![]() ![]() The most straight-forward and cost-effective setting in which to assess drug-drug interactions is in cultured cancer cell lines, and the information generated here can be translated into more complex cancer models. Although drug synergy is not necessarily required for clinical benefit, with an additive effect being sufficient to cure in some instances, synergy/antagonism scoring remains an important parameter to evaluate when designing combination therapies or working to understand the mechanisms underpinning current treatment regimens. ![]() Although there is a lack of nomenclature standardisation, synergy can be broadly defined as a combination effect that is stronger than expected from the sum of the drugs individual effects, whilst antagonism is a combination effect that is less active than the additive effect. One parameter of combination therapy that is routinely the focus of pre-clinical research is drug synergy/antagonism scoring. It is thus critical to robustly assess drug-drug interactions at the pre-clinical stage and to translate this knowledge into the clinic. As oncology continues to move towards personalised treatment strategies, be it with traditional cytotoxic chemotherapies or with targeted therapies, ultimately these agents will be used in a combination regimen, and it is important to ensure these combinations are developed in a rational manner. There is ample clinical evidence documenting the benefit of this approach for cancer patients, with one of the first being in acute leukaemias. The rationale behind the use of two or more drugs in cancer therapy is to enhance cancer cell killing, reduce treatment toxicity, and prevent the onset of treatment resistance. Overall, the optimisation of drug synergy scoring as presented here could inform future medium- to high-throughput drug synergy screening strategies in pre-clinical research.Ĭurrent treatment regimens for many different diseases utilise combinations of pharmacological agents, and this is especially true in the treatment of cancer. Focusing on the antileukemic efficacy of the chemotherapy combination of cytarabine and inhibitors of ribonucleotide reductase, we could show that detection and quantification of drug synergy by three common synergy models was well-tolerated despite reducing matrix size from 8 × 8 to 4 × 4. ![]() These minimal matrices utilise substantially less reagents and data processing power than their typically used larger counterparts. We used a drug matrix reduction workflow that allowed the identification of a minimal drug matrix capable of robustly detecting and quantifying drug synergy. Thus, in this study, we set out to optimise a parameter of this methodology, determining the minimal matrix size that can be used to robustly detect and quantify synergy between two drugs. The state-of-the-art method to profile drug synergy in preclinical research is by using dose–response matrices in disease-appropriate models, however this approach is frequently labour intensive and cost-ineffective, particularly when performed in a medium- to high-throughput fashion. Combinations of pharmacological agents are essential for disease control and prevention, offering many advantages over monotherapies, with one of these being drug synergy. ![]()
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