Drug Synergy Tools

Configuration File: drug_synergy_tools.json Tool Type: Local Tools Count: 5

This page contains all tools defined in the drug_synergy_tools.json configuration file.

Available Tools

DrugSynergy_calculate_bliss (Type: DrugSynergyTool)

Calculate Bliss Independence synergy score for a drug combination. Model: E_expected = E_a + E_b …

DrugSynergy_calculate_bliss tool specification

Tool Information:

• Name: DrugSynergy_calculate_bliss

• Type: DrugSynergyTool

• Description: Calculate Bliss Independence synergy score for a drug combination. Model: E_expected = E_a + E_b - E_a*E_b. Synergy score = (E_combo - E_expected) * 100. Positive = synergy; Negative = antagonism. Inputs are fractional inhibition values (0-1). Based on Bliss (1939). Use for: single-concentration combination screening, high-throughput synergy assessment.

Parameters:

• operation (string) (required) Operation type

• effect_a (number) (required) Fractional inhibition of drug A alone (0=no effect, 1=complete inhibition)

• effect_b (number) (required) Fractional inhibition of drug B alone (0=no effect, 1=complete inhibition)

• effect_combination (number) (required) Observed fractional inhibition of the drug combination

Example Usage:

query = {
    "name": "DrugSynergy_calculate_bliss",
    "arguments": {
        "operation": "example_value",
        "effect_a": "example_value",
        "effect_b": "example_value",
        "effect_combination": "example_value"
    }
}
result = tu.run(query)

DrugSynergy_calculate_ci (Type: DrugSynergyTool)

Calculate Chou-Talalay Combination Index (CI) for drug synergy quantification. CI is derived from…

DrugSynergy_calculate_ci tool specification

Tool Information:

• Name: DrugSynergy_calculate_ci

• Type: DrugSynergyTool

• Description: Calculate Chou-Talalay Combination Index (CI) for drug synergy quantification. CI is derived from the Median Effect Equation: fa/fu = (D/Dm)^m. CI < 1 indicates synergy, CI = 1 is additive, CI > 1 is antagonism. Also computes the Dose Reduction Index (DRI) showing how much each drug dose can be reduced in combination. Supports both mutually exclusive (drugs with similar mechanisms) and mutually non-exclusive (drugs with different mechanisms) assumptions. Based on Chou & Talalay (1984). Use for: quantitative synergy assessment, dose optimization, isobologram analysis.

Parameters:

• operation (string) (required) Operation type

• doses_a_single (array) (required) Concentration values for drug A single-agent dose-response (e.g., [0.01, 0.1, 1, 10, 100])

• effects_a_single (array) (required) Fractional inhibition of drug A alone at each dose (0-1 scale)

• doses_b_single (array) (required) Concentration values for drug B single-agent dose-response

• effects_b_single (array) (required) Fractional inhibition of drug B alone at each dose (0-1 scale)

• dose_a_combo (number) (required) Dose of drug A used in the combination

• dose_b_combo (number) (required) Dose of drug B used in the combination

• effect_combo (number) (required) Observed fractional inhibition of the combination (0-1 scale, must be between 0 and 1 exclusive)

• assumption (string) (optional) Whether drugs have similar (mutually_exclusive) or different (mutually_non_exclusive) mechanisms of action. Default: mutually_exclusive

Example Usage:

query = {
    "name": "DrugSynergy_calculate_ci",
    "arguments": {
        "operation": "example_value",
        "doses_a_single": ["item1", "item2"],
        "effects_a_single": ["item1", "item2"],
        "doses_b_single": ["item1", "item2"],
        "effects_b_single": ["item1", "item2"],
        "dose_a_combo": "example_value",
        "dose_b_combo": "example_value",
        "effect_combo": "example_value"
    }
}
result = tu.run(query)

DrugSynergy_calculate_hsa (Type: DrugSynergyTool)

Calculate Highest Single Agent (HSA) synergy score for drug combinations across dose points. HSA …

DrugSynergy_calculate_hsa tool specification

Tool Information:

• Name: DrugSynergy_calculate_hsa

• Type: DrugSynergyTool

• Description: Calculate Highest Single Agent (HSA) synergy score for drug combinations across dose points. HSA expected = max(E_a, E_b) at each dose. Synergy = observed - HSA expected. Requires matching arrays of effects for drug A, drug B, and the combination. Use for: multi-dose combination matrices, identifying synergistic dose ranges.

