Enhancing the identification of voriconazole-associated hepatotoxicity by targeted metabolomics.

Voriconazole-associated hepatotoxicity is a common condition that generally manifests as elevated liver enzymes and can lead to drug discontinuation. Careful monitoring of voriconazole-associated hepatotoxicity is needed but there are no specific plasma biomarkers for this condition. Metabolomics has emerged as a promising technique for investigating biomarkers associated with drug-induced toxicity. The aim of this study was to use targeted metabolomics to evaluate seven endogenous metabolites as potential biomarkers of voriconazole-associated hepatotoxicity. Patients undergoing therapeutic drug monitoring of voriconazole were classified into a hepatotoxicity group (18 patients) or a control group (153 patients). Plasma samples were analysed using ultra-high-performance liquid chromatography coupled to mass spectrometry. Metabolite concentrations in the two groups were compared. Areas under the receiver operating characteristic (AUROC) curves generated from logistic regressions were used to correlate the concentrations of these seven metabolites with voriconazole trough concentrations and conventional liver biochemistry tests. Glycocholate and α-ketoglutarate levels were significantly higher in the hepatotoxicity group compared with the control group (false discovery rate-corrected P < 0.001 and P = 0.024, respectively). The metabolites glycocholate (AUROC = 0.795) and α-ketoglutarate (AUROC = 0.696) outperformed voriconazole trough concentrations (AUROC = 0.555) and approached the performance of alkaline phosphatase (AUROC = 0.876) and total bilirubin (AUROC = 0.815). A panel of glycocholate combined with voriconazole trough concentrations (AUROC = 0.827) substantially improved the performance of voriconazole trough concentrations alone in predicting hepatotoxicity. In conclusion, the panel integrating glycocholate with voriconazole trough concentrations has great potential for identifying voriconazole-associated hepatotoxicity.

When Machine Learning and Deep Learning Come to the Big Data in Food Chemistry.

Since the first food database was released over one hundred years ago, food databases have become more diversified, including food composition databases, food flavor databases, and food chemical compound databases. These databases provide detailed information about the nutritional compositions, flavor molecules, and chemical properties of various food compounds. As artificial intelligence (AI) is becoming popular in every field, AI methods can also be applied to food industry research and molecular chemistry. Machine learning and deep learning are valuable tools for analyzing big data sources such as food databases. Studies investigating food compositions, flavors, and chemical compounds with AI concepts and learning methods have emerged in the past few years. This review illustrates several well-known food databases, focusing on their primary contents, interfaces, and other essential features. We also introduce some of the most common machine learning and deep learning methods. Furthermore, a few studies related to food databases are given as examples, demonstrating their applications in food pairing, food-drug interactions, and molecular modeling. Based on the results of these applications, it is expected that the combination of food databases and AI will play an essential role in food science and food chemistry.

Comparison of controlled drugs and new psychoactive substances (NPS) regulations in East and Southeast Asia.

New psychoactive substances (NPS) are substances of abuse that easily evade existing controlled drug regulations. This study conducted a systematic review on controlled drug regulations and analyzed the numbers of new psychoactive substances (NPS) reported in six East and Southeast Asian countries in comparison to US and UK from 2009 to 2020. Generally, more NPS were reported in the US (551) and UK (400), compared to Japan (379), China (221), Singapore (142), South Korea (99), Malaysia (41), and Taiwan (35). Legislative mechanisms including the specific listing of individual substances, generic control of a family of substances, analogue control of similar substances, temporary bans of new substances were evaluated. In this review, countries that have adopted a combination of legislative mechanisms were able to identify higher numbers of NPS for regulatory control, such as the US, UK, Japan, Singapore, and South Korea. These findings can provide references to countries like Malaysia and Taiwan, to strengthen NPS-related regulations nationally. Countries in the East and Southeast Asian region should be encouraged to collaborate more closely and to implement additional legislative approaches most relevant to the regional NPS trends to bridge the regulatory gap and to prevent the spread of emerging NPS.