Back

The delivery of nano-formulated drugs to solid tumours is selectively increased by co-application of the vascular disrupting agent CA4P

Kitowski, A.; Heise, C.; Sperling, S.; Hotz, J.; Bajic, D.; Rubey, M.; Klar, K.; Hoefner, G.; Vollaire, J.; Josserand, V.; Coll, J.-L.; Thorn-Seshold, O.; Marinkovic, P.; Thorn-Seshold, J.

2025-08-12 cancer biology
10.1101/2025.08.10.669501 bioRxiv
Show abstract

Improving the efficacy of existing cytotoxic chemotherapeutics requires increasing drug delivery to tumours while minimising systemic toxicity. Formulating these drugs as nanoparticles can reduce their exposure to healthy tissues, but broadly applicable strategies to enhance tumoral accumulation are lacking. Here, we show that co-administering small molecule vascular disrupting agents together with nanoparticle formulations (e.g. diagnostic reporters, or clinical drugs irinotecan and doxorubicin) increases their tumoral uptake by up to threefold, without raising systemic exposure. In a syngeneic mouse model of triple-negative breast cancer, this enhancement diminished when co-treatments were repeated, limiting its therapeutic benefit. However, since most solid tumour types are susceptible to vascular disrupting agents, this approach may be a broadly applicable strategy to improve the selectivity of drug delivery: with particular relevance for single dose use in diagnostic or research settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/669501v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@a5b07corg.highwire.dtl.DTLVardef@1e5dcd2org.highwire.dtl.DTLVardef@49134org.highwire.dtl.DTLVardef@1d90aae_HPS_FORMAT_FIGEXP M_FIG C_FIG

Published in British Journal of Pharmacology · training set

Matching journals

The top 10 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.