Brotizolam

In the last few years, the interest in pharmaceutical drugs and their metabolites as environmental pollutants is gaining growing importance.Regular monitoring and timely actions are decisive to implement appropriate water resource management strategies and to evaluate the efficiency of traditional or innovative wastewater treatment plants (WWTPs).This study describes the development and validation of an anal. procedure using 10 mL sample volume followed by direct-injection in ultra-high-performance liquid chromatog.-tandem mass spectrometry (UHPLC-MS/MS) for the simultaneous determination of 95 pharmaceutical drugs and 10 of their main metabolites in wastewater.Adequate sensitivity was recorded for all target analytes, with limits of detection below 5 ng/L for 60 out of 95 analytes and recoveries exceeding 80% for all the analytes under study.A total of 42 target analytes were detected in almost all sites, and limited differences were observed among several pharmaceutical drug arrays found at different sampling sites.In addition, an abatement yield higher than 50% was observed for only 12 of the 42 detected substances.The procedure, which combined direct injection and small sample volume collection, showed great potential and efficiency for the high-performance determination of pharmaceutical drugs in wastewater.

Accurate prediction of human clearance (CL) is essential in early drug development. Single Species Scaling (SSS) using rat pharmacokinetic (PK) data, particularly with unbound plasma fraction (f_u,plasma), is widely used. However, its accuracy declines for compounds with extremely low f_u,plasma, and no systematic method has addressed this limitation.

We developed a new approach, called Fraction-based Linear EXtrapolation SSS (FLEX-SSS fu Rat), which switches between SSS fu Rat and SSS Rat formulas based on an optimized fu threshold. The threshold and scaling coefficients were derived using a training set of 200 compounds. Additionally, a random forest (RF) machine learning model was built using molecular descriptors. Both models were validated using an external dataset of 62 compounds.

All five predictive models showed comparable performance; among them, the consensus model combining FLEX-SSS fu Rat and RF yielded the best result: 40.3% within 2-fold error, only 16.1% above 5-fold, and GMFE of 2.7.

This study is the first to systematically validate SSS fu Rat using an independent dataset. The integration of threshold-based allometry and machine learning enabled more accurate human CL prediction, supporting informed decisions in first-in-human dose selection.