Budipine Hydrochloride

We report development and prospective validation of a QSAR model of the unbound brain-to-plasma partition coefficient, K_p,uu,brain, based on the in-house data set of ∼1000 compounds. We discuss effects of experimental variability, explore the applicability of both regression and classification approaches, and evaluate a novel, model-within-a-model approach of including P-glycoprotein efflux prediction as an additional variable. When tested on an independent test set of 91 internal compounds, incorporation of P-glycoprotein efflux information significantly improves the model performance resulting in an R² of 0.53, RMSE of 0.57, Spearman's Rho correlation coefficient of 0.73, and qualitative prediction accuracy of 0.8 (kappa = 0.6). In addition to improving the performance, one of the key advantages of this approach is the larger chemical space coverage provided indirectly through incorporation of the in vitro, higher throughput data set that is 4 times larger than the in vivo data set.

Active pharmaceutical ingredients (APIs) have raised considerable concern over the past decade due to their widespread detection in water resources and their potential to affect ecosystem health. This triggered many attempts to prioritize the large number of known APIs to target monitoring efforts and testing of fate and effects. However, so far, a comprehensive approach to screen for their presence in surface waters has been missing. Here, we explore a combination of an automated suspect screening approach based on liquid chromatography coupled to high-resolution mass spectrometry and a model-based prioritization using consumption data, readily predictable fate properties and a generic mass balance model for activated sludge treatment to comprehensively detect APIs with relevant exposure in wastewater treatment plant effluents. The procedure afforded the detection of 27 APIs that had not been covered in our previous target method, which included 119 parent APIs. The newly detected APIs included seven compounds with a high potential for bioaccumulation and persistence, and also three compounds that were suspected to stem from point sources rather than from consumption as medicines. Analytical suspect screening proved to be more selective than model-based prioritization, making it the method of choice for focusing analytical method development or fate and effect testing on those APIs most relevant to the aquatic environment. However, we found that state-of-the-practice exposure modeling used to predict potential high-exposure substances can be a useful complement to point toward oversights and known or suspected detection gaps in the analytical method, most of which were related to insufficient ionization.