolic profiles in ALS patients.…”