Application of Hybrid Approach Based on Empirical and Physiological Concept for Predicting Pharmacokinetics in Humans—Usefulness of Exponent on Prospective Evaluation of Predictability

Abstract

We developed a hybrid method for predicting plasma concentration-time curves in humans by integrating species differences in in vitro intrinsic clearance (CL int ) into the Dedrick approach based on the allometry concept. With prediction of clearance (CL) by allometric scaling, taking in vitro CL int into consideration improved the accuracy and reduced the average fold error from 2.72 to 1.99. With the hybrid approach of applying the same concept to the Dedrick approach, the predictability of plasma concentration profiles was compared with the results of the conventional Dedrick approach and the physiologically based pharmacokinetic model using 15 compounds with widely ranging physicochemical and pharmacokinetic profiles. The hybrid approach showed the highest predictability among the examined methods. For CL and the apparent volume of distribution at the steady state (V ss ), the relationship between the exponent of allometric equation and fold error was also evaluated with the hybrid approach. The relationship appeared to be a horseshoe curve. Six compounds with exponents ranging from 0.7 to 1.1 for both CL and V ss [antipyrine, caffeine, epiroprim, propafenone, theophylline, and verapamil] displayed higher predictability. Three compounds with an exponent ranging from 0.7 to 1.1 for CL showed better predictability for CL, and the other four compounds appeared to display similar relationship between the exponent and predictability for V ss . These findings indicated that the exponent becomes a preliminary index to speculate on predictability. Combination of the hybrid approach and exponent allows us to prospectively draw human plasma concentration-time curves, with the implication of possible prediction accuracy prior to clinical studies.

Development of a Hybrid Physiologically Based Pharmacokinetic Model with Drug‐Specific Scaling Factors in Rat to Improve Prediction of Human Pharmacokinetics

“…In the comparative study that we had previously conducted, it was revealed that the prediction accuracy of V ss estimated from plasma concentration–time curves predicted with the PBPK model was lower than that with the hybrid Dedrick approach (AFE; 1.95 vs. 1.54). The PBPK model utilizes TC‐based equations in which plasma protein binding, clog P , and p K a are substituted to estimate the K p value of each tissue.…”

“…The clog P , pH‐dependent measure of lipophilicity (clog D ), and p K a were calculated using CLOGP, version 4.82 (Daylight Chemical Information Systems Inc., Laguna, Niguel, CA, USA) and Pallas, version 4.4.1 (CompuDrug Inc., Sedona, AZ, USA). The compounds were divided into acidic, basic, and neutral classes based on the difference between the clog D values at pH 6.5 and 7.4, according to the previous report …”

“… where the log P / D is clog P for basic compounds and clog D at pH 7.4 for acidic and neutral compounds. In the previous report, we predicted the plasma concentration–time profiles of 15 model compounds of which reports of the plasma concentration data in animals and humans after intravenous administration were available, using three different methods (c.f. the conventional Dedrick approach, the hybrid Dedrick approach, and the PBPK model).…”

“…). The model, which is composed of 11 tissue compartments (lungs, adipose tissue, bones, brain, heart, muscles, kidneys, spleen, liver, skin, and small intestine), considers the liver only as an elimination site . The principles of mass balance equations for noneliminating tissues and eliminating tissue (the liver) are indicated by the following differential equations: where V is the volume, Q is the blood flow, and C is the concentration of tissues (T) and arteries (a).…”

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

“…PBPK model and interspecies allometric scaling are two common methodologies for the prediction (Poulin et al, 2011; Vuppugalla et al, 2011). Although interspecies allometric scaling has been frequently used (Iavarone et al, 1999; Kang et al, 2009; Sayama et al, 2013), PBPK model, considered as a mechanism-based methodology, shows several advantages (Pang and Durk, 2010; Poulin et al, 2011; Jones and Rowland-Yeo, 2013). Interspecies allometric scaling highly depends on animal study, which should be performed with at least three animal species.…”