Ferroptosis is a non-apoptotic form of regulated cell death caused by the failure of the glutathione-dependent lipid-peroxide-scavenging network. FINO2 is an endoperoxide-containing 1,2-dioxolane that can initiate ferroptosis selectively in engineered cancer cells. We investigated the mechanism and structural features necessary for ferroptosis initiation by FINO2. We found that FINO2 requires both an endoperoxide moiety and a nearby hydroxyl head group to initiate ferroptosis. In contrast to previously described ferroptosis inducers, FINO2 does not inhibit system xc− or directly target GPX4, as do erastin and RSL3, respectively, or deplete GPX4 protein, as does FIN56. Instead, FINO2 causes both indirect loss of GPX4 enzymatic function and directly oxidizes iron, ultimately causing widespread lipid peroxidation. These findings suggest that endoperoxides such as FINO2 can initiate a multi-pronged mechanism of ferroptosis.

SUMMARY Design of small molecules that disrupt protein-protein interactions, including the interaction of RAS proteins and their effectors, have potential use as chemical probes and therapeutic agents. We describe here the synthesis and testing of potential small molecule pan-RAS ligands, which were designed to interact with adjacent sites on the surface of oncogenic KRAS. One compound, termed 3144, was found to bind to RAS proteins using microscale thermophoresis, nuclear magnetic resonance spectroscopy and isothermal titration calorimetry, and to exhibit lethality in cells partially dependent on expression of RAS proteins. This compound was metabolically stable in liver microsomes and displayed anti-tumor activity in xenograft mouse cancer models. These findings suggest that pan-RAS inhibition may be an effective therapeutic strategy for some cancers, and that structure-based design of small molecules targeting multiple adjacent sites to create multivalent inhibitors may be effective for some proteins.

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC 50 < 100 nM, and four unique cores with a predicted IC 50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns. File list (3) download file view on ChemRxiv Pathfinder-manuscript.pdf (3.52 MiB) download file view on ChemRxiv PathFinder-manuscript-SI.pdf (4.09 MiB) download file view on ChemRxiv TOC-graphic.png (2.65 MiB)

Carbon-carbon bond formation is the basis for the biogenesis of nature's essential molecules. Consequently, it lies at the heart of the chemical sciences. Chiral catalysts have been developed for asymmetric C-C bond formation to yield single enantiomers from several organometallic reagents. Remarkably, for extremely reactive organolithium compounds, which are among the most broadly used reagents in chemical synthesis, a general catalytic methodology for enantioselective C-C formation has proven elusive, until now. Here, we report a copper-based chiral catalytic system that allows carbon-carbon bond formation via allylic alkylation with alkyllithium reagents, with extremely high enantioselectivities and able to tolerate several functional groups. We have found that both the solvent used and the structure of the active chiral catalyst are the most critical factors in achieving successful asymmetric catalysis with alkyllithium reagents. The active form of the chiral catalyst has been identified through spectroscopic studies as a diphosphine copper monoalkyl species.

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC₅₀ < 100 nM, and four unique cores with a predicted IC₅₀ < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.