Back

Synthetic inhibitors of pregnancy-associated plasma protein-A regulate IGF-1 signaling in vitro and in vivo

Kertisova, A.; Shih, S.-W.; Kirk, N. S.; Picha, J.; Rucklova, A.; Kozak, J.; Mrzilkova, K.; Lubos, M.; Tloustova, E.; Budesinsky, M.; Duan, C.; Jiracek, J.; Selicharova, I.

2026-10-02 biochemistry
10.64898/2026.09.30.755852 bioRxiv
Show abstract

Dysregulation of insulin-like growth factor (IGF) signaling contributes to diverse human diseases. Therapeutic strategies have largely targeted the IGF-1 receptor, but the complexity of the IGF system has limited their clinical efficacy. Pregnancy-associated plasma protein-A (PAPP-A) is a secreted metalloproteinase that regulates local IGF signaling through proteolytic cleavage of IGF-binding proteins (IGFBPs) and represents an attractive therapeutic target. Here, we report the first synthetic PAPP-A inhibitors designed to target the enzyme active site using an IGFBP-5-derived anchor sequence. Replacing the scissile bond with phosphinate or phosphonate moieties yielded potent and selective PAPP-A inhibitors. Cryo-electron microscopy revealed the molecular basis of inhibition and key active-site interactions. The compounds inhibited cleavage of IGFBP-4 and IGFBP-5, two physiological PAPP-A substrates. They suppressed IGF-1 signaling in A549 lung cancer cells, with efficacy comparable to the endogenous PAPP-A inhibitor stanniocalcin-1. They also inhibited Papp-aa-dependent IGF signaling and cell proliferation in wild-type and stc1a-/- mutant zebrafish in vivo in a dose-dependent manner. Together, these findings establish the first class of synthetic, selective PAPP-A inhibitors capable of modulating IGF signaling. They provide new chemical tools for studying and modulating pathological IGF signaling and offer new directions for developing PAPP-A-targeted therapeutic strategies.

Matching journals

The top 6 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.