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Drug Metabolism and Disposition Fast Forward
First published on April 14, 2008; DOI: 10.1124/dmd.108.020834

0090-9556/08/3607-1194-1197$20.00
DMD 36:1194-1197, 2008

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SHORT COMMUNICATION

Hepatocellular Binding of Drugs: Correction for Unbound Fraction in Hepatocyte Incubations Using Microsomal Binding or Drug Lipophilicity Data

Peter J. Kilford, Michael Gertz, J. Brian Houston, and Aleksandra Galetin

School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, United Kingdom

(Received February 8, 2008; Accepted April 9, 2008)


   Abstract
Top
 Abstract
Materials and Methods
 Results and Discussion
 References
 
Analogous to the fraction unbound in microsomes (fumic), fraction unbound in hepatocyte incubations (fuhep) is an important parameter in the prediction of intrinsic clearance and potential drug-drug interactions. A recent study by Austin et al. (Drug Metab Dispos 33:419–425, 2005) proposed a linear 1:1 relationship between the extent of binding to microsomes at 1 mg/ml and to hepatocytes at 106 million cells/ml. The current study collates a fumic and fuhep database for 39 drugs to examine the relationship between binding in microsomes and hepatocytes. A new nonlinear empirical equation is proposed as an alternative to the linear relationship to relate binding between the two systems. The nonlinear equation results in higher prediction accuracy and lower bias in comparison to the linear relationship, in particular for drugs with fuhep < 0.4. The proposed equation is further extended to allow direct prediction of fuhep from drug lipophilicity data by substituting the fumic term by the Hallifax and Houston predictive equation (Drug Metab Dispos 34:724–726, 2006). The prediction accuracy of this approach is high for relatively hydrophilic drugs (logP/D ≤ 2.5), whereas less accurate prediction of the fuhep is observed for lipophilic drugs (logP > 3), consistent with the limitations observed for microsomal binding predictive tools. In conclusion, the proposed nonlinear equations provide an accurate predictive tool to estimate fuhep for the in vitro-in vivo extrapolation of intrinsic clearance and inhibition parameters.

The need to incorporate the fraction unbound in microsomes (fumic) to obtain meaningful drug concentrations for the prediction of intrinsic clearance and cytochrome P450 inhibition potential is widely accepted (Obach, 1996Go; Ito and Houston, 2005Go; Rostami-Hodjegan and Tucker, 2007Go). Recently, two equations based on drug lipophilicity have been developed for prediction of fumic (Austin et al., 2002Go; Hallifax and Houston, 2006Go) that avoid experimental determinations. The limitations of these empirical predictive tools and their applicability for fumic predictions over a range of lipophilicity and microsomal protein concentrations have been addressed (Gertz et al., 2008Go).

Analogous to applying a correction for microsomal drug binding to in vitro clearance and inhibition parameters, it is important that the fraction unbound in hepatocyte incubations (fuhep) is also considered for in vitro-in vivo extrapolation (McGinnity et al., 2006Go; Brown et al., 2007bGo). However, this need has yet to be broadly applied, perhaps due to the lack of comprehensive demonstration of its value. In a recent study by Austin et al. (2005Go), the extent of binding between the microsomal and hepatocyte incubations was compared using hepatocyte data (n = 14) and the corresponding fumic from a previous study (Austin et al., 2002Go). The authors proposed a linear 1:1 relationship between microsomal and hepatocyte binding for incubations of 1 mg/ml and 106 cells/ml, respectively, indicating that the fuhep can be extrapolated from microsomal studies. However, the applicability of this correlation has been questioned because of the small number of compounds investigated (Hallifax and Houston, 2007Go).

To further explore the relationship between binding in microsomal and hepatocyte systems, a detailed analysis was carried out involving 39 drugs. A nonlinear empirical equation is proposed as an alternative to the linear relationship to relate binding between the two systems. In addition, prediction of fuhep directly from the logP/D metric is assessed over a wide range of lipophilicity (-0.13 to 5.93). The implications of these findings on the estimation of hepatocellular drug concentration for intrinsic clearance and inhibition parameter predictions are discussed.


