Zimmer

class synergy.combination.Zimmer(drug1_model=None, drug2_model=None, **kwargs)

The Effective Dose synergy model from Zimmer et al.

This model uses the multiplicative survival principle (i.e., Bliss), but adds a parameter for each drug describing how it affects the potency of the other.

Synergy by Zimmer is described by these parameters

Interpretation of synergy parameters

Parameter	Values	Synergy/Antagonism	Interpretation
a12	< 0	Synergism	Drug 2 increases the effective dose (potency) of drug 1
	> 0	Antagonism	Drug 2 decreases the effective dose (potency) of drug 1
a21	< 0	Synergism	Drug 1 increases the effective dose (potency) of drug 2
	> 0	Antagonism	Drug 1 decreases the effective dose (potency) of drug 2

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.