Pharmacological depletion of RNA splicing factor RBM39 by indisulam synergizes with PARP inhibitors in high-grade serous ovarian carcinoma.

Ovarian high-grade serous carcinoma (HGSC) is the most common subtype of ovarian cancer with limited therapeutic options and a poor prognosis. In recent years, poly-ADP ribose polymerase (PARP) inhibitors have demonstrated significant clinical benefits, especially in patients with BRCA1/2 mutations. However, acquired drug resistance and relapse is a major challenge. Indisulam (E7070) has been identified as a molecular glue that brings together splicing factor RBM39 and DCAF15 E3 ubiquitin ligase resulting in polyubiquitination, degradation, and subsequent RNA splicing defects. In this work, we demonstrate that the loss of RBM39 induces splicing defects in key DNA damage repair genes in ovarian cancer, leading to increased sensitivity to cisplatin and various PARP inhibitors. The addition of indisulam also improved olaparib response in mice bearing PARP inhibitor-resistant tumors. These findings demonstrate that combining RBM39 degraders and PARP inhibitors is a promising therapeutic approach to improve PARP inhibitor response in ovarian HGSC.

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.