pkpd.protocols.protocol_pkpd_two_compartments_conv module

class pkpd.protocols.protocol_pkpd_two_compartments_conv.ProtPKPDTwoCompartmentsConv(**kwargs)[source]

Bases: pkpd.protocols.protocol_pkpd_ode_base.ProtPKPDODEBase

Fit a two-compartmentx model to a set of measurements (any arbitrary dosing regimen is allowed)

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

where C is the concentration of the central compartment, Cl the 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. 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.

The forward model is implemented by convolution instead of by numerical solution of the differential equation.

Protocol created by http://www.kinestatpharma.com

createModel()[source]
forwardModel(parameters, x=None)[source]