Cefetamet Sodium

Fibroblast activation protein (FAP) has been extensively studied as a cancer biomarker for decades. Recently, small-molecule FAP inhibitors have been widely adopted as a targeting moiety of experimental theranostic radiotracers. Here we present a fast qPCR-based analytical method allowing FAP inhibition screening in a high-throughput regime. To identify clinically relevant compounds that might interfere with FAP-targeted approaches, we focused on a library of FDA-approved drugs. Using the DNA-linked Inhibitor Antibody Assay (DIANA), we tested a library of 2667 compounds within just a few hours and identified numerous FDA-approved drugs as novel FAP inhibitors. Among these, prodrugs of cephalosporin antibiotics and reverse transcriptase inhibitors, along with one elastase inhibitor, were the most potent FAP inhibitors in our dataset. In addition, by employing FAP DIANA in the quantification mode, we were able to determine FAP concentrations in human plasma samples. Together, our work expands the repertoire of FAP inhibitors, analyzes the potential interference of co-administered drugs with FAP-targeting strategies, and presents a sensitive and low-consumption ELISA alternative for FAP quantification with a detection limit of 50 pg/ml.

Diseases related to the central nervous system (CNS) are major health concerns and have serious social and economic impacts. Developing new drugs for CNS-related disorders presents a major challenge as it actively involves delivering drugs into the CNS. Therefore, it is imperative to develop in silico methodologies to reliably identify potential lead compounds that can penetrate the blood–brain barrier (BBB) and help to thoroughly understand the role of different physicochemical properties fundamental to the BBB permeation of molecules. In this study, we have analysed the chemical space of the CNS drugs and compared it to the non-CNS-approved drugs. Additionally, we have collected a feature selection dataset from Muehlbacher et al. (J Comput Aided Mol Des 25(12):1095–1106, 2011. 10.1007/s10822-011-9478-1) and an in-house dataset. This information was utilised to design a molecular fingerprint that was used to train machine learning (ML) models. The best-performing models reported in this study achieved accuracies of 0.997 and 0.98, sensitivities of 1.0 and 0.992, specificities of 0.971 and 0.962, MCCs of 0.984 and 0.958, and ROC-AUCs of 0.997 and 0.999 on an imbalanced and a balanced dataset, respectively. They demonstrated overall good accuracies and sensitivities in the blind validation dataset. The reported models can be applied for fast and early screening drug-like molecules with BBB potential. Furthermore, the bbbPythoN package can be used by the research community to both produce the BBB-specific molecular fingerprints and employ the models mentioned earlier for BBB-permeability prediction.