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# *
# * Authors: Carlos Oscar Sorzano (info@kinestat.com)
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# * Kinestat Pharma
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import pyworkflow.protocol.params as params
from .protocol_pkpd_ode_base import ProtPKPDODEBase
from pkpd.models.pk_models import PK_Monocompartment
# TESTED in test_workflow_gabrielsson_pk01.py
# TESTED in test_workflow_gabrielsson_pk02.py
# TESTED in test_workflow_gabrielsson_pk03.py
# TESTED in test_workflow_gabrielsson_pk04.py
# TESTED in test_workflow_gabrielsson_pk05.py
# TESTED in test_workflow_gabrielsson_pk06.py
# TESTED in test_workflow_gabrielsson_pk15.py
# TESTED in test_workflow_gabrielsson_pk17.py
# TESTED in test_workflow_gabrielsson_pk43.py
# TESTED in test_workflow_deconvolution.py
# TESTED in test_workflow_ivivc.py
[docs]class ProtPKPDMonoCompartment(ProtPKPDODEBase):
""" Fit a monocompartmental model to a set of measurements obtained by oral doses (any arbitrary dosing regimen is allowed)\n
The differential equation is dC/dt = -Cl * C/V + 1/V * dD/dt\n
where C is the concentration, Cl the clearance, V the distribution volume, and D the input dosing regime.
Confidence intervals calculated by this fitting may be pessimistic because it assumes that all model parameters
are independent, which are not. Use Bootstrap estimates instead.\n
Protocol created by http://www.kinestatpharma.com\n"""
_label = 'pk monocompartment'
def __init__(self,**kwargs):
ProtPKPDODEBase.__init__(self,**kwargs)
#--------------------------- DEFINE param functions --------------------------------------------
def _defineParams(self, form):
self._defineParams1(form, True, "t", "Cp")
form.addParam('bounds', params.StringParam, label="Parameter bounds ([tlag], sourceParameters, Cl, V)", default="",
help="Bounds for the tlag (if it must be estimated), parameters for the source, clearance and volume. Example: (0.01,0.04);(0.2,0.4);(10,20). "\
'Make sure that the bounds are expressed in the expected units (estimated from the sample itself).'\
'Be careful that Cl bounds must be given here. If you have an estimate of the elimination rate, this is Ke=Cl/V. Consequently, Cl=Ke*V ')
[docs] def createModel(self):
return PK_Monocompartment()