Fragment-based virtual screening identifies a first-in-class preclinical drug candidate for Huntington's disease

Galyan, Simon Marius; Ewald, Collin Y.; Jalencas i Giménez, Xavier, 1981-; Masrani, Shyam; Meral, Selin; Mestres i López, Jordi

doi:http://dx.doi.org/10.1038/s41598-022-21900-2

Fragment-based virtual screening identifies a first-in-class preclinical drug candidate for Huntington's disease

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Galyan SM, Ewald CY, Jalencas X, Masrani S, Meral S, Mestres J. Fragment-based virtual screening identifies a first-in-class preclinical drug candidate for Huntington's disease. Sci Rep. 2022 Nov 16;12(1):19642. DOI: 10.1038/s41598-022-21900-2

Abstract

Currently, there are no therapies available to modify the disease progression of Huntington's disease (HD). Recent clinical trial failures of antisense oligonucleotide candidates in HD have demonstrated the need for new therapeutic approaches. Here, we developed a novel in-silico fragment scanning approach across the surface of mutant huntingtin (mHTT) polyQ and predicted four hit compounds. Two rounds of compound analoging using a strategy of testing structurally similar compounds in an affinity assay rapidly identified GLYN122. In vitro, GLYN122 directly binds and reduces mHTT and induces autophagy in neurons. In vivo, our results confirm that GLYN122 can reduce mHTT in the cortex and striatum of the R/2 mouse model of Huntington's disease and subsequently improve motor symptoms. Thus, the in-vivo pharmacology profile of GLYN122 is a potential new preclinical candidate for the treatment of HD.