Treating Duchenne Muscular Dystrophy: The Promise of Stem Cells, Artificial Intelligence, and Multi-Omics

Abstract

Muscular dystrophies are chronic and debilitating disorders caused by progressive muscle wasting. Duchenne muscular dystrophy (DMD) is the most common type. DMD is a well-characterized genetic disorder caused by the absence of dystrophin. Although some therapies exist to treat the symptoms and there are ongoing efforts to correct the underlying molecular defect, patients with muscular dystrophies would greatly benefit from new therapies that target the specific pathways contributing directly to the muscle disorders. Three new advances are poised to change the landscape of therapies for muscular dystrophies such as DMD. First, the advent of human induced pluripotent stem cells (iPSCs) allows researchers to design effective treatment strategies that make up for the gaps missed by conventional “one size fits all” strategies. By characterizing tissue alterations with single-cell resolution and having molecular profiles for therapeutic treatments for a variety of cell types, clinical researchers can design multi-pronged interventions to not just delay degenerative processes, but regenerate healthy tissues. Second, artificial intelligence (AI) will play a significant role in developing future therapies by allowing the aggregation and synthesis of large and disparate datasets to help reveal underlying molecular mechanisms. Third, disease models using a high volume of multi-omics data gathered from diverse sources carry valuable information about converging and diverging pathways. Using these new tools, the results of previous and emerging studies will catalyze precision medicine-based drug development that can tackle devastating disorders such as DMD.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

“…Integrative multi-omics approaches have been employed to dissect the disease pathophysiology ( Ruffini et al, 2020 ; Volonte et al, 2020 ; Straub et al, 2021 ). PandaOmics is a fully integrated AI-based platform with a wide range of omics and text data sources ( Vera et al, 2022 ). Compared to other existing tools for target discovery, PandaOmics has several unique advantages with respect to user experience, algorithms, the comprehensive database, and the time machine validation approach ( Zhavoronkov et al, 2019 ).…”

“…During the target identification process, our AI models dynamically assessed disease targets based on a variety of measures, such as novelty, accessibility by small molecules and biologics, safety and tissue expression specificity, to collectively generate hypotheses around their potential druggability profiles. Previous studies demonstrated the effectiveness of PandaOmics to identify novel and repurposing therapeutic targets ( Insilico Medicine, 2022 ; Pun et al, 2022 ; Vera et al, 2022 ).…”

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

“…Another example refers to the prompt discovery of potent inhibitors against discoidin domain receptor 1 (DDR1) kinase including de novo small molecule design, synthesis and in vivo validation [ 46 ]. Analysis of multiomics-based data and application of PandaOmics has been further highlighted for acceleration of advancements in treatment of Duchenne muscular dystrophy [ 47 ]. In this regard, the possibilities for AI-driven target discovery became broadly unleashed for remarkably faster progression of discovered hits into clinical trials.…”

How Can Proteomics Help to Elucidate the Pathophysiological Crosstalk in Muscular Dystrophy and Associated Multi-System Dysfunction?

“…Combining findings from these types of molecular and cellular analyses will be extremely helpful in predicting the trajectory of disease progression in clinical subtypes of DMD and determining the potential influence of environmental factors and lifestyle. To achieve a maximum yield of data from integrative analyses [ 35 ], multi-omics approaches will be assisted by employing big data analytic tools and using optimized machine learning and artificial intelligence approaches [ 487 , 488 , 489 , 490 , 491 ]. A crucial aspect of the development of novel therapies to treat dystrophinopathies is the elucidation of the underlying mechanisms that generate mild forms of DMD [ 492 ] or naturally protective phenotypes, such as the spared muscular systems of the tongue, intrinsic laryngeal muscles and extraocular muscles [ 196 , 493 , 494 ].…”