A Precision Medicine Tool for Optimal Personalized Treatment in Patients with Acute Myeloid Leukemia

用于急性髓系白血病患者最佳个性化治疗的精准医疗工具

• 批准号: 10547266
• 负责人: Athanasios Mantalaris
• 金额: $ 39.69万
• 依托单位: SANICKA, INC
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10547266
• 关键词: Achievement; Acute Myelocytic Leukemia; Address; Adoption; Adult; Adult Acute Myeloblastic Leukemia; Algorithms; Blood; Body Surface Area; Body Weight; Bone Marrow; Cell Count; Cell Cycle; Cell Cycle Kinetics; Cells; Chemoresistance; Clonal Evolution; Code; Combination immunotherapy; Computer software; Confidence Intervals; Coupled; DNA analysis; Data; Decision Making; Development; Diagnosis; Disease; Disease Outcome; Disease Resistance; Disease remission; Dose; Drug Kinetics; FDA approved; Family; Future; Genomics; Gold; Health Care Costs; Health Personnel; Health Professional; Hepatic; Heterogeneity; Hospital Costs; Immuno-Chemotherapy; Intervention Studies; Kidney; Kinetics; Leukemic Cell; Malignant Bone Neoplasm; Malignant Neoplasms; Measurable; Medical Device; Mission; Mutation; Observational Study; Oncologist; Online Systems; Outcome; Overweight; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Pharmacodynamics; Pharmacology Study; Phase; Plasma; Precision therapeutics; Process; Prognosis; Protein Analysis; Publishing; Pythons; Quality of life; Recovery; Refractory Disease; Regimen; Relapse; Residual Tumors; Resistance; Resistance development; Risk; Schedule; Secure; Selection for Treatments; Services; Standardization; Surveys; Techniques; Therapeutic; Time; Toxic effect; Treatment Cost; Treatment Efficacy; Validation; Vertebral column; Visualization; acute myeloid leukemia cell; base; chemotherapy; clinical decision support; diagnostic value; dosage; improved; improved outcome; individual patient; leukemia; mathematical model; multiple omics; neutrophil; novel; novel drug class; optimal treatments; patient oriented; peripheral blood; personalized medicine; pharmacokinetics and pharmacodynamics; precision medicine; prevent; prospective; rapid growth; recruit; response; satisfaction; simulation; small molecule inhibitor; standard of care; support tools; targeted agent; therapy outcome; tool; treatment optimization; treatment response; tumor; user-friendly

Acute myeloid leukemia (AML) is an aggressive cancer of the bone marrow and peripheral blood with poor prognosis mostly due to relapse. Despite decades of improvements in chemo-immunotherapy (CIT) and, more recently, the use of hypomethylating agent (HMAs) and addition of novel small molecule inhibitors (SMIs) to back-bone chemotherapy, AML treatment selection and dosage remains mostly empiric, with standard first- and second-line regimens, each with potential toxic consequences; dosing is based on body surface area, renal and hepatic function and pharmacokinetics/pharmacodynamics (PK/PD), ignoring tumor-specific parameters (tumor bulk, heterogeneity and cell cycle kinetics). Consequently, up to 60% of patients are under- or over-dosed and a further 10-40% of patients have primary refractory disease (non-responders) to gold-standard of care first-line CIT resulting in poor outcomes with high healthcare costs. Advances in genomic techniques are now able to assess AML clonal dynamics and measurable residual disease in patients throughout therapy with reasonable turn-around times. This rapid growth in diagnostic capabilities in conjunction with an ever-increasing number of available FDA-approved targeted treatments for patients with AML, present a constant and ongoing gap between practice and potential resulting in significant lag-time between use and know-how to improve outcomes. A framework for personalized treatment selection and optimization is therefore an unmet need in precision therapy for patients with AML. To address this need, “πCITTM Simulator”, a Clinical Decision Support service, was developed to assist Oncologists with treatment selection by providing (before treatment begins) simulations of disease response, progression, AML clonal evolution and normal blood count recovery in patients receiving therapy with different CIT, SMI and HMA options and combinations. In order to improve on the selected treatment for best patient outcome and reduced toxicity, “πCITTM Optimizer”, a Software as Medical Device, was developed to optimize drug, dose and schedule. πCITTM Simulator and Optimizer provide healthcare professionals with critical data, prior to treatment initiation, to prevent over- or under-dosage and administration of ineffective drugs for patients with resistant disease, thereby reducing treatment and hospitalization costs. In Phase 1 of this fast- track application, SANICKA will develop its first minimum viable product by (1) expanding πCITTM to incorporate novel SMIs/HMAs resulting in the launch to the market of πCITTM Simulator and (2) creating a web-based Clinician Portal for Oncologists to upload patient and tumor data, and visualize results. During Phase 2, SANICKA will (1) expand πCITTM Optimizer to capture AML sub-clonal kinetics and sensitivity to CIT/SMIs/HMAs using retrospectively-collected multi-center patient data for validation and (2) prospectively validate πCITTM Optimizer with an observational study in patients with AML as they undergo treatment. The use of πCITTM will improve patient outcomes and quality of life and reduce healthcare costs by introducing a step-change in the approach to AML therapy: 1) personalization, 2) precision simulation and, 3) dynamic optimization of treatment.

