BACKGROUND: Prediction of pharmacokinetics in humans is essential for translating preclinical data to humans and planning safe and efficient clinical studies. The performance of various methods in extrapolation of preclinical pharmacokinetic data to humans is usually benchmarked by the fraction of predictions falling within a predefined interval that is centred on the value observed clinically. Recently, such an approach was used to compare physiologically based pharmacokinetic (PBPK) modelling and allometry in predicting the pharmacokinetics of a set of compounds in humans. Here, we present an analysis of the same dataset, focusing on predictions falling outside such a relatively narrow and centrally located interval. These are the main risk determinants in extrapolation of preclinical pharmacokinetic data to humans and should therefore be thoroughly understood in a risk mitigation approach to the design of early-phase human studies. METHODS: Values that had been previously predicted by allometry and by PBPK modelling in terms of the apparent total clearance after oral administration, apparent volume of distribution, area under the plasma concentration-time curve, maximum plasma drug concentration, time to reach the maximum plasma concentration and terminal elimination half-life in humans were used to generate a log-transformed dataset of predicted/observed ratios. The probabilities of mispredicting the values of these pharmacokinetic parameters using PBPK modelling and allometry were estimated by a bootstrap procedure on this set of ratios. RESULTS: Our results, albeit from a limited dataset, indicated that although PBPK modelling yielded higher fractions of satisfactory predictions than allometry, both methodologies were associated with a significant and occasionally high probability of obtaining mispredictions of pharmacokinetic parameters by factors of >2, >3 and >10. In line with recent proposals to extend the goals of early-phase human studies beyond safety and tolerability, and considering the need to mitigate risks in studies dealing with novel and highly potent drug candidates, we discuss these results in a pharmacological context. CONCLUSIONS: Concise recommendations are given regarding the use of allometric and PBPK extrapolation methodologies in the translation process. The results presented here should alert clinical investigators to the limitations inherent in all approaches to prediction of human pharmacokinetics from preclinical data. We propose an adaptive approach to the design of early-phase clinical studies, particularly when dealing with compounds that are characterized by novel and only partially understood pharmacological profiles.
BACKGROUND: Prediction of pharmacokinetics in humans is essential for translating preclinical data to humans and planning safe and efficient clinical studies. The performance of various methods in extrapolation of preclinical pharmacokinetic data to humans is usually benchmarked by the fraction of predictions falling within a predefined interval that is centred on the value observed clinically. Recently, such an approach was used to compare physiologically based pharmacokinetic (PBPK) modelling and allometry in predicting the pharmacokinetics of a set of compounds in humans. Here, we present an analysis of the same dataset, focusing on predictions falling outside such a relatively narrow and centrally located interval. These are the main risk determinants in extrapolation of preclinical pharmacokinetic data to humans and should therefore be thoroughly understood in a risk mitigation approach to the design of early-phase human studies. METHODS: Values that had been previously predicted by allometry and by PBPK modelling in terms of the apparent total clearance after oral administration, apparent volume of distribution, area under the plasma concentration-time curve, maximum plasma drug concentration, time to reach the maximum plasma concentration and terminal elimination half-life in humans were used to generate a log-transformed dataset of predicted/observed ratios. The probabilities of mispredicting the values of these pharmacokinetic parameters using PBPK modelling and allometry were estimated by a bootstrap procedure on this set of ratios. RESULTS: Our results, albeit from a limited dataset, indicated that although PBPK modelling yielded higher fractions of satisfactory predictions than allometry, both methodologies were associated with a significant and occasionally high probability of obtaining mispredictions of pharmacokinetic parameters by factors of >2, >3 and >10. In line with recent proposals to extend the goals of early-phase human studies beyond safety and tolerability, and considering the need to mitigate risks in studies dealing with novel and highly potent drug candidates, we discuss these results in a pharmacological context. CONCLUSIONS: Concise recommendations are given regarding the use of allometric and PBPK extrapolation methodologies in the translation process. The results presented here should alert clinical investigators to the limitations inherent in all approaches to prediction of human pharmacokinetics from preclinical data. We propose an adaptive approach to the design of early-phase clinical studies, particularly when dealing with compounds that are characterized by novel and only partially understood pharmacological profiles.
Authors: Johan G C van Hasselt; Natasha K A van Eijkelenburg; Jos H Beijnen; Jan H M Schellens; Alwin D R Huitema Journal: Br J Clin Pharmacol Date: 2013-07 Impact factor: 4.335