Combination of acamprosate and baclofen (PXT864) as a potential new therapy for amyotrophic lateral sclerosis.

There is currently no therapy impacting the course of amyotrophic lateral sclerosis (ALS). The only approved treatments are riluzole and edaravone, but their efficacy is modest and short-lasting, highlighting the need for innovative therapies. We previously demonstrated the ability of PXT864, a combination of low doses of acamprosate and baclofen, to synergistically restore cellular and behavioral activity in Alzheimer's and Parkinson's disease models. The overlapping genetic, molecular, and cellular characteristics of these neurodegenerative diseases supported investigating the effectiveness of PXT864 in ALS. As neuromuscular junction (NMJ) alterations is a key feature of ALS, the effects of PXT864 in primary neuron-muscle cocultures injured by glutamate were studied. PXT864 significantly and synergistically preserved NMJ and motoneuron integrity following glutamate excitotoxicity. PXT864 added to riluzole significantly improved such protection. PXT864 activity was then assessed in primary cultures of motoneurons derived from SOD1G93A rat embryos. These motoneurons presented severe maturation defects that were significantly improved by PXT864. In this model, glutamate application induced an accumulation of TDP-43 protein in the cytoplasm, a hallmark that was completely prevented by PXT864. The anti-TDP-43 aggregation effect was also confirmed in a cell line expressing TDP-43 fused to GFP. These results demonstrate the value of PXT864 as a promising therapeutic strategy for the treatment of ALS.

Synergistic PXT3003 therapy uncouples neuromuscular function from dysmyelination in male Charcot-Marie-Tooth disease type 1A (CMT1A) rats.

Charcot-Marie-Tooth disease 1 A (CMT1A) is caused by an intrachromosomal duplication of the gene encoding for PMP22 leading to peripheral nerve dysmyelination, axonal loss, and progressive muscle weakness. No therapy is available. PXT3003 is a low-dose combination of baclofen, naltrexone, and sorbitol which has been shown to improve disease symptoms in Pmp22 transgenic rats, a bona fide model of CMT1A disease. However, the superiority of PXT3003 over its single components or dual combinations have not been tested. Here, we show that in a dorsal root ganglion (DRG) co-culture system derived from transgenic rats, PXT3003 induced myelination when compared to its single and dual components. Applying a clinically relevant ("translational") study design in adult male CMT1A rats for 3 months, PXT3003, but not its dual components, resulted in improved performance in behavioral motor and sensory endpoints when compared to placebo. Unexpectedly, we observed only a marginally increased number of myelinated axons in nerves from PXT3003-treated CMT1A rats. However, in electrophysiology, motor latencies correlated with increased grip strength indicating a possible effect of PXT3003 on neuromuscular junctions (NMJs) and muscle fiber pathology. Indeed, PXT3003-treated CMT1A rats displayed an increased perimeter of individual NMJs and a larger number of functional NMJs. Moreover, muscles of PXT3003 CMT1A rats displayed less neurogenic atrophy and a shift toward fast contracting muscle fibers. We suggest that ameliorated motor function in PXT3003-treated CMT1A rats result from restored NMJ function and muscle innervation, independent from myelination.

Next-generation drug repurposing using human genetics and network biology.

Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatments have high costs that are difficult to justify for small markets that include rare diseases. Drug repurposing, involving the identification of single or combinations of existing drugs based on human genetics data and network biology approaches represents a next-generation approach that has the potential to increase the speed of drug discovery at a lower cost. This Pharmacological Perspective reviews progress and perspectives in combining human genetics, especially genome-wide association studies, with network biology to drive drug repurposing for rare and common diseases with monogenic or polygenic etiologies. Also, highlighted here are important features of this next generation approach to drug repurposing, which can be combined with machine learning methods to meet the challenges of personalized medicine.

biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data.

High-throughput technologies such as transcriptomics, proteomics, and metabolomics show great promise for the discovery of biomarkers for diagnosis and prognosis. Selection of the most promising candidates between the initial untargeted step and the subsequent validation phases is critical within the pipeline leading to clinical tests. Several statistical and data mining methods have been described for feature selection: in particular, wrapper approaches iteratively assess the performance of the classifier on distinct subsets of variables. Current wrappers, however, do not estimate the significance of the selected features. We therefore developed a new methodology to find the smallest feature subset which significantly contributes to the model performance, by using a combination of resampling, ranking of variable importance, significance assessment by permutation of the feature values in the test subsets, and half-interval search. We wrapped our biosigner algorithm around three reference binary classifiers (Partial Least Squares-Discriminant Analysis, Random Forest, and Support Vector Machines) which have been shown to achieve specific performances depending on the structure of the dataset. By using three real biological and clinical metabolomics and transcriptomics datasets (containing up to 7000 features), complementary signatures were obtained in a few minutes, generally providing higher prediction accuracies than the initial full model. Comparison with alternative feature selection approaches further indicated that our method provides signatures of restricted size and high stability. Finally, by using our methodology to seek metabolites discriminating type 1 from type 2 diabetic patients, several features were selected, including a fragment from the taurochenodeoxycholic bile acid. Our methodology, implemented in the biosigner R/Bioconductor package and Galaxy/Workflow4metabolomics module, should be of interest for both experimenters and statisticians to identify robust molecular signatures from large omics datasets in the process of developing new diagnostics.