Synergy Score

A probabilistic framework assuming drugs act independently on separate pathways. The expected combined effect is calculated as E_bliss = E_A + E_B - E_A * E_B, where E represents fractional effects. A synergy score is derived by subtracting this expected effect from the observed effect.

• Key Assumption: Statistical independence of drug actions
• Positive Score: Observed effect exceeds expectation (synergy)
• Negative Score: Observed effect falls below expectation (antagonism)
• Common Application: High-throughput screening of cancer drug combinations

A dose-equivalence model based on the assumption that a drug cannot synergize with itself. It uses the Combination Index (CI), where CI < 1 indicates synergy, CI = 1 indicates additivity, and CI > 1 indicates antagonism.

• Isobologram Analysis: Graphical method plotting isoboles (lines of equal effect)
• Concave Isobole: Indicates synergy (lower doses needed in combination)
• Convex Isobole: Indicates antagonism (higher doses needed)
• Gold Standard: Widely regarded as the most rigorous reference model

A conservative reference model that compares the combination effect to the maximum effect achieved by any single agent at the same concentration. Synergy is declared only when the combination exceeds the best individual performance.

• Threshold: Effect_combination > max(Effect_A, Effect_B)
• Strength: Simple to compute and interpret
• Weakness: Fails to detect synergy when both drugs have similar high efficacy
• Use Case: Initial screening before applying more sophisticated models like Loewe or Bliss

A hybrid model that combines the philosophies of both Loewe additivity and Bliss independence to capture shifts in both potency and efficacy. ZIP computes a delta score by comparing observed response surfaces against expected non-interactive surfaces.

• Two-Dimensional Scoring: Accounts for changes in both drug potency (left/right shift) and efficacy (up/down shift)
• Delta Score: Positive values indicate synergy across the dose matrix
• Advantage: More sensitive than Bliss or Loewe alone for complex interaction patterns
• Visualization: Often displayed as 3D synergy landscape plots

A computational workflow that transforms raw dose-response data into interpretable synergy metrics. The pipeline typically involves normalization, reference model fitting, and statistical significance testing.

• Step 1: Measure cell viability across a checkerboard dose matrix
• Step 2: Normalize to fractional inhibition (0 to 1)
• Step 3: Compute expected additivity surface using chosen reference model
• Step 4: Subtract expected from observed to generate synergy score matrix
• Step 5: Bootstrap resampling to estimate confidence intervals