In silico simulation of pre-clinical and clinical data may accelerate pre-clinical and clinical trial advances, leading to benefits for therapeutic outcomes, toxicity and cost savings. Combining this with clonal architecture data may permit truly personalized therapy. Chronic lymphocytic leukemia (CLL) exhibits clonal diversity, evolution and selection, spontaneously and under treatment pressure. We apply a dynamic simulation model to published CLL clonal architecture data to explore alternative therapeutic strategies, focusing on BTK inhibition. By deriving parameters of clonal growth and death behavior we model continuous vs time-limited ibrutinib therapy, and find that, despite persistence of disease, time to clinical progression may not differ. This is a testable hypothesis. We model IgVH-mutated CLL vs unmutated CLL by varying proliferation and find, based on the limited available data about clonal dynamics after such therapy, that there are differences predicted in response to anti-CD20 efficacy. These models can suggest potential clinical trials, and also indicate what additional data are needed to improve predictions. Ongoing work will expand modeling to agents such as venetoclax and to T cell therapies.

临床前和临床数据的计算机模拟可能会加速临床前和临床试验的进展，从而带来治疗效果、毒性和成本节约的好处。将其与克隆结构数据相结合，可能会实现真正的个性化治疗。慢性淋巴细胞白血病 (CLL) 在治疗压力下自发地表现出克隆多样性、进化和选择。我们将动态模拟模型应用于已发布的 CLL 克隆架构数据，以探索替代治疗策略，重点是 BTK 抑制。通过推导克隆生长和死亡行为的参数，我们对连续与死亡进行建模限时依鲁替尼治疗，并发现尽管疾病持续存在，但临床进展的时间可能没有差异。这是一个可检验的假设。我们通过不同的增殖对IgVH 突变的 CLL与未突变的 CLL 进行建模，并基于此类治疗后有关克隆动力学的有限可用数据发现，预测抗 CD20 疗效存在差异。这些模型可以建议潜在的临床试验，还可以表明需要哪些额外的数据来改进预测。正在进行的工作将建模扩展到诸如 venetoclax 和 T 细胞疗法之类的药物。