   Materials and Methods
Top
 Abstract
 Materials and Methods
Results and Discussion
 References
 
A database of 39 drugs and their corresponding fumic and fuhep values was collated either from in house data or from the literature (Austin et al., 2002Go, 2005Go; Brown et al., 2007aGo; Hallifax and Houston, 2007Go). In the aforementioned studies, different methods were used to determine the drug binding in hepatocyte incubations, namely oil centrifugation (using live cells), dialysis (using dead cells), and ultrafiltration (using dead cells). Microsomal and hepatocyte binding are defined by eqs. 1 and 2, respectively:

Formula 1(1)

Formula 2(2)
where Ka represents microsomal protein binding affinity, P the microsomal protein concentration (mg/ml), Kp the hepatocyte/medium concentration ratio, and VR a Vcell/Vinc ratio, where Vcell is the cell volume and Vinc the incubation volume (Brown et al., 2007bGo; Hallifax and Houston, 2007Go). VR is 0.005 at the cell concentration of 106 cells/ml (Brown et al., 2007bGo). Where multiple fumic/hep values were obtained, a mean value was used for the comparison. In cases where the fumic or fuhep were obtained at different microsomal protein or cell concentrations, reported values were standardized to give fumic or fuhep values at 1 mg/ml and 106 cells/ml, respectively.

To investigate the relationship between drug binding in hepatocytes and microsomes, an equation was obtained by dividing eq. 2 by 1 and rearranging it to produce:

Formula 3(3)
To allow the calculation of fuhep from drug lipophilicity, the fumic term in eq. 3 was substituted with the Hallifax and Houston (2006Go) equation (eq. 4). This fumic predictive equation is based on either logD7.4 (for acidic and neutral drugs) or logP (for bases) as descriptors for drug lipophilicity:

Formula 4(4)

The final equation for the prediction of fuhep directly from logP/D data is eq. 5:

Formula 5(5)
Predicted fuhep for 39 drugs was compared with the observed values and their respective logP/D, ranging from -0.13 to 5.39.


   Results and Discussion
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 Abstract
 Materials and Methods
 Results and Discussion
References
 
The database (Table 1) covered a range of physicochemical properties, including 8 acids, 18 bases, and 13 neutrals. The fumic ranged from 0.01 to 1.00 for astemizole and warfarin, respectively, whereas the fuhep ranged from 0.03 to 0.99 for {alpha}-naphthoflavone and tolbutamide, respectively.


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  		TABLE 1 LogP/D values for the 39 drugs investigated and their respective fumic and fuhep values at microsomal and cell concentrations of 1 mg/ml and 106 cells/ml, respectively

 

At the conditions investigated by Austin et al. (2005Go), that is, at P = 1 mg/ml and cell concentration of 106 cells/ml, the nonlinear relationship (eq. 3) between the experimentally obtained fumic and fuhep was found to be a better descriptor than the proposed linear relationship. Figure 1A shows the data for 39 drugs together with the nonlinear fit using eq. 3 with a Kp/Ka ratio of 125 and VR/P of 0.005; also shown is the linear relationship (line of unity). The proposed relationship between fumic and fuhep (eq. 3) results in predictions for 87% of the compounds investigated within 1.5-fold of the observed value. When a linear relationship between microsomal and hepatocyte binding is assumed, the proportion outside 1.5-fold from the line of unity is higher (28% in comparison with 13% in the case of nonlinear equation). However, in both cases the outliers include drugs with a logP/D >3, with astemizole, fluoxetine, and quinine as drugs with the most pronounced discrepancy between fumic and fuhep. In addition, the precision error (Fig. 1B) for linear relationship is greater, in particular for drugs with fuhep <0.4. Equation 3 allows the prediction of fuhep from existing microsomal binding data.


