Back

Monotherapy efficacy of BBB-permeable small molecule activators of PP2A in glioblastoma

Merisaari, J.; Denisova, O. V.; Doroszko, M.; Le Joncour, V.; Johansson, P.; Leenders, W. P. J.; Kastrinsky, D. B.; Nilesh, Z.; Laakkonen, P.; Nelander, S.; Ohlmeyer, M.; Westermarck, J.

2019-09-23 cancer biology
10.1101/777276 bioRxiv
Show abstract

Glioblastoma (GB) is a fatal disease in which most targeted therapies have clinically failed. However, pharmacological reactivation of tumor suppressors has not been thoroughly studied as yet as a GB therapeutic strategy. Tumor suppressor Protein Phosphatase 2A (PP2A), is inhibited by non-genetic mechanisms in GB, and thus it would be potentially amendable for therapeutic reactivation. Here we demonstrate, that small molecule activators of PP2A (SMAPs), NZ-8-061 and DBK-1154, effectively cross the in vitro model of blood-brain barrier (BBB), and in vivo partition to mouse brain tissue after oral dosing. In vitro, SMAPs exhibit robust cell killing activity against five established GB cell lines, and nine patient-derived primary glioma cell lines. Collectively these cell lines have heterogenous genetic background, kinase inhibitor resistance profile, and stemness properties; and they represent different clinical GB subtypes. Oral dosing of either of the SMAPs significantly reduced growth of infiltrative intracranial GB tumors. DBK-1154, with both higher degree of brain/blood distribution, and more potent in vitro activity against all tested GB cell lines, also significantly increased survival of mice bearing orthotopic GB xenografts. In summary, this report presents a proof-of-principle data for BBB-permeable tumor suppressor reactivation therapy for glioblastoma cells of heterogenous molecular background.

Matching journals

The top 5 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.