Parameters:

• operation (string) (required) Operation type

• effects_a (array) (required) Array of drug A inhibition effects at each dose point (0-1 or 0-100)

• effects_b (array) (required) Array of drug B inhibition effects at each dose point (same length as effects_a)

• effects_combo (array) (required) Array of combination inhibition effects at each dose point (same length as effects_a)

Example Usage:

query = {
    "name": "DrugSynergy_calculate_hsa",
    "arguments": {
        "operation": "example_value",
        "effects_a": ["item1", "item2"],
        "effects_b": ["item1", "item2"],
        "effects_combo": ["item1", "item2"]
    }
}
result = tu.run(query)

DrugSynergy_calculate_loewe (Type: DrugSynergyTool)

Calculate Loewe Additivity synergy score for a drug combination. The Loewe model is the most wide…

DrugSynergy_calculate_loewe tool specification

Tool Information:

• Name: DrugSynergy_calculate_loewe

• Type: DrugSynergyTool

• Description: Calculate Loewe Additivity synergy score for a drug combination. The Loewe model is the most widely used reference model for drug interaction: d_a/D_a(E) + d_b/D_b(E) = 1 at additivity. If sum < 1, the combination is synergistic (needs less drug than expected); if > 1, it is antagonistic. Requires single-agent dose-response data for each drug (to fit Hill curves) plus the combination doses and observed effect. Based on Loewe & Muischnek (1926). Use for: rigorous synergy quantification with dose-response data, comparison with Bliss/HSA/ZIP models.

Parameters:

• operation (string) (required) Operation type

• doses_a_single (array) (required) Concentration values for drug A single-agent dose-response (e.g., [0.01, 0.1, 1, 10, 100])

• effects_a_single (array) (required) Fractional inhibition of drug A alone at each dose (0-1 scale, same length as doses_a_single)

• doses_b_single (array) (required) Concentration values for drug B single-agent dose-response

• effects_b_single (array) (required) Fractional inhibition of drug B alone at each dose (0-1 scale, same length as doses_b_single)

• dose_a_combo (number) (required) Dose of drug A used in the combination

• dose_b_combo (number) (required) Dose of drug B used in the combination

• effect_combo (number) (required) Observed fractional inhibition of the combination (0-1 scale)

Example Usage:

query = {
    "name": "DrugSynergy_calculate_loewe",
    "arguments": {
        "operation": "example_value",
        "doses_a_single": ["item1", "item2"],
        "effects_a_single": ["item1", "item2"],
        "doses_b_single": ["item1", "item2"],
        "effects_b_single": ["item1", "item2"],
        "dose_a_combo": "example_value",
        "dose_b_combo": "example_value",
        "effect_combo": "example_value"
    }
}
result = tu.run(query)

DrugSynergy_calculate_zip (Type: DrugSynergyTool)

Calculate ZIP (Zero Interaction Potency) delta synergy score from a full dose-response matrix. Fi…

DrugSynergy_calculate_zip tool specification

Tool Information:

• Name: DrugSynergy_calculate_zip

• Type: DrugSynergyTool

• Description: Calculate ZIP (Zero Interaction Potency) delta synergy score from a full dose-response matrix. Fits Hill curves to marginal dose-response data for each drug and calculates expected additivity. Returns delta matrix where positive values indicate synergy. Based on Yadav et al. (2015). IMPORTANT: Each drug must have ≥3 non-zero dose points with measurable inhibition for Hill curve fitting to succeed. A 3×3 matrix with non-zero doses_a and doses_b (no zero-dose rows/columns) is the minimum; 4×4 or larger matrices are recommended. If Hill fitting fails, use calculate_bliss instead. Interpretation: score > 10 = Synergy, -10 to 10 = Additivity, < -10 = Antagonism. Use for: comprehensive combination screening matrices.

Parameters:

• operation (string) (required) Operation type

• doses_a (array) (required) Concentration values for drug A (e.g., [0.01, 0.1, 1, 10])

• doses_b (array) (required) Concentration values for drug B (e.g., [0.01, 0.1, 1, 10])

• viability_matrix (array) (required) 2D matrix of cell viability percentages (0-100). Rows = doses_a, Columns = doses_b.

Example Usage:

query = {
    "name": "DrugSynergy_calculate_zip",
    "arguments": {
        "operation": "example_value",
        "doses_a": ["item1", "item2"],
        "doses_b": ["item1", "item2"],
        "viability_matrix": ["item1", "item2"]
    }
}
result = tu.run(query)

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