Source code for pkpd.protocols.protocol_pkpd_monocompartment_conv

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# * Authors:     Carlos Oscar Sorzano (
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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

[docs]class ProtPKPDMonoCompartmentConv(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 The forward model is implemented by convolution instead of by numerical solution of the differential equation.\n Protocol created by\n""" _label = 'pk monocompartment conv' def __init__(self,**kwargs): ProtPKPDODEBase.__init__(self,**kwargs)
[docs] def forwardModel(self, parameters, x=None): return self.forwardModelByConvolution(parameters, x)
#--------------------------- 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 ') form.addParam('tFImpulse', params.StringParam, label="Maximum length of the impulse response [min]", default="", help="This is the time length for which the impulse response will be simulated. Leave empty if it must be taken from the input signal.")
[docs] def createModel(self): return PK_Monocompartment()