Structure–toxicity relationship and structure–activity relationship study of 2-phenylaminophenylacetic acid derived compounds

Development of Novel Diclofenac Analogs Designed to Avoid Metabolic Activation and Hepatocyte Toxicity

Diclofenac (DCF) is widely used as a nonsteroidal anti-inflammatory drug; however, it is associated with severe liver injury. This adverse reaction is thought to be related to the reactive quinone imine (QI) and acyl glucuronide (AG) metabolites of DCF, but it remains controversial which reactive metabolites mainly contribute to DCF-induced toxicity. In this study, we synthesized five types of DCF analogs that were designed to mitigate the formation of reactive QI and/or AG metabolites and evaluated their metabolic stability, cyclooxygenase (COX) inhibitory activity, and toxicity to cryopreserved human hepatocytes. Compounds with fluorine at the 5- and 4'-positions of aromatic rings exhibited modest and high metabolic stability to oxidation by cytochrome P450, respectively, but induced cytotoxicity comparable to DCF. Replacing the carboxylic group of DCF with its bioisosteres was effective in terms of stability to oxidative metabolism and glucuronidation; however, sulfonic acid and sulfonamide groups were not preferable for COX inhibition, and tetrazole-containing analogs induced strong cytotoxicity. On the other hand, compounds that have fluorine at the benzylic position were resistant to glucuronidation and showed little toxicity to hepatocytes. In addition, among these compounds, those with hydrogen at the 4'-position (2a and 2c) selectively inhibited the COX-2 enzyme. Throughout these data, it was suggested that compounds 2a and 2c might be novel safer and more efficacious drug candidates instead of DCF.

In-silico approach for drug induced liver injury prediction: Recent advances

Drug induced liver injury (DILI) is the prime cause of liver disfunction which may lead to mild non-specific symptoms to more severe signs like hepatitis, cholestasis, cirrhosis and jaundice. Not only the prescription medications, but the consumption of herbs and health supplements have also been reported to cause these adverse reactions resulting into high mortality rates and post marketing withdrawal of drugs. Due to the continuously increasing DILI incidences in recent years, robust prediction methods with high accuracy, specificity and sensitivity are of priority. Bioinformatics is the emerging field of science that has been used in the past few years to explore the mechanisms of DILI. The major emphasis of this review is the recent advances of in silico tools for the diagnostic and therapeutic interventions of DILI. These tools have been developed and widely used in the past few years for the prediction of pathways induced from both hepatotoxic as well as hepatoprotective Chinese drugs and for the identification of DILI specific biomarkers for prognostic purpose. In addition to this, advanced machine learning models have been developed for the classification of drugs into DILI causing and non-DILI causing. Moreover, development of 3 class models over 2 class offers better understanding of multi-class DILI risks and at the same time providing authentic prediction of toxicity during drug designing before clinical trials.