Modelling and simulation are now being recognized as important aids to the drug development process. An important element of this is the ability to predict in vivo pharmacokinetic behaviour from in vitro data.
Most attempts at in vitro–in vivo extrapolation (IVIVE) have focused on predicting mean values of drug clearance and the impact of drug–drug interactions in the 'average' subject. This review considers methodology to assess outcomes in populations of virtual patients, thereby adding the capacity to identify the characteristics of individuals at particular risk of inadequate or excessive drug exposure.
Factors influencing variability in hepatic drug clearance are considered in detail. These include the abundance of different hepatic drug metabolizing enzymes, hepatic microsomal protein and hepatocellularity, liver weight, hepatic blood flow, plasma binding and haematocrit.
Based on Monte Carlo simulation, the incorporation of variability in pharmacokinetic factors in mechanistic, physiologically based models, together with demographic data and specific information on the genetic variability of enzymes, allows prediction of the net exposure and time-course of drugs in the body across patient populations.
Applications of the approach in predicting drug clearance in adults from different ethnic backgrounds and in neonates, infants and children are described, as well as its utility in the assessment of inter-individual variability in metabolically based drug–drug interactions.
The authors conclude that considerable progress has been made towards predicting pharmacokinetic behaviour from in vitro information on the interaction between drug molecules and enzymes. Further challenges are indicated in the area of enzyme-transporter interplay, and in the linkage of pharmacokinetic predictions to models of pharmacodynamic response.
The perceived failure of new drug development has been blamed on deficiencies in in vivo studies of drug efficacy and safety. Prior simulation of the potential exposure of different individuals to a given dose might help to improve the design of such studies. This should also help researchers to focus on the characteristics of individuals who present with extreme reactions to therapy. An effort to build virtual populations using extensive demographic, physiological, genomic and in vitro biochemical data to simulate and predict drug disposition from routinely collected in vitro data is outlined.
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The authors wish to thank Ben Meakin for organizing the bibliography for this review.
A.R.H. and G.T. are founding members of and shareholders in Simcyp, a University of Sheffield spin-out company that develops algorithms for the in vitro prediction of drug metabolism. Simcyp's databases and related software are available free to appropriate non-profit organizations to facilitate research into in vitro–in vivo extrapolation (IVIVE).
- Monte Carlo methods
A method of generating virtual entities with randomly assigned characteristics.
A computer programming language, which is not commonly used anymore.
- Quantitative Framework
In this context, a quantitative framework is essentially the systems biology of the model relevant to ADME (absorption, distribution, metabolism and excretion). It enables one to rationalize non-linear relationships between different ADME elements in a way that is not possible using non-quantitative or rank order frameworks.
The proportion of the volume of a sample of blood that is represented by red blood cells.
- Central tendency
Central tendency refers to a representation of a widely variable distribution of sets of numbers by their mode, median, average and so on. It is a known phrase in referring to average, but sometimes the average may not be the right measure, so central tendency is wider than the meaning of average but it may include average in many cases.
- Allometric scaling methods
Methods that extrapolate pharmacokinetic parameters between different species, or various age groups within the same species, based on the assumption that there is a link between the body size and the magnitude of each pharmacokinetic parameter.
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Rostami-Hodjegan, A., Tucker, G. Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov 6, 140–148 (2007). https://doi.org/10.1038/nrd2173
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