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.
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