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Chapter 2: Identification of synergistic combinations
Introduction
The first attempt at accurately describing the effect of a mixture of two agents was made in 1913 when Wilhelm Frei first published on the matter. He suggested that the combination of agents ‘from the same pharmacological class’ were suggested to obey the principle of isoadditivism, whereas mixtures of agents from separate classes are described as heteroadditivism (Frei, 1913). However, these descriptions were continually revised. Finally Bliss described a model for classifying combination therapy (Bliss, 1939):
1. Independent combined effect, where two agents with different mechanisms of action are combined to yield an effect which can be estimated from the dose response curves obtained for each agent.
2. Similar combined effect, where agents with similar mechanisms of actions are combined in constant proportions to yield an effect which can be estimated from the dose response curves obtained for each agent.
3. Synergistic effect, where the effect obtained from the combination of two agents are significantly enhanced when compared to that of each agent alone, and cannot be estimated from the dose response curves obtained for each agent.
These classifications are known as the Bliss Independence theory. However, this theory is limited as it cannot be used to describe combinations of agents which do not exert a greater effect than any of the agents by itself, i.e. additive combinations. Despite this shortcoming, the Bliss Independence theory, along with the Loewe Additivity theory, remains one of the most often used models in the search for synergistic combinations. According to Loewe’s Additivity theory if two compounds have no interaction the combination would be regarded as additive, if the compounds have a negative interaction the combination would be regarded as antagonistic, and if the compounds have a positive interaction the combination would be regarded as synergistic (Loewe, 1953; Fitzgerald et al., 2006). Unfortunately the contradictions between these models have muddled research into combination therapy as, in certain cases, Bliss independence can be interpreted as Loewe antagonism and vice versa. However, as the Loewe model does not require any information about the mechanism of action of the agents, it is the preferred model to identify synergy (Lee, 2010).
Originally, synergy was identified by comparing the LD50 obtained from dose response curves for various fixed ratios of the two components (Bliss, 1939). Even though this method provides a precise protocol to identify synergy, it requires vast quantities of experimental reagents, animals, funds and time. Alternatively, isobolograms can be constructed based on dose-response data for each agent alone and for the mixture of agents (Martinez-Irujo et al., 1996). An isobologram, or isoffective graph, is then constructed based on the curvature of the isobologram. However, this method also requires large quantities of experimental data to accurately determine synergy (Martinez-Irujo et al., 1996). A more time- and cost-effective approach became possible with the development of the Median Effect equation (Eq. 1) of Chou and Talalay (Chou and Talalay, 1984) which is based on the Loewe Additivity model whereby the dose-effect of combinations of agents were described in terms of the combination index (CI) of the mixture.
The Chou-Talalay method relies on the assumption that the two agents in the combination exert mutually exclusive effects and CalcuSyn software, based on the Median Effect equation, relies on this assumption (Lee, 2010). These indices can be calculated for a fixed ratio, or any other combination of agents, based on as few as two data points which significantly decreases the resources required to identify synergy. Even though fewer data points are required when fixed ratios are used as simulation can be performed to identify synergy over a range of concentrations, it is not always feasible as agents must be approximately equipotent to be combined in such a fashion. For agents which are not equipotent, the checkerboard approach or Latin square design of experimental combinations is often used (Chou, 2010).
In direct contrast to synergistic action, antagonism may also be elicited when agents are combined. In such cases the action of the two agents interfere with each other resulting in a decreased effect (Bliss, 1939). In the clinical setting, agents acting antagonistically may present as ineffective combinations or combinations which induce significant toxicity (Ocaña et al., 2014).
Both synergistic and additive combinations may have advantages in a clinical setting (Ocaña et al., 2014). There are four possible positive clinical outcomes when combining two effective anti-cancer agents (Frei et al., 1961b; Greco et al., 1996; Chou, 2006). These include:
1. Enhanced cancer eradicating power
2. Reduced dosages of anti-neoplastics may achieve the same or greater therapeutic effect, decreasing the risk of unacceptable toxicity in the patient
3. Limited risk of the cancer developing drug resistance
4. Greater selectivity for heterogeneous populations of cancer cells, with differing existing resistance
The first report of the therapeutic use of combinations of anti-cancer agents was published by Frei and colleagues. In that study, patients with acute leukaemia were treated with a purine analogue, folic acid antagonists or a combination of the two with mixed success: in children the combination treatment resulted in increased remission rate but had no effect on long-term survival, whereas in adults treatment with the combination did not result in the highest remission rate but increased the survival rate by a slight margin. It was suggested, however, that the combination of a purine analogue with a folic acid antagonist diminished the incidence of resistance to either compound (Frei et al., 1961a).
From this initial study, several guidelines as to the suitable combinations of agents were suggested. The suitability of compounds for use in combination regimens and the efficacy of such a combination in the clinical setting are determined by the toxicity profiles and mechanisms of action of each drug in the combination (Frei, 1972). Compounds with overlapping cytotoxic mechanisms are generally not used in combination as this increases the likelihood of adverse side effects leading to dose reduction, and potential loss of clinical benefit (Frei, 1972). Combining compounds with differing mechanisms of action are generally used in order to avoid drug resistance.
It has been reported that 40% of breast tumours demonstrate intracellular hypoxia with a median oxygen concentration below 0.3%, whilst non-cancerous breast tissue has a median oxygen concentration greater than 9% (Nualart et al., 2009). The hypoxic intra-tumour environment induces aerobic glycolysis that requires large quantities of glucose for the cell’s energy requirements and therefore glucose transporter inhibitors, such as fasentin, indinavir and quercetin, and glycolysis inhibitors, including 2-deoxyglucose, 3-bromopyruvate and lonidamine, demonstrate selective cytotoxicity towards malignant cells (Gatenby and Gillies, 2007).
