Back

The glycosphingolipid inhibitor eliglustat inhibits autophagy in osteoclasts to increase bone mass and reduce myeloma bone disease

Leng, H.; Zhang, H.; Li, L.; Zhang, S.; Wang, Y.; Ersek, A.; Morris, E.; Sezgin, E.; Lee, Y.-H.; Li, Y.; Mi, J.; Zhong, Q.; Edwards, C.; Simon, A. K.; Horwood, N. J.

2021-02-06 cell biology
10.1101/2021.02.05.429906 bioRxiv
Show abstract

Multiple myeloma (MM) is a fatal hematological malignancy, where the majority of patients are diagnosed with, or develop, destructive and debilitating osteolytic bone lesions. Current treatments for MM bone disease such as the bisphosphonate zoledronic acid can result in deleterious side effects at high doses. In this study, eliglustat, an FDA approved glycosphingolipid inhibitor, was shown to reduce MM bone disease in preclinical models of MM. Mechanistically, eliglustat alters the lipid composition and plasma membrane fluidity and acts as an autophagy flux inhibitor in bone-resorbing osteoclasts (OC). Autophagic degradation of the signaling molecule TRAF3 is key step in OC differentiation; this was prevented by eliglustat in OC precursors. In addition, eliglustat works depend on TRAF3 in vivo. Furthermore, the combination of eliglustat and zoledronic acid was found to have an additive effect to reduce MM bone disease, suggesting the potential for combination therapies that would allow for drug dose reductions. Taken together, this project identifies a novel mechanism in which glycosphingolipid inhibition reduces osteoclastogenesis via autophagy and highlights the translational potential of eliglustat for the treatment of bone loss disorders such as MM. One Sentence SummaryTranslational use of eliglustat as an autophagy inhibitor to limit bone lesions in multiple myeloma.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.