Clinical, Basic, Translational and Kinetic Studies of Drug Action and Resistance

药物作用和耐药性的临床、基础、转化和动力学研究

基本信息

批准号：
9154251
负责人：
Antonio Fojo
金额：
$ 55.8万
依托单位：
DIVISION OF CLINICAL SCIENCES - NCI
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/9154251
关键词：
Accounting Adjuvant Age American Animal Experiments Animal Model Biology Caring Cellular biology Cessation of life Clinic Clinical Clinical Data Clinical Trials Complex Cytotoxic agent Data Data Analyses Development Drug effect disorder Drug resistance Educational process of instructing Enrollment Equation Goals Growth Harvest Human Image Kinetics Knowledge Laboratories Learning Malignant Neoplasms Malignant neoplasm of prostate Measurement Mining Modeling Molecular Multiple Myeloma Mus Outcome Output Patients Pharmaceutical Preparations Pharmacodynamics Pharmacology Pre-Clinical Model Process Progression-Free Survivals Randomized Clinical Trials Research Research Personnel Resistance Resistance development Resources Role Scientist Serum Markers Time Treatment Efficacy Tumor Biology Work base cancer stem cell career design drug development drug efficacy effective therapy experience in vivo insight malignant breast neoplasm mathematical model novel oncology pre-clinical prevent randomized trial research study resistance mechanism response targeted treatment tumor tumor growth tumor progression

项目摘要

Our long-term goals are to understand in as much depth as possible the mechanisms of drug efficacy. Experience has taught us that not only for our traditional cytotoxic agents, but also for our new "targeted therapies" the major reason why our drugs fail to benefit patients is the development of drug resistance. Both intrinsic and acquired drug resistance are the major impediments to a successful outcome. We firmly believe that while drug resistance can be complex it is not an insurmountable problem. We also firmly believe that a limited number of mechanisms exist, especially at a molecular level, and that the better we understand these the more likely we are to develop effective therapies. We believe that the lessons learned in our models of drug resistance - be they pre-clinical or clinical - will have broad applicability to other drugs. Thus exploiting our in depth understanding of the mechanisms we study we are confident that we will gain knowledge with broad applicability. We do not focus solely on one mechanism of resistance. We examine the full breadth of resistance mechanisms. Increasingly we do this by examining large clinical trial data and conducting detailed, incisive and in-depth analysis of the kinetics of tumor growth and regression We have developed a novel paradigm for assessing therapeutic efficacy using tumor measurements obtained while patients are enrolled in a clinical trial. This mathematical model has applications to many tumor types and may aid in evaluating outcomes. Modeling tumor progression using data gathered while patients are enrolled on a clinical trial could be valuable in drug development and in primary oncology care. We developed an equation based on the model that tumor size decreases exponentially (i.e., as a first-order process) but that there is also independent exponential re-growth of the tumor and these are reflected in the quantity of a serum marker or imaging measurements. This equation is: f = exp(minus d x t) + exp(g x t) minus 1 where exp is the base of the natural logarithm, e = 2.7182 ..., and f is the tumor quantity at time t, normalized to the value at day 0, the time at which treatment is commenced. The rate constant d (decay, in days raised to the minus 1) accounts for the exponential decrease in the tumor quantity, whereas the rate constant g (growth, also in days raised to the minus 1) represents the exponential re-growth of the tumor following treatment. We now have extensive data that in prostate cancer, RCCs, breast cancer and multiple myeloma that show the growth rate constant (g) correlates exceptionally well with overall survival while the regression rate constant (d) does not. This is not unexpected since death is not caused by the fraction of tumor that regresses, but by the fraction that survives and grows and how fast it grows. Our ongoing analyses are designed to confirm this unequivocally so that we may propose the growth rate constant (g) as a valuable clinical trial endpoint. But we are also developing the further by developing novel paradigms to assess clinical trial data that heretofore has only be presented as three simple endpoints: progression-free survival, overall survival and response rate. Despite collecting a large amount of data these are the only endpoints we define. Our data analysis looks to expand this so that we may better understand how drugs work, how different combinations compare, what makes one therapy better, which therapy might be more likely to succeed in an adjuvant or neo-adjuvant setting and which one more likely to fail and numerous other analyses and correlations. We also plan to use the data to investigate and interrogate hypotheses heretofore confined to pre-clinical models. In effect we will use the ultimate experiment in the ultimate model, humans with cancer, to understand basic biologic principles. This section will describe submitted, ongoing and proposed studies to understand the mechanisms of drug resistance clinically and to better define how our therapies work. Our research goals are to (1) understand the molecular basis of drug resistance; (2) comprehend how/why these changes occur; (3) devise strategies to reduce or prevent their occurrence and (4) validate and develop a novel paradigm for assessing therapeutic efficacy. In the clinic, Drs. Bates and Fojo continue to conduct trials in drug-resistant cancers. This section represents where I started my career as a researcher in the field of oncology. It also reflects an acknowledgement on my part that in this age of clearly defined resources, not all research questions that one would like to investigate can be pursued. So it proposes no laboratory experiments. Rather it seeks to leverage what I believe is an enormous amount of data that is mined only superficially. With many large randomized clinical trials enrolling hundreds of patients and capturing an enormous amount of data from each one, it is remarkable that the output from all of this data is usually three numbers: (1) overall response rate; (2) median progression-free survival; and (3) median overall survival. Several of the trials that we have been lucky to obtain have ten to fifteen thousand data points. What would we think if a basic scientist conducted an in vivo experiment with 750 mice, collected all the data regularly collected in a clinical trial and his/her the analysis referred to only three endpoints? As we are discovering there is a trove of data to be mined and it can provide us insights into many of the questions we have been trying to answer for many years - in actual patients!

