Eigenmann Miro J, Frances Nicolas, Lavé Thierry, Walz Antje-Christine
Roche Pharma Research and Early Development, Quantitative Systems Pharmacology, Hoffmann-La Roche Ltd., Grenzacherstrasse, 124, 4070, Basel, Switzerland.
Pharmaceutical Sciences Roche Innovation Centre Basel, Hoffmann-La Roche Ltd., Basel, Switzerland.
J Pharmacokinet Pharmacodyn. 2017 Dec;44(6):617-630. doi: 10.1007/s10928-017-9553-x. Epub 2017 Oct 31.
Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.
非小细胞肺癌（NSCLC）患者从靶向表皮生长因子受体（EGFR）的酪氨酸激酶抑制剂（TKIs）治疗中受益匪浅。然而，大多数患者在长期治疗后不可避免地会出现获得性耐药，这限制了临床疗效的提高。在本研究中，评估了患者来源的NSCLC异种移植小鼠对药物治疗的耐药性，并应用建模和模拟来了解耐药动态，以此作为探索更有益药物方案的基础。用厄洛替尼或吉非替尼治疗期间及治疗后的肿瘤生长抑制曲线拟合了两个半机制模型。基础模型提出，药物治疗后，肿瘤细胞停止增殖，在最终死亡前经历几个损伤阶段。获得性耐药模型在基础模型中增加了一个耐药项，假定耐药细胞从受损肿瘤细胞池中产生。结果，对药物治疗敏感的肿瘤细胞要么死亡，要么转化为耐药细胞群体，与敏感细胞相比，其增殖速度较慢。耐药模型能更好地描述观察到的肿瘤生长曲线，并得出耐药出现的结论。在模拟研究中，探讨了耐药细胞的选择以及耐药细胞与敏感细胞随时间变化的比例。所提出的模型深入了解了耐药出现的动态过程。它预测了治疗期间由耐药细胞选择驱动的肿瘤再生长，并解释了为什么在停用TKI治疗后可能会出现更快的肿瘤再生长。最后，展示了半机制模型如何用于探索不同情况并在临床试验中确定最佳治疗方案。
