Prospect for application of mathematical models in combination cancer treatments

TL;DR

This paper reviews mathematical models of combination cancer therapies, emphasizing their potential to improve treatment efficacy, overcome drug resistance, and guide future research in cancer management.

Contribution

It provides a comprehensive overview of existing mathematical models for combination cancer treatments and discusses open questions and promising drug combinations.

Findings

Mathematical models can help optimize combination therapy strategies.

Models highlight the importance of considering toxicity and resistance.

Future research directions are identified for improving cancer treatment.

Abstract

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.