Computational modeling and preclinical validation support targeting somatic instability for Huntington's disease treatment
Huntingtons disease (HD) is caused by an expanded CAG trinucleotide repeat within the huntingtin (HTT) gene. Genetic modifiers of disease onset and progression in HD implicate somatic instability (SI) of the expanded CAG repeat as a key pathogenic driver, with MSH3 emerging as a leading therapeutic target. Reducing SI, particularly in the most affected neuronal cell type, medium spiny neurons (MSNs) of the striatum, is thus a rational therapeutic strategy for HD. To inform the development of an SI-targeted therapy, we generated a computational model simulating SI in MSNs to infer therapeutic effects on MSN survival resulting from an intervention that reduces SI. The model takes advantage of HD patient data to predict therapeutic benefit across a range of inherited CAG lengths and ages of intervention, considering the degree of target engagement regionally and per cell. To target SI experimentally, we designed an artificial microRNA to lower MSH3 mRNA (miMSH3) after delivery with AAV-DB-3, a previously described MSN-targeting AAV capsid variant. AAV-DB-3.miMSH3 achieved from 48 to 94% MSH3 mRNA reduction in MSNs of nonhuman primates (NHPs), which, when modeled, would reduce the composite Unified Huntington Disease Rating Scale change over baseline from 50 to over 120% as well as delay motor symptom onset by many years. AAV-DB-3.miMSH3 also showed robust target engagement in vivo with up to 46% reduction in SI in HdhQ111 mice. The integration of preclinical experimental data and the computational model support the translational potential of AAV-DB-3.miMSH3 as a disease-modifying therapy applicable for HD patients with a broad range of inherited repeat lengths. One Sentence SummaryPredictive modeling to guide therapeutic targeting of disease modifiers in Huntingtons disease.