Back

The Rise and Fall of SARM1 Base-Exchange Inhibitors

Lundback, T.; Chandrasekar, V.; Gu, C.; Ju, H.; McAdam, R.; Palomero, M.; Sader, K.; Peter, B.; Wissler, L.; Nevin, P.; Foster, E.; Jamier, T.; Manjappa, P.; Johansson, C.; Sandmark, J.; Ding, M.; Persson-Kry, A.; Mitra, S.; Satir, T. M.; Bilican, B.; Messa, M.; Fraser, G.; Linley, J.; Plant, H.; Moore, R.; Seifert, T.; Lerche, M.; Raynochek, C.; Nilsson, E.; Majbour, N.; Lucey, R.; Maia de Oliveira, T.; Wang, Q.; Chessell, I.; Breccia, P.; Jarvis, R.

2025-11-12 neuroscience
10.1101/2025.11.11.687870 bioRxiv
Show abstract

The sterile alpha and TIR motif containing 1 (SARM1) enzyme is a key driver of axonal degeneration in response to injury, making it an attractive target for treating chemotherapy-induced peripheral neuropathy (CIPN) and other nervous system diseases. In this study, we identified and optimised a new class of base-exchange inhibitors (BEXi) targeting SARM1 and explored their molecular interactions and conformational effects using cryo-EM, HDX-MS and SAXS. Although BEXi produced robust inhibition across all biochemical and cellular assay formats, application at sub-inhibitory concentrations consistently led to paradoxical SARM1 activation, and in neuronal assays, accelerated neurite degeneration. Further analysis showed that BEXi only delayed, rather than prevented, neurite degeneration when applied to primary neuronal cells, even at exceedingly high inhibitor concentrations. These results prompted us to discontinue BEXi development in favour of alternative strategies, underscoring the complexity of SARM1 as a therapeutic target and the need for comprehensive, mechanistically informed screening cascades.

Published in Communications Chemistry (predicted rank #23) · training set

Matching journals

The top 3 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.