Prediction of drug indications based on chemical interactions and chemical similarities

Combination effect of nanoparticles on the acute pulmonary inflammogenic potential: additive effect and antagonistic effect

The combination effect of co-exposed different types of nanomaterials is little known although humans are generally exposed to a mixture of nanomaterials from urban ultrafine particles or industrial nanomaterials. Herein, we evaluated the combined effect of nanoparticles (NPs) using three types of NPs in different inflammogenic categories: carbon black (CB), nickel oxide (NiO), and copper oxide (CuO). A single type of NPs or NPs in combination was intratracheally instilled into the lungs of rats and the bronchoalveolar lavage fluid (BALF) was analyzed at 24 h after instillation to evaluate the acute inflammogenic potential. The percentage of neutrophils in BALF was selected as a toxicity endpoint and the potential for reactive oxygen species (ROS) generation, dose-response of the combined effect, sequential treatment of CB and NiO, and uptake of NiO to alveolar macrophages after combined treatment of CB and NiO were evaluated for the mechanism of the combined effect. Co-exposure of CuO and NiO showed an additive effect on the percentage of neutrophils and ROS generation potential, which implies that the physicochemical properties of each NP are not influenced by the other type. While CB exerted an antagonistic effect on the percentage of neutrophils in combined treatment with CuO or NiO. The antagonistic effect of CB was due to the scavenging activity of the ROS generated by the CuO and NiO rather than the competition in cellular uptake to target cells (i.e. alveolar macrophages), which highlight the importance of the combined effect of NPs in the risk assessment.

Relating Substructures and Side Effects of Drugs with Chemical-chemical Interactions

Background:

Drugs are very important for human life because they can provide treatment, cure, prevention, or diagnosis of different diseases. However, they also cause side effects, which can increase the risks for humans and pharmaceuticals companies. It is essential to identify drug side effects in drug discovery. To date, lots of computational methods have been proposed to predict the side effects of drugs and most of them used the fact that similar drugs always have similar side effects. However, previous studies did not analyze which substructures are highly related to which kind of side effect.

Method:

In this study, we conducted a computational investigation. In this regard, we extracted a drug set for each side effect, which consisted of drugs having the side effect. Also, for each substructure, a set was constructed by picking up drugs owing such substructure. The relationship between one side effect and one substructure was evaluated based on linkages between drugs in their corresponding drug sets, resulting in an Es value. Then, the statistical significance of Es value was measured by a permutation test.

Results and Conclusion:

A number of highly related pairs of side effects and substructures were obtained and some were extensively analyzed to confirm the reliability of the results reported in this study.