SynergyFinder™ Explained

Identification of 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

  • Request the list of anti-cancer agents
Pre-profiled anti cancer drugs in clusters

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

Dose-based and Effect-based Synergy
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).
SynergyFinder MEK + PI3K Curves excl CI Waarde
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.
SynergyFinder Triple Combination
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.

Combination index analysis (CI) for the combination of a PI3-kinase and MEK inhibitor. Circles represent experimentally determined CI values (Chou Talalay method).
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.

Combination index analysis (CI) for the combination of a PI3-kinase and MEK inhibitor. Circles represent experimentally determined CI values (Chou Talalay method).
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.

Fixed Dose Combination Experiment

In some cases it can be advantageous to combine fixed and variable compound doses in a synergy experiment e.g. if single agents show little activity. In this experiment NTRC combines a fixed concentration of the first compound with various concentrations of the second compound. The single agent activities have first been assessed in duplicate via cellular profiling.

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 a targeted synergy (Uitdehaag et al., 2015)
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

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