Import Observed Data Into A GastroPlus Project

This Jupyter notebook reads exposure data from an Excel file and imports them as observed data into a GastroPlus 10.2 project. After importing the data, the data is retrieved from the project and plotted.

Load required libraries

PY

import gastroplus_api as gp
import pandas as pd
from pprint import pprint
import os

Start the GastroPlus service

PY

try:
    gastroplus_service = gp.start_service(verbose=False)
except Exception as e:
    print(f"Error starting GastroPlus service: {e}")

CODE

GastroPlus Service configured. Listening on port: 8700

If not using start_service() to start the GastroPlus service, adjust the port variable below to match the port of the GastroPlus Service instance

The port set here must match the listening port of the running GastroPlus Service.

PY

#port=8700
port = gastroplus_service.port
host = f"http://localhost:{port}"
client = gp.ApiClient(gp.Configuration(host = host))
gastroplus = gp.GpxApiWrapper(client)

Setup Input Information

Set the following variables as needed in the following cell

SCRIPT_PATH: The location of this script (if you wish to specify other paths relative to this directory)
PROJECT_DIRECTORY: The directory where the project is located
PROJECT_NAME: The name of the project
EXCEL_FILE: The name of the Excel file to import
INDEPENDENT_COLUMN_NAME: The column heading in the Excel file that corresponds to the independent variable
DEPENDENT_COLUMN_NAME: The column heading in the Excel file that corresponds to the dependent variable
INDEPENDENT_UNIT: The units of the independent variable
DEPENDENT_UNIT: The units of the dependent variable
NEW_GROUP_NAME: The group name to be assigned to the imported data
NEW_SERIES_NAME: The series name to be assigned to the imported data
SAVE_PROJECT: Whether to save the project after importing the data

PY

SCRIPT_PATH = os.getcwd()
PROJECT_DIRECTORY = os.path.normpath(os.path.join(SCRIPT_PATH, '../../ProjectFiles'))
PROJECT_NAME = "Metoprolol.gpproject"
PROJECT_FILE_NAME = os.path.join(PROJECT_DIRECTORY, PROJECT_NAME)
EXCEL_FILE = 'External_data.xlsx'
EXCEL_FILE_NAME = os.path.join(SCRIPT_PATH, EXCEL_FILE)
# These are the names of the columns in the Excel file
INDEPENDENT_COLUMN_NAME = "Time (h)"
DEPENDENT_COLUMN_NAME = "Conc. (µg/mL)"
# These are the units of the data columns
INDEPENDENT_UNIT = "hour"
DEPENDENT_UNIT = "microgram/milliliter"
NEW_GROUP_NAME = "IVP 500mg Pentikainen"
NEW_SERIES_NAME = "Plasma"
SAVE_PROJECT = False

Open Project and Read Existing Observed Data

Open the project and get the list of observed data that the project initially contains

PY

gastroplus.open_project(PROJECT_FILE_NAME)
observed_data_inventory = gastroplus.experimental_data_inventory()
pd.json_normalize(observed_data_inventory.to_dict(), record_path=['observed_series_keys'])

	group_type	group_name	series_type	series_name
0	InVitroDissolutionRelease	Fast CR tablet	TimeRealSeries	Fast CR tablet
1	InVitroDissolutionRelease	Slow CR tablet	TimeRealSeries	Slow CR tablet
2	InVitroDissolutionRelease	Moderate CR tablet	TimeRealSeries	Moderate CR tablet
3	InVitroDissolutionRelease	New Form CR tablet	TimeRealSeries	New Form CR tablet
4	ExposureData	Fast CR tablet 100mg	UncertainConcentrationSeries	Fast CR tablet 100mg
5	ExposureData	Moderate CR tablet 100mg	UncertainConcentrationSeries	Moderate CR tablet 100mg
6	ExposureData	Slow CR tablet 100mg	UncertainConcentrationSeries	Slow CR tablet 100mg

Load and process data from an Excel file

Load data from the Excel file and rename column headings to prepare for adding to the project

PY

external_data = pd.read_excel(EXCEL_FILE_NAME)

# prepare data for import by renaming the columns
observed_data =  external_data.rename(columns={INDEPENDENT_COLUMN_NAME : "independent", DEPENDENT_COLUMN_NAME : "dependent"})

Import the data into the project

PY

series_key = gp.SeriesKey(group_type = gp.ObservedDataGroupType.ExposureData,
                                          group_name = NEW_GROUP_NAME,
                                          series_type = gp.ObservedDataSeriesType.UncertainConcentrationSeries,
                                          series_name = NEW_SERIES_NAME)

# Create list of ObservedSeriesPoints objects from the observed_data Dataframe
series = [gp.ObservedSeriesPointsInner(independent = row.independent, dependent = row.dependent) for row in observed_data.itertuples()]

# Define the experimental setup for the observed data
experimental_setup = gp.ExposureSeriesMetadataExposureExperimentalSetup(dose = gp.ScalarValue(value = 300, unit = "milligram"),
                                                        body_mass = gp.ScalarValue(value = 60.3, unit = "kilogram"),
                                                        infusion_time= gp.ScalarValue(value = 0, unit = "hour"))

datakey = gp.DataKey(module = "SystemicCirculation",
                  compartment = "VenousReturn",
                  state = "ConcentrationPlasma")

# combine the experimental setup and data key into a ExposureSeriesMetadata object
exposure_series_metadata = gp.ExposureSeriesMetadata(exposure_experimental_setup = experimental_setup,
                                                     data_key = datakey)

set_series_data_request = gp.SetSeriesDataRequest(dependent_unit = DEPENDENT_UNIT,
                                                  independent_unit = INDEPENDENT_UNIT,
                                                  exposure_series_metadata = exposure_series_metadata,
                                                  series = series)

