Bone metastasis is the most frequent complication in prostate cancer patients and associated outcome remains fatal. Radium223 (Rad223), a bone targeting radioisotope improves overall survival in patients (3.6 months vs. placebo). However, clinical response is often followed by relapse and disease progression, and associated mechanisms of efficacy and resistance are poorly understood. Research efforts to overcome this gap require a substantial investment of time and resources. Computational models, integrated with experimental data, can overcome this limitation and drive research in a more effective fashion. Accordingly, we developed a predictive agent-based model of prostate cancer bone metastasis progression and response to Rad223 as an agile platform to maximize its efficacy. The driving coefficients were calibrated on ad hoc experimental observations retrieved from intravital microscopy and the outcome further validated, in vivo. In this work we offered a detailed description of our data-integrated computational infrastructure, tested its accuracy and robustness, quantified the uncertainty of its driving coefficients, and showed the role of tumor size and distance from bone on Rad223 efficacy. In silico tumor growth, which is strongly driven by its mitotic character as identified by sensitivity analysis, matched in vivo trend with 98.3% confidence. Tumor size determined efficacy of Rad223, with larger lesions insensitive to therapy, while medium- and micro-sized tumors displayed up to 5.02 and 152.28-fold size decrease compared to control-treated tumors, respectively. Eradication events occurred in 65 ± 2% of cases in micro-tumors only. In addition, Rad223 lost any therapeutic effect, also on micro-tumors, for distances bigger than 400 μm from the bone interface. This model has the potential to be further developed to test additional bone targeting agents such as other radiopharmaceuticals or bisphosphonates.

中文翻译：

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基于试剂的前列腺癌骨转移进展和对镭223的反应模型。


骨转移是前列腺癌患者最常见的并发症，相关结果仍然是致命的。 Radium223 (Rad223) 是一种骨靶向放射性同位素，可改善患者的总生存期（与安慰剂相比为 3.6 个月）。然而，临床反应之后往往会出现复发和疾病进展，而相关的疗效和耐药机制却知之甚少。克服这一差距的研究工作需要投入大量的时间和资源。与实验数据相结合的计算模型可以克服这一限制并以更有效的方式推动研究。因此，我们开发了一种基于预测剂的前列腺癌骨转移进展模型以及对 Rad223 的反应，作为一个灵活的平台，以最大限度地提高其功效。根据从活体显微镜中检索到的临时实验观察结果来校准驱动系数，并在体内进一步验证结果。在这项工作中，我们详细描述了我们的数据集成计算基础设施，测试了其准确性和鲁棒性，量化了其驱动系数的不确定性，并展示了肿瘤大小和距骨距离对 Rad223 功效的作用。在硅片中，肿瘤生长受到敏感性分析确定的有丝分裂特征的强烈驱动，与体内趋势相匹配，置信度为 98.3%。肿瘤大小决定了 Rad223 的疗效，较大的病灶对治疗不敏感，而中型和微型肿瘤的大小与对照治疗的肿瘤相比分别减少了 5.02 倍和 152.28 倍。仅在微肿瘤病例中，65 ± 2% 的病例发生了根除事件。此外，Rad223 对距离骨界面超过 400 μm 的微肿瘤也失去了任何治疗效果。 该模型有可能进一步开发以测试其他骨靶向剂，例如其他放射性药物或双膦酸盐。