Study on the Mechanism of Lipid Peroxidation Induced by Carbonate Radicals.

Based on the reported research, hydroxyl radicals can be rapidly transformed into carbonate radicals in the carbonate-bicarbonate buffering system in vivo. Many of the processes considered to be initiated by hydroxyl radicals may be caused by carbonate radicals, which indicates that lipid peroxidation initiated by hydroxyl radicals can also be caused by carbonate radicals. To date, theoretical research on reactions of hydrogen abstraction from and radical addition to polyunsaturated fatty acids (PUFAs) of carbonate radicals has not been carried out systematically. This paper employs (3Z,6Z)-nona-3,6-diene (NDE) as a model for polyunsaturated fatty acids (PUFAs). Density functional theory (DFT) with the CAM-B3LYP method at the 6-311+g(d,p) level was used to calculate the differences in reactivity of carbonate radicals abstracting hydrogen from different positions of NDE and their addition to the double bonds of NDE under lipid solvent conditions with a dielectric constant of 4.0 (CPCM model). Grimme's empirical dispersion correction was taken into account through the D3 scheme. The energy barrier, reaction rate constants, internal energy, enthalpy and Gibbs free energy changes in these reactions were calculated With zero-point vibrational energy (ZPVE) corrections. The results indicated that carbonate radicals initiate lipid peroxidation primarily through hydrogen abstraction from diallyl carbon atoms. The reaction of hydrogen abstraction from diallyl carbon atoms exhibits the highest reaction rate, with a reaction rate constant approximately 43-fold greater than the second-ranked hydrogen abstraction from allyl carbon atoms. This process has the lowest energy barrier, internal energy, enthalpy, and Gibbs free energy changes, indicating that it is also the most spontaneous process.

QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites.

Malaria, caused by Plasmodium parasites, results in >400,000 deaths annually. There is no effective vaccine, and new drugs with novel modes of action are needed because of increasing parasite resistance to current antimalarials. Histone deacetylases (HDACs) are epigenetic regulatory enzymes that catalyze post-translational protein deacetylation and are promising malaria drug targets. Here, we describe quantitative structure-activity relationship models to predict the antiplasmodial activity of hydroxamate-based HDAC inhibitors. The models incorporate P. falciparum in vitro activity data for 385 compounds containing a hydroxamic acid and were subject to internal and external validation. When used to screen 22 new hydroxamate-based HDAC inhibitors for antiplasmodial activity, model A7 (external accuracy 91%) identified three hits that were subsequently verified as having potent in vitro activity against P. falciparum parasites (IC50 = 6, 71, and 84 nM), with 8 to 51-fold selectivity for P. falciparum versus human cells.

Targeting Class IIa HDACs: Insights from Phenotypes and Inhibitors.

This review summarizes key literature defining the phenotypes of individual class IIa HDAC proteins and compounds that selectively target their enzymatic catalytic domain (CD). The focus is on the effects of class IIa HDACs in physiological and pathological conditions, both in vitro and in vivo, and on their mode of action in regulating genes, upstream proteins and signaling pathways. Phenotype studies further demonstrate either beneficial or detrimental effects of silencing selected class IIa HDACs or their enzymatic properties. We also summarize the knowledge gained from structure-activity relationships of CD inhibitors as well as molecular mechanisms underpinning isozyme selectivity where crystal structures or modelling studies are available. Given that the number of genes affected by silencing class IIa HDACs is much smaller than class I, the role of gene regulation of class IIa HDACs could be much more selective. Since class IIa HDACs have restricted tissue distributions and multiple functions independent of their CD, targeting the CD of class IIa HDACs could lead to more selective therapeutic agents with significantly fewer side-effects than other HDAC ligands.

Quinazolinone derivatives as inhibitors of homologous recombinase RAD51.

RAD51 is a vital component of the homologous recombination DNA repair pathway and is overexpressed in drug-resistant cancers, including aggressive triple negative breast cancer (TNBC). A proposed strategy for improving therapeutic outcomes for patients is through small molecule inhibition of RAD51, thereby sensitizing tumor cells to DNA damaging irradiation and/or chemotherapy. Here we report structure-activity relationships for a library of quinazolinone derivatives. A novel RAD51 inhibitor (17) displays up to 15-fold enhanced inhibition of cell growth in a panel of TNBC cell lines compared to compound B02, and approximately 2-fold increased inhibition of irradiation-induced RAD51 foci formation. Additionally, compound 17 significantly inhibits TNBC cell sensitivity to DNA damage, implying a potentially targeted therapy for cancer treatment.

Docking Analysis and Multidimensional Hybrid QSAR Model of 1,4-Benzodiazepine-2,5-Diones as HDM2 Antagonists.

The inhibitors of p53-HDM2 interaction are attractive molecules for the treatment of wild-type p53 tumors. In order to search more potent HDM2 inhibitors, docking operation with CDOCKER protocol in Discovery Studio 2.1 (DS2.1) and multidimensional hybrid quantitative structure-activity relationship (QSAR) studies through the physiochemical properties obtained from DS2.1 and E-Dragon 1.0 as descriptors, have been performed on 59 1,4-benzodiazepine- 2,5-diones which have p53-HDM2 interaction inhibitory activities. The docking results indicate that π-π interaction between the imidazole group in HIS96 and the aryl ring at 4-N of 1,4-benzodiazepine-2,5-dione may be one of the key factors for the combination of ligands with HDM2. Two QSAR models were obtained using genetic function approximation (GFA) and genetic partial least squares (G/PLS) based on the descriptors obtained from DS2.1 and E-dragon 1.0, respectively. The best model can explain 85.5% of the variance (R (2) adj ) while it could predict 81.7% of the variance (R (2) cv ). With this model, the bioactivities of some new compounds were predicted.