Isoxsuprine Hydrochloride

Since their discovery as impurities in numerous pharmaceuticals beginning in 2018, there has been a strong push to predict and prevent the formation of mutagenic nitrosamines. Several experimental methods, particularly the Nitrosation Assay Procedure, have been developed to predict a molecule's susceptibility to nitrosation. Here, we have compiled the results of hundreds of these experiments from the literature to construct two structure-activity relationship models: a statistical model and an expert rule-based model. The statistical model has been built with graph neural networks and was trained on a dataset of 207 nitrogen-containing molecules. This model makes a binary call for each nitrogen center, predicting if it is likely to be nitrosated or not. Conversely, the rule-based model labels each possible nitrosamine product as one of four categories, ranging from "unlikely" to "very likely". It makes this determination based on 15 rules, which cover 12 deactivating (inhibit nitrosation) and 3 activating (favor nitrosation) features that have been drawn from the literature. Both models perform remarkably well, with accuracies of ∼80%. The rule-based model is generally biased toward favoring nitrosation while the statistical model is more likely to classify an amine as un-nitrosatable due to the makeup of the dataset. Using the models together can balance these biases and further improve the reliability of both.

Pharmaceutical pollution in wastewater poses significant environmental and public health concerns worldwide. Chloramphenicol (CP), an antibiotic widely used in medical and veterinary applications, is among the active pharmaceutical ingredients (APIs) frequently detected in aquatic environments. This study explored the encapsulation of chloramphenicol API in contaminated wastewater using rationally designed fibrations based on the silver metal ion-directed self-assembly of fibrillator-type self-assembling ligand (ANS-3). We further investigated the removal of various commonly prescribed drugs, including antibiotics such as β-lactam (amoxicillin), fluoroquinolone (ciprofloxacin), aminoglycoside (neomycin), and tetracycline; antiparasitic agents with antiprotozoal properties (praziquantel and metronidazole); nonsteroidal anti-inflammatory drugs (NSAIDs) such as phenylbutazone and ketoprofen; the vasodilator isoxsuprine; amphiphilic antidepressants (amitriptyline); and the antiviral drug amantadine. The findings validated the crucial influence of polar multifunctionality and structural complexity in enhancing interactions with Ag.ANS-3 matrix, emphasizing its potential for efficient drug sequestration. First, picolinic acid (PA) and phenylalanine (F) were evaluated for their ability to form fibrillar structures, and their morphological characterization revealed well-defined fibrillar networks with varying degrees of porosity and interconnectivity. Then, the strategic inclusion of leucine in synthesizing ANS-3 facilitated the formation of robust fibrillar networks, employing its hydrophobic interactions to drive the self-assembly process. Finally, the encapsulation of APIs was evaluated using Ag(I) metal ion-driven ANS-3 based self-assembled nanofibrous material. This research contributes to the development of innovative physicochemical wastewater treatment strategies for environmental remediation and validates the importance of rational design in encapsulation-based wastewater remediation technologies.