The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects |
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Authors: | Phylinda L S Chan Philippe Jacqmin Marc Lavielle Lynn McFadyen Barry Weatherley |
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Institution: | 1.Global Pharmacometrics, Pfizer Primary Care Business Unit,Kent,UK;2.Exprimo NV,Mechelen,Belgium;3.Laboratoire de Mathématiques,INRIA Saclay and University,Orsay,France |
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Abstract: | Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to
perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed
algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM
algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag
time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was
then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last
step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates
(EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous
PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated
data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual
estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design,
it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor
binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability.
Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the
sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of
the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation
step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD
modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic
models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics
parameters, as well as investigation of different model sub-components during the model building process. This was not possible
with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy
computation times and convergence problems with complex models. |
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