急性髓样白血病（AML）是骨髓和外周血的侵袭性癌症 预后主要是由于继电器。尽管化学免疫疗法（CIT）有数十年的改善，并且更多 最近，使用甲基化剂（HMA）和新型小分子抑制剂（SMI）的使用 背骨化学疗法，AML治疗选择和剂量仍然是经验性的，具有标准的第一和标准 二线方案，每种方案都有潜在的有毒后果；剂量基于身体表面积，肾脏和 肝功能和药代动力学/药效学（PK/PD），忽略肿瘤特异性参数（肿瘤 散装，异质性和细胞周期动力学）。因此，多达60％的患者剂量不足或过度剂量，并且 另外10-40％的患者患有原发性难治性疾病（无反应者）至一线护理标准 CIT导致较差的医疗保健成本结果不佳。基因组技术的进步现在能够 在整个治疗过程中评估患者的AML克隆动力学和可测量的残留疾病 周转时间。诊断能力的快速增长与数量不断增加 可用的FDA批准的针对AML患者的靶向治疗方法，呈现出恒定且持续的差距 练习和潜在的使用，导致使用和专业知识之间的滞后时间大大改善结果。一个 因此，用于个性化治疗选择和优化的框架是精确治疗的未满足需求 适用于AML的患者。为了满足这种需求，临床决策支持服务“πcittm模拟器”是 通过提供（在开始开始之前）模拟肿瘤学家来协助肿瘤学家进行治疗的选择 接受的患者的疾病反应，进展，AML克隆进化和正常血数恢复 具有不同的CIT，SMI和HMA选项和组合的治疗。为了改善所选治疗 为了最好的患者结局和降低毒性，开发了一种医疗设备的软件“πcittm优化器” 优化药物，剂量和时间表。 πCITTM模拟器和优化器为医疗保健专业人员提供 在治疗计划之前，关键数据可以防止过度或剂量不足和无效药物给药 对于耐药性疾病的患者，从而降低了治疗和住院费用。在这个快速的第1阶段 轨道应用，Sanicka将通过（1）扩展πCittm开发其第一个最低可行产品 新颖的SMI/HMA导致推出πCittm模拟器市场，（2）创建基于Web的市场 肿瘤学家上载患者和肿瘤数据的临床医生门户，并可视化结果。在第2阶段， Sanicka将（1）扩展πCittm优化器，以捕获AML亚连锁动力学和对CIT/SMIS/HMAS的敏感性 使用回顾性收集的多中心患者数据进行验证和（2）前瞻性验证πCITTM 在接受治疗时，对AML患者进行观察性研究的优化器。 πCittm的使用将 通过引入逐步改变 AML治疗方法：1）个性化，2）精确模拟和3）治疗的动态优化。