Back

A drug repurposing screen identifies altiratinib as a selective inhibitor of a key regulatory splicing kinase and a potential therapeutic for toxoplasmosis and malaria

SWALE, C.; BELLINI, V.; Bowler, M. W.; NARDELLA, F.; Brenier-Pinchart, M.-P.; Cannella, D.; Belmudes, L.; MAS, C.; Coute, Y.; LAURENT, F.; Scherf, A.; BOUGDOUR, A.; HAKIMI, M.-a.

2021-11-03 microbiology
10.1101/2021.11.03.467097 bioRxiv
Show abstract

Introductory paragraphThe apicomplexa comprise a large phylum of single-celled, obligate intracellular protozoa that infect humans and animals and cause severe parasitic diseases. Available therapeutics against these devastating diseases are limited by suboptimal efficacy and frequent side effects, as well as the emergence and spread of resistance. Here, we use a drug repurposing strategy and identify altiratinib, a compound originally developed to treat glioblastoma, as a promising drug candidate with broad spectrum activity against apicomplexans. Altiratinib is parasiticidal and blocks the development of intracellular zoites in the nanomolar range and with a high selectivity index. We have identified TgPRP4K of T. gondii as the primary target of altiratinib by genetic target deconvolution, highlighting key residues within the kinase catalytic site that, when mutated, confer resistance to the drug. We have further elucidated the molecular basis of the inhibitory mechanism and species selectivity of altiratinib for TgPRP4K as well as for its P. falciparum counterpart PfCLK3. Our data also point to structural features critical for binding of the other PfCLK3 inhibitor, TCMDC-135051. Consistent with the role of this kinase family in splicing in a broad spectrum of eukaryotes, we have shown that altiratinib causes global disruption of splicing, primarily through intron retention in both T. gondii and P. falciparum. Thus, our data establish parasitic PRP4K/CLK3 as a promising pan-apicomplexan target whose repertoire of inhibitors can be expanded by the addition of altiratinib.

Matching journals

The top 4 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.