New Tools for Cancer Chemotherapy: Computational Assistance for Tailoring Treatments

Computational models of cancer chemotherapy have the potential to streamline clinical trial design, contribute to the design of rational, tailored treatments, and facilitate our understanding of experimental results. Mechanistic models based on functional data from tumor biopsies will enable physicians to predict response to treatment for a specific patient, in contrast to statistical models in which the probability of response for a given patient may differ substantially from the population average. While microarray analyses of gene expression also show promise for guiding individualized treatments, it may be difficult to link statistical mining of microarray data with mechanistic, tailored treatments. Furthermore, gene expression does not identify how drugs should be scheduled. This review summarizes mechanistic mathematical models developed to improve the design of chemotherapy regimens. Mechanistic models that incorporate both genetic resistance and cell cycle-mediated resistance during treatment with multiple drugs will be most useful in designing treatment regimens tailored for individuals. Because there are already a number of papers that address the applications of microarray technology, we will limit our discussion to the contrasts between mechanistic computational models and microarray technology, and how these two approaches may complement one another.