Clinical Development of Rhenium Nanoliposomes (RNL186) for Glioblastoma

胶质母细胞瘤(GBM)是成人最常见和最具侵袭性的原发恶性脑瘤， 综合治疗后中位生存期19.6个月。中的主要限制因素 提供杀瘤辐射剂量是对周围大脑的毒性。治疗性放射性核素，由于 较短的组织路径和放射生物学上的差异，有可能延长放射治疗窗口 单位为GBM。然而，需要一种载体将同位素输送到大脑，并将其定位在 想要的地点，否则他们很快就会散去。脂质体包埋有可能促进 放射性同位素在组织内的滞留，但有效地将放射性同位素装载到脂质体中的方法 是必要的。这一直是这项技术发展的一个重要限制因素，现在已经 已成功解决。为了克服这一点，我们开发了一种使用自定义 亲脂性分子(BMEDA)，它携带放射性核素Re进入水舱。 脂质体纳米粒。最终的研究产品是Re纳米脂质体(186RNL)。 为了表征186RNL的保留性、耐受性和活性，我们进行了186RNL的瘤内注射 在携带胶质母细胞瘤肿瘤的大鼠身上。持续增加高达30倍的典型外束剂量 表明动物能耐受所有剂量而没有伤害的证据，并与显著的生存有关 不同之处。此外，许多大鼠没有肿瘤残留。一项毒性研究在比格犬身上进行， 186RNL或空白对照纳米脂质体，在系统或在狗的大脑中没有产生明显的变化 24小时或14天。为了进一步表征药物产品并解决化学、制造、 和FDA的控制问题，我们与NanoTechnology达成了一项合作协议 美国国家癌症研究所(NCI)的特征实验室(NCL)。NCL提供了制造 UTHSA生产的方案、试剂和代表性批次。未观察到显著差异。 在两个地点生产的RNL之间，观察到最终产品的显著稳定性。这种药是 被FDA批准在不久之后进行临床研究。我们的具体假设是186RNL可以 对于复发的进展性GBM患者，以远高于正常剂量的辐射剂量安全地给予 186RNL的治疗将显著提高GBM患者的存活率。 继续临床开发是有必要的。因此，我们建议测试最大可耐受剂量和安全性 186RNL在复发性胶质瘤患者中的分布，确定186RNL治疗复发性胶质母细胞瘤的疗效 建立和验证预测186RNL分布的数学模型。这样做的直接目标是 目的是使用早期时间点、患者特定的数据来校准基于机制的模型，从而允许 对186RNL随时间分布的准确预测。此模型将使用以下工具开发 在AIM 1中建立的数据，然后在186RNL交付之前用于选择最佳注射点 在目标2中。

Glioblastoma (GBM) is the most common and most aggressive of the primary malignant brain tumor in adults, with a median overall survival of 19.6 months following multi-modality therapy. The main limiting factor in delivering a tumoricidal radiation dose is the toxicity to surrounding brain. Therapeutic radionuclides, due to a short tissue path and differences in radiobiology, have the potential to extend the therapeutic window for radiation in GBM. However, a carrier is needed to deliver the isotope to the brain and maintain its localization at the desired site, as otherwise they quickly disperse. Liposomal encapsulation has the potential to facilitate radioisotope retention within the tissue, but a method for the efficient loading of liposomes with the radioisotopes was needed. This has been an essential limiting factor in the development of this technology, and has now been successfully addressed. To overcome this, we have developed an encapsulation method using a custom lipophilic molecule (BMEDA) that carries the rhenium radionuclides into the aqueous compartment of the liposome nanoparticles. The final investigational product is Rhenium nanoliposomes (186RNL). To characterize the retention, tolerability, and activity of 186RNL, we performed intratumoral infusions of 186RNL in rats bearing glioblastoma tumors. Increasing doses as high as 30-fold typical external beam doses consistently showed that animals tolerated all doses without evidence of harm, and were associated with marked survival differences. In addition, many of the rats had no residual tumor. A toxicity study was performed in beagles with 186RNL or blank control nanoliposomes and produced no significant changes systemically or in the brains of dogs at 24 hours or 14 Days. In order to further characterize the drug product and address chemistry, manufacturing, and control concerns of FDA, we entered into a collaborative agreement with the Nanotechnology Characterization Laboratory (NCL) of the National Cancer Institute (NCI). NCL was provided with manufacturing protocols, reagents, and representative lots manufactured at the UTHSA. No significant difference was observed between RNL manufactured at the two sites and with marked stability of final product observed. The drug was cleared by the FDA to proceed to clinical study shortly thereafter. It is our specific hypothesis that 186RNL can safely be administered to patients with recurrent progressive GBM at much higher radiation doses than can be achieved with current techniques, and that treatment with 186RNL will markedly improve survival in GBM patients. Continued clinical development is warranted. We therefore propose to test the maximum tolerable dose and safety profile of 186RNL in patients with recurrent glioma, determine the efficacy of 186RNL in recurrent glioblastoma, and to develop and validate a mathematical model to predict the distribution of 186RNL. The immediate goal of this Aim is to use early time point, patient-specific data, to calibrate a mechanism-based model, thereby allowing for the accurate prediction of the distribution of 186RNL as a function of time. This model will be developed using data established in Aim 1, then used before delivery of 186RNL in the selection of the optimal point of injection in in Aim 2.