Radiolabeled inhibitors as probes for imaging mutant IDH1 expression in gliomas: Synthesis and preliminary evaluation of labeled butyl-phenyl sulfonamide analogs.

INTRODUCTION: Malignant gliomas frequently harbor mutations in the isocitrate dehydrogenase 1 (IDH1) gene. Studies suggest that IDH mutation contributes to tumor pathogenesis through mechanisms that are mediated by the neomorphic metabolite of the mutant IDH1 enzyme, 2-hydroxyglutarate (2-HG). The aim of this work was to synthesize and evaluate radiolabeled compounds that bind to the mutant IDH1 enzyme with the goal of enabling noninvasive imaging of mutant IDH1 expression in gliomas by positron emission tomography (PET). METHODS: A small library of nonradioactive analogs were designed and synthesized based on the chemical structure of reported butyl-phenyl sulfonamide inhibitors of mutant IDH1. Enzyme inhibition assays were conducted using purified mutant IDH1 enzyme, IDH1-R132H, to determine the IC50 and the maximal inhibitory efficiency of the synthesized compounds. Selected compounds, 1 and 4, were labeled with radioiodine ((125)I) and/or (18)F using bromo- and phenol precursors, respectively. In vivo behavior of the labeled inhibitors was studied by conducting tissue distribution studies with [(125)I]1 in normal mice. Cell uptake studies were conducted using an isogenic astrocytoma cell line that carried a native IDH1-R132H mutation to evaluate the potential uptake of the labeled inhibitors in IDH1-mutated tumor cells. RESULTS: Enzyme inhibition assays showed good inhibitory potency for compounds that have iodine or a fluoroethoxy substituent at the ortho position of the phenyl ring in compounds 1 and 4 with IC50 values of 1.7 μM and 2.3 μM, respectively. Compounds 1 and 4 inhibited mutant IDH1 activity and decreased the production of 2-HG in an IDH1-mutated astrocytoma cell line. Radiolabeling of 1 and 4 was achieved with an average radiochemical yield of 56.6 ± 20.1% for [(125)I]1 (n = 4) and 67.5 ± 6.6% for [(18)F]4 (n = 3). [(125)I]1 exhibited favorable biodistribution characteristics in normal mice, with rapid clearance from the blood and elimination via the hepatobiliary system by 4 h after injection. The uptake of [(125)I]1 in tumor cells positive for IDH1-R132H was significantly higher compared to isogenic WT-IDH1 controls, with a maximal uptake ratio of 1.67 at 3 h post injection. Co-incubation of the labeled inhibitors with the corresponding nonradioactive analogs, and decreasing the normal concentrations of FBS (10%) in the incubation media substantially increased the uptake of the labeled inhibitors in both the IDH1-mutant and WT-IDH1 tumor cell lines, suggesting significant non-specific binding of the synthesized labeled butyl-phenyl sulfonamide inhibitors. CONCLUSIONS: These data demonstrate the feasibility of developing radiolabeled probes for the mutant IDH1 enzyme based on enzyme inhibitors. Further optimization of the labeled inhibitors by modifying the chemical structure to decrease the lipophilicity and to increase potency may yield compounds with improved characteristics as probes for imaging mutant IDH1 expression in tumors.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.