BioMed Research International
Volume 2015 (2015), Article ID 857327, 9 pages
http://dx.doi.org/10.1155/2015/857327

Research Article

How to Choose In Vitro Systems to Predict In Vivo Drug Clearance: A System Pharmacology Perspective

Lei Wang,1,2 ChienWei Chiang,3,4 Hong Liang,1,2 Hengyi Wu,3,4 Weixing Feng,1,5 Sara K. Quinney,3,6 Jin Li,1,2 and Lang Li3,7

¹Bioinformatics Research Center, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
²Biomedical Engineering Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
³Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
⁴School of Informatics and Computing, Indiana University, Indianapolis, IN 46202, USA
⁵Pattern Recognition and Intelligent System Institute, College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, China
⁶Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
⁷Department of Medical and Molecular Genomics, School of Medicine, Indiana University, Indianapolis, IN 46202, USA

Received 13 November 2014; Revised 23 January 2015; Accepted 4 February 2015

Academic Editor: Stelvio M. Bandiera

Copyright © 2015 Lei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The use of in vitro metabolism data to predict human clearance has become more significant in the current prediction of large scale drug clearance for all the drugs. The relevant information (in vitro metabolism data and in vivo human clearance values) of thirty-five drugs that satisfied the entry criteria of probe drugs was collated from the literature. Then the performance of different in vitro systems including Escherichia coli system, yeast system, lymphoblastoid system and baculovirus system is compared after in vitro-in vivo extrapolation. Baculovirus system, which can provide most of the data, has almost equal accuracy as the other systems in predicting clearance. And in most cases, baculovirus system has the smaller CV in scaling factors. Therefore, the baculovirus system can be recognized as the suitable system for the large scale drug clearance prediction.

1. Introduction

In vivo drug clearance is a very significant pharmacokinetic parameter, which largely determines the drug exposure in human body [1, 2]. Predicting the clinical in vivo drug clearance from the preclinical in vitro experiments is essential during the drug development. Specifically, hepatic clearance () is the most important clearance parameter as the majority of the drugs are metabolized in human liver [3].

The most common in vitro drug clearance methods include the use of human liver microsomes (HLMs) or hepatocytes [4], which are well documented in the literature [5–8]. The advantage of HLMs and human hepatocytes is that they are physiologically closer to human liver [4, 9, 10]. Their disadvantages include enormous problems between sample variations with unknown causes and relative high expense [11, 12]. In particular, the large variation of in vitro experiments in HLMs or hepatocytes causes the doubts in reproducibility. On the other hand, the commercial availability of recombinant human enzyme expression systems makes the prediction of human drug clearance cheaper and more reproducible [13, 14]. The advantages and limitations of each in vitro approach are well documented [15–21].

In order to predict in vivo clearance from in vitro experiments, system pharmacology model, such as the physiologically based pharmacokinetic (PBPK) model have been developed rapidly [22–25]. Yap et al. [26] present statistical learning models based on mixed physicochemical and topological descriptors. Demir-Kavuk et al. [27] develop a single application called DemQSAR. Simcyp [28] and Gastroplus [29] are developed originally in collaboration with major pharmaceutical companies to simulate and predict drug clearance and drug interaction in virtual patient populations.

Despite the previously described advances in both technology and system pharmacology modeling, in vitro drug clearance prediction still faces some new challenges [25, 30]. There are a number of in vitro recombinant enzyme systems available, but it is not clear whether they all perform similarly or differently. The performance of different recombinant systems can also be enzyme dependent, but little was known about it [30]. Finally and most importantly, unlike traditional physiologically based pharmacokinetics modeling that investigates one or a few drugs a time, current translational bioinformatics desires a system model that can conduct large scale drug clearance for all the drugs [31]. This is a new challenge that poses not only the accuracy of the in vitro-in vivo clearance prediction, but also the completeness and variations of the annotated in vitro recombinant experiment data on drug metabolisms. As the in vitro-in vivo clearance prediction has been well documented in the literature, this paper will address the completeness and variations of various in vitro recombinant experiments.

