A streamlined search technology for identification of synergistic drug combinations

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).

Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

“…Key advantages of the FSC approach are that it is output-driven and thus, does not require any knowledge or assumptions about the biology of the system and that it greatly reduces the number of experimental drug combinations that must be tested (11). The previously developed FSC.I platform has been used to identify optimal drug regimens in several other biological systems, including antiretroviral therapy for HSV (12), and the FSC.II platform has been applied to identifying drug combinations for cancer chemotherapy (13). However, neither the FSC.I platform nor the FSC.II platform has been used previously for evaluation of antibacterial drug combination therapy.…”

Treatment of Hematological Malignancies with Glycyrrhizic Acid

“…IM is a selective tyrosine kinase inhibitor that targets the BCR-ABL fusion protein present in CML and a receptor for KIT (27). It is well-tolerated in CD34 + cells in vitro up to doses of 10 μM for 48 h far above our working dose of 0.4 μM (9). Additionally, IM is relatively non-toxic in clinical applications with few sideeffects on non-malignant hematological cells (28).…”

“…The challenge of targeting high tumor loads becomes feasible by utilizing multiple vectors near their max therapeutic dose. Several advances using combinational therapies have been reported and show great promise in the treatment of hematological malignancies (9). For example, combination therapy with hypomethylating agents, histone deacetylase inhibitors, along with other chemotherapeutic agents has shown promise over monotherapies in the treatment of myelodysplastic syndromes (10).…”

“…Limitations include robust survival signaling pathways, resistance developed from genomic instability, and cell heterogeneity (9 therapy was developed to address these limitations and has been employed successfully in a wide variety of chemotherapy regimens (25). Key features of combination candidates include 1) drugs should achieve significant responses in mono-therapy, 2) administered at the highest tolerable doses, 3) unique mechanisms of activity to obtain synergistic responses and, 4) alternative mechanisms of cellular resistance development (26).…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…Weiss et al [76] used a statistical design of experiment (DOE) approach with an orthogonal array composite design (OACD). Through a series of designed experiments and data analysis based on regression modelling, they were able to identify a set of effective and synergistic drugs for viability inhibition of renal carcinoma cells.…”