Microsomal oxidative stress induced by NADPH is inhibited by nitrofurantoin redox biotranformation

In Vitro Models for Studying Chronic Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major clinical problem in terms of patient morbidity and mortality, cost to healthcare systems and failure of the development of new drugs. The need for consistent safety strategies capable of identifying a potential toxicity risk early in the drug discovery pipeline is key. Human DILI is poorly predicted in animals, probably due to the well-known interspecies differences in drug metabolism, pharmacokinetics, and toxicity targets. For this reason, distinct cellular models from primary human hepatocytes or hepatoma cell lines cultured as 2D monolayers to emerging 3D culture systems or the use of multi-cellular systems have been proposed for hepatotoxicity studies. In order to mimic long-term hepatotoxicity in vitro, cell models, which maintain hepatic phenotype for a suitably long period, should be used. On the other hand, repeated-dose administration is a more relevant scenario for therapeutics, providing information not only about toxicity, but also about cumulative effects and/or delayed responses. In this review, we evaluate the existing cell models for DILI prediction focusing on chronic hepatotoxicity, highlighting how better characterization and mechanistic studies could lead to advance DILI prediction.

Prediction of Synergistic Antibiotic Combinations by Graph Learning

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

Hazardous ecotoxicological impact of two commonly used nitrofuran-derived antibacterial drugs: Furazolidone and nitrofurantoin

This paper discusses the impact of two nitrofuran-derived drugs, namely furazolidone and nitrofurantoin on growth of oat and common radish as well as their impact on bacteria Allivibrio fischeri and crustaceans Heterocypris incongruens. Results indicated that both compounds were highly phytotoxic for radish (R. sativus) being simultaneously nearly not harmful for oat (A. sativa). Growing inhibition of shoots, roots, fresh matter and photosynthetic pigments is correlated with growing concentration of drugs in soil. Ecotoxicological impact of both compounds on model luminescence bacteria Aliivibrio fischeri and freshwater crustaceans Heterocypris incongruens as a representative organisms of two different level of food chain, is also reported herein, and the obtained data show significant toxicity against these two organisms. Basing on obtained results, it was concluded that both nitrofuran drugs in case of distribution through environment, by improper utilisation after use or unplanned environmental intoxication with unused drugs may cause serious environmental problems and therefore both should be handled with a reasonable care at any step of their production or utilisation.

Cucurbit[7]uril-stabilized gold nanoparticles as catalysts of the nitro compound reduction reaction

Catalytic performance of cucurbit[7]uril-stabilized gold nanoparticles on the reduction reaction of 4-nitrophenol and nitrofurantoin.