Development of personalized ex vivo predictive technology for rapidly matching patient tumors with chemotherapy regimens before treatment.

开发个性化离体预测技术，用于在治疗前将患者肿瘤与化疗方案快速匹配。

基本信息

批准号：
10303439
负责人：
Karim I Budhwani
金额：
$ 5.2万
依托单位：
CERFLUX, INC.
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-10 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10303439
关键词：
Age Biomimetics Cell Line Cessation of life Chemotherapy-Oncologic Procedure Clinical Collaborations Comprehensive Cancer Center Data Development Diagnosis Excision Goals Human In Vitro Infusion procedures Legal patent Machine Learning Malignant Neoplasms Malignant neoplasm of pancreas Modeling Ohio Operative Surgical Procedures Pancreas Pancreatic Ductal Adenocarcinoma Patients Performance Status Pharmaceutical Preparations Pharmacology Prognostic Factor Protocols documentation Provider Quality of life Regimen Serum Albumin Small Business Innovation Research Grant Survival Rate Systemic Therapy Technology Testing Therapeutic Uses Tissues Universities Work Xenograft procedure base cancer therapy clinical decision-making cost effective therapy efficacy evaluation efficacy testing improved individual patient ineffective therapies overtreatment pancreatic ductal adenocarcinoma cell personalized medicine personalized predictions precision oncology predictive tools response standard of care tool treatment response treatment strategy tumor

项目摘要

Project Summary/Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers with <9% five-year survival rate and an estimated 60,000 deaths/year by 2030. PDAC is often diagnosed at an advanced stage thereby precluding surgical resection for most patients. While new systemic therapy regimens have improved survival, availability of multiple options, without tools to select an optimal regimen from these (on an individualized basis), has created a frustrating paradox in clinical decision-making. Due to a lack of personalized predictive tools, current standard of care treatment strategy is based on prognostic factors such as age, stage, performance status, serum albumin, etc. There is a critical, urgent and unmet need to develop predictive tools that can identify optimal systemic therapy regimens and eliminate from consideration ineffective options, on an individualized basis, to improve quality of life and reduce overtreatment. CerFlux, Inc. is developing such predictive technology with its low-cost and rapid Personalized Oncology Efficacy Test (POET) to match each patient with the right treatment – before treatment – to transform pancreatic cancer treatment in the near-term and make a difference in the lives of patients and providers around the world. Our personalized medicine approach is unique and further enhanced by a commercial-academic collaboration between CerFlux, Inc. and the James Comprehensive Cancer Center at the Ohio State University. The proposed project will build on recent work by our team including a patented (US 10,114,010B1) biomimetic in vitro platform for pharmacological transport and pancreatic microtissue tumor models. The commercial goal of this proposal is to identify best practices for using POET in personalized therapy. Our hypothesis is that response to treatment observed in POET will approximate the response in the corresponding patient. Our objective is to predict both effective and ineffective treatments for each patient prior to initiating treatment. We propose the following aims to achieve our objective: Aim 1: Calibrate and optimize POET for evaluating therapeutics using human PDAC cell-line xenografts for subsequent testing with patient tissue. Aim 2: Evaluate efficacy of various systemic therapy agents in POET on an individualized basis to establish protocols and best practices for using POET in personalized therapy. We envision substantial continuing commercial-academic collaboration between CerFlux, Inc. and the James Comprehensive Cancer Center at the Ohio State University including the integration of machine learning to derive a “POET Score” – a personalized quantitative efficacy score – based on a combination of factors. Data from POET and the POET Score will help clinical teams rank treatments for individual patients before the first drug infusion. If successful, this SBIR-driven study has the potential to transform pancreatic cancer treatment in the near-term and make a positive impact around the world.

项目摘要/摘要胰腺导管腺癌（PDAC）是最致命的癌症之一，五年生存率<9％估计到2030年估计每年60,000例。PDAC经常在高级阶段被诊断出来，从而排除大多数患者的手术切除。虽然新的系统治疗方案的生存率提高了，但可用性有多种选项，没有工具来从这些选项中选择最佳方案（以个性化的基础）创建临床决策中令人沮丧的悖论。由于缺乏个性化的预测工具，当前标准护理治疗策略的基于预后因素，例如年龄，阶段，性能状态，血清白蛋白，等等。有一个至关重要的，紧急和未满足的需要开发可以识别最佳系统性的预测工具治疗方案并从个性化的基础上消除了无效的选择，以改善生活质量并减少过度治疗。 Cerflux，Inc。正在以低成本开发这种预测技术和快速个性化的肿瘤学测试（诗人）使每个患者与正确的治疗相匹配 - 之前治疗 - 在短期内改变胰腺癌治疗，并改变世界各地的患者和提供者。我们的个性化医学方法是独一无二的，进一步增强了由Cerflux，Inc。和James综合癌症中心之间的商业学术合作在俄亥俄州立大学。拟议的项目将基于我们团队最近的工作，包括专利（US 10,114,010B1）用于药物运输和胰岛微动物肿瘤的仿生体内平台型号。该提案的商业目标是确定在个性化治疗中使用诗人的最佳实践。我们的假设是，在诗人中观察到的对治疗的反应将近似相应的患者。我们的目标是预测每位患者的有效治疗和无效治疗开始治疗。我们提出以下目的以实现我们的目标：目标1：使用人PDAC细胞系Xenographographics校准和优化用于评估治疗的诗人随后对患者组织进行测试。目标2：在个性化的基础上评估各种系统治疗剂的效率在个性化治疗中使用诗人的方案和最佳实践。我们设想Cerflux，Inc。和James之间的实质性持续的商业学术合作俄亥俄州立大学的综合癌症中心，包括将机器学习的整合到基于因素的组合，得出“诗人分数”（诗人得分”（个性化的定量效率得分）。数据从诗人和诗人分数中，临床团队将在第一次之前对个别患者的治疗进行排名药物输液。如果成功，这项以SBIR驱动的研究有可能改变胰腺癌治疗近期并对世界产生积极影响。