Terbutaline Sulfate

Hospital wastewater (HWW) contains pharmaceutical drugs, antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARG), and pathogens, posing potential risks to environmental and public health. However, data on the occurrence of these contaminants are scarce, especially in low- and middle-income countries. This study employed LC-QTOF MS for a qualitative suspect screening of 1922 pharmaceutical drugs and metabolites in HWW (n = 8 samples). Results were used in an environmental risk assessment applied with the support of in silico tools, to predict potential risks to aquatic organisms. ARB were analyzed using plate count agar method and ARG by Real-time PCR. 113 drugs were identified in HWW, and antibiotics were the most frequently detected (15 compounds). Environmental risk assessment indicated that 29 compounds have the potential to cause toxic effects to aquatic fauna, among which 4 compounds (ampicillin, escitalopram, iopromide, and topiramate) may pose a very high potential risk (RQ > 2). In addition, ARB resistant to azithromycin and cefazolin (10⁶-10⁷ CFU/mL) were abundant in HWW samples, while low levels of bacteria resistant to meropenem (10⁵ CFU/mL) were detected. The relative number of copies of blaKPC, catA1, sul1, ermB, qnrS, vanB, floR, and aadA were higher in HWW from one of the hospitals. However, there was no significant difference for blaOXA (p value = 0.1271) or mecA genes (p value = 0.6077) in HWW samples. These results call attention to potential risks posed by the discharge of HWW and reveal the need to raise data for improving management practices for environmental protection.

Retention of molecules on immobilized artificial membrane (IAM) chromatography is a key physicochemical property for predictive models of permeability across biological barriers, with applications in drug design and ecotoxicology. Currently, IAM retention is solely experimentally determined, which limits its utility for screening virtual compound libraries or for predictions of yet not synthesized molecules. The present study focuses on developing predictive models of IAM retention factors (logk_w(IAM)) for a structurally diverse set of drug compounds, scrutinizing the role of lipophilicity, experimental and calculated, as well as the contribution of additional molecular parameters, selected from a pool of physicochemical, constitutional, topological and 3D descriptors. After obtaining a data overview by principal component analysis, both multiple linear regression (MLR) and partial least squares (PLS) analyses were used to construct lipophilicity-based models and lipophilicity-independent models. Bulk, polarity and fraction of anionic species were common descriptors in all models. It was demonstrated that calculated lipophilicity values introduced additional uncertainty, depending on the software used. On the other hand, lipophilicity-independent MLR and PLS models, which relied solely on computational descriptors, showed comparable performance with lipophilicity-based models, while offering the advantage to more useful for screening large libraries in early drug discovery. The reliability of lipophilicity-independent MLR and PLS models was assessed by external validation as well as by using a blind test set. Error distribution between lipophilicity-based and lipophilicity-independent models was also investigated and found to be comparable, while it was better than the differences between experimental and calculated lipophilicity values.