CYP2C8-Mediated Formation of a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Cytochrome P450 and Aldehyde Oxidase Substrate.

Predicting human disproportionate metabolites is difficult, especially when drugs undergo species-specific metabolism mediated by cytochrome P450s (P450s) and/or non-P450 enzymes. This study assessed human metabolites of DS-1971a, a potent Nav1.7-selective blocker, by performing human mass balance studies and characterizing DS-1971a metabolites, in accordance with the Metabolites in Safety Testing guidance. In addition, we investigated the mechanism by which the major human disproportionate metabolite (M1) was formed. After oral administration of radiolabeled DS-1971a, the major metabolites in human plasma were P450-mediated monoxidized metabolites M1 and M2 with area under the curve ratios of 27% and 10% of total drug-related exposure, respectively; the minor metabolites were dioxidized metabolites produced by aldehyde oxidase and P450s. By comparing exposure levels of M1 and M2 between humans and safety assessment animals, M1 but not M2 was found to be a human disproportionate metabolite, requiring further characterization under the Metabolites in Safety Testing guidance. Incubation studies with human liver microsomes indicated that CYP2C8 was responsible for the formation of M1. Docking simulation indicated that, in the formation of M1 and M2, there would be hydrogen bonding and/or electrostatic interactions between the pyrimidine and sulfonamide moieties of DS-1971a and amino acid residues Ser100, Ile102, Ile106, Thr107, and Asn217 in CYP2C8, and that the cyclohexane ring of DS-1971a would be located near the heme iron of CYP2C8. These results clearly indicate that M1 is the predominant metabolite in humans and a human disproportionate metabolite due to species-specific differences in metabolism. SIGNIFICANCE STATEMENT: This report is the first to show a human disproportionate metabolite generated by CYP2C8-mediated primary metabolism. We clearly demonstrate that DS-1971a, a mixed aldehyde oxidase and cytochrome P450 substrate, was predominantly metabolized by CYP2C8 to form M1, a human disproportionate metabolite. Species differences in the formation of M1 highlight the regio- and stereoselective metabolism by CYP2C8, and the proposed interaction between DS-1971a and CYP2C8 provides new knowledge of CYP2C8-mediated metabolism of cyclohexane-containing substrates.

Novel cancer subtyping method based on patient-specific gene regulatory network.

The identification of cancer subtypes is important for the understanding of tumor heterogeneity. In recent years, numerous computational methods have been proposed for this problem based on the multi-omics data of patients. It is widely accepted that different cancer subtypes are induced by different molecular regulatory networks. However, only a few incorporate the differences between their molecular systems into the identification processes. In this study, we present a novel method to identify cancer subtypes based on patient-specific molecular systems. Our method realizes this by quantifying patient-specific gene networks, which are estimated from their transcriptome data, and by clustering their quantified networks. Comprehensive analyses of The Cancer Genome Atlas (TCGA) datasets applied to our method confirmed that they were able to identify more clinically meaningful cancer subtypes than the existing subtypes and found that the identified subtypes comprised different molecular features. Our findings also show that the proposed method can identify the novel cancer subtypes even with single omics data, which cannot otherwise be captured by existing methods using multi-omics data.

System-Based Differential Gene Network Analysis for Characterizing a Sample-Specific Subnetwork.

Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.