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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.

项目总结/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年每年有6万人死亡。PDAC通常在晚期被诊断，从而排除了大多数患者手术切除。虽然新的全身治疗方案提高了生存率，多个选项，没有工具来选择最佳方案，从这些（在个性化的基础上），创造了临床决策中令人沮丧的悖论。由于缺乏个性化的预测工具，目前的标准护理治疗策略是基于预后因素，如年龄，阶段，性能状态，血清白蛋白，等开发预测工具，以确定最佳的系统性，这是一个关键、紧迫和未满足的需求。治疗方案，并从考虑无效的选择，在个性化的基础上，以改善提高生活质量，减少过度治疗。CerFlux公司正在开发这种预测技术，和快速个性化肿瘤疗效测试（POET），以匹配每个患者的正确治疗-之前治疗-在短期内改变胰腺癌的治疗，并改变人们的生活。世界各地的患者和供应商。我们的个性化医疗方法是独特的，并进一步加强由CerFlux，Inc.詹姆斯综合癌症中心在俄亥俄州州立大学。拟议的项目将建立在我们的团队最近的工作，包括专利 (US 10，114，010 B1）用于药物转运和胰腺微组织肿瘤的仿生体外平台模型该提案的商业目标是确定在个性化治疗中使用POET的最佳实践。我们的假设是，POET中观察到的治疗反应将接近相应的病人。我们的目标是预测有效和无效的治疗，为每一个病人事先开始治疗为实现我们的目标，我们提出以下目标：目的1：校准和优化POET，以评估使用人PDAC细胞系异种移植物的治疗方法，随后用患者组织进行测试。目的2：在个体化的基础上评估各种全身治疗药物在POET中的疗效，在个性化治疗中使用POET的协议和最佳实践。我们设想CerFlux，Inc.和詹姆斯俄亥俄州州立大学的综合癌症中心，包括将机器学习整合到根据多种因素的组合得出“POET评分”--个性化的定量疗效评分。数据 POET和POET评分将帮助临床团队在第一次治疗前对个体患者的治疗进行排名，药物输注如果成功，这项SBIR驱动的研究有可能改变胰腺癌的治疗，短期内，并在世界各地产生积极影响。