Analysis of Cytotoxic Agents in Xenograft Models

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Retrospective evaluation of activity of established cytotoxic agents in clinical trials versus preclinical models shows that, in general, activity in xenograft models is predictive of some level of clinical response. Several reports have detailed good correlations between xenograft response and clinical response for rhabdomyosarcoma, colon cancer, lung cancer (particularly small-cell lung cancer), breast cancer, and myeloma.26'52'53 Table 1 shows a partial compilation of data generated in our department examining the activity of numerous standard cytotoxic agents as well as selected novel targeted agents in a variety of xenograft and syngeneic flank tumor models. Both early treatment (ET) and staged tumor (ST) trials are included and it can be seen that in some cases the magnitude of response is quite similar in the two cases (e.g., paclitaxel in the A549 model), while in others there are significant differences in response (e.g., paclitaxel in breast cancer).

The data include several examples of cytotoxic agents that show significant xenograft activity that is reflective of clinical activity: irinotecan is highly active in several models of colon cancer, temozolomide is efficacious in a model of glioma and one of two models of melanoma, paclitaxel is active in models of bladder, breast, lung, and prostate cancer, rituximab is highly active in two models of B-cell lymphoma, and gemcitabine is active in a model of pancreatic cancer. Note, however, that this activity can be quite dependent on the dose and schedule employed (note irinotecan in colon cancer models). Correlating dose and schedule with PK (e.g., efficacy driven by Cmax, area under the curve (AUC), time over threshold, etc.) is a vital aspect of preclinical evaluation in order to help define the optimum clinical schedule (once- versus twice-daily administration, continuous versus intermittent therapy, etc.). Table 1 also highlights some of the differences in activity that can be observed for a given agent in different models of the same tumor type (note carboplatin and cisplatin in different models of lung cancer). There are also examples of drugs that are known to be clinically active yet show little to no activity in the representative models shown here (for example, 5-fluorouracil in colon cancer). While these results indicate a reasonable correlation between activity, it is much more difficult to correlate the extent of activity. For example, complete regression and/or curative activity can be attained with agents such as irinotecan in some models of colon cancer, but this extent of response is difficult to achieve clinically, resulting in part from toxicity-associated complications (algorithmic dosing/differential drug metabolism).54

On the other hand, there are also published reports suggesting that xenograft models are not predictive of cyto-toxic activity, or that they are not predictive for a given tumor type.23,28 To some extent these studies suffered from limited critical review of the legitimacy of the claims of preclinical activity and/or failure to consider issues such as whether optimal drug levels were achieved clinically. It is interesting to note, however, that there seems to be a trend toward more favorable correlations between preclinical and clinical activity when the xenografts used were from early passage tumor lines compared to lines that have been maintained in culture for extended periods of time.28'52'53 This is in agreement with the assertion made by Peterson and Houghton that early passage lines are preferable as xenograft models.26

There is little debate that xenograft models (and probably all tumor models) have significant limitations as perfect predictors of clinical activity for experimental anticancer agents. However, it also appears quite clear that, when used properly and augmented by appropriate biological validation' these models can be very useful tools for the development of active agents, especially for broadly acting cytotoxic agents. The current challenge is to determine whether these models, or any existing tumor models, will be predictive of activity for the newer generation of 'targeted' therapies that are being developed. The first decade of attempts to develop more targeted therapies has resulted in a few wonderful success stories but also some great disappointments.50'55-58 The tremendous unmet need of effective therapies for cancer patients has undoubtedly increased the pressure to initiate clinical trials with agents where significant preclinical efficacy was somewhat lacking.

Our ability to make significant advances in the development of cancer therapeutics is also hindered by the cumbersome and overly rigid methods of clinical evaluation. Lessons can be learnt from the antiviral field, where the utilization of cocktails of experimental agents led to profound improvements in clinical response.59

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