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-based approaches in drug development. It allows Simcyp models, including ADME, PBPK , drug-drug interaction, ADAM and both PK and PD, to be fitted to observed clinical data for the purpose of estimating unknown/uncertain drug or physiological parameters. Simultaneously fitting on two sets of observed clinical data can be accommodated, which includes any combination of PK &PD, parent & metabolite, victim & perpetrator, blood & tissue/organ etc.
Data entry templates, which are to a large extent compatible with formats of common POP-PK packages, are designed to be easily copied and pasted from different sources. Dosing schedules can be defined independently for each individual and multiple irregular dosing schemes are accommodated. Covariates such as age, body weight, height, sex, renal function, haematocrit level, albumin level and smoking status, can also be incorporated and any missing covariates can be generated from prior knowledge. 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 the nonlinear mixed effect model, maximum likelihood (ML) and maximum a posteriori (MAP) algorithms using the Monte Carlo expectation maximisation approach can be employed. Some common useful goodness-of-fit diagnostic tools are also provided.
This framework accelerates model selection, model building and covariate recognition and expands modelling and simulation in drug development to a new paradigm. 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.
