SynergyFinder™ to Identify Synergistic Combination Therapies
Identification of Novel Synergistic Combinations
SynergyFinder™ helps to ensure that new therapies are combined in the best way possible. Combination therapies are applied to halt cancer growth and to overcome emerging resistance mechanisms. To identify the right combination of your lead compound with standards of care or new drugs, NTRC has developed SynergyFinder™.
SynergyFinder™ Characteristics
Synergy testing in in vitro cellular assays
Evaluates if combinations of two or three anti-cancer drugs have a greater than additive effect
Allows rapid identification of the best out of many possible combination treatments
Scalable from 1 to 1,000 combinations within a study
Database of anti-cancer drugs pre-profiled in Oncolines™ for rapid experimental progress
Unique Features
Determination of full dose response curves for single agents and mixtures
Synergies are determined by Fixed Ratio experiments and Fixed Dose combination experiments
- Quantitative synergy scoring with Chou-Talalay method (Dose-based) and Bliss scoring (Effect-based)
Visualization of synergy by curve (IC50) shift and isobolograms
Reproducibility of cancer drug synergy is covered by independent repeat experiments before finalisation of the study
Equipotent Mixtures
Synergy drug screening via fixed ratio combination experiments uses equipotent mixtures of the anti-cancer drugs. The mixtures contain fixed ratios of Compound A at IC50 and Compound B at IC50. Since Compound A and Compound B are equipotent (viz. both are at IC50), they are interchangeable and there is no dilution effect of mixing the compounds. To expedite synergy testing, NTRC has predetermined IC50 values, curve shapes and efficacy parameters of many relevant anti-cancer drugs, covering the relevant anti-cancer drug classes. The anti-cancer drugs have been profiled in the full OncolinesTM panel, so the IC50 values of these drugs are available for 102 cancer cell lines.
Pre-profiled Anti-Cancer Drugs
Recently approved small molecule oncology drugs
Targeted agents
Cytostatic agents
Epigenetic modulators
Selective tool compounds for investigational pathways
Cluster dendrogram of the response of more than 150 anti-cancer agents in the Oncolines™ cancer cell line panel.
Distinguishing Synergy from Additivity
The goal of a SynergyFinderTM experiment is to identify cases where the joint effect of a compound combination is improved compared to the additive effects of the individual compounds (Uitdehaag et al., 2015). At NTRC we have developed two experimental set-ups to measure synergy, the fixed IC50 ratio experiment and the fixed dose combination experiment. Synergistic effects are evaluated by two approaches, the curve shift analysis and the combination matrix experiment with Bliss-scoring. The curve shift analysis determines synergy as a decrease in dose to achieve the same effect (Dose-based), which is quantified with the CI value. The Bliss approach bases synergy on an increase in the maximum effect (Effect-based). The synergistic mechanism of a specific combination may result in either one or the other, or both. NTRC has merged the two methods into one approach, generating a comprehensive view on synergistic effects
Left: Dose-based synergy, lower dose required to achieve the same effect.
Right: Effect-based synergy, higher maximum effect achieved.
Full dose response curves cover the sigmoidal nature of drug-effect relations. The exponential behaviour may be overlooked in a dose-matrix approach.
Example of sigmoidal dose-effect curve (Chou, 2006).
Fixed IC50 Ratio Experiment
This is the most robust experiment provided that the single agents show well-behaved dose-response curves. Based on predetermined IC50 values of the single agents, mixtures are generated with theoretical equipotency compared to the single agents. Subsequently, dose response curves of these mixtures are measured in duplicate via cellular profiling. A left-ward curve shift compared to the single agent curves indicates synergy, as described by Zhao et al. (2004).
SynergyFinder™ allows the testing of mixtures of three compounds. The individual contribution of each compound to the total synergy can be determined. In one such experiment, NTRC has shown that the FDA-approved combination of the BRAF inhibitor dabrafenib and the MEK inhibitor trametinib can be further supplemented with the PI3K inhibitor pictilisib to enhance synergy. By establishing such super-synergistic combinations in vitro, it is possible to effectively focus resources for in vivo studies on the most successful multi-combinations.
Dose response curves of single agents (green, blue) and fixed ratio mixtures (orange, yellow, red) allow for curve-shift analysis. The mixture curves are shifted to the left and CI value < 1, meaning synergy. This example is for the combination of a MEK inhibitor and PI3K inhibitor.
Example of curve-shift analysis for a combination of three compounds: dabrafenib (green curve), trametinib (blue curve) and pictilisib (purple curve). The combination of compounds (grey curve) results in a curve-shift to the left, indicating synergy.
CI Index
As a secondary analysis, we use the experimental data to generate Chou Talalay Combination Indices (Chou T., 2010) providing an independent, quantitative assessment of synergy. The CI Index is the most-used measure of synergy. It can be seen as the ‘dose reduction’ caused by synergy.
The CI is dependent on the fraction effect considered (e.g. 75% growth inhibition). NTRC determines CIs at various effect sizes and drug ratiosGenerally, a CI < 1 indicates synergy for a compound pair, a CI < 0.3 indicates strong synergy, as described by Haagensen et al. (2012). CI > 1 indicates antagonism.
