A staged screening of registered drugs highlights remyelinating drug candidates for clinical trials

There is no treatment for the myelin loss in multiple sclerosis, ultimately resulting in the axonal degeneration that leads to the progressive phase of the disease. We established a multi-tiered platform for the sequential screening of drugs that could be repurposed as remyelinating agents. We screened a library of 2,000 compounds (mainly Food and Drug Administration (FDA)-approved compounds and natural products) for cellular metabolic activity on mouse oligodendrocyte precursors (OPC), identifying 42 molecules with significant stimulating effects. We then characterized the effects of these compounds on OPC proliferation and differentiation in mouse glial cultures, and on myelination and remyelination in organotypic cultures. Three molecules, edaravone, 5-methyl-7-methoxyisoflavone and lovastatin, gave positive results in all screening tiers. We validated the results by retesting independent stocks of the compounds, analyzing their purity, and performing dose-response curves. To identify the chemical features that may be modified to enhance the compounds' activity, we tested chemical analogs and identified, for edaravone, the functional groups that may be essential for its activity. Among the selected remyelinating candidates, edaravone appears to be of strong interest, also considering that this drug has been approved as a neuroprotective agent for acute ischemic stroke and amyotrophic lateral sclerosis in Japan

GWAS-associated Variants, Non-genetic Factors, and Transient Transcriptome in Multiple Sclerosis Etiopathogenesis: a Colocalization Analysis [preprint]

A clinically actionable understanding of multiple sclerosis (MS) etiology goes through GWAS interpretation, prompting research on new gene regulatory models. Our previous works on these topics suggested a stochastic etiologic model where small-scale random perturbations could eventually reach a threshold for MS onset and progression. A new sequencing technology has mapped the transient transcriptome (TT), including intergenic RNAs, and antisense intronic RNAs. Through a rigorous colocalization analysis, here we show that genomic regions coding for the TT were significantly enriched for both MS-associated GWAS variants, and DNA binding sites for molecular transducers mediating putative, non-genetic, etiopathogenetic factors for MS (e.g., vitamin D deficiency, Epstein Barr virus latent infection, B cell dysfunction). These results suggest a model whereby TT-coding regions are hotspots of convergence between genetic ad non-genetic factors of risk/protection for MS (and plausibly for other complex disorders). Our colocalization analysis also provides a freely available data resource at www.mscoloc.com for future research on transcriptional regulation in MS

Fragility, robustness, and antifragility in deep neural networks

This PhD thesis investigates the relationship between network architectures and the robustness against adversarial attacks using a novel methodology that considers both aspects as part of the robustness analysis. Through an investigation on the adversarial targeting of neurons, specifically in the first convolutional layer of a deep neural network (DNN), we observe a relationship between neurons that affect the test accuracy of the DNN, when inferring on a clean, unperturbed dataset, subsequently characterising them as fragile, and those neurons targeted by a potential adversarial attack. We show how the fragile neurons of a DNN convolutional layer evolve over the network training procedure and propose an algorithm to show the targeting of fragile neurons by adversarial attacks. Using the developed adversarial targeting algorithm we show that adversarial attacks focus on specific components of the convolutional layer, framing the adversarial perturbations as attacks on fragile neurons. The task of analysing the robustness of DNNs, thus, leads us to the identification of fragile and non-fragile network parameters, where non-fragile refers to any parameters that do not degrade the performance when subjected to perturbations, as opposed to fragile parameters that do degrade network performance. When discussing perturbations, we consider both variations to the network parameters and the input dataset, in the form of adversarial attacks. We further extend the analysis to characterise the parameters of deep neural networks as either fragile, robust, or antifragile, and show that network accuracy is impacted negatively, invariantly, or positively w.r.t. defined global and local robustness scores that are computed using a baseline network performance. We design a signal processing technique in the form of synaptic filters that identify the fragility, robustness and antifragility characteristics of deep neural network parameters. We subject a network to synaptic filters and compare the network responses for both clean and adversarial datasets, subsequently exposing parameters targeted by the adversary. Our results identify the structural fragility of network architectures and show how they evolve over the training process, thus informing us on the learning landscapes of DNNs. We find that, for a given network architecture, global and local filtering responses have invariant features to different datasets over the learning landscape. Vice-versa, for a given dataset we identify invariant features across different network architectures. Our proposed analysis of fragility, robustness and antifragility of deep neural networks is useful for designing compact networks by removing particularly the antifragile parameters. We improve the adversarial robustness of networks using a selective backpropagation method that, upon identification of parameter characterisations, retrains only the robust and antifrgaile parameter updates, whilst omitting the fragile parameter updates during the training procedure. Following this, we develop DNNs for two novel, real-world applications; a DNN designed to identify the the optimum denoising filter for noisy ECG waveforms, and DNNs designed to classify human activities and motion intensities from signals measured using an ultra wide-band radar system. We use original datasets for both tasks and develop novel DNN architectures for the classification tasks. Subsequently, we apply the developed selective backpropagation method to train the custom-designed DNNs and observed an increase in adversarial robustness for the DNNs evaluated. Furthermore, for both the signal denoising filter selection and activity classification tasks, we discern an improvement in the test accuracy when applied to the clean, unperturbed dataset. We successfully show that the proposed selective backpropagation method is capable of improving the adversarial robustness of networks, and in certain instances, also the regular test accuracy. Supporting results for these findings are presented across the chapters of this thesis

Design of robust metabolic pathways

In this paper we investigate plant photosynthesis and microbial fuel cells. We report the following: 1) we introduce and validate a novel multi-objective optimization algorithm, PMO2; 2) in photosynthesis we increase the yield of 135%, while in Geobacter sulfurreducens we determine the tradeoff for growth versus redox properties; 3) finally, we discuss Pareto-Front as an estimator of robust metabolic pathways. © 2011 ACM