2. Methods

2.1. In Vitro Experimental Data Collection

All the in vitro data of selected drugs were collated from the published literature after identifying sources using PubMed. The criteria for drug selection were that they were recognized as probes for specific cytochrome P450s (CYP) or metabolized mostly by a single CYP enzyme [49, 50]. Probe drugs refer to drugs whose plasma AUC values had been shown to increase 5-fold or higher when coadministered with a known CYP inhibitor or AUC ratio in poor metabolizers versus 1280 extensive metabolizers is greater than 5-fold [50]. These literature data including (pmol min−1 pmol⁻¹CYP), (pM) were obtained from various systems for heterologous expression of recombinant P450 enzymes containing bacterial expression in Escherichia coli, expression in yeast cells, lymphoblastoid expression systems, and baculovirus-driven expression in insect cells. Fraction unbound in plasma (fu) of drugs was also collected. If intervals of the fraction of drug unbound in plasma parameters were collected, the mean of an interval was the acceptable value.

2.2. In Vivo Data Collection

Human clearance values were taken from published original work and in part reported by Obach et al. [32]. Both intravenous data and oral data were accepted. In the case of oral clearance, the clearance was taken as a product of oral clearance and absolute bioavailability of the drug, in order to calculate drugs’ intravenous clearance. The bioavailability was got through Drug Bank [39] and published original literature with a single point or the mean of an interval. At the end, only the intravenous clearance was used to assess in vitro-in vivo clearance prediction.

2.3. In Vitro-In Vivo Extrapolation

Prediction of drug hepatic clearance using in vitro recombinant P450 enzyme kinetic parameters was performed in three main steps. Initially, intrinsic clearance per unit enzyme () was calculated by the following:

The median value of the same recombinant P450 enzymes expression systems for each drug was taken, respectively.

After that, was converted to a whole organ intrinsic metabolic clearance () using enzyme abundance, MPPGL, and the liver weight as shown in the following:

where there were CYPs with corresponding values for different pathways in each recombinant system; enzyme abundance refers to the amount (pmol P450) per milligram of microsomal protein; MPPGL means the amount (mg) of microsomal protein per gram of liver; and the liver weight means the weight (g) of human liver. Enzyme abundance, MPPGL, liver weight, and liver blood flow were generated by Simcyp with 1000 Sim-healthy volunteers (age: 20–50), female/male ratio 1, and 100% of extensive metabolizer for all major CYP enzymes [51].

At last, the value of was combined with binding parameters () and liver blood flow () to extrapolate to whole organ clearance by well-stirred model by the following:

where is the fraction of drug unbound in blood. So, it could be calculated by ratios. While ratios were not all available from the literature, a default value of 0.55 was used. Meanwhile, nonspecific microsomal binding was ignored.

2.4. Scaling Factor

The scaling factor of each probe drug was assessed from the difference between predicted and observed in vivo values as described in the following:

where is the observed in vivo clearance and is the predicted value. Then, the scaling factor for different enzymes was determined by averaging scaling factor of probe drugs with the same recombinant P450 enzymes expression systems. This value also could assess the accuracy of clearance predicting. For one drug, if the scaling factor in one system was identical to the others, they had the same accuracy in predicting.

2.5. Statistical Analysis

All data were presented as mean ± S.E., unless stated otherwise. To measure the variability of prediction, the coefficient of variation (CV) was utilized. This CV measures the technical variations of in vitro metabolism experiments published from different labs.

3. Results

3.1. Literature Data Collection

Thirty-five drugs were considered as probe drugs for various enzymes, CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A, from different expression systems as they had relatively adequate kinetic data, as shown in Table 1 [49, 50].

Table 1: Drug set.

Drug
Expression systems
In vivo clearance
(L/h)
References

Caffeine
Baculovirus
5.88
Obach et al. (2008) [32]

Melatonin
Baculovirus
57.96
Mallo et al. (1990) [33]

Tacrine
Yeast
235.2
Obach et al. (2008) [32]

Theophylline (1,3-DMX)
E. coli and lymphoblastoid
3.612
Obach et al. (2008) [32]

Bupropion
Baculovirus
5.415
Lei et al. (2010) [34], Hill et al. (2007) [35]

Efavirenz
Baculovirus
5.483
Gengiah et al. (2012) [36], Chiappetta et al. (2010) [37]

Repaglinide
Baculovirus
32.76
Obach et al. (2008) [32]

Paclitaxel
Baculovirus
26.88
Obach et al. (2008) [32]

