Synthesis and biological activities of vitamin D-like inhibitors of CYP24 hydroxylase.

Selective inhibitors of CYP24A1 represent an important synthetic target in a search for novel vitamin D compounds of therapeutic value. In the present work, we show the synthesis and biological properties of two novel side chain modified 2-methylene-19-nor-1,25(OH)(2)D(3) analogs, the 22-imidazole-1-yl derivative 2 (VIMI) and the 25-N-cyclopropylamine compound 3 (CPA1), which were efficiently prepared in convergent syntheses utilizing the Lythgoe type Horner-Wittig olefination reaction. When tested in a cell-free assay, both compounds were found to be potent competitive inhibitors of CYP24A1, with the cyclopropylamine analog 3 exhibiting an 80-1 selective inhibition of CYP24A1 over CYP27B1.

Optimizing QSAR models for predicting ligand binding to the drug-metabolizing cytochrome P450 isoenzyme CYP2D6.

The cytochrome P450 isozyme CYP2D6 binds a large variety of drugs, oxidizing many of them, and plays a crucial role in establishing in vivo drug levels, especially in multidrug regimens. The current study aimed to develop reliable predictive models for estimating the CYP2D6 inhibition properties of drug candidates. Quantitative structure-activity relationship (QSAR) studies utilizing 51 known CYP2D6 inhibitors were carried out.

Quantitative structure-activity relationship models for predicting biological properties, developed by combining structure- and ligand-based approaches: an application to the human ether-a-go-go-related gene potassium channel inhibition.

A strategy for developing accurate quantitative structure-activity relationship models enabling predictions of biological properties, when suitable knowledge concerning both ligands and biological target is available, was tested on a data set where molecules are characterized by high structural diversity. Such a strategy was applied to human ether-a-go-go-related gene K(+) channel inhibition and consists of a combination of ligand- and structure-based approaches, which can be carried out whenever the three-dimensional structure of the target macromolecule is known or may be modeled with good accuracy. Molecular conformations of ligands were obtained by means of molecular docking, performed in a previously built theoretical model of the channel pore, so that descriptors depending upon the three-dimensional molecular structure were properly computed.

Development of QSAR models for predicting hepatocarcinogenic toxicity of chemicals.

A dataset comprising 55 chemicals with hepatocarcinogenic potency indices was collected from the Carcinogenic Potency Database with the aim of developing QSAR models enabling prediction of the above unwanted property for New Chemical Entities. The dataset was rationally split into training and test sets by means of a sphere-exclusion type algorithm. Among the many algorithms explored to search regression models, only a Support Vector Machine (SVM) method led to a QSAR model, which was proved to pass rigorous validation criteria, in accordance with the OECD guidelines.

Molecular modelling of human CYP2D6 and molecular docking of a series of ajmalicine- and quinidine-like inhibitors.

3D-models were created and refined for CYP2D6 and for its complexes with ajmalicine and quinidine. The influence of the conformation of the enzyme active site on its interaction with ligands was evaluated by performing three series of molecular docking on selected ajmalicine- and quinidine-like inhibitors. The results suggested that the experimental binding values of ajmalicine- and quinidine-like inhibitors better fit with the energetic terms derived from their interaction with structures of CYP2D6 obtained by, respectively, optimizing the ajmalicine/CYP2D6 and the quinidine/CYP2D6 complexes, rather than exploiting the 3D-strucure of the enzyme not subjected to a ligand-induced conformational change.

Prediction of hERG potassium channel affinity by the CODESSA approach.

The problem of predicting torsadogenic cardiotoxicity of drugs is afforded in this work. QSAR studies on a series of molecules, acting as hERG K+ channel blockers, were carried out for this purpose by using the CODESSA program. Molecules belonging to the analyzed dataset are characterized by different therapeutic targets and by high molecular diversity.