Design and synthesis of novel fluorene derivatives as inhibitors of pyruvate dehydrogenase kinase.

Activation of pyruvate dehydrogenase (PDH) by inhibition of pyruvate dehydrogenase kinase (PDHK) has the potential for the treatment of diabetes mellitus and its complications, caused by the malfunction of the glycolytic system and glucose oxidation. In this paper, we describe the identification of novel PDHK inhibitors with a fluorene structure. High-throughput screening using our in-house library provided compound 6 as a weak inhibitor that occupied the allosteric lipoyl group binding site in PDHK2. Structure-based drug design (SBDD) while addressing physicochemical properties succeeded in boosting inhibitory activity approximately 700-fold. Thus obtained compound 32 showed favorable pharmacokinetics profiles supported by high membrane permeability and metabolic stability, and exhibited activation of PDH in rat livers and a glucose lowering effect in Zucker fatty rats.

Optimization Efforts for Identification of Novel Highly Potent Keap1-Nrf2 Protein-Protein Interaction Inhibitors.

In research focused on protein-protein interaction (PPI) inhibitors, the optimization process to achieve both high inhibitory activity and favorable physicochemical properties remains challenging. Our previous study reported the discovery of novel and bioavailable Keap1-Nrf2 PPI inhibitor 8 which exhibited moderate in vivo activity in rats. In this work, we present our subsequent efforts to optimize this compound. Two distinct approaches were employed, targeting high energy water molecules and Ser602 as "hot spots" from the anchor with good aqueous solubility, metabolic stability, and membrane permeability. Through ligand efficiency (LE)-guided exploration, we identified two novel inhibitors 22 and 33 with good pharmacokinetics (PK) profiles and more potent in vivo activities, which appear to be promising chemical probes among the existing inhibitors.

Methyl and Fluorine Effects in Novel Orally Bioavailable Keap1-Nrf2 PPI Inhibitor.

Oxidative stress is one of the causes of progression of chronic kidney disease (CKD). Activation of the antioxidant protein regulator Nrf2 by inhibition of the Keap1-Nrf2 protein-protein interaction (PPI) is of interest as a potential treatment for CKD. We report the identification of the novel and weak PPI inhibitor 7 with good physical properties by a high throughput screening (HTS) campaign, followed by structural and computational analysis. The installation of only methyl and fluorine groups successfully provided the lead compound 25, which showed more than 400-fold stronger activity. Furthermore, these dramatic substituent effects can be explained by the analysis of using isothermal titration calorimetry (ITC). Thus, the resulting 25, which exhibited high oral absorption and durability, would be a CKD therapeutic agent because of the dose-dependent manner for up-regulation of the antioxidant protein heme oxigenase-1 (HO-1) in rat kidneys.

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important factors for evaluating pharmacokinetic properties, and many previous studies have attempted to use computational methods to extrapolate these values from nonclinical laboratory animal models to human subjects. However, it is difficult to obtain sufficient, comprehensive experimental data from these animal models, and many studies are missing critical values. This means that studies using nonclinical data as explanatory variables can only apply a small number of compounds to their model training. In this study, we perform missing-value imputation and feature selection on nonclinical data to increase the number of training compounds and nonclinical datasets available for these kinds of studies. We could obtain novel models for total body clearance (CLtot) and steady-state Vd (Vdss) (CLtot: geometric mean fold error [GMFE], 1.92; percentage within 2-fold error, 66.5%; Vdss: GMFE, 1.64; percentage within 2-fold error, 71.1%). These accuracies were comparable to the conventional animal scale-up models. Then, this method differs from animal scale-up methods because it does not require animal experiments, which continue to become more strictly regulated as time passes.

Structure-based drug design of novel and highly potent pyruvate dehydrogenase kinase inhibitors.

