A team from Genethon used an artificial intelligence approach to develop an optimized gene therapy vector able to target muscle tissue specifically. Their innovation, tested in Duchenne muscular dystrophy and limb-girdle muscular dystrophy models, opens new, promising perspectives for improving the efficacy and safety of treatments for neuromuscular diseases. It may also be deployable for gene therapies targeting other tissues.
In gene therapy, a vector is needed to get the therapeutic gene and its regulatory sequences to the concerned cells. Currently, the most efficient vectors are viruses that have been re-engineered to remove any pathogenicity and render them apt for transgene delivery. In neuromuscular diseases, adeno-associated viruses (AAVs) are most frequently used. That virus’s genetic material is enclosed in a proteinaceous shell called a capsid. This latter plays a key role in the virus’s—and thus the vector’s—ability to target particular cells. However, the true specificity of that targeting ability of AAVs needs to be improved to reach their full potential as therapeutic vectors. Indeed, in neuromuscular diseases, very high levels of vectors are needed to bring enough of them to the targeted muscle cells, but this causes an overflow of vectors that ends up in the liver, causing a risk of hepatic complications.
To address that problem, Genethon’s “Progressive Muscular Dystrophies team“, headed by Isabelle Richard, has developed an innovative, artificial-intelligence-based method for the development of gene therapy vectors able to more specifically target skeletal muscle, thus improving the efficacy and safety of the treatment.
Their work was published on 11 September 2024 in Nature Communications and had been presented in part by researcher Ai Vu Hong at a conference entitled “Optimizing innovative therapies: Therapeutic targeting and combinatorial strategies”, co-organized by Genopole and the University of Évry Paris-Saclay and held on 2 July 2024.
LICA1: a promising vector born of artificial intelligence
The team’s innovation involves the modification of the AAV capsid so that it specifically recognizes an integrin complex named αVβ6, which is particularly abundant on the muscle cell surface. The in silico method for protein conception involved modifying a characteristic protein on the capsid by adding a binding motif to it, one specific to αVβ6, and modifying its surrounding amino acids (see image).
These changes also improved the protein’s stability and folding, and thus the stability of the vector.
Of six variants conceived in silico, then produced and studied, one, named LICA1, appeared particularly promising. The research team thus tested that vector in vitro on human differentiated muscle cells and thereafter in animal models of Duchenne muscular dystrophy and limb-girdle muscular dystrophy. They found that it was indeed able to effectively deliver and express therapeutic transgenes.
20-fold fewer vectors and greater efficacy than with current AAVs in muscular dystrophy models
More precisely, the LICA1 vector presented muscle transduction efficacy significantly higher than that of current vectors, even at doses 20-times lower than those normally required. In murine muscular dystrophy models, LICA1 was able to transduce more than 80% of muscle fibers and confer a notable improvement in muscle function. It reduced muscle lesion biomarkers by 57.5% and 67.2% in Duchenne muscular dystrophy and limb-girdle muscular dystrophy models respectively. Moreover, it provided the most favorable muscle-to-liver transduction rate among all of the tested AAVs, thus minimizing adverse effects associated with vector accumulation in the liver. This increased specificity for skeletal muscle was confirmed in histological and transcriptomics analyses, underlining LICA1’s increased effectiveness for correcting dystrophic phenotypes, transcriptional dysregulations and muscular function in dystrophy models.
A validated approach extendable to other organs and diseases
The results of this study suggest that LICA1 may be able to improve muscular dystrophy gene therapies by offering better vectoral efficacy and safety. It could help reduce treatment dosages, which has great importance for minimizing not only adverse effects but also treatment costs, making gene therapies more accessible to patients with myopathic diseases.
More generally, the work done by the Genethon team underlines the potential of associating artificial intelligence with viral vector engineering expertise to improve specificity, efficacy and safety in gene therapies. The methodology developed by the Évry team for selecting pertinent receptors and using artificial intelligence to design a thereto-tailored capsid is also applicable for other systems and diseases.
