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Michael Fernandes

Publications and source records attributed to Michael Fernandes.

6 recordsLinked to original sources
PubMed2004

Cytostatic anticancer drug development.

This review focuses on clinical trials and the approval process in order to understand the discrepancy between vibrant science and the continuing failure of mechanism-based anticancer drugs. CLINICAL TRIALS: Mechanistic trials in cancer require at least three elements: the assurance of tumor definition, knowledge of the natural history, and earlier intervention. Histologic identity is not a reliable surrogate of the functional nature or a predictor of the natural history. cDNA arrays and computational models have promise in improving diagnosis and prediction, and thereby making tailored therapy possible. The latter requires: the incorporation of initial and earlier rational combination therapy, dynamic models of disease progression, and methods to discourage the emergence of resistance. For cytostatics, and in early cancer, a delay in progression may represent a better index of survival than tumor shrinkage. APPROVAL PROCESS: Since mechanistic similarities may outweigh therapeutic predictions based on organ-and histology-defined cancer, there is a need for a revised process that would allow for tailored treatment and initial combination therapy to improve safety, efficacy, and circumvent resistance. CONCLUSION: In order to translate the major and immediate potential of cytostatic drugs, clinical trials and the approval process may need to shift to a mechanism-based framework.

Antineoplastic Agents↗
PubMed2003

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.

Drug Resistance, Neoplasm↗