Combining chemotherapeutics to treat malignant tumors has been shown to be effective in preventing drug resistance, tumor recurrence, and reducing tumor size. We modeled combination drug therapy in PC-3 human prostate cancer cells using mixture design response surface methodology (MDRSM), a statistical technique designed to optimize compositions that we applied in a novel manner to design combinations of chemotherapeutics. Conventional chemotherapeutics (mitoxantrone, cabazitaxel, and docetaxel) and natural bioactive compounds (resveratrol, piperlongumine, and flavopiridol) were used in 12 different combinations containing three drugs at varying concentrations. Cell viability and cell cycle data were collected and used to plot response surfaces in MDRSM that identified the most effective concentrations of each drug in combination. MDRSM allows for extrapolation of data from three or more compounds in variable ratio combinations, unlike the Chou-Talalay method. MDRSM combinations were compared with combination index data from the Chou-Talalay method and were found to coincide. We propose MDRSM as an effective tool in devising combination treatments that can improve treatment effectiveness and increase treatment personalization, because MDRSM measures effectiveness rather than synergism, potentiation, or antagonism.
Copyright © 2018 by The Author(s).