Immobilized artificial membrane-chromatographic and computational descriptors in studies of soil-water partition of environmentally relevant compounds

Systematic Study of Steroid Drugs' Ability to Cross Biomembranes-The Possible Environmental Impact and Health Risks Associated with Exposure During Pregnancy

Thirty-seven steroid drugs of different types were investigated in silico for their environmental and pharmacokinetic properties (partition between soil and water, bioaccumulation in aquatic organisms, ability to be absorbed from the gastrointestinal tract and to cross biological barriers-skin, blood-brain barrier and placenta) using on-line tools and novel QSAR models. The same drugs were studied by Molecular Docking in the context of their ability to interact with two enzymes-glutathione S-transferase (GST) and human N-acetyltransferase 2 (NAT2), which are involved in the placenta's protective system against harmful xenobiotics. Steroid drugs are released to the environment from households, hospitals, manufacturing plants and farms (e.g., with natural fertilizers) and they can affect the aquatic life (reproduction and development of aquatic organisms), even at sub-ng/L concentrations. It was established that the majority of studied drugs are mobile in soil, so they may reach surface waters far from point of discharge, e.g., from farming; however, only a few of them are likely to bioaccumulate. All of them can be absorbed orally or through skin, and they are also expected to cross the placenta. Over 30% of studied compounds are likely to pass through the blood-brain barrier (although five compounds in this group are likely P-gp substrates, which may reduce their activity in the central nervous systems); they have also very high affinity for both studied enzymes.

Affinity of Compounds for Phosphatydylcholine-Based Immobilized Artificial Membrane-A Measure of Their Bioconcentration in Aquatic Organisms

The BCF (bioconcentration factor) of solutes in aquatic organisms is an important parameter because many undesired chemicals enter the ecosystem and affect the wildlife. Chromatographic retention factor log k_w^IAM obtained from immobilized artificial membrane (IAM) HPLC chromatography with buffered, aqueous mobile phases and calculated molecular descriptors obtained for a group of 120 structurally unrelated compounds were used to generate useful models of log BCF. It was established that log k_w^IAM obtained in the conditions described in this study is not sufficient as a sole predictor of bioconcentration. Simple, potentially useful models based on log k_w^IAM and a selection of readily available, calculated descriptors and accounting for over 88% of total variability were generated using multiple linear regression (MLR), partial least squares (PLS) regression and artificial neural networks (ANN). The models proposed in the study were tested on an external group of 120 compounds and on a group of 40 compounds with known experimental log BCF values. It was established that a relatively simple MLR model containing four independent variables leads to satisfying BCF predictions and is more intuitive than PLS or ANN models.