home > research & development > simcyp science > parameter estimation
Parameter estimation
The parameter estimation (PE) module within the Simcyp Simulator bridges typical ‘bottom-up’ PBPK approaches and common pharmacometric analyses of clinical data to accelerate model building and covariate recognition in drug development. It allows Simcyp models, including PBPK , drug-drug interaction and ADAM , to be fitted to observed clinical data (e.g. concentration-time profiles) for the purpose of estimating unknown/uncertain drug or physiological parameters.
In the presence or absence of inhibitors, up to ten parameters can be estimated simultaneously as part of the fitting procedure. Data entry templates, which are to a large extent compatible with formats of common POP-PK packages, facilitate the input of observed po or iv clearances, volumes of distribution, or plasma or blood concentration values in different concentration and time units. Dosing schedules can be defined independently for each individual and multiple irregular dosing schemes are accommodated. Covariates such as age, body weight and sex, can also be incorporated so that each virtual subject generated in the Simulator is similar to the individual in the clinical study. Oral, infusion, intravenous, as well as lung and dermal routes of administration are all supported.
In addition to classical optimisation algorithms, users may select genetic algorithms or hybrid methods which enhance the performance of the PE module for individual fitting of observed data. For population fitting, maximum likelihood (ML) and maximum a posteriori (MAP) algorithms using the Monte Carlo expectation maximisation approach can be employed.
This framework accelerates model building and covariate recognition. Further, it provides a platform for scientists to optimally use information accumulated during drug discovery and development combined with knowledge on systems biology of healthy and disease populations.