Abstract

BackgroundCombinations of drugs can improve the efficacy of cancer treatment, enable the reduction of side effects and the occurrence of acquired drug resistance.MethodsWe approached this challenge mathematically by using the validated technology called the Therapeutically Guided Multidrug Optimization (TGMO) method. In a set of genetically distinct human renal cell carcinoma (RCC) cell lines, either treated chronically with sunitinib (−ST) or sunitinib-naive, we identified cell line-specific low-dose-optimised drug combinations (ODC).ResultsSix cell-type-specific low-dose drug combinations for three sunitinib-naive as well as three sunitinib pre-treated cells were established. These ODCs effectively inhibited the RCC cell metabolic activity while being ineffective in non-cancerous cells. Based on a single screening test and three searches, starting with ten drugs, we identified highly efficacious drug mixtures containing four drugs. All ODCs contained AZD4547 (FGFR signalling pathway inhibitor) and pictilisib (pan-phosphatidylinositol 3-kinase inhibitor), but varied in the third and fourth drug. ODC treatment significantly decreased cell metabolic activity (up to 70%) and induced apoptosis, independent of the pretreatment with sunitinib. The ODCs outperformed sunitinib, the standard care for RCC. Moreover, short-term starvation potentiated the ODC activity. The translation of the 2D-based results to 3D heterotypic co-culture models revealed significant inhibition of the spheroid growth (up to 95%).ConclusionWe demonstrate a promising low-dose drug combination development to obtain drug combinations effective in naive as well as resistant tumours. Nevertheless, we emphasise the need for further mechanistic investigation and preclinical development.

Highlights

  • Combinations of drugs can improve the efficacy of cancer treatment, enable the reduction of side effects and the occurrence of acquired drug resistance

  • We investigated the improvement of renal cell carcinoma (RCC) treatment via the identification of synergistic drug combinations effective in sunitinib-naive and sunitinib pre-treated RCC cells

  • Therapeutically Guided Multidrug Optimization (TGMO)-based identification of synergistic four-drug combinations specific to each RCC cell line Starting with a set of ten small-molecule-based drugs that are well known and well-characterised, either FDA approved or in clinical evaluation or with existing clinical data (Supplementary Table S1), we performed an experimental search using a simple in vitro cell metabolic activity assay

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Summary

Introduction

Combinations of drugs can improve the efficacy of cancer treatment, enable the reduction of side effects and the occurrence of acquired drug resistance. The clinical benefit of using low-dose drug combinations to treat complex diseases has become a major interest in the medical community because of the advantages, such as enhanced efficacy, lack of intrinsic or acquired resistance and the limitation of doserelated toxicities This holds true in the case of cancer therapy, where in many cases, adequate treatments are still lacking due to intra-patient and/or inter-/intra-tumour heterogeneity. Sunitinib (SutentTM) is one of the first-line treatment therapies for RCC.[5] Many preclinical and clinical efforts for this indication have focused on identifying effective combinations of existing targeted agents using either simultaneous or sequential administration to maximise their clinical benefits Large studies, such as the BEST, RECORD, INTORACT, CALGB, TORAVA, CheckMate 214 and Keynote-426 studies,[6,7,8,9,10,11] investigated first-line combination regimens and, in most cases, did not show superior outcomes over single agents.[12] Instead of enhanced efficacy, substantial toxicity has been a recurrent observation in these studies,[12] even when combinations are designed to target complementary pathways. The application of appropriate methods to design optimal drug combination regimens with optimal drug selections and use at appropriate doses is of major importance

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