Terfenadine

Emerging contaminants pose a potential risk to aquatic ecosystems in the Pearl River Basin, China, owing to the high population density and active industry. This study investigated samples from eight sewage treatment plants, and five surface water bodies of related watersheds. To screen the risk of emerging contaminants (ECs), and clarify their sources, this study calculated the risk quotient of detected chemical and performed source identification/apportionment using the positive matrix factorization method. In total, 149 organic pollutants were identified. Pharmaceuticals showed significant concentrations in sewage treatment plant samples (120.87 ng/L), compared with surface water samples (1.13 ng/L). The ecological risk assessment identified three chemicals with a heightened risk to aquatic organisms: fipronil sulfide, caffeine, and roxithromycin. Four principal sources of contaminants were identified: pharmaceutical wastewater, domestic sewage, medical effluent, and agricultural runoff. Pharmaceutical wastewater was the primary contributor (60.4 %), to the cumulative EC concentration and to ECs in sewage treatment plant effluent. Agricultural drainage was the main source of ECs in surface water. This study provides a strategy to obtain comprehensive information on the aquatic risks and potential sources of EC species in areas affected by artificial activities, which is of substantial importance to pollutant management and control.

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R² of calibration = 0.80, Q² of validation = 0.64) and post-digestion (R² of calibration = 0.76, Q² of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R² = 0.70, and post-digestion R² = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.