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 developed a workflow including different methods like traditional dose dependant indexes, surface modeling¹ (Greco et al., 1995) and the new MuSyC²,³ algorithm to perform a complete drug interaction study.

Drug interaction study phases

  • Setup phase. An initial phase to determine the optimal condition for drug interaction study.
  • Doses
  • Single Drug Doses. Single drugs are tested to select the most suitable doses for each drug.
  • Combination Doses. A dose matrix (4x4, 5x5, NxN) is created based on single drugs EC50s to have the more informative and reliable data to calculate the drug interaction.
  • Incubation times. The most suitable incubation time(s) are selected to grant the best data collection for the drug interaction synergy calculation.
  • Experimental phase. Data information collected during the setup phase is used to perform the final experiment.
  • Analysis phase
  • Dose-dependent
  • Combination Index
  • Loewe Combination Index
  • Loewe Delta
  • Interaction surface modeling
  • MuSyC²,³ algorithm
  • Loewe¹

MuSyC algorithm different synergies for drug interaction

Figure 01 Synergy
Figure 1. MuSyC algorithm is applicable to any metric of drug interaction effect and is able to differentiate between potency (α), efficacy (β) and cooperativity (γ) synergy.

Drug interaction study results dashboard

Figure 02 Synergy
Figure 2. Drug interaction study analysis results in Spotfire. Combination Indexes, 3D surface model and synergy parameters results are displayed together for a better overview.

Drug interaction 3D surface

Figure 03 Synergy
Figure 3. 3D interaction surface examples for potency synergy. MuSyC algorithm calculates fold changes in the potency on drug B induced by drug A (α12) and vice versa (α21).

Interactive Demo - Drug interaction 3D surface

Highlights

  • Innovative workflow using a complete set of synergy calculation methods.
  • Pre-analysis phase to maximize per well information collection without quality loss. (Single drug & combination doses selection, incubation time…)
  • Efficacy and cooperativity synergy added to the traditional potency synergy.
  • Asymmetric synergy detection (α12, α21, γ12 & γ21)
  • Interactive Spotfire synergy data analysis and visualization (3D Surface viewer, drug interaction parameters, synergy score, single drug dose response curves …)

References

  1. Greco, W.R., Bravo, G., and Parsons, J.C. (1995). The search for synergy: a critical review from a response surface perspective. Pharmacol. Rev. 47, 331–385.
  2. Meyer, C.T., Wooten, D.J., Paudel, B.B., Bauer, J., Hardeman, K.N., Westover, D., Lovly, C.M., Harris, L.A., Tyson, D.R., and Quaranta, V. (2019). Quantifying drug combination synergy along potency and efficacy axes. Cell Syst. 8, 97–108.e16. https://doi.org/10.1016/j.cels.2019.01.003
  3. Wooten, David J, and Albert, Réka. synergy – A Python library for calculating, analyzing, and visualizing drug combination synergy. (2020) Bioinformatics. https://doi.org/10.1093/bioinformatics/btaa826
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