Experiments

Kupferschmidt1995

Models

Datasets

Figures

fig1

Code

../../experiments/kupferschmidt1995.py

from typing import Dict, List

from sbmlsim.data import DataSet, load_pkdb_dataframes_by_substance
from sbmlsim.fit import FitMapping, FitData
from sbmlsim.plot import Figure, Axis
from sbmlsim.simulation import Timecourse, TimecourseSim

from . import MidazolamSimulationExperiment


class Kupferschmidt1995(MidazolamSimulationExperiment):
    def datasets(self) -> Dict[str, DataSet]:
        dsets = {}
        for fig_id in ["Fig1", "Fig2"]:
            dframes = load_pkdb_dataframes_by_substance(
                f"{self.sid}_{fig_id}", data_path=self.data_path)
            for substance, df in dframes.items():
                dset = DataSet.from_df(df, self.ureg)
                if substance == "midazolam":
                    dset.unit_conversion("mean", 1 / self.Mr.mid)
                elif substance == "1-hydroxymidazolam":
                    dset.unit_conversion("mean", 1 / self.Mr.mid1oh)
                else:
                    raise ValueError

                for intervention in df.intervention.unique():
                    # tag route
                    if "po" in intervention:
                        route = "po"
                    elif "iv" in intervention:
                        route = "iv"
                    else:
                        raise ValueError
                    # tag intervention type
                    if "GRAP1" in intervention:
                        continue
                    else:
                        type = "control"

                    dsets[f"{fig_id}_{substance}_{route}_{type}"] = dset[dset.intervention == intervention]
        return dsets

    def simulations(self) -> Dict[str, TimecourseSim]:
        return super(Kupferschmidt1995, self).simulations(
            simulations={**self.simulations_mid()}
        )

    def simulations_mid(self) -> Dict[str, TimecourseSim]:
        """ Kupferschmidt1995

        - midazolam, iv, 5 [mg]
        - midazolam, po, 15 [mg]

        - grapefruit juice, po, 200 [mg], -60min and -15min

        """
        Q_ = self.Q_
        bodyweight = Q_(70, 'kg') # avg. bodyweight of 8 individuals. (se=1kg)
        mid_iv = Q_(5, 'mg')
        mid_po = Q_(15, 'mg')

        sim_def = {
            "mid_iv_c": {'end': 1500, 'steps': 3000, 'dose': {'IVDOSE_mid': mid_iv}, 'change': self.default_changes()},
            "mid_po_c": {'end': 1500, 'steps': 3000, 'dose': {'PODOSE_mid': mid_po}, 'change': self.default_changes()},
        }
        tcsims = {}
        for key, value in sim_def.items():
            tcsims[key] = TimecourseSim([
                Timecourse(start=0, end=value['end'], steps=value['steps'],
                           changes={**value['change'],
                                    **value['dose'],
                                    "BW": bodyweight}
                           )
        ])

        return tcsims

    def fit_mappings(self) -> Dict[str, FitMapping]:
        # fit mapping: which data maps on which simulation
        fit_dict = {
            "fm_mid_iv": {"ref": "Fig1_midazolam_iv_control", "obs": "task_mid_iv_c", "yid": "[Cve_mid]"},
            "fm_mid1oh_iv": {"ref": "Fig2_1-hydroxymidazolam_iv_control", "obs": "task_mid_iv_c", "yid": "[Cve_mid1oh]"},
            "fm_mid_po": {"ref": "Fig1_midazolam_po_control", "obs": "task_mid_po_c", "yid": "[Cve_mid]"},
            "fm_mid1oh_po": {"ref": "Fig2_1-hydroxymidazolam_po_control", "obs": "task_mid_po_c", "yid": "[Cve_mid1oh]"},
        }

        mappings = {}
        for key, values in fit_dict.items():
            mappings[key] = FitMapping(
                self,
                reference=FitData(self, dataset=values["ref"], xid="time", yid="mean", yid_sd="mean_sd"),
                observable=FitData(self, task=values["obs"], xid="time", yid=values["yid"])
            )
        return mappings

    def figures(self) -> Dict[str, Figure]:
        return {
            **self.figure_mid()
        }

    def figure_mid(self):
        unit_time = "min"
        unit_mid = "nmol/ml"
        unit_mid1oh = "nmol/ml"

        fig = Figure(self, sid="Fig1",
                     num_rows=2, num_cols=2, name=self.sid)
        plots = fig.create_plots(
            Axis("time", unit=unit_time),
            legend=True
        )

        # set titles and labs
        plots[0].set_title("midazolam iv, 5 [mg]")
        plots[1].set_title("midazolam iv, 5 [mg] + Grapefruit Juice")
        plots[2].set_title("midazolam po, 15 [mg]")
        plots[3].set_title("midazolam po, 15 [mg] + Grapefruit Juice")
        for k in (0, 1):
            plots[k].set_yaxis("midazolam", unit_mid)
            plots[k].xaxis.label_visible = False
        for k in (2, 3):
            plots[k].set_yaxis("1-hydroxymidazolam", unit_mid1oh)

        # simulation
        plot_dict = {
            "mid_iv_c": {'plot': (0, 1), 'label': 'mid (ve blood; control)', "color": "black"},
            "mid_po_c": {'plot': (2, 3), 'label': 'mid (ve blood; control)', "color": "black"},
        }

        for key, value in plot_dict.items():
            for suffix in ["_sensitivity", ""]:
                task_id = f"task_{key}{suffix}"
                # plot midazolam
                p = plots[value["plot"][0]]
                p.add_data(task=task_id, xid='time', yid='[Cve_mid]',
                           label=value['label'], color=value['color'], linewidth=2)
                # plot 1-hydroxymidazolam
                p = plots[value["plot"][1]]
                p.add_data(task=task_id, xid='time', yid='[Cve_mid1oh]',
                           label=value['label'], color=value['color'], linewidth=2)

        # plot data
        data_def = {
            "Fig1_midazolam_iv_control": {'plot': 0, 'key': 'control', 'color': 'black'},
            "Fig1_midazolam_po_control": {'plot': 1, 'key': 'control', 'color': 'black'},
            "Fig2_1-hydroxymidazolam_iv_control": {'plot': 2, 'key': 'control', 'color': 'black'},
            "Fig2_1-hydroxymidazolam_po_control": {'plot': 3, 'key': 'control', 'color': 'black'},
        }

        for dset_key, dset_info in data_def.items():
            p = plots[dset_info['plot']]
            p.add_data(dataset=dset_key,
                       xid="time", yid="mean", yid_sd="mean_sd",
                       count=None, color=dset_info['color'],
                       label=dset_info['key'])

        return {"fig1": fig}