BACKGROUND The standard treatment for high-grade non-Hodgkin lymphoma involves the combination of chemotherapy and immunotherapy. We characterize in-silico the optimal combination protocol that maximizes the overall survival probability. We rely on a pharmacokinetics/pharmacodynamics (PK/PD) model that describes the joint evolution of tumor and effector cells, as well as the effects of both chemotherapy and immunotherapy. The toxicity is taken into account through ad-hoc constraints. We develop an optimization algorithm that belongs to the class of Monte-Carlo tree search algorithms. Our simulations rely on an in-silico population of heterogeneous patients differing with respect to their PK/PD parameters. The optimization objective consists in characterizing the combination protocol that maximizes the overall survival probability of the patient population under consideration. RESULTS We compare using in-silico experiments our results to standard protocols and observe a gain in overall survival probabilities that vary from 4 to 9 percentage points. The gains increase with the complexity of the potential protocol. Gains are larger in presence of a higher number of injections or of an actual combination with immunotherapy. CONCLUSIONS In in-silico experiments, optimal protocols achieve significant gains over standard protocols when considering overall survival probabilities. Our optimization algorithm enables us to efficiently tackle this numerical problem with a large dimensionality. The in-vivo implications of our in-silico results remain to be explored.

中文翻译：

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使用优化试探法优化淋巴瘤的治疗组合。

背景技术高级非霍奇金淋巴瘤的标准治疗涉及化学疗法和免疫疗法的组合。我们在计算机上描述最佳组合方案，该方案可最大程度地提高整体生存率。我们依靠药代动力学/药效学（PK / PD）模型，该模型描述了肿瘤细胞和效应细胞的联合进化，以及化学疗法和免疫疗法的作用。通过临时限制考虑了毒性。我们开发了一种优化算法，该算法属于Monte-Carlo树搜索算法的类别。我们的模拟依赖于异类患者的计算机内人口，其PK / PD参数有所不同。优化目标在于表征组合方案，该方案可使所考虑的患者群体的总生存概率最大化。结果我们使用计算机模拟实验将我们的结果与标准方案进行了比较，并观察到总体生存率的提高在4到9个百分点之间。增益随着潜在协议的复杂性而增加。在注射次数较多或与免疫疗法实际结合时，获益更大。结论在计算机模拟实验中，考虑整体生存概率时，最佳方案比标准方案获得了明显的收益。我们的优化算法使我们能够有效解决大尺寸的数值问题。