Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

Bioavailability of organic micropollutants in cell-based bioassays

The application of in vitro cell-based bioassays is increasing in chemical risk and hazard assessment and their implementation in high-throughput screening (HTS) format can contribute significantly to meet the high demands on effect data for the increasing number and variety of anthropogenic chemicals. Their suitability to replace whole-organism tests in human health risk assessment depends on the ability to quantitatively predict effects in humans, referred to as quantitative in vitro-in vivo extrapolation (QIVIVE). The determination of chemical bioavailability is an important prerequisite for the QIVIVE, but is challenging to do by experiment due to the small medium volumes used in HTS. The thesis aimed to develop and experimentally parameterize models that enable the prediction of the bioavailability of neutral and ionizable chemicals in various cell-based bioassays. The results emphasize the complexity of in vitro exposure as it results from several physiochemical properties interacting with different biological molecules and processes. The presentation demonstrates that simple equations based on predicted input parameters can prospectively improve chemical dosing and prevent experimental artifacts leading to poor data quality. The developed experimentally verified mass balance and kinetic models can be used for improved data analysis to quantify reliable freely dissolved and cellular concentrations that can subsequently be applied to QIVIVE models