Back

Single-sample, multi-omic mass spectrometry for investigating mechanisms of drug toxicity

Mahmud, I.; Chan, W.-K.; Yannell, K.; Simmermaker, C.; Van de Bittner, G.; Wu, L.; Chan, D.; Mohsin, S. B.; Liu, Y.; Sausen, J.; Weinstein, J. N.; Lorenzi, P. L.

2025-02-17 cancer biology
10.1101/2025.02.13.638125 bioRxiv
Show abstract

Poor therapeutic index is a principal cause of drug attrition during development. A case in point is L-asparaginase (ASNase), an enzyme-drug approved for treatment of pediatric acute lymphoblastic leukemia (ALL) but too toxic for adults. To elucidate potentially targetable mechanisms for mitigation of ASNase toxicity, we performed multi-omic profiling of the response to sub-toxic and toxic doses of ASNase in mice. We collected whole blood samples longitudinally, processed them to plasma, and extracted metabolites, lipids, and proteins from a single 20-{micro}L plasma sample. We analyzed the extracts using multiple reaction monitoring (MRM) of 500+ water soluble metabolites, 750+ lipids, and 375 peptides on a triple quadrupole LC-MS/MS platform. Metabolites, lipids, and peptides that were modulated in a dose-dependent manner appeared to converge on antioxidation, inflammation, autophagy, and cell death pathways, prompting the hypothesis that inhibiting those pathways might decrease ASNase toxicity while preserving anticancer activity. Overall, we provide here a streamlined, three-in-one LC-MS/MS workflow for targeted metabolomics, lipidomics, and proteomics and demonstrate its ability to generate new insights into mechanisms of drug toxicity.

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.