Evaluation of distribution coefficients and concentration ratios of (90)Sr and (137)Cs in the Techa River and the Miass River.

Empirical data on the behavior of radionuclides in aquatic ecosystems are needed for radioecological modeling, which is commonly used for predicting transfer of radionuclides, estimating doses, and assessing possible adverse effects on species and communities. Preliminary studies of radioecological parameters including distribution coefficients and concentration ratios, for (90)Sr and (137)Cs were not in full agreement with the default values used in the ERICA Tool and the RESRAD BIOTA codes. The unique radiation situation in the Techa River, which was contaminated by long-lived radionuclides ((90)Sr and (137)Cs) in the middle of the last century allows improved knowledge about these parameters for river systems.

Computational prediction of human drug metabolism.

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery.

A combined approach to drug metabolism and toxicity assessment.

The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction.