Biomedical subjects

Chenzhong Liao

Publications and source records attributed to Chenzhong Liao.

6 recordsLinked to original sources

PubMed2006

Biotinylated biphenyl ketone-containing 2,4-dioxobutanoic acids designed as HIV-1 integrase photoaffinity ligands.

The diketo acid (DKA) class of HIV-1 integrase inhibitors are thought to function by chelating divalent metal ions within the enzyme catalytic center. However, differences in mutations conferring resistance among sub-families of DKA inhibitors suggest that multiple binding orientations may exist. In order to facilitate identification of DKA-binding sites, biotin-tagged biphenyl ketone-containing 2,4-dioxobutanoic acids were prepared as DKA photoaffinity probes. Introduction of biotin was obtained by means of Huisgen [3+2] cycloaddition 'click chemistry.' Two photoprobes, 5a and 5b, were prepared bearing short and long linker segments, respectively, between the biotin and DKA nucleus. The greatest inhibitory potency was shown by 5b, which inhibited 3'-processing and strand transfer reactions with IC50 values of > 333 microM and 12.4 microM, respectively. In cross-linking assays designed to measure disruption of substrate DNA binding, the photoprobes behaved similarly to a reference DKA inhibitor. Analogues 5a and 5b represent novel photoaffinity ligands, which may be useful in clarifying the HIV-1 binding interactions of DKA inhibitors.

Acetoacetates↗

PubMed2005

Construction of a virtual combinatorial library using SMILES strings to discover potential structure-diverse PPAR modulators.

Based on the structural characters of PPAR modulators, a virtual combinatorial library containing 1226,625 compounds was constructed using SMILES strings. Selected ADME filters were employed to compel compounds having poor drug-like properties from this library. This library was converted to sdf and mol2 files by CONCORD 4.0, and was then docked to PPARgamma by DOCK 4.0 to identify new chemical entities that may be potential drug leads against type 2 diabetes and other metabolic diseases. The method to construct virtual combinatorial library using SMILES strings was further visualized by Visual Basic.net that can facilitate the needs of generating other type virtual combinatorial libraries.

Combinatorial Chemistry Techniques↗

PubMed2004

Design, synthesis, and evaluation of a new class of noncyclic 1,3-dicarbonyl compounds as PPARalpha selective activators.

Lipid accumulation in nonadipose tissues is increasingly linked to the development of type 2 diabetes in obese individuals. We report here the design, synthesis, and evaluation of a series of novel PPARalpha selective activators containing 1,3-dicarbonyl moieties. Structure-activity relationship studies led to the identification of PPARalpha selective activators (compounds 10, 14, 17, 18, and 21) with stronger potency and efficacy to activate PPARalpha over PPARgamma and PPARdelta. Experiments in vivo showed that compounds 10, 14, and 17 had blood glucose lowering effect in diabetic db/db mouse model after two weeks oral dosing. The data strongly support further testing of these lead compounds in other relevant disease animal models to evaluate their potential therapeutic benefits.

Adipose Tissue↗

PubMed2004

3D QSAR studies on peroxisome proliferator-activated receptor gamma agonists using CoMFA and CoMSIA.

The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure-activity relationship (3D QSAR) based on existing agonists of PPARgamma (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r2 and cross-validated coefficient (q2) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPARgamma, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPARgamma.

Computer Simulation↗

PubMed2004

Quantitative structure-activity relationship study of histone deacetylase inhibitors.

Histone deacetylases (HDACs) play a critical role in gene transcription and have become a novel target for the discovery of drugs against cancer and other diseases. During the past several years there have been extensive efforts in the identification and optimization of histone deacetylase inhibitors (HDACIs) as novel anticancer drugs. Here we report a comprehensive quantitative structure-activity relationship (QSAR) study of HDACIs in the hope of identifying the structural determinants for anticancer activity. We have identified, collected, and verified the structural and biological activity data for 124 compounds from various literature sources and performed an extensive QSAR study on this comprehensive data set by using various QSAR and classification methods. A highly predictive QSAR model with R(2) of 0.76 and leave-one-out cross-validated R(2) of 0.73 was obtained. The overall rate of cross-validated correct prediction of the classification model is around 92%. The QSAR and classification models provided direct guidance to our internal programs of identifying and optimizing HDAC inhibitors. Limitations of the models were also discussed.

Animals↗

PubMedDate not supplied

Eigenvalue analysis of peroxisome proliferator-activated receptor gamma agonists.

Eigenvalue analysis (EVA) was conducted on a series of potent agonists of peroxisome proliferator-activated receptor gamma (PPARgamma). Predictive EVA quantitative structure-activity relationship (QSAR) models were established using the SYBYL package, which had conventional r2 and cross-validated coefficient (q2) values up to 0.920 and 0.587 for the AM1 method and 0.863 and 0.586 for the PM3 method, respectively. These models were validated by a test set containing 18 compounds. The capability to predict by these two models for PPARgamma agonists, with the best predictive r2pred value of 0.614 for AM1 and 0.822 for PM3 methods, set a successful example for applying a similar approach in building QSAR models for PPARalpha and -delta that could potentially offer a new opportunity in the design of novel PPAR modulators.

Models, Chemical↗