Pyruvate dehydrogenase kinases (PDHKs) are fascinating drug targets for numerous diseases, including diabetes and cancers. In this report, we describe the result of our structure-based drug design from tricyclic lead compounds that led to the discovery of highly potent PDHK2 and PDHK4 dual inhibitors in enzymatic assay. The C3-position of the tricyclic core was explored, and the PDHK2 X-ray structure with a representative compound revealed a novel ATP lid conformation in which the phenyl ring of Phe326 mediated the interaction of the Arg258 sidechain and the compound. Compounds with amide linkers were designed to release the ATP lid by forming an intramolecular pi-pi interaction, and these compounds showed single-digit nM IC50 values in an enzymatic assay. We also explored the C4-position of the tricyclic core to reproduce the interaction observed with the C3-position substitution, and the pyrrolidine compound showed the same level of IC50 values. By optimizing an interaction with the Asn255 sidechain through a docking simulation, compounds with 2-carboxy pyrrole moiety also showed single-digit nM IC50 values without having a cation-pi interaction with the Arg258 sidechain.

Prediction of Total Drug Clearance in Humans Using Animal Data: Proposal of a Multimodal Learning Method Based on Deep Learning.

Research into pharmacokinetics plays an important role in the development process of new drugs. Accurately predicting human pharmacokinetic parameters from preclinical data can increase the success rate of clinical trials. Since clearance (CL) which indicates the capacity of the entire body to process a drug is one of the most important parameters, many methods have been developed. However, there are still rooms to be improved for practical use in drug discovery research; "improving CL prediction accuracy" and "understanding the chemical structure of compounds in terms of pharmacokinetics". To improve those, this research proposes a multimodal learning method based on deep learning that takes not only the chemical structure of a drug but also rat CL as inputs. Good results were obtained compared with the conventional animal scale-up method; the geometric mean fold error was 2.68 and the proportion of compounds with prediction errors of 2-fold or less was 48.5%. Furthermore, it was found to be possible to infer the partial structure useful for CL prediction by a structure contributing factor inference method. The validity of these results of structural interpretation of metabolic stability was confirmed by chemists.

Quinolone carboxylic acids as a novel monoketo acid class of human immunodeficiency virus type 1 integrase inhibitors.

Human immunodeficiency virus type 1 (HIV-1) integrase is a crucial target for antiretroviral drugs, and several keto-enol acid class (often referred to as diketo acid class) inhibitors have clinically exhibited marked antiretroviral activity. Here, we show the synthesis and the detailed structure-activity relationship of the quinolone carboxylic acids as a novel monoketo acid class of integrase inhibitors. 6-(3-Chloro-2-fluorobenzyl)-1-((2S)-1-hydroxy-3,3-dimethylbutan-2-yl)-7-methoxy-4-oxo-1,4-dihydroquinoline-3-carboxylic acid 51, which showed an IC50 of 5.8 nM in the strand transfer assay and an ED50 of 0.6 nM in the antiviral assay, and 6-(3-chloro-2-fluorobenzyl)-1-((2S)-1-hydroxy-3-methylbutan-2-yl)-7-methoxy-4-oxo-1,4-dihydroquinoline-3-carboxylic acid 49, which had an IC50 of 7.2 nM and an ED50 of 0.9 nM, were the most potent compounds in this class. The monoketo acid 49 was much more potent at inhibiting integrase-catalyzed strand transfer processes than 3'-processing reactions, as is the case with the keto-enol acids. Elvitegravir 49 was chosen as a candidate for further studies and is currently in phase 3 clinical trials.

Novel HIV-1 integrase inhibitors derived from quinolone antibiotics.

The viral enzyme integrase is essential for the replication of human immunodeficiency virus type 1 (HIV-1) and represents a remaining target for antiretroviral drugs. Here, we describe the modification of a quinolone antibiotic to produce the novel integrase inhibitor JTK-303 (GS 9137) that blocks strand transfer by the viral enzyme. It shares the core structure of quinolone antibiotics, exhibits an IC50 of 7.2 nM in the strand transfer assay, and shows an EC50 of 0.9 nM in an acute HIV-1 infection assay.