A streamlined search technology for identification of synergistic drug combinations.

Andrea Weiss,Xianting Ding,Patrycja Nowak-Sliwinska,Paul J Dyson,Hubert Van Den Bergh,Arjan W Griffioen,Robert H Berndsen,Chih-Ming Ho

doi:10.1038/srep14508

Abstract

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p < 0.001, CI < 0.3).

Highlights

Embryonic stem cells[16] the differentiation of mesenchymal stem cells[17], and the inhibition of tumor angiogenesis[18]
We introduce a streamlined route to drug combination optimization and implement this novel approach in the optimization of a multi-drug combination inhibiting the viability of the human renal adenocarcinoma cell line 786-O
The s-FSC technique is based on an iterative cycle, where drug combinations are tested in vitro and analyzed in a series of ‘search rounds’, each resulting in the development of a second-order linear regression model (Fig. 1)

Summary

Introduction

Embryonic stem cells[16] the differentiation of mesenchymal stem cells[17], and the inhibition of tumor angiogenesis[18]. We introduce a streamlined route to drug combination optimization and implement this novel approach in the optimization of a multi-drug combination inhibiting the viability of the human renal adenocarcinoma cell line 786-O The selection of this cell line was motivated by the fact that renal cell carcinoma (RCC) is generally chemo- and radio-therapy resistant, and its clinical treatment is largely dependent on the use of targeted agents[20]. After only three rounds of experimental search efforts, we identified an optimal drug combination containing axitinib[21]; erlotinib[22,23], dasatinib and AZD4547. This drug combination induced the specific and synergistic[24] inhibition of 786-O cell viability, while having a reduced effect on non-malignant cell types

Methods

Results

Conclusion