(R)-Warfarin
Baculovirus, E. coli, and lymphoblastoid
0.231
Obach et al. (2008) [32]

Phenytoin
Baculovirus and lymphoblastoid
3.906
Hayes et al. (1975) [38]

Celecoxib
Lymphoblastoid and yeast
21.05
Drug Bank [39], Paulson et al. (2001) [40]

Clobazam
Baculovirus
2.49
Drug Bank [39]

(R)-Lansoprazole (dexlansoprazole)
Baculovirus
18.48
Obach et al. (2008) [32]

(R)-Omeprazole
Baculovirus and lymphoblastoid
35.28
Obach et al. (2008) [32]

Atomoxetine
Baculovirus
15.435
Drug Bank [39]

Dextromethorphan
Baculovirus, E. coli, yeast, and lymphoblastoid
40.59
Moghadamnia et al. (2003) [41], Kukanich and Papich (2004) [42]

Metoprolol
Lymphoblastoid
54.6
Obach et al. (2008) [32]

Perphenazine
Baculovirus
113.4
Obach et al. (2008) [32]

Tolterodine
Baculovirus
10.5
Brynne et al. (1997) [43]

Venlafaxine
Lymphoblastoid and yeast
40.95
Drug Bank [39]

Alfentanil
Baculovirus
16.38
Obach et al. (2008) [32]

Astemizole
Yeast
82.6
Lefebvre et al. (1997) [44]

Cisapride
Baculovirus
14.20
Lowry et al. (2003) [45]

Cyclosporine
Baculovirus
31.5
Obach et al. (2008) [32]

Felodipine
Baculovirus and lymphoblastoid
46.2
Obach et al. (2008) [32]

Indinavir
Baculovirus
75.6
Obach et al. (2008) [32]

Maraviroc
Baculovirus
44
Abel et al (2008) [46]

Midazolam
Baculovirus, E. coli, and lymphoblastoid
22.26
Obach et al. (2008) [32]

Pimozide
Baculovirus
0.042
Desta et al. (1999) [47]

Quinidine
Lymphoblastoid
16.8
Obach et al. (2008) [32]

Sildenafil
Baculovirus
38.22
Obach et al. (2008) [32]

Sirolimus
Baculovirus
2.73
Brattstram et al. (2000) [48]

Tacrolimus
Baculovirus
4.63
Obach et al. (2008) [32]

Triazolam
Baculovirus and lymphoblastoid
12.6
Obach et al. (2008) [32]

Vardenafil
Baculovirus
54.6
Obach et al. (2008) [32]

3.2. Comparison of Clearance Predictions for Different Enzyme Probe Drugs from the Same Expression System

Since most drugs had baculovirus system data, they were used to predict probe drugs’ clearance. The predicted clearance was within 3-fold of the observed in vivo value for 6 of the 15 (40%) drugs for CYP3A probe drugs. While for CYP 2D6, none of the predicted values was within 3-fold the observed in vivo value. Only one drug was within 3-fold the observed value for CYP1A2, 2B6, 2C8, 2C9, and 2C19, which accounted for 50%, 50%, 50%, 50%, and 33% of the total. These results were illustrated in Figure 1 and Table 2.

Table 2: Predicted value and observed in vivo value of probe drugs.

Drug
Expression systems
The predicted value (L/h)
The observed in vivo value (L/h)
Scaling factor

