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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：
10080473
负责人：
Karim I Budhwani
金额：
$ 24.91万
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/10080473
关键词：
Age Alabama Architecture Area Biomimetics Biopsy Biopsy Specimen Cell Death Cell Line Cessation of life Chemotherapy-Oncologic Procedure Clinical Collaborations Comprehensive Cancer Center Core Biopsy Data Development Devices Diagnosis Diagnostic Diffuse Drug Delivery Systems Excision Exposure to Future Goals Harvest Human In Vitro Infusion procedures Legal patent Machine Learning Malignant Neoplasms Malignant neoplasm of pancreas Modeling Operative Surgical Procedures Pain Pancreas Pancreatic Ductal Adenocarcinoma Patient Rights Patients Performance Status Pharmaceutical Preparations Pharmacology Phase Preparation Prognostic Factor Protocols documentation Provider Quality of life Regimen Right to Treatments Serum Albumin Small Business Innovation Research Grant Specimen Surface Properties Survival Rate Systemic Therapy Technology Testing Therapeutic Uses Time Tissues Tumor Tissue Universities Validation Work Xenograft procedure base cancer therapy chemotherapy clinical decision-making clinically translatable commercialization cost design drug testing effective therapy efficacy testing human tissue image processing improved individual patient ineffective therapies overtreatment personalized medicine personalized predictions pre-clinical precision oncology predictive test predictive tools response screening standard of care tissue culture tool treatment response treatment strategy tumor tumor xenograft

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 O’Neil Comprehensive Cancer Center at the University of Alabama at Birmingham. 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 O’Neil Comprehensive Cancer Center at the University of Alabama at Birmingham 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/文摘