Terbutaline Sulfate

The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure-performance relationships (QSPR) for rapid and accurate in silico BCS classification. The present study aims to develop a web platform for high-throughput BCS classification based on high-performance ML models. Initially, four data sets of BCS-related molecular properties: log S, log P, log D, and log P_app were curated. Subsequently, 6 ML algorithms or deep learning frameworks were employed to construct models, with diverse molecular representations ranging from one-dimensional molecular fingerprints, descriptors, and molecular graphs to three-dimensional molecular spatial coordinates. By comparing different combinations of molecular representations and learning algorithms, LightGBM exhibited excellent performance in solubility prediction, with an R² of 0.84; AttentiveFP outperformed others in permeability prediction, with R² values of 0.96 and 0.76 for log P and log D, respectively; and XGBoost was the most accurate for log P_app prediction, with an R² of 0.71. When externally validated on a marketed drug BCS category data set, the best-performing models achieved classification accuracies of over 77 and 73% for solubility and permeability, respectively. Finally, the well-trained models were embedded into the first ML-based BCS class prediction web platform (x f), enabling pharmaceutical scientists to quickly determine the BCS category of candidate drugs, which will aid in the high-throughput BCS assessment for candidate drugs during the preformulation stage, thereby promoting reduced risk and enhanced efficiency in drug development and regulation.

The aim of this study was to optimize a Liquid Chromatography Mass Spectrometry (LC-MS) method using a zwitterionic phosphorylcholine HILIC column for the determination of several Persistent and Mobile Organic Contaminants (PMOC) in wastewater samples. An experimental design approach was implemented to both better understand the retention mechanisms of several polar compounds and to find the optimal operating conditions for their detection and quantification. Eleven PMOCs, with logD_pH=7 ranging from -5.27 to 0.24, were considered, including pesticides, artificial sweeteners, pharmaceuticals, and central nervous system stimulants. Key chromatographic variables-such as the initial percentage of the organic mobile phase, temperature, flow rate, gradient time, acid percentage, and the type and concentration of two different salts- were studied to assess their influence on peak areas, retention times and separation efficiency. The results indicated buffer type, flow rate, and initial percentage of organic mobile phase as the most influential factors affecting retention, though the effects were closely related to the chemical and physicochemical properties of the analytes. The optimized instrumental method demonstrated acceptable figures of merit, with recoveries ranging from 49 % to 100 % for all analytes (except taurine, which may require a different experimental preprocessing step). The method also showed satisfactory precision (repeatability of the entire experimental procedure), in terms of Relative Standard Deviation (RSD %), which was <10 % for all analytes. The developed method was successfully applied to the analysis of reclaimed water samples collected in six wastewater treatment plants in two regions of northern Italy. All target ECs were detected and quantified, except for clenbuterol, terbutaline, acesulfame K and 2,4-dichlorophenoxyacetic acid, which were below the detection limit.