stats.ddi¶

Classification of drug-drug interactions by the change of the exposure, and its publication table.

The FDA guidance (FDA 2020) classifies a perpetrator by the ratio of the AUC of a sensitive substrate with and without it: a strong, moderate or weak inhibitor raises the AUC at least 5-fold, 2- to 5-fold or 1.25- to 2-fold; a strong, moderate or weak inducer lowers it by at least 80 %, 50-80 % or 20-50 %. A substrate is sensitive when a strong inhibitor raises its AUC at least 5-fold and moderately sensitive at 2- to 5-fold. The EMA guideline (EMA 2012) uses the same thresholds; the index substrates, inhibitors and inducers are the FDA's tables (FDA drug interaction table), the study designs follow the industry perspective of Bjornsson et al. (2003).

DDIKind ¶

Bases: StrEnum

Direction of an interaction.

DDIStrength ¶

Bases: StrEnum

Strength of an interaction.

Sensitivity ¶

Bases: StrEnum

Sensitivity of a substrate to a strong inhibitor.

DDIThresholds `dataclass` ¶

DDIThresholds(
    inhibitor_weak=1.25,
    inhibitor_moderate=2.0,
    inhibitor_strong=5.0,
    inducer_weak=0.8,
    inducer_moderate=0.5,
    inducer_strong=0.2,
    sensitive=5.0,
    moderately_sensitive=2.0,
    source="FDA 2020",
)

Thresholds of the classification, as AUC ratios with / without the perpetrator.

An inhibitor threshold is the smallest ratio of its class, an inducer threshold the largest (inducer_strong = 0.2 is an 80 % decrease).

Attributes:

Name	Type	Description
`inhibitor_weak`	`float`	weak inhibitor at or above this ratio
`inhibitor_moderate`	`float`	moderate inhibitor at or above this ratio
`inhibitor_strong`	`float`	strong inhibitor at or above this ratio
`inducer_weak`	`float`	weak inducer at or below this ratio
`inducer_moderate`	`float`	moderate inducer at or below this ratio
`inducer_strong`	`float`	strong inducer at or below this ratio
`sensitive`	`float`	sensitive substrate at or above this ratio
`moderately_sensitive`	`float`	moderately sensitive substrate at or above this ratio
`source`	`str`	the guidance the thresholds come from

fda `classmethod` ¶

fda()

The thresholds of the FDA clinical drug interaction guidance (FDA 2020).

Returns:

Type	Description
`DDIThresholds`	The thresholds.

ema `classmethod` ¶

ema()

The thresholds of the EMA guideline on the investigation of drug interactions (EMA 2012).

These equal the FDA ones.

Returns:

Type	Description
`DDIThresholds`	The thresholds.

classify ¶

classify(auc_ratio)

Kind and strength of an interaction from an AUC ratio.

Parameters:

Name	Type	Description	Default
`auc_ratio`	`float`	AUC with / without the perpetrator, positive.	required

Returns:

Type	Description
`tuple[DDIKind, DDIStrength]`	The kind and the strength.

Raises:

Type	Description
`ValueError`	if the ratio is not positive.

DDIResult `dataclass` ¶

DDIResult(
    kind,
    strength,
    auc_ratio,
    cmax_ratio,
    ci_low,
    ci_high,
    uncertain,
    kind_low,
    strength_low,
    kind_high,
    strength_high,
    thresholds,
)

Classification of an interaction.

Attributes:

Name	Type	Description
`kind`	`DDIKind`	the classification (from the bound of the interval closer to 1 when an interval is given)
`strength`	`DDIStrength`	the strength
`auc_ratio`	`float`	the AUC ratio
`cmax_ratio`	`float \| None`	the Cmax ratio, reported only
`ci_low`	`float`	lower bound of the interval of the AUC ratio, `NaN` without one
`ci_high`	`float`	upper bound of the interval
`uncertain`	`bool`	whether the interval spans a boundary of the classes
`kind_low`	`DDIKind`	classification of `ci_low`
`strength_low`	`DDIStrength`	strength of `ci_low`
`kind_high`	`DDIKind`	classification of `ci_high`
`strength_high`	`DDIStrength`	strength of `ci_high`
`thresholds`	`DDIThresholds`	the thresholds used

to_dict ¶

to_dict()

The fields as a dictionary with the enumerations as strings.