Caffeine
Baculovirus
1.21
5.88
2.28

(R)-Lansoprazole (dexlansoprazole)
Baculovirus
81.74
18.48
−2.12

(R)-Omeprazole
Baculovirus
31.81
35.28
0.15

(R)-Omeprazole
Lymphoblastoid
15.56
35.28
1.18

(R)-Warfarin
Baculovirus
0.66
0.231
−1.51

(R)-Warfarin
E.coli
0.0041
0.231
5.82

(R)-Warfarin
Lymphoblastoid
0.018
0.1512
3.07

7-Epi-10-deacetyl-paclitaxel
Baculovirus
1.77
26.88
3.93

Alfentanil
Baculovirus
56.32
16.38
−1.79

Astemizole
Yeast
40.24
82.6
1.04

Atomoxetine
Baculovirus
1.65
20.99875
3.67

Bupropion
Baculovirus
7.27
5.415
−0.43

Celecoxib
Lymphoblastoid
41.26
21.05
−0.97

Celecoxib
Yeast
5.66
21.05
1.90

Cisapride
Baculovirus
20.76
14.1975
−0.56

Clobazam
Baculovirus
23.02
2.49
−3.18

Cyclosporine
Baculovirus
29.07
31.5
0.11

Dextromethorphan
Baculovirus
11.53
15.435
0.42

Dextromethorphan
E.coli
0.26
15.435
5.89

Dextromethorphan
Lymphoblastoid
11.87
15.435
0.38

Dextromethorphan
Yeast
21.66195
15.435
−0.49

Efavirenz
Baculovirus
0.19
5.483
4.85

Felodipine
Lymphoblastoid
0.83
46.2
5.80

Indinavir
Baculovirus
89.11
75.6
−0.23

Maraviroc
Baculovirus
33.78
44
0.38

Melatonin
Baculovirus
35.52
57.96
0.70

Metoprolol
Lymphoblastoid
44.05
54.6
0.31

Midazolam
Baculovirus
54.9
22.26
−1.29

Midazolam
E.coli
44.97
22.26
−1.03

Midazolam
Lymphoblastoid
46.51
22.26
−1.06

Perphenazine
Baculovirus
34.35
113.4
1.72

Phenytoin
Baculovirus
0.37
3.906
3.40

Phenytoin
Lymphoblastoid
1.07
3.906
1.87

Pimozide
Baculovirus
6.87
0.042471
−6.64

Quinidine
Lymphoblastoid
76.93
16.8
−2.18

Repaglinide
Baculovirus
27.29
32.76
0.26

Sildenafil
Baculovirus
5.17
38.22
2.89

Sirolimus
Baculovirus
19.47
2.73
−2.84

Tacrine
Yeast
16.96
235.2
3.79

Tacrolimus
Baculovirus
89.11
4.634
−4.32

Theophylline (1,3-DMX)
E. coli
6.6
3.612
−0.86

Theophylline (1,3-DMX)
Lymphoblastoid
8.04
3.612
−1.15

Tolterodine
baculovirus
2.22
10.5
2.24

Triazolam
Baculovirus
46.65
12.6
−1.89

Triazolam
Lymphoblastoid
4.65
12.6
1.44

Vardenafil
Baculovirus
27.75
54.6
0.98

Venlafaxine
Lymphoblastoid
13.91
40.95
1.56

Venlafaxine
Yeast
2.76
40.95
3.89

Figure 1: Predicted versus observed clearances of 28 drugs of baculovirus expression system.

3.3. Comparisons of Clearance Predictions in the Different Expression Systems

Dextromethorphan and midazolam were selected to compare different expression systems, because these two drugs were investigated and published under all these systems. For dextromethorphan, the predicted values from yeast system were only within 3-fold the in vivo value. And baculovirus system and lymphoblastoid system had almost the same prediction accuracy (Figure 2, Table 2).

Figure 2: Predicted versus observed clearances of 2 drugs with different expression systems.

For midazolam, all of the predicted clearance values were within 3-fold the in vivo clearance values. The most accurate predicted value was from E. coli system. And the three expression systems had almost the same prediction accuracy (Figure 2, Table 2).

3.4. Comparison of Data Availability from Different Expression Systems

All the in vitro recombinant enzyme expression system data were collated from the published literature. The total number of data points was 293. Figure 3 showed the proportion of data from different expression systems. In general, baculovirus and lymphoblastoid system were more abundant than the others. Baculovirus system has the largest proportion, 67%. Lymphoblastoid system was the second one, 20%. Only 8% and 5% of the data came from E. coli system and yeast system.

Figure 3: Proportion of data from different expression systems.

If we mapped all the data to different drugs, the majority of the drugs (28/35) were tested in the baculovirus expression system; part (12/35) of the selected drugs were test in the lymphoblastoid expression systems and only 4/35 were from E. coli and yeast systems, respectively.

3.5. Comparisons of Scaling Factors

Scaling factors of different enzymes based on CYP expression systems were calculated and shown in Table 3. These scaling factor ranged from −1.735 to 3.794. In the baculovirus expression system, the values of scaling factors varied a lot across the enzymes (Figure 4). And 71.4% (5/7) of the values, whose range was −1.735 to 2.394, were positive.

