  The long-term efficacy of targeted therapeutics for cancer treatment can be
significantly limited by the type of therapy and development of drug
resistance, inter alia. Experimental studies indicate that the factors
enhancing acquisition of drug resistance in cancer cells include cell
heterogeneity, drug target alteration, drug inactivation, DNA damage repair,
drug efflux, cell death inhibition, as well as microenvironmental adaptations
to targeted therapy, among others. Combination cancer therapies (CCTs) are
employed to overcome these molecular and pathophysiological bottlenecks and
improve the overall survival of cancer patients. CCTs often utilize multiple
combinatorial modes of action and thus potentially constitute a promising
approach to overcome drug resistance. Considering the colossal cost, human
effort, time and ethical issues involved in clinical drug trials and basic
medical research, mathematical modeling and analysis can potentially contribute
immensely to the discovery of better cancer treatment regimens. In this
article, we review mathematical models on CCTs developed thus far for cancer
management. Open questions are highlighted and plausible combinations are
discussed based on the level of toxicity, drug resistance, survival benefits,
preclinical trials and other side effects.

Tissues and Organs