Using a chemical biology approach to develop novel inhibitors of mitochondrial oxidative phosphorylation for the treatment of ovarian cancer

点击翻译按钮获取中文摘要

The number of women Veterans is rapidly increasing and expected to reach to 16-17% of the total Veteran population within the next two decades. Furthermore, female Veterans are an aging population, the 45-64 age group being the largest cohort. As female Veterans age, an increasing number are expected to present with cancers. Gynecologic tumors constitute 12% of all cancers in women Veterans with ovarian malignancy being the second most common in this group. Ovarian cancer predominantly develops in postmenopausal women. Therefore, the incidence of this cancer is expected to increase as the population ages. Ovarian cancer is an especially lethal cancer with none of the modern treatment regimens showing major benefits on overall survival of patients. Our proposed research focuses on the development of novel oxidative phosphorylation (OXPHOS) inhibitors for the treatment of ovarian cancer. Leads for therapeutics emerging from this work have great potential benefit to women Veterans and is highly significant to the VA Healthcare system. Recurrent tumors, cancer stem cells, and non-malignant cells from the tumor microenvironment engage OXPHOS for their metabolic needs. Therefore, there is renewed interest in developing OXPHOS inhibitors as novel agents for the treatment of cancer. We have identified a group of compounds that inhibit OXPHOS by interfering with ubiquinone in transporting electrons from NADH and FADH2 to molecular oxygen. The compounds we are testing, citral, plumbagin, curcumin, atovaquone and others, have an unsaturated carbonyl functional group that mimics ubiquinone’s benzoquinone substructure. As a result, these compounds interfere in OXPHOS, cause rapid increase in intracellular oxygen radicals that damage DNA, and induce apoptosis through the activation of p53. Such ubiquinone mimetics are therefore candidates for cancer therapy. However, the OXHPOS inhibitors we have examined so far, show poor potency and pharmacokinetic characteristics. In this application we propose to employ rapid and cost-effective computational approaches to identify novel and potent OXPHOS inhibitors with the goal of developing leads for treatment of high-grade serous ovarian tumors. In Aim 1, we will develop and validate a data mining pipeline to identify potent and drug-like OXPHOS inhibitors from extensive assay data available in PubChem public repository. In Aim 2 we will employ a machine learning model, based on previous screening data, to guide selection of new compounds for iterative screening in cell biological assays reporting on different aspects of the OXPHOS pathway. In Aim 3, we will test combination dosing of OXPHOS and PARP inhibitors for synergistic potency and efficacy in ovarian cancer models. The methods developed here could be applied generally in leveraging prior assay data to build models that guide iterative screening as a more efficient approach than standard high throughput screening. The proposed studies should help us to identify the most promising biochemical targets and inhibitory compound classes for OXPHOS-targeted ovarian cancer therapies. Lead OXPHOS inhibitors discovered through our approach will be tested in future clinical trials that include women Veterans diagnosed for high grade serous ovarian cancer.