Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, Florida.
Department of Biochemistry, University of Washington, Seattle, Washington.
Clin Cancer Res. 2019 Jul 15;25(14):4413-4421. doi: 10.1158/1078-0432.CCR-19-0006. Epub 2019 Apr 16.
Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies.
We examine evolution-based strategies for two-drug therapy and identify an approach that divides the treatment drugs into primary and secondary roles. The primary drug has the greatest efficacy and/or lowest toxicity. The secondary drug is applied solely to reduce the resistant population to the primary drug.
Simulations from the mathematical model demonstrate that the primary-secondary approach increases time to progression (TTP) compared with conventional strategies in which drugs are administered without regard to evolutionary dynamics. We apply our model to an ongoing adaptive therapy clinical trial of evolution-based administration of abiraterone to treat metastatic castrate-resistant prostate cancer. Model simulations, parameterized with data from individual patients who progressed, demonstrate that strategic application of docetaxel during abiraterone therapy would have significantly increased their TTP.
Mathematical models can integrate evolutionary dynamics into multidrug cancer clinical trials. This has the potential to improve outcomes and to develop clinical trials in which these mathematical models are also used to estimate the mechanism(s) of treatment failure and explore alternative strategies to improve outcomes in future trials.
将进化动力学纳入转移性癌症的系统治疗中，可以延长肿瘤控制时间，与标准最大耐受剂量（MTD）策略相比。先前的研究主要集中在单药治疗上，但许多临床癌症治疗方法结合了两种或更多药物。由于复杂的细胞相互作用和药物、剂量和治疗方案的潜在变化的参数空间很大，优化多药治疗中的进化动力学具有挑战性。然而，多药治疗也为使用基于进化的策略进一步改善结果提供了机会。
我们研究了两药治疗的基于进化的策略，并确定了一种将治疗药物分为主要药物和次要药物的方法。主要药物具有最大疗效和/或最低毒性。次要药物仅用于减少主要药物的耐药人群。
数学模型的模拟表明，与不考虑进化动力学而施用药物的传统策略相比，主-次方法增加了进展时间（TTP）。我们将我们的模型应用于正在进行的基于进化的阿比特龙给药的适应性治疗临床试验，以治疗转移性去势抵抗性前列腺癌。使用个体进展患者的数据参数化模型模拟表明，在阿比特龙治疗期间战略性地应用多西他赛将显著增加他们的 TTP。
数学模型可以将进化动力学纳入多药癌症临床试验。这有可能改善结果，并开发临床试验，其中这些数学模型也用于估计治疗失败的机制，并探索改善未来试验结果的替代策略。
