Lindsay Danika, Garvey Colleen M, Mumenthaler Shannon M, Foo Jasmine
School of Mathematics, University of Minnesota, Minneapolis, Minnesota, United States of America.
Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, United States of America.
PLoS Comput Biol. 2016 Aug 25;12(8):e1005077. doi: 10.1371/journal.pcbi.1005077. eCollection 2016 Aug.
Experimental studies have shown that one key factor in driving the emergence of drug resistance in solid tumors is tumor hypoxia, which leads to the formation of localized environmental niches where drug-resistant cell populations can evolve and survive. Hypoxia-activated prodrugs (HAPs) are compounds designed to penetrate to hypoxic regions of a tumor and release cytotoxic or cytostatic agents; several of these HAPs are currently in clinical trial. However, preliminary results have not shown a survival benefit in several of these trials. We hypothesize that the efficacy of treatments involving these prodrugs depends heavily on identifying the correct treatment schedule, and that mathematical modeling can be used to help design potential therapeutic strategies combining HAPs with standard therapies to achieve long-term tumor control or eradication. We develop this framework in the specific context of EGFR-driven non-small cell lung cancer, which is commonly treated with the tyrosine kinase inhibitor erlotinib. We develop a stochastic mathematical model, parametrized using clinical and experimental data, to explore a spectrum of treatment regimens combining a HAP, evofosfamide, with erlotinib. We design combination toxicity constraint models and optimize treatment strategies over the space of tolerated schedules to identify specific combination schedules that lead to optimal tumor control. We find that (i) combining these therapies delays resistance longer than any monotherapy schedule with either evofosfamide or erlotinib alone, (ii) sequentially alternating single doses of each drug leads to minimal tumor burden and maximal reduction in probability of developing resistance, and (iii) strategies minimizing the length of time after an evofosfamide dose and before erlotinib confer further benefits in reduction of tumor burden. These results provide insights into how hypoxia-activated prodrugs may be used to enhance therapeutic effectiveness in the clinic.
实验研究表明，实体瘤中驱动耐药性出现的一个关键因素是肿瘤缺氧，这会导致局部环境龛的形成，耐药细胞群体可在其中进化和存活。缺氧激活前体药物（HAPs）是设计用于穿透肿瘤缺氧区域并释放细胞毒性或细胞生长抑制药物的化合物；其中几种HAPs目前正在进行临床试验。然而，一些试验的初步结果并未显示出生存获益。我们假设，涉及这些前体药物的治疗效果在很大程度上取决于确定正确的治疗方案，并且数学建模可用于帮助设计将HAPs与标准疗法相结合的潜在治疗策略，以实现长期肿瘤控制或根除。我们在表皮生长因子受体（EGFR）驱动的非小细胞肺癌这一特定背景下建立此框架，这种癌症通常用酪氨酸激酶抑制剂厄洛替尼治疗。我们开发了一个随机数学模型，使用临床和实验数据进行参数化，以探索将一种HAP（依沃福酰胺）与厄洛替尼相结合的一系列治疗方案。我们设计联合毒性约束模型，并在可耐受方案空间内优化治疗策略，以确定导致最佳肿瘤控制的特定联合方案。我们发现：（i）将这些疗法联合使用比单独使用依沃福酰胺或厄洛替尼的任何单一疗法方案更能延缓耐药性；（ii）依次交替单剂量使用每种药物可使肿瘤负担最小化，并最大程度降低产生耐药性的概率；（iii）使依沃福酰胺给药后至厄洛替尼给药前的时间长度最小化的策略在减轻肿瘤负担方面有进一步益处。这些结果为缺氧激活前体药物如何用于提高临床治疗效果提供了见解。