Returns:

Type	Description
`dict[str, Any]`	Field name to value.

ddi_classification ¶

ddi_classification(
    auc_ratio, *, cmax_ratio=None, ci=None, thresholds=None
)

Classify a perpetrator by the AUC ratio of a substrate with and without it.

With an interval (given as ci or carried by a RatioResult) the classification is conservative: it uses the bound closer to 1 (the lower bound of an increase, the upper bound of a decrease), an interval which contains 1 gives no interaction, and uncertain is set when the two bounds fall into different classes.

Parameters:

Name	Type	Description	Default
`auc_ratio`	`float \| RatioResult`	AUC ratio with / without the perpetrator, or the `ratio` result.	required
`cmax_ratio`	`float \| RatioResult \| None`	Cmax ratio, reported next to the classification.	`None`
`ci`	`tuple[float, float] \| None`	interval of the AUC ratio; overrides the interval of a `RatioResult`.	`None`
`thresholds`	`DDIThresholds \| None`	the thresholds, FDA 2020 by default.	`None`

Returns:

Type	Description
`DDIResult`	The classification.

Raises:

Type	Description
`ValueError`	if the interval is reversed or a ratio is not positive.

substrate_sensitivity ¶

substrate_sensitivity(auc_ratio, *, thresholds=None)

Sensitivity of a substrate from its AUC ratio with a strong inhibitor.

Parameters:

Name	Type	Description	Default
`auc_ratio`	`float \| RatioResult`	AUC ratio with / without the strong inhibitor.	required
`thresholds`	`DDIThresholds \| None`	the thresholds, FDA 2020 by default.	`None`

Returns:

Type	Description
`Sensitivity`	`SENSITIVE` at or above `thresholds.sensitive`, `MODERATELY_SENSITIVE`
`Sensitivity`	at or above `thresholds.moderately_sensitive`, else `NONE`.

Raises:

Type	Description
`ValueError`	if the ratio is not positive.

ddi_table ¶

ddi_table(
    test,
    reference,
    parameters=("auc_inf_obs", "cmax"),
    *,
    dim="individual",
    thresholds=None,
    digits=3,
    ci_level=0.9,
    paired=None,
    include_excluded=False,
    **indexers,
)

The interaction table of a publication: one row per parameter, formatted.

Every parameter is taken from both results with ParameterResult.sample(name, dim, **indexers), its geometric mean ratio with and without the perpetrator is computed with pkpdutils.stats.ratio and classified with ddi_classification, which reads the bound of the interval closer to 1 (FDA 2020; EMA 2012). The classes are defined for the AUC; they are applied to every parameter of the table, so that the row of the maximum is read next to the row of the exposure.

Parameters:

Name	Type	Description	Default
`test`	`ParameterResult`	the result with the perpetrator.	required
`reference`	`ParameterResult`	the result without it.	required
`parameters`	`Sequence[str]`	the parameters of the table.	`('auc_inf_obs', 'cmax')`
`dim`	`str`	the sample dimension of the individuals.	`'individual'`
`thresholds`	`DDIThresholds \| None`	the thresholds, FDA 2020 by default.	`None`
`digits`	`int`	significant digits of the numbers.	`3`
`ci_level`	`float`	level of the intervals, 0.90 as in bioequivalence.	`0.9`
`paired`	`bool \| None`	pair the samples, `None` pairs when both carry the same labels, as in `pkpdutils.stats.ratio`.	`None`
`include_excluded`	`bool`	read the samples a result marks `excluded` (`pkpdutils.nca.NCAResult.exclude`) as well.	`False`
`**indexers`	`Any`	coordinate label per remaining sample dimension.	`{}`

Returns:

Type	Description
`DataFrame`	The table with the columns `parameter`, `unit`, `n_test`,
`DataFrame`	`n_reference`, `ratio`, `ci_low`, `ci_high`, `kind`, `strength`,
`DataFrame`	`uncertain` and `source`; every cell is a string.

Raises:

Type	Description
`ValueError`	as `ParameterResult.sample` and `pkpdutils.stats.ratio`.

stats.ddi¶

DDIKind ¶

DDIStrength ¶

Sensitivity ¶

DDIThresholds dataclass ¶

fda classmethod ¶

ema classmethod ¶

classify ¶

DDIResult dataclass ¶

to_dict ¶

ddi_classification ¶

substrate_sensitivity ¶

ddi_table ¶

DDIThresholds `dataclass` ¶

fda `classmethod` ¶

ema `classmethod` ¶

DDIResult `dataclass` ¶