Figure 1
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  		FIG. 1. A, comparison of binding in hepatocytes at a concentration of 106 cells/ml and binding in microsomes at a concentration of 1 mg/ml for 39 drugs. Data shown as fractions unbound in the respective incubations. The solid line represents the fit to eq. 3 with a Kp/Ka ratio of 125 and dashed line represents the line of unity. B, comparison between precision error (expressed as predicted/observed value on a log scale) and predicted fuhep for 39 drugs. bullet and {circ} represent predicted fuhep using either nonlinear (eq. 3) or linear empirical model, respectively.

 
Equation 3 was extended to allow calculation of fuhep directly from drug lipophilicity by substituting the fumic term by the Hallifax and Houston (2006Go) equation (eq. 4). This equation was selected over the equation proposed by Austin et al. (2002Go), as recent analysis has shown that this tool provided more accurate fumic predictions, in particular for lipophilic drugs (logP/D = 2.5–5) and at higher microsomal protein concentrations (Gertz et al., 2008Go). The resulting nonlinear empirical model (eq. 5) allowed the prediction of fuhep directly from logP/D.

The ratio of predicted (eq. 5) to observed fuhep values was compared with the respective drug lipophilicity ranging from -0.13 to 5.93 (caffeine and miconazole, respectively). Figure 2 shows the relationship between the predicted/observed fuhep and the logP/D for the 39 drugs investigated. The prediction accuracy of the derived equation is high for relatively hydrophilic drugs (logP/D ≤ 2.5), with 89% falling within 1.5-fold of the line of unity. The only outlier in this lipophilicity range is cortisol, where the predicted fuhep is 4.4-fold greater than the observed value. In the intermediate lipophilicity range (logP/D = 2.5–5), 35% of the compounds are outside 2-fold of the predicted/observed ratio. Less accurate prediction of the fuhep for lipophilic drugs (logP > 3) is consistent with the limitations observed for microsomal binding predictions (Gertz et al., 2008Go). In addition, the impact of variability in the logP/D estimates on the prediction of fuhep was investigated. Analogous to microsomal binding situation (Gertz et al., 2008Go), a propagation of 20% variation in logP had a negligible effect on fuhep at low lipophilicity. However, at increasing lipophilicity (logP ≥ 5), 20% variation on logP resulted in more than 15-fold difference in the fuhep predictions.


Figure 2
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  		FIG. 2. Comparison between precision error (expressed as predicted/observed fuhep ratio) and lipophilicity (logP/D) using eq. 5. Areas of low (logP/D ≤ 2.5), intermediate (logP/D = 2.5–5), and high (logP/D ≥ 5) lipophilicity are indicated. Dashed and dotted lines indicate 1.5- and 2-fold of the line of unity, respectively.

 

In conclusion, a nonlinear empirical equation has been proposed that allows successful prediction of fuhep using either fumic or a lipophilicity metric. The nonlinear equation shows comparable accuracy to microsomal binding prediction equations and results in lower bias in the fuhep prediction in comparison with a previously proposed linear relationship. This is particularly evident for drugs with fuhep <0.4, for which the accurate assessment of nonspecific binding will have a significant impact on prediction of intrinsic clearance. Prediction of hepatocyte binding must be undertaken with caution for drugs with logP/D ≥3 because of the impact of inaccuracies in logP/D estimates and the general inaccuracy of the predictive tools in this lipophilicity area. Based on the current data set, this is evident for basic and neutral drugs, whereas hydrophilic acidic drugs tend to have high fuhep (fumic) values and are generally well predicted. In addition, for drugs for which hepatocellular uptake is not limited to simple partitioning [e.g., lysosomal uptake for bases at low drug concentrations (Hallifax and Houston, 2007Go) or active uptake in hepatocytes (Hirano et al., 2006Go; Ho et al., 2006Go)], the prediction of fuhep will be further complicated. However, as a general guide, eqs. 3 and 5 provide a reasonably accurate and straightforward method to calculate fuhep to allow correction of hepatocellular drug concentration for intrinsic clearance and inhibition parameter predictions.


   Footnotes
 
The work was funded by a consortium of pharmaceutical companies (GlaxoSmithKline, Lilly, Novartis, Pfizer, and Servier) within the Centre for Applied Pharmacokinetic Research at the University of Manchester. M.G. and P.J.K. are recipients of Ph.D. studentships from Pfizer and Biotechnology and Biological Sciences Research Council/Novartis, respectively.

