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

急性髓性白血病（AML）是一种侵袭性骨髓和外周血的癌症， 预后多因复发。尽管化学免疫疗法（CIT）经过数十年的改进， 最近，低甲基化剂（HMAs）的使用和新的小分子抑制剂（SMI）的添加， 骨干化疗，AML治疗的选择和剂量仍然主要是经验性的，标准的第一和第二次化疗， 二线方案，每种方案都有潜在的毒性后果;剂量基于体表面积、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 体积、异质性和细胞周期动力学）。因此，高达60%的患者剂量不足或过量， 另有10-40%的患者患有对金标准一线治疗无效的原发性难治性疾病 CIT导致不良结局和高医疗费用。基因组技术的进步现在能够 在整个治疗过程中评估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，以及越来越多的 FDA批准的针对AML患者的靶向治疗在以下方面存在持续和持续的差距： 实践和潜力导致使用和专门知识之间的显著滞后时间，以改善结果。一 因此，个性化治疗选择和优化的框架是精确治疗中未满足的需求 对于AML患者。为了满足这一需求，“πCITTM模拟器”，一个临床决策支持服务， 通过提供（在治疗开始前）模拟 接受治疗的患者中的疾病缓解、进展、AML克隆演变和正常血细胞计数恢复 不同的CIT，SMI和HMA选项和组合的治疗。为了改善所选的治疗方法 为了获得最佳的患者结果和降低毒性，开发了“πCITTM Optimizer”，即医疗器械软件， 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 治疗开始前的关键数据，以防止过量或剂量不足以及无效药物的给药 对于耐药疾病患者，从而降低治疗和住院费用。在这个快速的第一阶段- 跟踪应用，SANICKA将开发其第一个最小可行的产品，通过（1）扩展πCITTM， 新的SMI/HMA导致πCITTM模拟器上市，以及（2）创建基于Web的 肿瘤学家的临床医生门户网站，用于上传患者和肿瘤数据，并可视化结果。在第二阶段， SANICKA将（1）扩展πCITTM Optimizer以捕获AML亚克隆动力学和对CIT/SMI/HMA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 Optimizer与AML患者接受治疗时的观察性研究。πCITTM的使用将 改善患者的治疗效果和生活质量，并通过逐步改变 AML治疗方法：1）个性化，2）精确模拟，3）治疗的动态优化。

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.