Stochastic modeling of drug resistance in cancer.
One of the main causes of failure in the treatment of cancer is the development of drug resistance by the cancer cells. Employing multi-drug therapeutic strategies is a promising way to prevent resistance and improve the chances of treatment success. We formulate and analyse a stochastic model for multi-drug resistance and investigate the dependence of treatment outcomes on the initial tumor load, mutation rates and the turnover rate of cancerous cells. We elucidate the general principles of the emergence and evolution of resistant cells inside the tumor, before and after the start of treatment. We discover that for non-mutagenic drugs, pre-existence contributes more to resistance generation than the treatment phase; this result holds for the case where all drugs are applied simultaneously, and is not applicable for sequential therapy models. The application of mathematical modelling to aspects of adjuvant chemotherapy scheduling. J. Math. Biol. 48(4), 375-422]. Also, we find that treatment success is independent on the turnover rate for one drug, and it depends strongly on it for multi-drug therapies. For low-turnover rates, increasing the number of drugs will increase the probability of successful therapy. For very high-turnover rates, increasing the number of drugs used does not significantly increase the chances of treatment success.
Komarova
Natalia
N
Department of Mathematics and Ecology and Evolution, University of California, Irvine, CA 92697, USA. komarova@math.uci.edu
D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D018432:Drug Resistance, Multiple / Q000235:genetics; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D006801:Humans; D008954:Models, Biological; D009154:Mutation; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000473:pathology; D013269:Stochastic Processes; D016896:Treatment Outcome