ABSTRACT
Mutations at the arginine residue (R132) in isocitrate dehydrogenase 1 (IDH1) are frequently identified in various human cancers. Inhibition of mutant IDH1 (mIDH1) with small molecules has been clinically validated as a promising therapeutic treatment for acute myeloid leukemia and multiple solid tumors. Herein, we report the discovery and optimization of a series of quinolinones to provide potent and orally bioavailable mIDH1 inhibitors with selectivity over wild-type IDH1. The X-ray structure of an early lead 24 in complex with mIDH1-R132H shows that the inhibitor unexpectedly binds to an allosteric site. Efforts to improve the in vitro and in vivo absorption, distribution, metabolism, and excretion (ADME) properties of 24 yielded a preclinical candidate 63. The detailed preclinical ADME and pharmacology studies of 63 support further development of quinolinone-based mIDH1 inhibitors as therapeutic agents in human trials.
Subject(s)
Enzyme Inhibitors/chemistry , Enzyme Inhibitors/pharmacology , Isocitrate Dehydrogenase/antagonists & inhibitors , Quinolones/chemistry , Quinolones/pharmacology , Allosteric Site/drug effects , Animals , Biological Availability , Cell Line, Tumor , Crystallography, X-Ray , Dogs , Drug Discovery , Enzyme Inhibitors/pharmacokinetics , Female , Humans , Isocitrate Dehydrogenase/chemistry , Isocitrate Dehydrogenase/genetics , Madin Darby Canine Kidney Cells , Mice , Mice, Inbred BALB C , Models, Molecular , Point Mutation , Quinolones/pharmacokineticsABSTRACT
The discovery, structure-activity relationships, and optimization of a novel class of fatty acid synthase (FASN) inhibitors is reported. High throughput screening identified a series of substituted piperazines with structural features that enable interactions with many of the potency-driving regions of the FASN KR domain binding site. Derived from this series was FT113, a compound with potent biochemical and cellular activity, which translated into excellent activity in in vivo models.
Subject(s)
Fatty Acid Synthases/antagonists & inhibitors , Piperazines/chemistry , Administration, Oral , Animals , Binding Sites , Cell Line, Tumor , Cell Proliferation/drug effects , Drug Evaluation, Preclinical , Fatty Acid Synthases/metabolism , Half-Life , Humans , Malonyl Coenzyme A/metabolism , Mice , Mice, Nude , Molecular Docking Simulation , Neoplasms/drug therapy , Neoplasms/metabolism , Piperazines/administration & dosage , Piperazines/pharmacokinetics , Piperazines/pharmacology , Protein Structure, Tertiary , Rats , Structure-Activity RelationshipABSTRACT
A protein structure-guided drug design approach was employed to develop small molecule inhibitors of the BET family of bromodomains that were distinct from the known (+)-JQ1 scaffold class. These efforts led to the identification of a series of substituted benzopiperazines with structural features that enable interactions with many of the affinity-driving regions of the bromodomain binding site. Lipophilic efficiency was a guiding principle in improving binding affinity alongside drug-like physicochemical properties that are commensurate with oral bioavailability. Derived from this series was tool compound FT001, which displayed potent biochemical and cellular activity, translating to excellent in vivo activity in a mouse xenograft model (MV-4-11).
ABSTRACT
Riverbank filtration (RBF) is a low-cost water treatment technology in which surface water contaminants are removed or degraded as the infiltrating water moves from the river/lake to the pumping wells. The removal or degradation of contaminants is a combination of physicochemical and biological processes. This paper illustrates the development and application of three types of artificial neural networks (ANNs) to estimate the effectiveness of two RBF facilities in the US. The feed-forward back-propagation network (BPN) and radial basis function network (RBFN) model prediction results produced excellent agreement with measured data at a correlation coefficient above 0.99 for filtrate water quality parameters, including temperature as well as turbidity, heterotrophic bacteria, and coliform removal. In comparison, the fuzzy inference system network (FISN) predicted only temperature and bacteria removal with reasonable accuracy. It is shown that the predictive performances of the ANNs depend on the model structure and model inputs.
