Innovative Drug Interaction Studies
Drug combinations are critical in many diseases, including cancer. Synergistic drugs allow the use of lower doses in the treatment and therefore the adverse reactions would probably be reduced.
Vivia has recently implemented new workflows and procedures for drugs interaction analysis covering those more traditional [1] as well as novel complex surface modeling methods [2].

Success in drugs-interaction projects depends on the effective application of these methods to datasets generated following optimal experimental conditions. We believe that design is critical. We count with a large experience in setting up combination experiments that are always a result of a collaborative work carried out together with our customers.

Drug interaction study phases

  • Design and setup. Aligned with project goals this critical initial phase is aimed to determine the optimal condition of the study.
    •  Drug concentrations:
      • Single Drug. If not available, single drugs are tested in advance to select the most suitable concentration for mixtures.
      • Combination. Based on single drugs EC50s, a combination matrix of concentrations is defined to have the more informative and reliable data to fit the complex surface interaction model.
    • Incubation times.
    • Others: compound management, controls, plate design, etc.
  • Experimental phase. According to the defined design and following quality control criteria.
  • Analysis.
    • Analytical methods are applied, and results are shown in fully customized figures and visualizations.
    • Deliverables: Pdf report, Excel data tables and Tibco Spotfire dynamic reports in customized applications running on a secure dedicated server.

Analytical procedures for drugs interaction measurement

Individual pre-fits of each drug at every tested concentration of the other are generated resulting in parallel dose-response curves.

Later, such fitting functions are used for feeding complex interaction 3D surface model with expected values coming out from the interpolation of tested dose of each drug in the corresponding fitting functions. Thus, we overcame variability due to high residual error and improved the results.

Calculation methods may use either the observed or the expected values described above as input data, and can be divided in 3 categories:

  • Calculated indexes based on ratios of concentrations that provide the same effect with each drug alone or combined with the other (Combination Index and Loewe)
  • Differences between observed and expected values from a reference additivity surface (Bliss and ZIP)
  • Estimated parameters from a regression analysis to a complex surface model MuSyC (Multidimensional Synergy of Combinations [3]) that integrates and unifies benefits from previous methods.

Key aspects of MuSyC interaction model algorithm

  • Multiple pharmacological activity parameters evaluated
  • Split synergy parameters to reflect potential asymmetry

Results visualization

  • Heatmaps and distribution plots
  • Drug effect. Control %
Combination index *
* Also available for other indexes: Bliss, Loewe and ZIP

Results visualization 2

  • Interactive free-rotation 3D chart and contour-plot for interaction surface including references for additive behavior.
  • Model parameters output table including 95% confidence intervals regression coefficients and other statistics to evaluate goodness of tit



  1. R. Greco, G. Bravo, and J. C. Parsons. The search for synergy: a critical review from a response surface perspective. Pharmacol Rev, vol. 47, no. 2, p. 331, (1995).
  2. Meyer, C. T. et al. Quantifying drug combination synergy along potency and efficacy axes. Cell Syst. 8, 97–108 (2019).
  3. Wooten, D. J., Meyer, C. T., Vito Quaranta, A. L. R. L. & Lope, C. F. Musyc is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery. Nat. Commun. 12, 1–16 (2021)