A limited sampling strategy based on maximum a posteriori Bayesian estimation for a five-probe phenotyping cocktail.
OBJECTIVE
Cocktail approach using a combination of probes to phenotype several cytochromes P450 or transporters is of high interest in anticipating drug–drug interactions and personalized medicine. Its clinical use remains however limited by the intensive sampling scheme required to obtain phenotyping indexes (PI) which consists in calculating the area under the concentration–time curves. We proposed to use maximum a posteriori Bayesian estimation (MAPBE) that incorporates available information from the whole population to derive PI from a few individual observations. The performance of a limited sampling strategy (LSS) based on MAPBE was evaluated for a five-probe cocktail.


METHODS
The studied cocktail included midazolam, tolbutamide, caffeine, dextromethorphan, omeprazole and their relevant metabolites. Prior information for MAPBE was obtained by nonlinear mixed-effect modelling of data from a pilot study. Sampling times were chosen based on optimal design theory using the Bayesian Fisher information matrix. Through a simulation study, we investigated the predicted PI in terms of bias and imprecision for varying number and timing of samples.


RESULTS
Some three-point Bayesian designs gave mean prediction errors in [−5 %, 5 %], root mean square errors below 30 % for all probes, except dextromethorphan whose model should be consolidated further with additional data. This approach gave overall less outlier predicted values than single-point metrics and was more flexible to the timing of the latest sampling.


CONCLUSIONS
MAPBE is accurate for predicting simultaneously several PI while being flexible in terms of integrating clinical constraints. Therefore, LSS based on MAPBE could help reduce the time of presence in hospital for individuals to be phenotyped.
Thu Thuy
TT
Nguyen
Henri
H
Bénech
Alain
A
Pruvost
Natacha
N
Lenuzza
D001499:Bayes Theorem; D002110:Caffeine / Q000097:blood / Q000493:pharmacokinetics*; D003198:Computer Simulation; D003915:Dextromethorphan / Q000097:blood / Q000493:pharmacokinetics*; D004347:Drug Interactions; D006801:Humans; D008874:Midazolam / Q000097:blood / Q000493:pharmacokinetics*; D008954:Models, Biological; D009853:Omeprazole / Q000097:blood / Q000493:pharmacokinetics*; D010641:Phenotype; D010865:Pilot Projects; D014044:Tolbutamide / Q000097:blood / Q000493:pharmacokinetics*
D002110:Caffeine; D003915:Dextromethorphan; D014044:Tolbutamide; D009853:Omeprazole; D008874:Midazolam