A physiologically-based digital twin for alcohol consumption—predicting real-life drinking responses and long-term plasma PEth¶

This is an in house modelling project where a model governing the dynamics of alcohol rate of appearance in plasma, following consumption of different alcoholic beverages paired with either:

i) non-alcoholic beverages
ii) food
iii) other alcoholic beverages
iV) solely the alcoholic drink itself

The article is published in NPJ digital twin, Podéus, H., Simonsson, C., Nasr, P. et al. A physiologically-based digital twin for alcohol consumption—predicting real-life drinking responses and long-term plasma PEth. npj Digit. Med. 7, 112 (2024), and can be found following this DOI: https://doi.org/10.1038/s41746-024-01089-6.

Model file¶

This model describe how beverages pass the stomach, and if they have an alcoholic content, how the alcohol is taken up into the intestines, transported into the blood, and then eliminated via enzymes in the liver. The rate of gastric emptying is influenced by the amount of available caloric content in the stomach and if there is food present.

The model is govern by multiple type of inputs.

There are anthropometric constants (sex, height, and weight) set by a sund.CONSTANT in the activity object.
The food and drink variables (alcohol concentration, rate of consumption, caloric content, and consumption duration) is govern by sund.PIECEWISE_CONSTANT in the activity object, as it is intent for multiple drinks and meals to be possible to occur in sequence.

To correctly keep track of the digestion of food and beverage kcal in the stomach, the model also includes events to: update the state goering the rate of gastric emptying with the remaining kcal (that has not been digested) once a new drink starts, and an event that similar updates the solid kcal, from food, once a new meal is consumed - as the digestion is dependent on the starting amount of kcal.

Implementation¶

An python implementation of the model, including a simulation with a) an alcoholic drink, b) an alcoholic drink paired with a meal, c) an non-alcoholic drink, and d) an alcoholic drink and plotting the corresponding results for the observables a) blood alcohol concentration (BAC), b) blood alcohol concentration, c) gastric volume, and d) phosphatidylethanol (PEth) is given below.

The model file used in the implementation can be downloaded here: alcoholmodel.

The python code requires the following packages to be installed.

uvpip

uv add numpy matplotlib

pip install numpy matplotlib

The python implementation is shown below and can be downloaded here.

import matplotlib.pyplot
import numpy

import sund

MODEL_NAME = "alcoholmodel"

#%% Install the model by using the SUND function 'install_model'
sund.install_model(f"modelfiles/{MODEL_NAME}.txt")

#%% Load the model by using the sund function 'load_model'
model = sund.load_model(f"{MODEL_NAME}")

#%% Create an activity and simulate an alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(sund.PIECEWISE_CONSTANT, "EtOH_conc",           t = [0, 20], f=[0, 5.1, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "vol_drink_per_time",  t = [0, 20], f=[0, 0.05135, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "kcal_liquid_per_vol", t = [0, 20], f=[0, 129.82, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "drink_length",        t = [0, 20], f=[0, 20, 0])
activity.add_output(sund.CONSTANT, "sex",    f=1)
activity.add_output(sund.CONSTANT, "weight", f=82.66)
activity.add_output(sund.CONSTANT, "height", f=1.77)

# Create a simulation object
sim = sund.Simulation(model, activities = activity, time_unit = model.time_unit, time_vector = numpy.arange(0, 250, 0.1))

sim.simulate()

# Plot the results
fig = matplotlib.pyplot.figure(figsize=(18*(1/2.54), 18*(1/2.54)))
ax = fig.add_subplot(2, 2, 1)
ax.plot(sim.time_vector, sim.feature_data[:, sim.feature_names.index('Blood alcohol concentration')], label='Blood alcohol concentration')
ax.set_xlabel('Time (min)')
ax.set_ylabel('BAC (mg/dL)')
ax.legend(edgecolor='None', loc='upper right')

#%% Create an activity and simulate an alcoholic drink with food
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(sund.PIECEWISE_CONSTANT, "EtOH_conc",           t = [20, 35],   f=[0, 20, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "vol_drink_per_time",  t = [20, 35],   f=[0, 0.009606, 0 ])
activity.add_output(sund.PIECEWISE_CONSTANT, "kcal_liquid_per_vol", t = [20, 35],   f=[0, 360, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "drink_length",        t = [20, 35],   f=[0, 15, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "kcal_solid",          t = [1e-8, 20], f=[0, 700, 0])
activity.add_output(sund.CONSTANT, "sex",    f=1)
activity.add_output(sund.CONSTANT, "weight", f=75.80)
activity.add_output(sund.CONSTANT, "height", f=1.83)

# Create a simulation object
sim = sund.Simulation(model, activities = activity, time_unit = model.time_unit, time_vector = numpy.arange(0, 400, 0.1))

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 2)
ax.plot(sim.time_vector, sim.feature_data[:, sim.feature_names.index('Blood alcohol concentration')], label='Blood alcohol concentration')
ax.set_xlabel('Time (min)')
ax.set_ylabel('BAC (mg/dL)')
ax.legend(edgecolor='None', loc='upper right')

#%% Create an activity and simulate a non-alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(sund.PIECEWISE_CONSTANT, "vol_drink_per_time",  t = [0, 3], f=[0, 0.5/3, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "kcal_liquid_per_vol", t = [0, 3], f=[0, 664, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "drink_length",        t = [0, 3], f=[0, 3, 0])
activity.add_output(sund.CONSTANT, "sex",    f=1)
activity.add_output(sund.CONSTANT, "weight", f=65.00)
activity.add_output(sund.CONSTANT, "height", f=1.71)

# Create a simulation object
sim = sund.Simulation(model, activities = activity, time_unit = model.time_unit, time_vector = numpy.arange(0, 125, 0.1))

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 3)
ax.plot(sim.time_vector, sim.feature_data[:, sim.feature_names.index('Gastric volume')], label='Gastric volume')
ax.set_xlabel('Time (min)')
ax.set_ylabel('Volume (mL)')
ax.legend(edgecolor='None', loc='upper right')

#%% Create an activity and simulate the PEth profile from an alcoholic drink
activity = sund.Activity(time_unit=model.time_unit)
activity.add_output(sund.PIECEWISE_CONSTANT, "EtOH_conc",           t = [0, 15], f=[0, 6.41, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "vol_drink_per_time",  t = [0, 15], f=[0, 0.0473173, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "kcal_liquid_per_vol", t = [0, 15], f=[0, 322.3, 0])
activity.add_output(sund.PIECEWISE_CONSTANT, "drink_length",        t = [0, 15], f=[0, 15, 0])
activity.add_output(sund.CONSTANT, "sex",    f=1)
activity.add_output(sund.CONSTANT, "weight", f=59.23)
activity.add_output(sund.CONSTANT, "height", f=1.78)

# Create a simulation object
sim = sund.Simulation(model, activities = activity, time_unit = model.time_unit, time_vector = numpy.arange(0, 400, 0.1))

sim.simulate()

# Plot the results
ax = fig.add_subplot(2, 2, 4)
ax.plot(sim.time_vector, sim.feature_data[:, sim.feature_names.index('PEth')], label='PEth')
ax.set_xlabel('Time (min)')
ax.set_ylabel('PEth (ng/mL)')
ax.legend(edgecolor='None', loc='upper right')


matplotlib.pyplot.show()