Developing new paradigms for overcoming drug resistance in cancer using novel humanised mouse models

虽然癌症新疗法的开发取得了重大进展，其中药物现在针对驱动肿瘤生长的遗传变化，但在将其转化为长期患者生存方面仍然存在重大挑战。此外，药物副作用，特别是新的联合治疗，仍然是一个主要问题。为什么患者的反应往往是戏剧性的，但没有导致持续的长期生存的原因是因为肿瘤迅速变得对这些非常有效的药物的高浓度耐药。鉴于目前对癌症基因组学、肿瘤细胞异质性、微环境等的重视，基本上没有关于药物药理学的研究，即如何最佳地使用新药。定义新兴靶向抗癌药物的药理学，而不是使用基于MTD的给药方案，必须成为开发新的有效癌症治疗方法的基本组成部分，特别是对于药物组合。了解如何最好地使用靶向药物可以使潜在的有前途的新治疗方案的成功和失败之间的差异。历史证据，特别是基于感染性疾病的治疗，告诉我们，耐药性可以通过同时使用药物组合或通过改变药物给药方案来克服。因此，改善癌症治疗的一个主要挑战是应该使用哪种药物组合以及给药方案是什么？基于大量新兴的靶向抗肿瘤药物，仅通过临床试验不可能解决这一挑战，迫切需要更能预测患者反应的新临床前模型。动物模型和人类之间的一个重要区别是身体如何处理药物。这导致不同的药物暴露和消除速率以及不同的药物代谢物。为了解决这个问题，我们已经做了一些基因改变的小鼠模型，其中药物的代谢方式与在人类中观察到的相似。该项目的目的是建立该模型系统预测人体药物反应的潜力，并使用该模型测试药物在患者可耐受的剂量下单独或联合使用是否仍然有效。我们的最终目标是开发一个基于计算机的模型，该模型将用于延迟耐药性发生的联合临床试验的知情设计。如果成功的话，该计划中开发的模型系统的应用可能会对改善癌症治疗带来巨大的好处，无论是在患者生存还是减少药物副作用方面。该计划中验证的模型对于新药的开发可能非常重要，不仅用于治疗癌症，还用于治疗其他人类疾病。

Although there have been significant advances in the development of new treatments for cancer, where the drugs are now targeted at the genetic changes which drive tumour growth, major challenges still exist in translating this into long-term patient survival. In addition, drug side-effects particularly of new combination treatments, continue to be a major problem. The reason why the often dramatic patient responses, have not resulted in sustained long-term survival is because the tumours rapidly become resistant to the high concentrations of these very potent drugs. Whereas there is currently an enormous emphasis on cancer genomics, tumour cell heterogeneity, microenvironment, etc., there are essentially no studies on drug pharmacology i.e. how to use the new drugs optimally. Defining the pharmacology of emerging targeted anti-cancer drugs, rather than using dosing regimens based on MTD, has to be a fundamental component of developing new effective treatments for cancer, particularly for drug combinations. Understanding how best to use targeted agents can make the difference between success and failure of potentially promising new treatment regimens.Historical evidence, based particularly on the treatment of infectious diseases, has taught us that drug resistance can be overcome by either using combinations of drugs concomitantly, or by altering the drug dosing regimen. A major challenge for the improved treatment of cancer is therefore which combination of drugs should be used and what is the dosing schedule? Based on the enormous number of emerging targeted anti-tumour agents, it is impossible to address this challenge by clinical trial alone, and new pre-clinical models which are more predictive of patient responses are urgently needed. An important difference between animal models and humans is how drugs are handled by the body. This results in different rates of drug exposure and elimination and different drug metabolites. To address this problem, we have made a number of genetically altered mouse models in which drugs are metabolised in a manner similar to that observed in humans. It is the aim of this project to establish the potential of this model system to predict drug responses in man and to use this model to test whether drugs are still effective, alone or in combination, at doses which will be tolerated by patients. Our ultimate aim is to develop a computer-based model which will be used for the informed design of combination clinical trials which delay the onset of drug resistance. If successful, the application of the model systems developed in this programme could be of enormous benefit for the improved treatment of cancer, both in terms of patient survival and reduction in drug side-effects. The model validated in this programme could be of great for the development of new drugs, not only for the treatment of cancer but also for the treatment of other human diseases.