BRAID

class synergy.combination.BRAID(drug1_model=None, drug2_model=None, mode='kappa', **kwargs)

Bases: ParametricSynergyModel2D

BRAID synergy.

kappa and delta are the BRAID synergy parameters, though E3 is related to how much more effective the combination is than either drug alone. Note though that lim_{d1 -> inf, d2 -> inf}E(d1, d2) does not equal E3 in BRAID.

Interpretation of synergy parameters

Parameter	Values	Synergy/Antagonism
kappa	< 0	Antagonism
	> 0	Synergism
delta	[0, 1)	Antagonism
	> 1	Synergism

Parameters:

• drug1_model (DoseResponseModel1D) – The model for the first drug.

• drug2_model (DoseResponseModel1D) – The model for the second drug.

• mode (str , default="kappa") – Options are “kappa”, “delta”, “both”. BRAID has model versions that fit synergy using the parameter “kappa”, the parameter “delta”, or both. The standard version only fits kappa, but the other variants are available.

E(d1, d2)

Calculate the expected effect of the combination of drugs at doses d1 and d2.

Parameters:

• d1 (ArrayLike) – Concentration of drug 1

• d2 (ArrayLike) – Concentration of drug 2

Return ArrayLike:

Expected effect of the combination of drugs at doses d1 and d2

E_reference(d1, d2)

Return the expected effect of the combination of drugs at doses d1 and d2.

Parameters:

• d1 (ArrayLike) – Concentration of drug 1

• d2 (ArrayLike) – Concentration of drug 2

Return ArrayLike:

Reference (additive) values of E at the given doses

fit(d1, d2, E, **kwargs)

Fit the model to data.

Parameters:

• d1 (ArrayLike) – Concentration of drug 1

• d2 (ArrayLike) – Concentration of drug 2

• E (ArrayLike) – Effect of the combination of drugs at doses d1 and d2

• kwargs (dict) –

  • p0: Initial parameter guesses

  • bootstrap_iterations: Number of bootstrap iterations to perform to estimate confidence intervals

  • use_jacobian: whether to use the model jacobian when fitting

  • Additional kwargs for scipy.optimize.curve_fit()

get_confidence_intervals(confidence_interval=95)

Return the lower bound and upper bound estimates for each parameter.

Parameters:

confidence_interval (float, default=95) – % confidence interval to return. Must be between 0 and 100.

Returns:

Lower and upper bounds for each parameter keyed by parameter name.

Return type:

Dict[str, Tuple[float, float]]

get_parameters()

Return the model’s parameters as a dict keyed by parameter name.

Return Dict[str, Any]:

Model parameters

Return type:

Dict[str, Any]

summarize(confidence_interval=95, tol=0.01)

Print a summary table of the synergy model.

Parameters:

• confidence_interval (float) – The confidence interval to use for parameter estimates (must be between 0 and 100).

• tol (float) – The tolerance around additivity for determining synergism or antagonism.

property is_converged: bool

True if model.fit() was called and the optimization converged.

property is_fit: bool

True if model.fit() was called.

property is_specified: bool

True if all parameters are set.