Research ArticlesPhysiologically Based Pharmacokinetic Modeling 1: Predicting the Tissue Distribution of Moderate-to-Strong Bases
Section snippets
INTRODUCTION
Obtaining rapid information regarding the pharmacokinetics of new drug candidates is a bottleneck in the discovery and development of new drugs, while unfavourable parameters are a common reason for the failure of many compounds. To expedite the acquisition of pharmacokinetic data, assist compound selection and ultimately reduce compound failures, a sound understanding as to the behaviour of a compound within the test system is required. Such knowledge improves the ability to predict in vivo
Underlying Considerations
Previous work with the individual enantiomers of a series of eight β-blocking drugs suggested that plasma protein binding and acidic phospholipid (phosphatidylserine (PS), mono- and di-phosphatidylglycerol (PG), phosphatidylinositol (PI) and phosphatidic acid (PA)) concentrations in blood and tissue cells were the primary factors controlling the distribution of this drug class within the body.4 The dominant interaction between β-blockers (pKa of ca. 9.5) and acidic phospholipids is thought to
New Equation (Equation 19)
Overall, the accuracy of the Kpu predictions for 28 moderate-to-strong bases in 13 rat tissues (n = 261) was good, and little difference was observed between Kpu values predicted using experimental compared to predicted LogP and pKa values (Figures 2 and 3, Tables 3 and 4), with 85.1% compared to 86.3% of the predicted values being within a factor of three of experimentally determined Kpu's. Predictions of brain and lung Kpu values were less accurate with ca67% of the predictions agreeing with
DISCUSSION
At present the mechanistic equations used for Kpu predictions are deterministic, in that they compute a fixed mean with no measure of the inherent variability and uncertainty associated with the biological system and parameter determinations. So to assess the accuracy of the Kpu predictions point estimates have been compared with mean in vivo Kpu values, but due to the aforementioned variability and uncertainty the likelihood of these two values being identical is minimal. As such, an arbitrary
ACKNOWLEDGEMENTS
One of the authors (Dr Trudy Rodgers) thanks the BBSRC and Cyprotex, Macclesfield, Cheshire for their financial support in the form of a CASE studentship during the initial phases of this research, and the Centre for Applied Pharmacokinetic Research, University of Manchester for funding the continuation of this work.
REFERENCES (57)
- et al.
A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery
J Pharm Sci
(2000) - et al.
Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs
J Pharm Sci
(2001) - et al.
Prediction of pharmacokinetics prior to in vivo studies. I. Mechanism-based prediction of volume of distribution
J Pharm Sci
(2002) - et al.
Tissue distribution of basic drugs: Accounting for enantiomeric, compound and regional differences amongst β-blocking drugs in rats
J Pharm Sci
(2005) Lipid composition of erythrocytes in various mammalian species
Biochim Biophys Acta
(1967)- et al.
A physiologically based pharmacokinetic model for nicotine disposition in the Sprague-Dawley rat
Toxicol Appl Pharmacol
(1992) - et al.
Effects of chronic ethanol ingestion on pharmacokinetics of procainamide in rats
J Pharm Sci
(1991) - et al.
NMR-Studies on the molecular basis of drug-induced phospholipidosis-II. Interaction between several amphiphilic drugs and phospholipids
Biochem Pharmacol
(1976) - et al.
Distribution of multi-drug resistance-associated p -glycoprotein in normal and neoplastic human tissues
Ann Oncol
(1990) - et al.
Thiamine whole blood and urinary pharmacokinetics in rats: Urethan-induced dose-dependent pharmacokinetics
J Pharm Sci
(1982)