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

我们的长期目标是尽可能深入地了解药物疗效的机制。经验告诉我们，不仅对于我们传统的细胞毒性药物，而且对于我们新的“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发展。内在和获得性耐药性都是取得成功的主要障碍。我们坚信，尽管耐药性可能很复杂，但它并非一个不可克服的问题。我们还坚信，存在有限数量的机制，特别是在分子水平上，我们越了解这些机制，我们就越有可能开发出有效的治疗方法。我们相信，从我们的耐药性模型中吸取的经验教训--无论是临床前还是临床--将对其他药物具有广泛的适用性。因此，利用我们对我们研究的机制的深入理解，我们相信我们将获得具有广泛适用性的知识。我们并不仅仅关注一种耐药机制。我们研究了抵抗机制的全部广度。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，精辟和深入的分析来做到这一点。我们已经开发了一种新的范例，用于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建模肿瘤进展可能在药物开发和初级肿瘤学护理中很有价值。我们开发了一个基于肿瘤大小呈指数下降的模型的方程（即，作为一级过程），但也存在肿瘤的独立指数再生长，并且这些反映在血清标记物或成像测量的量中。这个等式是：f = exp（minus d x t）+ exp（g x t）minus 1其中exp是自然对数的底，e = 2.7182. f是时间t的肿瘤量，标准化为第0天的值，第0天是治疗开始的时间。速率常数d（衰减，以天为单位上升到负1）说明肿瘤量的指数下降，而速率常数g（生长，也以天为单位上升到负1）表示治疗后肿瘤的指数再生长。我们现在有大量的数据表明，在前列腺癌、RCC、乳腺癌和多发性骨髓瘤中，生长速率常数（g）与总生存率非常相关，而回归速率常数（d）则不相关。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以及它生长的速度引起的。我们正在进行的分析旨在明确证实这一点，以便我们可以提出生长速率常数（g）作为有价值的临床试验终点。但我们也在通过开发新的范式来评估临床试验数据，从而进一步发展，迄今为止，临床试验数据仅以三个简单的终点呈现：无进展生存期，总生存期和反应率。尽管收集了大量数据，但这些是我们定义的唯一端点。我们的数据分析旨在扩展这一点，以便我们可以更好地了解药物如何工作，不同组合如何比较，什么使一种疗法更好，哪种疗法更有可能在辅助或新辅助治疗中成功，哪种疗法更有可能失败以及许多其他分析和相关性。我们还计划使用这些数据来调查和询问迄今为止仅限于临床前模型的假设。实际上，我们将使用终极模型中的终极实验，患有癌症的人类，来理解基本的生物学原理。

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.