CI values for the combination of a MEK and PI3K inhibitor at various effect levels. Circles represent experimentally determined CI values (Chou Talalay method).
Isobolograms
An isobologram is a graphic way of assessing if combinations show synergy. It shows the effect of a combination at a particular effect level, for instance 50% or 75%. The isobologram draws an isobolic line of theoretical additivity. It is a straight line between the concentration of Compound A to achieve the effect (x-axis) and the concentration of Compound B (y-axis). Mixtures below the line are synergistic. Below the line indicates that the compound concentrations can be decreased to achieve the same effect.
The isobologram for the combination of the MEK and PI3K inhibitor visualizes the effect of mixtures at a % effect. The blue line represents additivity. Mixtures that are below the blue line are synergistic, mixtures at the blue line are additive and mixtures above the line are antagonistic.
Custom Based Studies and Large Combinatorial Screens
The service SynergyFinder™ is highly suitable for small-scale, custom-based studies as well as large combinatorial screens. Small-scale studies can be performed for a selection of Oncolines™ cell lines and selected combinations of your compound with anti-cancer agents. Usually these studies are based on hypotheses regarding genetic background and targeting of compounds. Examples are provided by Uitdehaag et al. (2015) and Canté-Barrett et al. (2016).
Large combinatorial screens, referred to as SynergyScreen™, are generally performed for combinations of your compound with representatives of the diverse anti-cancer drug classes that are covered in the compound database. We have identified 42 exemplars covering approved and novel targets. You can also screen the full library of 180 pre-profiled anti-cancer drugs, with the purpose to look broadly for opportunities that may go beyond rationalisation.
Cancer Drug Synergy Prediction
The efficiency of combination screening can be improved by incorporating knowledge of a compound’s biological mechanism (a.o. Uitdehaag et al., 2019, Seashore-Ludlow et al., 2015, Lee et al., 2018). We constructed a model to predict synergy based on the profiling of single agents in the cancer cell line panel Oncolines™. More than 180 anti-cancer agents were profiled in dose response curves in the panel. The targeted synergy model assumes that drugs are synergistic when they have the same cancer drivers, yet inhibit different targets within a pathway. An example is the FDA approved synergistic combination of BRAF inhibitor dabrafenib (Tafinlar®) and MEK inhibitor trametinib (Mekinist®), which target BRAF[V600E] mutant cell lines. A priori selection of compounds can help in reducing size and cost of synergy screening experiments, but not replace them.
Example of targeted synergy for dabrafenib (Tafinlar®) and trametinib (Mekinist®). On the left are volcano plots (more info: Gene Mutation Analysis) showing that cell lines with a mutation in BRAF[V600E] are sensitive to the compounds. The right chart shows that the combination of dabrafenib and trametinib results in a curve shift to the left (Uitdehaag et al., 2015).
References
Uitdehaag et al. (2015) Selective Targeting of CTNNB1-, KRAS- or MYC-Driven Cell Growth by Combinations of Existing Drugs, PLoS ONE, 10 (5):e0125021.
http://dx.plos.org/10.1371/journal.pone.0125021
Chou T. (2006) Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies, Pharmacological Reviews, 58 (3):621-681.
http://pharmrev.aspetjournals.org/content/58/3/621
Zhao et al. (2004) Evaluation of combination chemotherapy: integration of nonlinear regression, curve shift, isobologram, and combination index analyses, Clinical Cancer Research, 10 (23):7994-8004.
http://clincancerres.aacrjournals.org/content/10/23/7994.full.pdf+html
Chou, T. (2010) Drug Combination Studies and Their Synergy Quantification Using the Chou-Talalay Method, Cancer Research, 70 (2):440-446.
https://cancerres.aacrjournals.org/content/70/2/440
Haagensen et al. (2012) The synergistic interaction of MEK and PI3K inhibitors is modulated by mTOR inhibition, British Journal of Cancer, 106:1386-1394.
https://www.nature.com/articles/bjc201270
Canté-Barrett et al. (2016) MEK and PI3K-AKT inhibitors synergistically block activated IL7 receptor signaling in T-cell acute lymphoblastic leukemia, Leukemia, 30:1832-1843.
https://www.nature.com/articles/leu201683
Uitdehaag et al. (2019) Combined cellular and biochemical profiling to identify predictive drug response biomarkers for kinase inhibitors approved for clinical use between 2013 and 2017, Molecular Cancer Therapeutics, 18 (2):470-481.
http://mct.aacrjournals.org/content/early/2018/10/31/1535-7163.MCT-18-0877
Seashore-Ludlow et al. (2015) Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset, Cancer Discovery 5:1210-1223.
https://cancerdiscovery.aacrjournals.org/content/5/11/1210
Lee et al. (2018) Harnessing synthetic lethality to predict the response to cancer treatment, Nature Communications, 9:2546.
https://www.nature.com/articles/s41467-018-04647-1