# import the observed data into the project
gastroplus.set_series_data(series_key.group_name, series_key.group_type, series_key.series_name, series_key.series_type, set_series_data_request)

Check the imported data

Get the list of observed data that the project to see that it includes the newly imported data

PY

updated_observed_data_inventory = gastroplus.experimental_data_inventory()
pd.json_normalize(updated_observed_data_inventory.to_dict(), record_path=['observed_series_keys'])

	group_type	group_name	series_type	series_name
0	InVitroDissolutionRelease	Fast CR tablet	TimeRealSeries	Fast CR tablet
1	InVitroDissolutionRelease	Slow CR tablet	TimeRealSeries	Slow CR tablet
2	InVitroDissolutionRelease	Moderate CR tablet	TimeRealSeries	Moderate CR tablet
3	InVitroDissolutionRelease	New Form CR tablet	TimeRealSeries	New Form CR tablet
4	ExposureData	IVP 500mg Pentikainen	UncertainConcentrationSeries	Plasma
5	ExposureData	Fast CR tablet 100mg	UncertainConcentrationSeries	Fast CR tablet 100mg
6	ExposureData	Moderate CR tablet 100mg	UncertainConcentrationSeries	Moderate CR tablet 100mg
7	ExposureData	Slow CR tablet 100mg	UncertainConcentrationSeries	Slow CR tablet 100mg

Retrieve the data that was just imported

PY

series_data_information = gastroplus.get_series_data(series_key.group_name,
                                                series_key.group_type,
                                                series_key.series_name,
                                                series_key.series_type).series_data
pprint(series_data_information.to_dict())

CODE

{'series_group_name': 'IVP 500mg Pentikainen',
 'series_map': [{'series_information': {'dependent_unit': 'ng/mL',
                                        'distribution_type': 'Unknown',
                                        'exposure_series_metadata': {'data_key': {'compartment': 'VenousReturn',
                                                                                  'module': 'SystemicCirculation',
                                                                                  'section': '',
                                                                                  'state': 'ConcentrationPlasma'},
                                                                     'exposure_experimental_setup': {'body_mass': {'unit': 'kg',
                                                                                                                   'value': 60.3},
                                                                                                     'dose': {'unit': 'mg',
                                                                                                              'value': 300.0},
                                                                                                     'infusion_time': {'unit': 'h',
                                                                                                                       'value': 0.0}}},
                                        'independent_unit': 'h',
                                        'series': [{'dependent': 15269.999999999998,
                                                    'independent': 0.04,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 12429.999999999998,
                                                    'independent': 0.17,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 9970.0,
                                                    'independent': 0.32,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 7679.999999999999,
                                                    'independent': 0.44,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 5150.0,
                                                    'independent': 0.72,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 3599.9999999999995,
                                                    'independent': 0.99,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 2450.0,
                                                    'independent': 1.49,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 1669.9999999999998,
                                                    'independent': 1.98,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 919.9999999999999,
                                                    'independent': 2.97,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 579.9999999999999,
                                                    'independent': 4.02,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 409.99999999999994,
                                                    'independent': 5.01,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 280.0,
                                                    'independent': 5.99,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 189.99999999999997,
                                                    'independent': 6.98,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 119.99999999999999,
                                                    'independent': 8.0,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 79.99999999999999,
                                                    'independent': 8.99,
                                                    'uncertainty_percentage': 0.0},
                                                   {'dependent': 59.99999999999999,
                                                    'independent': 10.01,
                                                    'uncertainty_percentage': 0.0}],
                                        'series_name': 'Plasma',
                                        'series_type': 'UncertainConcentrationSeries',
                                        'statistic_type': 'Unknown',
                                        'uncertainty_type': 'ArithmeticCoeffictionOfVariation',
                                        'use_in_distribution': True},
                 'series_name': 'Plasma'}]}

Extract the data that will be used to plot the observed data

PY

df = pd.json_normalize(
    series_data_information.to_dict(),
    record_path=["series_map", "series_information", "series"],
    meta=[
        "series_group_name",
        ["series_map", "series_name"],
        ["series_map", "series_information", "dependent_unit"],
        ["series_map", "series_information", "independent_unit"],
    ],
)
df

	independent	dependent	series_group_name	series_map.series_name	series_map.series_information.dependent_unit	series_map.series_information.independent_unit
0	0.04	15270.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
1	0.17	12430.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
2	0.32	9970.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
3	0.44	7680.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
4	0.72	5150.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
5	0.99	3600.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
6	1.49	2450.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
7	1.98	1670.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
8	2.97	920.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
9	4.02	580.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
10	5.01	410.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
11	5.99	280.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
12	6.98	190.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
13	8.00	120.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
14	8.99	80.0	IVP 500mg Pentikainen	Plasma	ng/mL	h
15	10.01	60.0	IVP 500mg Pentikainen	Plasma	ng/mL	h

Plot the observed data

PY

from plotnine import ggplot, aes, geom_point, labs, theme_classic

plot = (
      ggplot(df, aes(x="independent", y="dependent"))
      + geom_point(color="blue")
      + labs(
            x=df["series_map.series_information.independent_unit"].iloc[0],
            y=df["series_map.series_information.dependent_unit"].iloc[0],
            title=f"{df['series_group_name'].iloc[0]} - {df['series_map.series_name'].iloc[0]}",
      )
      + theme_classic()
)
plot.draw()

Save the project if SAVE_PROJECT is True

PY

if SAVE_PROJECT:
    gastroplus.save_project()