Flexible docking of pyridinone derivatives into the non-nucleoside inhibitor binding site of HIV-1 reverse transcriptase

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery. Herein, we survey advances on key topics on PLM with emphasis on questions often raised regarding the outcomes of the property landscape studies. We also emphasize on concepts of PLM that are being extended to other experimental areas beyond drug discovery. Topics discussed in this paper include applications of PLM to further characterize protein-ligand interactions, the utility of PLM as a quantitative and descriptive approach, and the statistical validation of property cliffs.

Advances in the development of pyridinone derivatives as non-nucleoside reverse transcriptase inhibitors

Medicinal chemistry, computational design and biological screening have advanced pyridin-2(1H)-one derivatives as a promising class of non-nucleoside reverse transcriptase inhibitors for the treatment of HIV/AIDS.

Design and synthesis of novel 5,6-disubstituted pyridine-2,3-dione-3-thiosemicarbazone derivatives as potential anticancer agents

A series of 5,6-disubstituted pyridine-2,3-dione-3-thiosemicarbazone derivatives(2a-2n) and 5,6-disubstituted pyridine-2,3-dione S-benzyl-3-thiosemicarbazones(3a-3g) were synthesized starting from 2,3-dihydroxypyridine via oxidation-Michael additions, condensations and nucleophilic substitutions. The structures of the compounds were established by IR, (1)H NMR, (13)C NMR, and HRMS. All newly synthesized compounds were screened for their anticancer activity against Breast cancer (MCF-7), Colon cancer (HCT-116) and hepatocellular cancer (BEL7402) cell lines. Bioassay results indicated that most of the prepared compounds exhibited cytotoxicity against various cancer cells in vitro. Some of the compounds exhibited promising antiproliferative activity, which were comparable to the positive control (5-fluorouracil). The structure-activity relationship was discussed.

Study on the interaction between HIV reverse transcriptase and its non-nucleoside inhibitor nevirapine by capillary electrophoresis

The HIV reverse transcriptase (RT) is an important antiviral target for the chemotherapy of AIDS because of its key role in virus replication. Nevirapine is a first generation of non-nucleoside reverse transcriptase inhibitors (NNRTIs), which is usually used for the therapy of AIDS. In this study, a high-performance analytical method based on capillary electrophoresis (CE) to investigate interactions between HIV RT and nevirapine was developed. Samples containing HIV RT and nevirapine at various ratios were incubated at 37 degrees C for 45 min and then separated by CE with Tris-acetate buffer at pH 7.3 containing 0.15% SDS. Both qualitative and quantitative characterizations of the binding were determined by CE for the first time. The binding constants of the interactions between HIV RT and nevirapine were calculated as (3.25+/-0.16)x10(4) and (1.25+/-0.07)x10(2) M(-1) by Scatchard analysis. HIV RT and nevirapine have two binding sites. The presented methodology should be generally applicable to study the interactions between HIV RT and nevirapine quantitatively and qualitatively.

Quantitative Structure–activity Relationship Analysis of Pyridinone HIV-1 Reverse Transcriptase Inhibitors using the k Nearest Neighbor Method and QSAR-based Database Mining

We have developed quantitative structure-activity relationship (QSAR) models for 44 non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs) of the pyridinone derivative type. The k nearest neighbor (kNN) variable selection approach was used. This method utilizes multiple descriptors such as molecular connectivity indices, which are derived from two-dimensional molecular topology. The modeling process entailed extensive validation including the randomization of the target property (Y-randomization) test and the division of the dataset into multiple training and test sets to establish the external predictive power of the training set models. QSAR models with high internal and external accuracy were generated, with leave-one-out cross-validated R2 (q2) values ranging between 0.5 and 0.8 for the training sets and R2 values exceeding 0.6 for the test sets. The best models with the highest internal and external predictive power were used to search the National Cancer Institute database. Derivatives of the pyrazolo[3,4-d]pyrimidine and phenothiazine type were identified as promising novel NNRTIs leads. Several candidates were docked into the binding pocket of nevirapine with the AutoDock (version 3.0) software. Docking results suggested that these types of compounds could be binding in the NNRTI binding site in a similar mode to a known non-nucleoside inhibitor nevirapine.