Development of a tetrahydroindazolone-based HDAC6 inhibitor with in-vivo anti-arthritic activity.

Histone deacetylase 6 (HDAC6) induces the expression of pro-inflammatory cytokines in macrophages; therefore, HDAC inhibitors may be beneficial for the treatment of macrophage-associated immune disorders and chronic inflammatory diseases, including atherosclerosis and rheumatoid arthritis. Structure-activity relationship studies were conducted on various phenyl hydroxamate HDAC6 inhibitors with indolone/indazolone-based bi- or tricyclic ring moieties as the cap group aiming to develop novel anti-arthritic drug candidates. Several compounds exhibited nanomolar activity and HDAC6 selectivity greater than 500-fold over HDAC1. Compound 21, a derivative with the tetrahydroindazolone cap group, is a potent HDAC6 inhibitor with an IC50 of 18 nM and 217-fold selectivity over HDAC1 and showed favorable oral bioavailability in animals. Compound 21 increases the acetylation level of tubulin without affecting histone acetylation in cutaneous T-cell lymphoma cells and inhibits TNF-α secretion in LPS-stimulated macrophage cells. The anti-arthritic effects of compound 21 were evaluated using a rat adjuvant-induced arthritis (AIA) model. Treatment with compound 21 significantly reduced the arthritis score, and combination treatment with methotrexate showed a synergistic effect in AIA models. We identified a novel HDAC6 inhibitor, compound 21, with excellent in vivo anti-arthritic efficacy, which can lead to the development of oral anti-arthritic drugs.

Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data.

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

Computer-aided identification of new histone deacetylase 6 selective inhibitor with anti-sepsis activity.

Histone deacetylase (HDAC) inhibitors have been recognized as promising approaches to the treatment of various human diseases including cancer, inflammation, neurodegenerative diseases, and metabolic disorders. Several pan-HDAC inhibitors are currently approved only as anticancer drugs. Interestingly, SAHA (vorinostat), one of clinically available pan-HDAC inhibitors, shows an anti-inflammatory effect at concentrations lower than those required for inhibition of tumor cell growth. It was also reported that HDAC6 selective inhibitor tubastatin A has anti-inflammatory and anti-rheumatic effect. In our efforts to develop novel HDAC inhibitors, we rationally designed various HDAC inhibitors based on the structures of two hit compounds identified by virtual screening of chemical database. Among them, 9a ((E)-N-hydroxy-4-(2-styrylthiazol-4-yl)butanamide) was identified as a HDAC6 selective inhibitor (IC50 values of 0.199 µM for HDAC6 versus 13.8 µM for HDAC1), and it did not show significant cytotoxicity against HeLa cells. In vivo biological evaluation of 9a was conducted on a lipopolysaccharide (LPS)-induced mouse model of sepsis. The compound 9a significantly improved 40% survival rate (P = 0.0483), and suppressed the LPS-induced increase of TNF-α and IL-6 mRNA expression in the liver of mice. Our study identified novel HDAC6 selective inhibitor 9a, which may serve as a potential lead for the development of anti-inflammatory or anti-sepsis agents.

Synthesis and antibacterial activity of 1 beta-methyl-2-(5-substituted imidazolino pyrrolidin-3-ylthio)carbapenem derivatives.

The synthesis of a new series of 1 beta-methylcarbapenems containing a substituted imidazolino pyrrolidine moiety is described. Their in vitro antibacterial activities against both Gram-positive and Gram-negative bacteria were tested and the effect of the substituent on the imidazoline ring was investigated. Compound 13 g which has a N-sulfonylmethyl substituted imidazoline moiety showed the most potent antibacterial activity.