Table 3: Scaling factor with different enzymes and expression systems.

Enzymes
Expression systems
Scaling factor (mean SD)
CV

1A2
Baculovirus
1.493 1.112
74.48%

1A2
E. coli
−0.869
—

1A2
Lymphoblastoid
−1.154
—

1A2
Yeast
3.794
—

2B6
Baculovirus
2.232 3.756
168.28%

2C8
Baculovirus
2.093 2.588
123.65%

2C9
Baculovirus
0.947 3.490
368.53%

2C9
E. coli
5.828
—

2C9
Lymphoblastoid
1.530 2.351
153.66%

2C9
Yeast
1.894
—

2C19
Baculovirus
−1.735 1.716
98.90%

2C19
Lymphoblastoid
1.181
—

2D6
Baculovirus
2.394 0.601
25.10%

2D6
Lymphoblastoid
1.213 0.790
65.13%

2D6
E. coli
7.290
—

2D6
Yeast
2.399 2.111
87.95%

3A
Baculovirus
−1.320 2.653
200.98%

3A
E. coli
−1.014
—

3A
Lymphoblastoid
0.993 3.541
356.60%

3A
Yeast
1.038
—

Figure 4: Comparisons of scaling factors in the baculovirus system.

The variability in the same enzyme between CYP expression systems was also different. In CYP2D6, yeast system and lymphoblastoid system had higher variability than baculovirus system with the coefficient of variation (CV) 65.13%, 87.95%, and 25.10%, respectively. In CYP3A, lymphoblastoid system (CV = 356.60%) had higher variability than baculovirus system (CV = 200.98%) similarly. However, in CYP2C9 the coefficient of variation in lymphoblastoid (CV = 153.66%) was smaller than baculovirus system (CV = 368.53%).

4. Discussion and Conclusion

In this paper, we compare the performance of different recombinant human enzyme expression systems (including Escherichia coli system, yeast system, lymphoblastoid system, and baculovirus system) for predicting hepatic clearance in human body. And we attempt to find out the most suitable one for the large scale drug clearance prediction. After collecting the in vitro pharmacokinetic parameters of thirty-five probe drugs, we use in vitro-in vivo extrapolation to predict the clearance. The experimental results (Table 2) show that half (24/48) of the predicted values in different in vitro systems are within 3-fold the observed in vivo clearance values.

The comparisons of clearance predictions for different enzyme probe drugs from the same expression system and in different expression systems, data availability from different expression systems, and scaling factors are further analyzed. Figure 2 shows that baculovirus system has almost equal accuracy as the other systems in predicting clearance. Meanwhile, it can provide more and sufficient data for prediction than the others (Figure 3). We should note that the scaling factor will be enzyme dependent as shown in Table 3 and in most cases baculovirus systems have the smaller CV in scaling factors. Therefore, we shall use data of the baculovirus system for the large scale drug clearance prediction.

Nevertheless, there are a few more caveats. Most important of all is that in vivo clearance of some probe drugs we collected contains the renal clearance. Some of the in vivo clearance is obtained as the systemic clearance. And the proportion of hepatic metabolism was not clear. Hence, the scaling factor estimation may have some bias.

In most closely related studies, the combination of HLM and recombinant enzymes is implemented to predict in vivo clearance for high accuracy of in vitro-in vivo extrapolation [52–54]. But most of them only focused on one drug, and the choice of in vitro systems was not taken into consideration.

We are fully aware that some drugs are metabolized through non-CYP pathways, such as oxidases, reductases, and other phase II metabolism enzymes. Our preliminary research on these enzymes revealed very limited in vitro experiment data on only a handful of drugs. Therefore, these data cannot be scaled up to do large scale in vitro-in vivo prediction and to evaluate their variations.

To our knowledge, this is the first study to compare the performance of different in vitro systems and make a decision. With the assistance of our work, the large scale drug clearance prediction should be more effective and efficient.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported in part by a grant from National Natural Science Foundation of China (61071174) and Fundamental Research Funds for the Central Universities (HEUCFT1102, HEUCFT1302, and HEUCFX41303), and United States National Institute of Health Grants R01GM10448301-A1 and R01LM011945-01.

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