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.

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.