Predictors of Human Interindividual Variability in Pharmacokinetic Parameters: The Effects of Population and Chemical Properties on Susceptibility to Drug Disposition. Prerna Banati, Dale Hattis, and Rob Goble
This report analyzes possible predictors of inter-individual variability in parameters describing absorption, transport, distribution or elimination characteristics of a chemical agent in a person or other organism using data assembled into a database from clinical studies. Studies presenting individual data of pharmacokinetic responses to specified drugs for at least 5 people were included in the development of the database. The indicator of variability is the standard deviation of the log-transformed values (the Log GSD). This analysis assumes a lognormal distribution of pharmacokinetic parameters related to individual susceptibility.
In our general observations of inter-individual variability from the pharmacokinetic database, the volume of distribution parameter presents the greatest departure from the assumed lognormal distribution and may be better represented by a mixed distribution. The Cmax parameter shows the greatest geometric mean log10 GSD but the variability of our selected samples among the four parameters studied is fairly similar around 0.2 geometric standard deviations. Body weight, age spread and gender differences (male vs mixed populations) as characteristics of test populations were investigated as possible predictors of inter-individual variability, while chemical lipophilicity and therapeutic use categories (affected target organs) were explored as biological and chemical characteristics of agents. Body weight variability as represented by the log GSD proved to be a weak predictor of variability in pharmacokinetic parameters than hydrophilic agents. Division of agents into their therapeutic use categories showed that variability within each group remained the same, at around 0.05 geometric standard deviations. Other chemical characteristics were also noted as possibly presenting an affect on variability including plasma protein binding.
In conclusion, a multiple regression analysis of the effectors that exemplified the greatest predictive power yielded non-significant results, in part due to small sample sizes. The outcome of this investigation included the development of a useful database to facilitate future studies of inter-individual variability of both pharmacokinetic and pharmacodynamic variability.
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