The anti-cancer properties of the oestrone analogues have been demonstrated in various in vitro cancer models including breast (Stander et al., 2011; Stander et al., 2012, 2013), oesophageal (Wolmarans et al., 2014) and cervical adenocarcinoma (Theron et al., 2013). It has been proposed that the oestrone analogues induce cell death in cancer cells by generating reactive oxygen species, dissipation of the mitochondrial membrane potential, abnormal spindle formation culminating in cell cycle arrest in the G2/M phase and apoptosis (Stander et al., 2011; Stander et al., 2012, 2013).
As the oestrone analogues, ESE-15-ol and ESE-16, and glycolysis inhibitors affect different hallmarks of cancer, it is proposed that the combination of these agents will improve existing treatment strategies aimed at effectively eradicating breast cancer. Therefore this study aimed at identifying synergistic combinations of oestrone analogues and glycolysis inhibitors on in vitro breast cancer models.
Materials
Cell culture reagents
Dulbecco’s Modified Eagle’s Medium (DMEM)
DMEM medium powder (Sigma-Aldrich, St Louis, USA) (67.35 g) was dissolved in 5 l sterile, distilled water and the pH adjusted to a final pH of 7.2 by the addition of 18.5 g NaHCO3.The final concentration of glucose was 4.5 g/l. The solution was sterilised by filtration using three 0.2 μm cellulose acetate filters and dispensed into sterile 500 ml bottles. A 1% penicillin/streptomycin solution was added and the medium stored at 4°C. Immediately prior to use foetal calf serum was added to the medium at a concentration of 2% or 10%.
Foetal Calf Serum (FCS)
Sterile foetal calf serum (Biochrom, Berlin, Germany) was heat-inactivated by incubating at 56°C for 40 min. Cell culture medium used to maintain cells was supplemented with 10% FCS, while medium supplemented with 2% FCS were used for all drug exposure experiments to minimise protein binding effects.
Nutrient Mixture F-12 Ham, Kaighn’s Modification (Ham’s F-12)
Ham’s F-12 medium powder (Sigma-Aldrich, St Louis, USA) (55.5 g) was dissolved in 5 l sterile, distilled water and the pH adjusted to a final pH of 7.2 by the addition of 12.5 g NaHCO3. The final concentration of glucose was 1.26 g/l.The solution was sterilised by filtration using three 0.2 μm cellulose acetate filters and dispensed into sterile 500 ml bottles. A 1% penicillin/streptomycin solution was added and the medium stored at 4°C. Immediately prior to use foetal calf serum was added to the medium at a concentration of 2% or 10%.
Declaration
Acknowledgements
Abstract .
Table of contents
List of figures
List of tables
List of abbreviations
Chapter 1: Introduction
1.1 A brief overview of the aetiology of cancer.
1.2 Altered metabolism: aerobic glycolysis
1.2.1 Glycolysis inhibitors
1.2.1.1 2-Deoxy-d-glucose (2DG)
1.2.1.2 3-Bromopyruvate (3-BrPA)
1.2.1.3 Lonidamine (LON)
1.2.1.4 Fasentin (FAS)
1.2.1.5 Indinavir (IND)
1.2.1.6 Quercetin (QUER)
1.3 The role of angiogenesis
1.4 Global cancer burden
1.4.1 Breast cancer
1.4.1.1 Prevalence and risk factors
1.4.1.2 Phenotypic subtypes and treatments
1.4.1.3 Limitations to successful treatment of malignancies
1.5 Drug combination therapy
1.6 2-Methoxyestradiol (2ME) and analogues
1.7 Aim of the study
1.8 Objectives of the study
Chapter 2: Identification of synergistic combinations
2.1 Introduction
2.2 Materials
2.2.1 Cell culture reagents
2.2.2 Reagents
2.2.3 Experimental compounds: Oestrone analogues
2.2.3.1 2-Ethyl-3-O-sulphamoyl-estra-1,3,5(10)15-tetraen-17-ol (ESE-15-ol)
2.2.3.2 2-Ethyl-3-O-sulfamoyl-estra-1,3,5(10)16-tetraene (ESE-16)
2.2.3.3 2-Methoxyestradiol (2ME)
2.2.4 Experimental compounds: Glycolysis inhibitors
2.2.4.1 2-Deoxy-D-glucose (2DG)
2.2.4.2 3-Bromopyruvate (3-BrPA)
2.2.4.3 Lonidamine (LON)
2.2.4.4 Fasentin (FAS)
2.2.4.5 Indinavir (IND)
2.2.4.6 Quercetin (QUER)
2.3 Methods
2.3.1 Maintenance of cell culture
2.3.2 Preparation of cells for assays and cell counting
2.3.3 Sulforhodamine B (SRB) cell enumeration assay
2.3.4 Assessment of synergism of combinations of oestrone analogues and glycolysis inhibitors
2.3.5 Interpretation of results
2.4 Results
2.4.1 Growth inhibition studies
2.4.2 Identification of synergism
2.5 Discussion
Chapter 3: Evaluation of toxicity sequence
3.1 Introduction
3.2 Materials
3.3 Pilot study: potential interference of QUER
3.4 Methods
3.5 Results
3.6 Discussion
Chapter 4: Angiogenesis studies
4.1 Introduction
4.2 Materials
4.3 Methods
4.4 Results
4.5 Discussion
Chapter 5: Concluding discussion
5.1 Identification of synergistic combinations
5.2 Evaluation of toxicity sequence .
5.3 Anti-angiogenic assessment of the selected combinations
5.4 Future directions
5.5 Conclusion
References
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