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Planning any experiment includes issues such as how many samples are to be taken and their location given some predictor variable. Often a model is used to explain these data; hence including this formally in the design will be beneficial for any subsequent parameter estimation and modelling. A number of criteria for model oriented experiments, which maximise the information content of the collected data are available. In this paper we present a program, Optdes, to investigate the optimal design of pharmacokinetic, pharmacodynamic, drug metabolism and drug-drug interaction models. Using the developed software the location of either a predetermined number of design points (exact designs) or together with the proportion of samples at each point (continuous designs) can be determined. Local as well as Bayesian designs can be optimised by either D- or A-optimality criteria. Although often the optimal design cannot be applied for practical reasons, alternative designs can be readily evaluated.

Three methods for estimation of the equilibrium tissue-to-plasma partition ratios (Kp values) in the presence of tissue concentration time data have been investigated. These are the area method, the open loop (tissue specific) method and the whole body model(closed loop) method, each with different model assumptions. Additionally, multiple imputations, a technique for dealing with deficiencies in data sets (i.e., missing tissues) is used. The estimated Kp values by the three methods have been compared and the limitations and advantages of each approach drawn. The area method, which is essentially model free, gives only a crude estimate of Kp without making any statement of its uncertainty; whereas both the open and closed loop methods provide an estimate of this. The closed loop method, where the most assumptions are made, is the approach that gives the best overall estimates of Kp, which was confirmed by comparing the predicted concentration–time profiles with experimental data. Although the estimates from the closed loop method, as well as the other two methods, are conditioned on the data, they are the most reliable for both propagating parameter variability and uncertainty through a whole body physiologically based model, as well as for extrapolation to human. A series of benzodiazepines, namely alprazolam, chlordiazepoxide, clobazam, diazepam, flunitrazepam, midazolam and triazolam in rat is used as a case study in the current investigation.