Analysis of cell death inducing compounds

A Measurement and Modeling Study of Hair Partition of Neutral, Cationic, and Anionic Chemicals

Various neutral, cationic, and anionic chemicals contained in hair care products can be absorbed into hair fiber to modulate physicochemical properties such as color, strength, style, and volume. For environmental safety, there is also an interest in understanding hair absorption to wide chemical pollutants. There have been very limited studies on the absorption properties of chemicals into hair. Here, an experimental and modeling study has been carried out for the hair-water partition of a range of neutral, cationic, and anionic chemicals at different pH. The data showed that hair-water partition not only depends on the hydrophobicity of the chemical but also the pH. The partition of cationic chemicals to hair increased with pH, and this is due to their electrostatic interaction with hair increased from repulsion to attraction. For anionic chemicals, their hair-water partition coefficients decreased with increasing pH due to their electrostatic interaction with hair decreased from attraction to repulsion. Increase in pH did not change the partition of neutral chemicals significantly. Based on the new physicochemical insight of the pH effect on hair-water partition, a new quantitative structure property relationship model has been proposed, taking into account of both the hydrophobic interaction and electrostatic interaction of chemical with hair fiber.

Plasma and liver proteomic analysis of 3Z-3-[(1H-pyrrol-2-yl)-methylidene]-1-(1-piperidinylmethyl)-1,3-2H-indol-2-one-induced hepatotoxicity in Wistar rats

3Z-3-[(1H-pyrrol-2-yl)-methylidene]-1-(1-piperidinylmethyl)-1,3-2H-indol-2-one (Z24), a synthetic anti-angiogenic compound, inhibits the growth and metastasis of certain tumors. Previous works have shown that Z24 induces hepatotoxicity in rodents. We examined the hepatotoxic mechanism of Z24 at the protein level and looked for potential biomarkers. We used 2-DE and MALDI-TOF/TOF MS to analyze alternatively expressed proteins in rat liver and plasma after Z24 administration. We also examined apoptosis in rat liver and measured levels of intramitochondrial ROS and NAD(P)H redox in liver cells. We found that 22 nonredundant proteins in the liver and 11 in the plasma were differentially expressed. These proteins were involved in several important metabolic pathways, including carbohydrate, lipid, amino acid, and energy metabolism, biotransformation, apoptosis, etc. Apoptosis in rat liver was confirmed with the terminal deoxynucleotidyl transferase dUTP-nick end labeling assay. In mitochondria, Z24 increased the ROS and decreased the NAD(P)H levels. Thus, inhibition of carbohydrate aerobic oxidation, fatty acid beta-oxidation, and oxidative phosphorylation is a potential mechanism of Z24-induced hepatotoxicity, resulting in mitochondrial dysfunction and apoptosis-mediated cell death. In addition, fetub protein and argininosuccinate synthase in plasma may be potential biomarkers of Z24-induced hepatotoxicity.

Computational toxicology: an overview of the sources of data and of modelling methods

BACKGROUND

Toxicology has the goal of ensuring the safety of humans, animals and the environment. Computational toxicology is an area of active development and great potential. There are tangible reasons for the emerging interest in this discipline from academia, industry, regulatory bodies and governments.

RESULTS

Pharmaceuticals, personal health care products, nutritional ingredients and products of the chemical industries are all potential hazards and need to be assessed. Toxicological tests for these products are costly, frequently use laboratory animals and are time-consuming. This delays end-user access to improved products or, conversely, the timely withdrawal of dangerous substances from the market. The aim of computational toxicology is to accelerate the assessment of potentially dangerous substances through in silico models.

CONCLUSIONS

In this review, we provide an overview of the development of models for computational toxicology. Addressing the significant divide between the experimental and computational worlds-believed to be a prime hindrance to computational toxicology-we briefly consider the fundamental issue of toxicological data and the assays they stem from. Different kinds of models that can be built using such data are presented: computational filters, models for specific toxicological endpoints and tools for the generation of testable hypotheses.

Toxicogenomic biomarkers for liver toxicity

Toxicogenomics (TGx) is a widely used technique in the preclinical stage of drug development to investigate the molecular mechanisms of toxicity. A number of candidate TGx biomarkers have now been identified and are utilized for both assessing and predicting toxicities. Further accumulation of novel TGx biomarkers will lead to more efficient, appropriate and cost effective drug risk assessment, reinforcing the paradigm of the conventional toxicology system with a more profound understanding of the molecular mechanisms of drug-induced toxicity. In this paper, we overview some practical strategies as well as obstacles for identifying and utilizing TGx biomarkers based on microarray analysis. Since clinical hepatotoxicity is one of the major causes of drug development attrition, the liver has been the best documented target organ for TGx studies to date, and we therefore focused on information from liver TGx studies. In this review, we summarize the current resources in the literature in regard to TGx studies of the liver, from which toxicologists could extract potential TGx biomarker gene sets for better hepatotoxicity risk assessment.

Toxicogenomics: transcription profiling for toxicology assessment

Toxicogenomics, the application of transcription profiling to toxicology, has been widely used for elucidating the molecular and cellular actions of chemicals and other environmental stressors on biological systems, predicting toxicity before any functional damages, and classification of known or new toxicants based on signatures of gene expression. The success of a toxicogenomics study depends upon close collaboration among experts in different fields, including a toxicologist or biologist, a bioinformatician, statistician, physician and, sometimes, mathematician. This review is focused on toxicogenomics studies, including transcription profiling technology, experimental design, significant gene extraction, toxicological results interpretation, potential pathway identification, database input and the applications of toxicogenomics in various fields of toxicological study.