A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: A tale of 'bottom-up' vs 'top-down' recognition of covariates.

An increasing number of failures in clinical stages of drug development have been related to the effects of candidate drugs in a sub-group of patients rather than the 'average' person. Expectation of extreme effects or lack of therapeutic effects in some subgroups following administration of similar doses requires a full understanding of the issue of variability and the importance of identifying covariates that determine the exposure to the drug candidates in each individual. In any drug development program the earlier these covariates are known the better. An important component of the drive to decrease this failure rate in drug development involves attempts to use physiologically-based pharmacokinetics 'bottom-up' modeling and simulation to optimize molecular features with respect to the absorption, distribution, metabolism and elimination (ADME) processes. The key element of this approach is the separation of information on the system (i.e. human body) from that of the drug (e.g. physicochemical characteristics determining permeability through membranes, partitioning to tissues, binding to plasma proteins or affinities toward certain enzymes and transporter proteins) and the study design (e.g. dose, route and frequency of administration, concomitant drugs and food). In this review, the classical 'top-down' approach in covariate recognition is compared with the 'bottom-up' paradigm. The determinants and sources of inter-individual variability in different stages of drug absorption, distribution, metabolism and excretion are discussed in detail. Further, the commonly known tools for simulating ADME properties are introduced.