Calcium oxalate (CaOx) crystal-induced nephropathies comprise a range of kidney disorders, for which there are no efficient pharmacological treatments. Although CaOx crystallization inhibitors have been suggested as a therapeutic modality already decades ago, limited progress has been made in the discovery of potent molecules with efficacy in animal disease models. Herein, an image-based machine learning approach to systematically screen chemically modified myo-inositol hexakisphosphate (IP6) analogues is utilized, which enables the identification of a highly active divalent inositol phosphate molecule. To date, this is the first molecule shown to completely inhibit the crystallization process in the nanomolar range, reduce crystal-cell interactions, thereby preventing CaOx-induced transcriptomic changes, and decrease renal CaOx deposition and kidney injury in a mouse model of hyperoxaluria. In conclusion, IP6 analogues based on such a scaffold may represent a new treatment option for CaOx nephropathies.
Keywords: calcium oxalate crystallization inhibitors; chronic kidney disease; image‐based drug screening; kidney calcification; kidney stones.
© 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim.