Arginase is a promising immuno-oncology target that can restore the innate immune response. However, it's highly polar active site often requires potent inhibitors to mimic amino acids, leading to poor passive permeability and low oral exposure. Using structure-based drug design, we discovered a novel proline-based arginase inhibitor () that was potent but had low oral bioavailability in rat.
View Article and Find Full Text PDFArginase has long been a target of interest in immuno-oncology, but discovering an orally bioavailable inhibitor is severely constrained by the requisite boronic acid pharmacophore. We began our drug discovery campaign by building off the β-position of the literature inhibitor ABH (). A divergent synthesis with an Ireland-Claisen rearrangement as the key step allowed access to numerous compounds, some of which we crystallized in the active site of arginase 2.
View Article and Find Full Text PDFThe optimization of an allosteric fragment, discovered by differential scanning fluorimetry, to an in vivo MAT2a tool inhibitor is discussed. The structure-based drug discovery approach, aided by relative binding free energy calculations, resulted in AZ'9567 (), a potent inhibitor in vitro with excellent preclinical pharmacokinetic properties. This tool showed a selective antiproliferative effect on methylthioadenosine phosphorylase (MTAP) KO cells, both in vitro and in vivo, providing further evidence to support the utility of MAT2a inhibitors as potential anticancer therapies for MTAP-deficient tumors.
View Article and Find Full Text PDFIn early drug discovery, hydrolytic chemical stability is routinely assessed to ensure future developability of quality compounds and stability in in vitro test environments. When conducting high-throughput hydrolytic stability analyses as part of the compound risk assessment, aggressive conditions are typically applied to allow for faster screening. However, it can be challenging to extrapolate the real stability risk and to rank compounds due to over-estimating risk based on aggressive conditions and the narrow discriminative window.
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