Sparse and unbalanced data

Using the PPK approach in the development of a new drag has the advantage that the relevant pharmacokinetic parameters for a reasonably large population can be obtained from only a few blood samples per subject. The PPK approach is the method of choice in all situations when only sparse and unbalanced data can be obtained. This situation exists when the PK needs to be studied in elderly, critically ill and pediatric patients, but also very often in preclinical studies investigating the effects of the drug in animals. 

Mixed effect modeling deals with the situation in between. Inter and intra individual variability are separated and calculated within the same step. Inter individual random effects are calculated for those parameters for which this information can be drawn from the data set. In general only one residual error is calculated. The method is very well suited for sparse and unbalanced data situations. 

The available concentration time data is typical for a clinical study there are relatively few observations on each of a large number of patients and samples are not taken at the same time points (sparse and unbalanced data). Neither the NAD method nor the STS method can be used. A one compartment model with absorption compartment and first order elimination was fitted to the data by mixed effect modeling with NONMEM. Clearance and volume of distribution were described by... 

Cosson et al. (54) provided another improvement for allometric scaling by using the PK/PD population approach. This approach enables one to use sparse and unbalanced data, which is most often the case in animal studies. Using this approach, they were able to estimate all allometric parameters and all interindividual variabilities in the population and for each species. An example of their code for implementation in NONMEM is presented in their publication (54). 

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