Ligand- and Structure-Based Drug Design Strategies and PPAR delta/alpha Selectivity

Peroxisome-proliferator-activated receptors are a class of nuclear receptors with three subtypes: a, ? and d. Their main function is regulating gene transcription related to lipid and carbohydrate metabolism. Currently, there are no peroxisome-proliferator-activated receptors d drugs being marketed. In this work, we studied a data set of 70 compounds with a and d activity. Three partial least square models were created, and molecular docking studies were performed to understand the main reasons for peroxisome-proliferator-activated receptors d selectivity. The obtained results showed that some molecular descriptors (log P, hydration energy, steric and polar properties) are related to the main interactions that can direct ligands to a particular peroxisome-proliferator-activated receptors subtype.

Drug-targeting in combined cancer chemotherapy: tumor growth inhibition in mice by association of paclitaxel and etoposide with a cholesterol-rich nanoemulsion

Lipid nanoemulsions (LDE) may be used as carriers of paclitaxel (PTX) and etoposide (ETP) to decrease toxicity and increase the therapeutic action of those drugs. The current study investigates the combined chemotherapy with PTX and ETP associated with LDE. Four groups of 10-20 B16F10 melanoma-bearing mice were treated with LDE-PTX and LDE-ETP in combination (LDE-PTX + ETP), commercial PTX and ETP in combination (PTX + ETP), single LDE-PTX, and single LDE-ETP. PTX and ETX doses were 9 mu mol/kg administered in three intraperitoneal injections on three alternate days. In two control groups mice were treated with saline solution or LDE alone. Tumor growth, metastasis presence, cell-cycle distribution, blood cell counts and histological data were analyzed. Toxicity of all treatments was evaluated in mice without tumors. Tumor growth inhibition was similarly strong in all treatment groups. However, there was a greater reduction in the number of animals bearing metastases in the LDE-PTX + ETP group (30 %) in comparison to the PTX + ETP group (82 %, p < 0.05). Reduction of cellular density, blood vessels and increase of collagen fibers in tumor tissues were observed in the LDE-PTX + ETP group but not in the PTX + ETP group, and in both groups reduced melanoma-related anemia and thrombocytosis were observed. Flow cytometric analysis suggested that LDE-PTX + ETP exhibited greater selectivity to neoplastic cells than PTX-ETP, showing arrest (65 %) in the G(2)/M phase of the cell cycle (p < 0.001). Toxicity manifested by weight loss and myelosuppression was markedly milder in the LDE-PTX + ETP than in the PTX + ETP group. LDE-PTX + ETP combined drug-targeting therapy showed markedly superior anti-cancer properties and reduced toxicity compared to PTX + ETP.

A fragment-based approach for ligand binding affinity and selectivity for the liver X receptor beta

Selective modulation of liver X receptor beta (LXR beta) has been recognized as an important approach to prevent or reverse the atherosclerotic process. In the present work, we have developed robust conformation-independent fragment-based quantitative structure-activity and structure-selectivity relationship models for a series of quinolines and cinnolines as potent modulators of the two LXR sub-types. The generated models were then used to predict the potency of an external test set and the predicted values were in good agreement with the experimental results, indicating the potential of the models for untested compounds. The final 2D molecular recognition patterns obtained were integrated to 3D structure-based molecular modeling studies to provide useful insights into the chemical and structural determinants for increased LXR beta binding affinity and selectivity. (C) 2011 Elsevier Inc. All rights reserved.

Pharmacokinetic properties and in silico ADME modeling in drug discovery

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME - absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.