Abstract
The existing procedures for quantitative in vitro-in vivo clearance prediction can be significantly biased either by totally neglecting the existing variability and uncertainty by using mean parameter values or by implementing Monte Carlo simulation with statistical distribution of the parameters reconstructed from very small sets of data. The aim of the present study is to develop a methodology for the prediction of in vivo hepatic clearance in the presence of semiquantitative or qualitative data and accounting for the existing uncertainty and variability. The method consists of two steps: 1) transformation of the information available into fuzzy sets (fuzzification); and 2) computation of the in vivo clearance using arithmetic operations with fuzzy sets. To illustrate the approach, rat hepatocyte and microsomal data for eight benzodiazepine compounds are used. A comparison with a standard Monte Carlo procedure is made. The methodology proposed can be used when Monte Carlo simulation may be biased or cannot be implemented. The obtained fuzzy in vivo clearance can be used subsequently in fuzzy simulations of pharmacokinetic models.
Footnotes
- Abbreviations used are::
- MC
- Monte Carlo
- probability distribution functions
- FST
- fuzzy set theory
- CLH
- predicted in vivo blood hepatic clearance
- QH
- total hepatic blood flow
- QHA
- hepatic arterial blood flow
- QHP
- blood flow perfusing hepatic portal vein draining the splanchnic organs
- fUB
- fraction unbound in blood
- fU
- fraction unbound in plasma
- R
- blood/plasma drug concentration ratio
- CLint
- intrinsic clearance scaled to the whole liver
- CLint,in vitro
- intrinsic clearance not scaled
- qHA
- fraction of cardiac output perfusing the hepatic artery
- qHP
- fraction of cardiac output perfusing the hepatic portal vein
- Received September 28, 2001.
- Accepted November 30, 2001.
- The American Society for Pharmacology and Experimental Therapeutics
DMD articles become freely available 12 months after publication, and remain freely available for 5 years.Non-open access articles that fall outside this five year window are available only to institutional subscribers and current ASPET members, or through the article purchase feature at the bottom of the page.
|