Validation of a decision support system for use in drug development: pharmacokinetic data

Computer-assisted drug development (CADD): an emerging technology for designing first-time-in-man and proof-of-concept studies from preclinical experiments

Computer-assisted drug development (CADD) is an emerging technology for accelerating drug development based on the integration of mathematical modelling and simulation. This methodology provides a knowledge-based decisional tool on alternative development strategies based on the evaluation of potential risks on drug safety, and the definition of experimental design of new trials with expected power and probability of success. An example of CADD implementation is presented to design the first-time-in-man (FTIM) and the proof-of-concept (PoC) study of a new CNS compound. The final objective of the example presented is not necessarily to supply a success story of a correct prediction of human data from animal studies but to define a credible strategy suitable to design FTIM and PoC studies using preclinical data without the support of any human in vivo information. Rhesus monkey and human PK were initially estimated using allometric scaling on data collected in dogs, cynomolgus monkeys and rats. A PK/PD model was derived from a study conducted in rodent and validated by comparing the model predicted response to the one observed in a PET experiment conducted in rhesus monkey. The final PK/PD model, incorporating potential variability and uncertainty on scaled human prediction together with a receptor affinity adjustment derived from in vitro binding studies, was used to design the first-time-in-man and the proof-of-concept study.

Bootstrapping for pharmacokinetic models: visualization of predictive and parameter uncertainty

PURPOSE

We explore use of "bootstrapping" methods to obtain a measure of reliability of predictions made in part from fits of individual drug level data with a pharmacokinetic (PK) model, and to help clarify parameter identifiability for such models.

METHODS

Simulation studies use four sets (A-D) of drug concentration data obtained following a single oral dose. Each set is fit with a two compartment PK model, and the "bootstrap" is employed to examine the potential predictive variation in estimates of parameter sets. This yields an empirical distribution of plausible steady state (SS) drug concentration predictions that can be used to form a confidence interval for a prediction.

RESULTS

A distinct, narrow confidence region in parameter space is identified for subjects A and B. The bootstrapped sets have a relatively large coefficient of variation (CV) (35-90% for A), yet the corresponding SS drug levels are tightly clustered (CVs only 2-9%). The results for C and D are dramatically different. The CVs for both the parameters and predicted drug levels are larger by a factor of 5 and more. The results reveal that the original data for C and D, but not A and B, can be represented by at least two different PK model manifestations, yet only one provides reliable predictions.

CONCLUSIONS

The insights gained can facilitate making decisions about parameter identifiability. In particular, the results for C and D have important implications for the degree of implicit overparameterization that may exist in the PK model. In cases where the data support only a single model manifestation, the "bootstrap" method provides information needed to form a confidence interval for a prediction.