我们的长期目标是尽可能深入地了解药物功效的机制。经验告诉我们，无论是传统的细胞毒药物，还是新的“靶向治疗”，我们的药物未能使患者受益的主要原因是耐药性的产生。内在和获得性耐药性都是成功结果的主要障碍。我们坚信，虽然耐药性可能很复杂，但这并不是一个无法克服的问题。我们还坚信，存在的机制数量有限，尤其是在分子水平上，我们对这些机制了解得越多，我们就越有可能开发出有效的疗法。我们相信，从我们的耐药模型中汲取的经验教训（无论是临床前还是临床）将广泛适用于其他药物。因此，利用我们对所研究机制的深入理解，我们相信我们将获得具有广泛适用性的知识。我们不仅仅关注一种抵抗机制。我们全面考察了耐药机制。我们越来越多地通过检查大型临床试验数据并对肿瘤生长和消退的动力学进行详细、深入和深入的分析来做到这一点。我们开发了一种新的范例，使用患者参加临床试验时获得的肿瘤测量值来评估治疗效果。该数学模型适用于许多肿瘤类型，并可能有助于评估结果。使用患者参加临床试验时收集的数据来建模肿瘤进展对于药物开发和初级肿瘤护理可能很有价值。我们基于以下模型开发了一个方程：肿瘤大小呈指数减小（即，作为一阶过程），但肿瘤也存在独立的指数再生长，这些都反映在血清标记物或成像测量的数量中。该方程为： f = exp(minus d x t) + exp(g x t) minus 1 其中 exp 是自然对数的底数，e = 2.7182 ...，f 是时间 t 时的肿瘤数量，标准化为 t 时的值第 0 天，治疗开始的时间。速率常数 d（衰减，以天数增加到负 1）表示肿瘤数量的指数减少，而速率常数 g（生长，也以天数增加到负 1）表示肿瘤数量的指数重新生长。治疗后的肿瘤。我们现在拥有前列腺癌、肾细胞癌、乳腺癌和多发性骨髓瘤的大量数据，这些数据表明生长速率常数 (g) 与总生存期密切相关，而回归速率常数 (d) 则不然。这并不意外，因为死亡不是由肿瘤消退的部分引起的，而是由存活和生长的部分及其生长速度引起的。我们正在进行的分析旨在明确证实这一点，以便我们可以提出增长率常数 (g) 作为有价值的临床试验终点。但我们还在进一步开发新的范式来评估临床试验数据，迄今为止，这些数据仅以三个简单的终点来呈现：无进展生存期、总生存期和缓解率。尽管收集了大量数据，但这些是我们定义的唯一端点。我们的数据分析希望扩展这一点，以便我们更好地了解药物如何发挥作用，不同的组合如何比较，什么使一种疗法更好，哪种疗法在辅助或新辅助环境中更有可能取得成功，以及哪种疗法更有可能取得成功。失败以及许多其他分析和关联。我们还计划使用这些数据来调查和质疑迄今为止仅限于临床前模型的假设。实际上，我们将使用最终模型（患有癌症的人类）中的最终实验来了解基本的生物学原理。本节将描述已提交的、正在进行的和拟议的研究，以了解临床耐药机制并更好地定义我们的疗法如何发挥作用。我们的研究目标是（1）了解耐药性的分子基础； (2) 理解这些变化如何/为何发生； (3) 制定策略来减少或预防其发生；(4) 验证和开发评估治疗效果的新范式。在诊所里，博士。贝茨和福霍继续进行耐药癌症的试验。本节代表了我作为肿瘤学领域研究员的职业生涯的起点。这也反映出我承认，在这个资源明确的时代，并不是所有想要调查的研究问题都可以得到解决。因此建议不要进行实验室实验。相反，它试图利用我认为只是表面挖掘的大量数据。许多大型随机临床试验招募了数百名患者，并从每个患者中获取大量数据，值得注意的是，所有这些数据的输出通常是三个数字：（1）总体缓解率； (2) 中位无进展生存期； (3) 中位总生存期。我们有幸获得的一些试验拥有一万到一万五千个数据点。如果一位基础科学家对 750 只小鼠进行了体内实验，收集了临床试验中定期收集的所有数据，并且他/她的分析仅涉及三个终点，我们会怎么想？正如我们发现的，有大量数据可供挖掘，它可以让我们深入了解多年来我们一直试图回答的许多问题 - 在实际患者中！