The concept of drug repositioning has recently received considerable attention in the field of oncology. In the present study, we propose that paroxetine can be used as a potent anticancer drug. Paroxetine, one of the selective serotonin reuptake inhibitors (SSRIs), has been widely prescribed for the treatment of depression and anxiety disorders. Recently, SSRIs have been reported to have anticancer activity in various types of cancer cells; however, the underlying mechanisms of their action are not yet known. In this study, we investigated the potential anticancer effect of paroxetine in human colorectal cancer cells, HCT116 and HT‐29. Treatment with paroxetine reduced cell viability, which was associated with marked increase in apoptosis, in both the cell lines. Also, paroxetine effectively inhibited colony formation and 3D spheroid formation. We speculated that the mode of action of paroxetine might be through the inhibition of two major receptor tyrosine kinases – MET and ERBB3 – leading to the suppression of AKT, ERK and p38 activation and induction of JNK and caspase‐3 pathways. Moreover, in vivo experiments revealed that treatment of athymic nude mice bearing HT‐29 cells with paroxetine remarkably suppressed tumour growth. In conclusion, paroxetine is a potential therapeutic option for patients with colorectal cancer.

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.

Statins are a class of lipid‐lowering drugs that have recently been used in drug repositioning in the treatment of human cancer. However, the underlying mechanism of statin‐induced cancer cell death has not been clearly defined. In the present study, we evaluated the anticancer effect of pitavastatin on oral squamous cell carcinoma (OSCC), SCC15 and SCC4 cells and found that FOXO3a might be a direct target in pitavastatin‐induced cancer cell death. Our data revealed that pitavastatin selectively suppressed cell viability and induced intrinsic apoptosis in a FOXO3a‐dependent manner in SCC15 cells while no effect was observed in SCC4 cells. Notably, treatment with pitavastatin in SCC15 cells induced the nuclear translocation of FOXO3a via dual regulation of two upstream kinases, AMPK and Akt, resulting in the up‐regulation of PUMA, a transcriptional target gene of FOXO3a. Furthermore, our data revealed that FOXO3a‐mediated PUMA induction plays a role in pitavastatin‐induced intrinsic apoptosis in SCC15 cells. Taken together, our findings suggest that pitavastatin activates the FOXO3a/PUMA apoptotic axis by regulation of nuclear translocation of FOXO3a via Akt/FOXO3a or AMPK/FOXO3a signalling. Therefore, these findings might help to elucidate the underlying mechanism of the anticancer effects of pitavastatin on OSCC.

Metabolomics is a powerful tool used in the description of metabolic system perturbations caused by diseases or abnormal conditions, and it usually involves qualitative and/or quantitative metabolome determination, accompanied by bioinformatics assessment. Methamphetamine is a psychostimulant with serious abuse potential and due to the absence of effective pharmacotherapy and a high recurrence potential, methamphetamine addiction is a grave issue. Moreover, its addiction mechanisms remain unclear, probably due to the lack of experimental models that reflect personal genetic variances and environmental factors determining drug addiction occurrence. The metabolic approach is only recently being used to study the metabolic effects induced by a variety of methamphetamine exposure statuses, in order to investigate metabolic disturbances related to the adverse effects and discover potential methamphetamine addiction biomarkers. To provide a critical overview of methamphetamine-associated metabolic changes revealed in recent years using the metabolomics approach, we discussed methamphetamine toxicity, applications of metabolomics in drug abuse and addiction studies, biological samples used in metabolomics, and previous studies on metabolic alterations in a variety of biological samples—including the brain, hair, serum, plasma, and urine—following methamphetamine exposure in animal studies. Metabolic alterations observed in animal brain and other biological samples after methamphetamine exposure were associated with neuronal and energy metabolism disruptions. This review highlights the significance of further metabolomics studies in the area of methamphetamine addiction research. These findings will contribute to a better understanding of metabolic changes induced by methamphetamine addiction progress and to the design of further studies targeting the discovery of methamphetamine addiction biomarkers and therapeutic targets.