Mathematical modeling and molecular imaging to maximize response while minimizing toxicities from systemic therapies in preclinical models of breast cancer

数学建模和分子成像可最大限度地提高乳腺癌临床前模型中全身治疗的反应，同时最大限度地降低毒性

• 批准号: 10564905
• 负责人: Anna C. Sorace
• 金额: $ 47.78万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10564905
• 关键词: Acceleration; Address; Affect; Aftercare; Animals; Biological; Biology; Brain; Brain imaging; Breast Cancer Model; Cardiac; Cardiotoxicity; Cognitive deficits; Combination Drug Therapy; Combined Modality Therapy; Cytotoxic Chemotherapy; Disease; Dose; Dose Limiting; Doxorubicin; ERBB2 gene; Goals; Health; Heart; Histology; Human; Image; Imaging technology; Immunohistochemistry; Immunotherapy; In complete remission; Injury; Long-Term Effects; Mathematics; Measurement; Measures; Membrane Potentials; Methods; Microglia; Neoadjuvant Therapy; Organ; Outcome; Pathologic; Patients; Phase I Clinical Trials; Positron-Emission Tomography; Pre-Clinical Model; Radiation therapy; Regimen; Risk; Schedule; Site; Standardization; Systemic Therapy; Testing; Therapeutic; Time; Toxic effect; Trastuzumab; Treatment Protocols; Tumor Burden; Xenograft procedure; advanced breast cancer; alternative treatment; cancer therapy; chemotherapy; clinically relevant; cognitive ability; glial activation; heart function; heart imaging; imaging modality; improved; malignant breast neoplasm; mathematical methods; mathematical model; molecular imaging; mouse model; neuroinflammation; optimal control theory; optimal treatments; patient derived xenograft model; pre-clinical; predictive modeling; prospective test; quantitative imaging; response; side effect; systemic toxicity; targeted treatment; treatment strategy; tumor; uptake

PROJECT SUMMARY Our overarching goal is to utilize biology-based mathematical models and advanced molecular imaging to dramatically decrease systemic toxicities while either maintaining or accelerating tumor control in preclinical models of breast cancer. Advances in systemic therapies have improved long-term survival in patients with locally-advanced breast cancer, however there has been a concomitant increase in the associated their long-term side effects, including cognitive deficits and cardiac problems. We have developed practical, biology- based mathematical models capable of systematically investigating the timing, order, dosing, and sequencing of combination therapies to identify therapeutic regimens that can potentially maximize response while minimizing toxicity. Preliminary results (both experimental and mathematical) reveal that alternating the order and dosing of combination chemotherapy (doxorubicin) and targeted therapy (Herceptin) can significantly and synergistically enhance response while reducing the chemotherapy dose by 50%. Furthermore, using optimal control theory, we have identified therapeutic regimens suggesting we can achieve tumor control 1.6x faster without increasing the amount of chemotherapy. We propose to develop the mathematical formalism that allows for systematically determining, on a patient specific basis, therapeutic regimens that maximize tumor response and minimize side effects. We then select the most promising options and test them experimentally against established treatment regimens and test for superior outcomes and toxicity. We also seek to develop quantitative imaging technologies capable of characterizing the temporal alterations in brain and cardiac function—organs known to be adversely affected by chemotherapies. We plan to achieve this goal with the following Specific Aims. Aim 1 will validate mathematical predictions for maintaining tumor control with minimal chemotherapy dose by employing optimal control theory to identify and biologically validate (with immunohistochemistry and overall tumor burden measurements) the three most promising combination treatment strategies. Aim 2 will implement advanced molecular imaging to quantify toxicity changes in critical organs during therapy by employing cardiac imaging of membrane potential (18F-TTP+-PET) and brain imaging of microglia activation (TSPO, measured with 18F-DPA- 714-PET) to determine longitudinal differences between long-term effects in animals treated with the standard and the optimized regimens. Completion of these aims will deliver a practical, experimental-computational approach for identifying optimal treatment strategies in pre-clinical mouse models, and appropriate for prospective testing in phase 1 clinical trials. As toxicity is the main dose-limiting factor in cancer treatments, developing methods to control it will dramatically effect patient health.

项目摘要 我们的首要目标是利用基于生物学的数学模型和先进的分子成像 显著降低全身毒性，同时维持或加速肿瘤控制， 乳腺癌的临床前模型。全身治疗的进展改善了患者的长期生存率 然而，与局部晚期乳腺癌相关的乳腺癌的发病率也随之增加， 长期副作用，包括认知缺陷和心脏问题。我们发展了实用的生物学- 基于数学模型，能够系统地调查时间，顺序，剂量和排序， 联合治疗，以确定可能最大限度地提高反应，同时最大限度地减少 毒性初步结果（实验和数学）表明，交替的顺序和剂量的 联合化疗（多柔比星）和靶向治疗（赫赛汀）可以显着协同作用， 提高反应，同时减少50%的化疗剂量。此外，利用最优控制理论， 我们已经确定了治疗方案，这表明我们可以在不增加肿瘤生长的情况下， 化疗的剂量我们建议发展数学形式主义，允许系统地 在患者特异性的基础上，确定使肿瘤反应最大化并使副作用最小化的治疗方案， 方面的影响.然后，我们选择最有希望的选择，并对既定的治疗方法进行实验测试 治疗方案和测试的优越结果和毒性。我们还寻求开发定量成像技术 能够表征大脑和心脏功能的时间变化，已知这些器官是不利的， 受到化疗的影响。我们计划通过以下具体目标实现这一目标。目标1将验证 通过采用最佳化疗剂量维持肿瘤控制的数学预测 控制理论，以确定和生物学验证（免疫组织化学和总体肿瘤负荷 测量）的三个最有前途的组合治疗策略。目标2将实现先进的 通过使用心脏成像来量化治疗期间关键器官中的毒性变化的分子成像， 膜电位（18F-TTP +-PET）和小胶质细胞激活的脑成像（TSPO，用18F-DPA-PET测量）。 714-PET），以确定用标准品治疗的动物的长期效应之间的纵向差异 和优化的治疗方案。这些目标的完成将提供一个实用的，实验计算 在临床前小鼠模型中确定最佳治疗策略的方法， 1期临床试验中的前瞻性测试。由于毒性是癌症治疗中的主要剂量限制因素， 开发控制它的方法将极大地影响病人的健康。