Identification and validation of microtubule depolymerizing agent, CYT997, as a potential drug candidate for hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is a typically fatal malignancy with limited treatment options and poor survival rates, despite recent FDA approvals of newer treatment options. We aim to address this unmet need by using a proprietary computational drug discovery platform that identifies drug candidates with the potential to advance rapidly and successfully through preclinical studies. METHODS: We generated an in silico model of HCC biology to identify the top 10 small molecules with predicted efficacy. The most promising candidate, CYT997, was tested for its in vitro effects on cell viability and cell death, colony formation, cell cycle changes, and cell migration/invasion in HCC cells. We used an HCC patient-derived xenograft (PDX) mouse model to assess its in vivo efficacy. RESULTS: CYT997 was significantly more cytotoxic against HCC cells than against primary human hepatocytes, and sensitized HCC cells to sorafenib. It arrested cell cycle at the G2/M phase with associated up-regulations of p21, p-MEK1/2, p-ERK, and down-regulation of cyclin B1. Cell apoptosis and senescence-like morphology were also observed. CYT997 inhibited HCC cell migration and invasion, and down-regulated the expressions of acetylated tubulins, ß-tubulin, glypican-3 (GPC3), ß-catenin, and c-Myc. In vivo, CYT997 (20 mg/kg, three times weekly by oral gavage) significantly inhibited PDX growth, while being non-toxic to mice. Immunohistochemistry confirmed the down-regulation of GPC3, c-Myc, and Ki-67, supporting its anti-proliferative effect. CONCLUSION: CYT997 is a potentially efficacious and non-toxic drug candidate for HCC therapy. Its ability to down-regulate GPC3, ß-catenin, and c-Myc highlights a novel mechanism of action.

Synergistic drug combinations from electronic health records and gene expression.

OBJECTIVE: Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding. METHOD: We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis. RESULTS: From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence. CONCLUSIONS: This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.