Article, publication date, and citation information can be found at http://dmd.aspetjournals.org.

doi:10.1124/dmd.108.020834.

ABBREVIATIONS: fumic, fraction unbound in microsomes; fuhep, fraction unbound in hepatocyte incubations; Ka, microsomal protein binding affinity; Kp, hepatocyte/medium concentration ratio; VR, ratio of the cell and incubation volume; logP/D, descriptor for lipophilicity (logP for drugs where pKa > 7.4; logD for drugs where pKa < 7.4); P, microsomal protein concentration.

Address correspondence to: Dr. A. Galetin, School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK. E-mail: aleksandra.galetin{at}manchester.ac.uk


   References
Top
 Abstract
 Materials and Methods
 Results and Discussion
 References
 


Austin RP, Barton P, Cockroft SL, Wenlock MC, and Riley RJ (2002) The influence of nonspecific microsomal binding on apparent intrinsic clearance, and its prediction from physicochemical properties. Drug Metab Dispos 30: 1497-1503.[Abstract/Free Full Text]

Austin RP, Barton P, Mohmed S, and Riley RJ (2005) The binding of drugs to hepatocytes and its relationship to physicochemical properties. Drug Metab Dispos 33: 419-425.[Abstract/Free Full Text]

Brown HS, Chadwick A, and Houston JB (2007a) Use of isolated hepatocyte preparations for cytochrome P450 inhibition studies: comparison with microsomes for Ki determination. Drug Metab Dispos 35: 2119-2126.[Abstract/Free Full Text]

Brown HS, Griffin M, and Houston JB (2007b) Evaluation of cryopreserved hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance. Drug Metab Dispos 35: 293-301.[Abstract/Free Full Text]

Gertz M, Kilford PJ, Houston JB, and Galetin A (2008) Drug lipophilicity and microsomal protein concentration as determinants in the prediction of the fraction unbound in microsomal incubations. Drug Metab Dispos 36: 535-542.[Abstract/Free Full Text]

Hallifax D and Houston JB (2006) Binding of drugs to hepatic microsomes: comment and assessment of current prediction methodology with recommendation for improvement. Drug Metab Dispos 34: 724-726.[Free Full Text]

Hallifax D and Houston JB (2007) Saturable uptake of lipophilic amine drugs into isolated hepatocytes: mechanisms and consequences for quantitative clearance prediction. Drug Metab Dispos 35: 1325-1332.[Abstract/Free Full Text]

Hirano M, Maeda K, Shitara Y, and Sugiyama Y (2006) Drug-drug interaction between pitavastatin and various drugs via oatp1b1. Drug Metab Dispos 34: 1229-1236.[Abstract/Free Full Text]

Ho RH, Tirona RG, Leake BF, Glaeser H, Lee W, Lemke CJ, Wang Y, and Kim RB (2006) Drug and bile acid transporters in rosuvastatin hepatic uptake: function, expression and pharmacogenetics. Gastroenterology 130: 1793-1806.[CrossRef][Medline]

Ito K and Houston JB (2005) Prediction of human drug clearance from in vitro and preclinical data using physiologically based and empirical approaches. Pharm Res 22: 103-112.[CrossRef][Medline]

McGinnity DF, Berry AJ, Kenny JR, Grime K, and Riley RJ (2006) Evaluation of time-dependent cytochrome P450 inhibition using cultured human hepatocytes. Drug Metab Dispos 34: 1291-1300.[Abstract/Free Full Text]

Obach RS (1996) The importance of non-specific binding in in vitro matrices, its impact on enzymes kinetic studies of drug metabolism reactions, and impact for in vitro-in vivo correlations. Drug Metab Dispos 24: 1047-1049.[Medline]

Rostami-Hodjegan A and Tucker GT (2007) Simulation and prediction of in vivo drug metabolism in human populations from in vitro data. Nat Rev Drug Discov 6: 140-148.[CrossRef][Medline]



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