Source code for pkpd.protocols.protocol_pkpd_two_compartments_autoinduction

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import pyworkflow.protocol.params as params
from .protocol_pkpd_ode_base import ProtPKPDODEBase
from pkpd.models.pk_models import PK_TwocompartmentsAutoinduction


[docs]class ProtPKPDTwoCompartmentsAutoinduction(ProtPKPDODEBase): """ Fit a two-compartment model to a set of measurements (any arbitrary dosing regimen is allowed)\n The central compartment is referred to as C, while the peripheral compartment as Cp. The differential equation is V dC/dt = -(Cl+Clp) * C + Clp * Cp + dD/dt, Vp dCp/dt = Clp * C - Clp * Cp\n being Cl=Cl0-a*Cp\n where C is the concentration of the central compartment, Cl0 the basal clearance, V and Vp the distribution volume of the central and peripheral compartment, Clp is the distribution rate between the central and the peripheral compartments, and D the input dosing regime. As the concentration in the peripheral compartment increases, the clearance is slowed. 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\n""" _label = 'pk two-compartments autoinduction' 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, Cl0, a, V, Clp, Vp)", default="", help="Bounds for time delay, central clearance and volume and peripheral clearance and volume. "\ '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_TwocompartmentsAutoinduction()