Gene traps and in vivo microinjection: a cancer drug discovery platform

描述(由申请人提供)：除了少数例外，癌症信号通路变化的广泛表征，以及我们识别不同分子靶标的有效调节器的能力的进步，都没有转化为突破性药物。《纽约时报》最近的一篇文章(2009年11月14日)引用了默克公司首席战略官默文·特纳的话：“我们在坏主意上投入的时间太长了。”“在周期的早期阶段阻止这种情况真的很重要。”这一点在抗癌药物开发中尤为明显，超过90%的抗癌新药在临床上失败。我们在这一领域的经验使我们相信，这个问题的一个原因是缺乏一个有效的、临床上可预测的平台来验证假说并在药物开发过程的最早阶段做出计划推进决策。目前的体内模型是资源密集型和速度受限的。此外，目前的体内药物疗效测试需要在化学方面进行大量投资，以优化化合物的全身给药。因此，大多数早期药物开发严重依赖于体外肿瘤细胞模型，其中大多数是在不代表天然肿瘤环境的条件下生长的，例如在血清中。人们普遍认为，这些模型并不能可靠地预测临床疗效。第二个基本问题是，几乎所有的肿瘤都通过重新布线信号网络来重新建立有效的致癌输出来获得对单剂靶向治疗的耐药性。积极和快速地识别可替代的可药物途径节点将使重点从开发针对特定分子的药物转移到设计关闭致癌网络的策略。为了解决这些问题，我们正在开发一个端到端平台，该平台能够对药物开发人员感兴趣的癌症途径进行全面的功能询问，与在抗癌药物开发的最早阶段快速验证体内联合临床模型的结果相兼容。在目标1中，我们将使用基因陷阱载体来生成仅在关键致癌途径节点被特定抑制时才发光的肿瘤模型。在目标2中，我们将使用我们的多孔针阵列技术，在体内生长的单个肿瘤的背景下，展示测试、比较和验证多种药物抗途径和抗肿瘤有效性的能力。Klinghoffer博士和Olson博士(Preage Biosciences Inc.和弗雷德·哈钦森癌症研究中心)开发了多孔针阵列技术，他们与芬尼博士(Xactagen LLC)联手，芬尼博士优化的基因陷阱载体系统允许通过内源性基因调节捕获途径调制，并对体内分析具有敏感性。多孔针阵列与高保真、高灵敏度的基因陷阱衍生的癌基因通路报告的成功结合，将促进范式的转变，从目前严重依赖于药物发现过程早期的标准体外模型，转向更具预测性和与疾病相关的体内模型。我们的平台将促进药物开发项目的合理推进，从而提高改善癌症患者健康的新疗法的临床成功率。

DESCRIPTION (provided by applicant): With few exceptions, extensive characterization of signal pathway alterations in cancer, and advances in our capacity to identify potent modulators of distinct molecular targets, has not translated into breakthrough drugs. In a recent New York Times article (Nov. 14, 2009), Mervyn Turner, Chief Strategy Officer for Merck was quoted: "We invest far too long in bad ideas." "It is really important to stop that at an earlier stage in the cycle". This is particularly evident in cancer drug development where over 90% of new anti-cancer drugs fail in the clinic. Our experiences in this area lead us to believe that one reason fo this problem is the lack of an efficient, clinically predictive platform for validating hypotheses nd making program advancement decisions at the earliest stages of the drug development process. Current in vivo models are resource intensive and rate limiting. Furthermore, current in vivo drug efficacy testing requires substantial investment in chemistry to optimize compounds for systemic delivery. Thus the majority of early drug development is heavily dependent on in vitro tumor cell models most of which are grown under conditions, such as in serum, which do not represent the native tumor environment. It is well accepted that these models do not reliably predict clinical efficacy. A second fundamental issue is that almost all tumors acquire resistance to single agent targeted therapies by rewiring signaling networks to reestablish effective oncogenic output. Proactive and rapid identification of alternative druggable pathway nodes would enable a shift in focus from developing drugs that target specific molecules to devising strategies to shut down oncogenic networks. To address these issues, we are developing an end-to-end platform that enables comprehensive functional interrogation of cancer pathways of interest to drug developers, compatible with rapid validation of findings in vivo co-clinical model at the earliest stages of cancer drug development. In Aim 1, we will employ a gene trap vector to generate tumor models that emit light only upon specific inhibition of key oncogenic pathway nodes. In Aim 2, we will use our porous needle array technology to demonstrate capacity to test, compare, and validate multiple agents, in the context of a single tumor grown in vivo, for anti-pathway and anti-tumor efficacy. Drs. Klinghoffer and Olson (Presage Biosciences Inc., and the Fred Hutchinson Cancer Research Center), who developed the porous needle array technology, have joined forces with Dr. Finney (Xactagen LLC), whose optimized gene trap vector system permits capture of pathway modulation via endogenous gene regulation with sensitivity amenable to in vivo analysis. Successful coupling of the porous needle array with high fidelity, high sensitivity gene trap derived oncogene pathway reporters will facilitate a paradigm shift from the current heavy reliance on standard in vitro based models early in the drug discovery process to more predictive and disease relevant in vivo models. Our platform will improve rational advancement of drug development projects, and in doing so, will increase the clinical success rate of new treatments that improve the health of cancer patients.