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

女退伍军人的人数正在迅速增加，预计将达到退伍军人总数的16-17%。 未来20年内的人口。此外，女性退伍军人是一个老龄化的人口，45-64岁 组是最大的队列。随着女性退伍军人年龄的增长，预计将有越来越多的人出现在 癌的妇科肿瘤占女性癌症的12%， 在这一组中第二常见。卵巢癌主要发生在绝经后妇女。 因此，这种癌症的发病率预计将随着人口老龄化而增加。卵巢癌是 尤其是致命的癌症，没有任何现代治疗方案显示出对总生存率的重大益处 病人。我们的研究重点是开发新的氧化磷酸化（OXPHOS） 用于治疗卵巢癌的抑制剂。从这项工作中产生的治疗方法具有巨大的潜力 这对退伍军人妇女有利，对退伍军人事务部医疗保健系统非常重要。复发性肿瘤，癌干 细胞和来自肿瘤微环境的非恶性细胞参与OXPHOS以满足其代谢需要。 因此，开发OXPHOS抑制剂作为治疗糖尿病的新药物重新引起了人们的兴趣。 癌我们已经鉴定了一组化合物，其通过干扰细胞中的泛醌来抑制OXPHOS。 将电子从NADH和FADH 2转移到分子氧。我们测试的化合物柠檬醛， 白花丹素、姜黄素、阿托伐醌等具有模拟 泛醌的苯醌亚结构。因此，这些化合物干扰OXPHOS，引起快速的 细胞内氧自由基增加，损伤DNA，并通过激活p53诱导细胞凋亡。 因此，这种泛醌模拟物是癌症治疗的候选物。然而，我们使用的OXHPOS抑制剂 到目前为止，已经检查过，显示出较差的效力和药代动力学特征。在本申请中，我们提出 采用快速和具有成本效益的计算方法来识别新的和有效的OXPHOS抑制剂 目的是开发治疗高级别浆液性卵巢肿瘤的先导物。在目标1中，我们将开发 并验证数据挖掘管道，以从广泛的分析数据中识别有效的药物样OXPHOS抑制剂 在PubChem公共资源库中提供。在目标2中，我们将采用基于先前的机器学习模型， 筛选数据，指导选择新化合物，用于细胞生物学测定报告中的迭代筛选 OXPHOS途径的不同方面。在目标3中，我们将测试OXPHOS和PARP的组合给药 抑制剂在卵巢癌模型中的协同效力和功效。这里开发的方法可以是 通常应用于利用先前的测定数据来构建模型，该模型引导迭代筛选作为更有效的方法。 方法比标准高通量筛选。拟议的研究应有助于我们确定 OXPHOS靶向卵巢癌治疗的有前途的生化靶点和抑制性化合物类别。 通过我们的方法发现的主要OXPHOS抑制剂将在未来的临床试验中进行测试，其中包括 被诊断为高级别浆液性卵巢癌的退伍军人。

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.