Send Orders for Reprints to
[email protected] Current Drug Metabolism, 2014, 15, 761-766
761
Quantitative Evaluation of Drug-Drug Interaction Potentials by in vivo InformationGuided Prediction Approach Feng Chen1, Zhe-Yi Hu2, Wei-Wei Jia3, Jing-Tao Lu4 and Yuan-Sheng Zhao5,* 1
School of Pharmacy, Hainan Medical University, Hainan Provincial Key Laboratory of R&D of Tropical Herbs, Haikou, Hainan, China; 2Department of Clinical Pharmacy, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA; 3Graduate School, Tianjin University of Traditional Chinese Medicine, Tianjin, China; 4Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA; 5Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY, USA Abstract: Drug-drug interaction (DDI) is one important topic in drug discovery, drug development and clinical practice. Recently, a novel approach, in vivo information-guided prediction (IVIP), was introduced for predicting the magnitude of pharmacokinetic DDIs which are caused by changes in cytochrome P450 (CYP) activity. This approach utilizes two parameters, i.e. CR (the apparent contribution of the target metabolizing enzyme to the clearance of the substrate drug) and IX (the apparent effect of a perpetrator on the target CYP) to describe the magnitude of DDI between a perpetrator and a victim drug. The essential concept of this method assumes that at a given dose level, the IX for a given perpetrator remains constant whatever the victim drug is. Usually, this IVIP method is only based on information from clinical studies and does not need in vitro information. In this review, basic concept, application and extension, as well as pros and cons of the IVIP method were presented. How to apply this approach was also discussed. Thus far, this method displayed good performance in predicting DDIs associated with CYPs, and can be used to forecast the magnitude of a large number of possible DDIs, of which only a small portion have been investigated in clinical studies. The key concept of this static approach could even be implemented in dynamic modeling to assess risks of DDIs involving drug transporters.
Keywords: Drug-drug interaction, in vivo information-guided, prediction method. INTRODUCTION Drug-drug interaction (DDI) has been one of the important issues in drug discovery, drug development and clinical practice. A DDI is said to occur if the pharmacological or clinical response of one drug is altered by another drug or the other drugs administered together. According to the underlying mechanisms, DDIs can be categorized as either pharmacodynamic or pharmacokinetic [1, 2]. When the co-administered drugs target the same or analogous receptor, or have similar or opposing pharmacological effects, a pharmacodynamic DDI may occur. Usually, DDIs in this type can be predicted and avoided through understanding the pharmacology of each drug. Therefore, compared with pharmacokinetic DDIs, pharmacodynamic interactions are less common. Pharmacokinetic DDIs are “indirect”, where the absorption, distribution, metabolism, or excretion of one “victim” drug is interfered by one “perpetrator” drug, leading to the change of the free drug concentration at the site of action and the alteration of resultant pharmacological responses [1, 2]. A large number of pharmacokinetic interactions involve inhibition and/or induction of drug metabolizing enzymes and/or drug transporters, which result in alternation of clearance of the affected drugs. Because DDIs may lead to serious adverse events, it is necessary to identify and predict potential DDIs. Well-controlled clinical DDI studies are capable of providing definitive information on whether a DDI occurs or not. However, it is not practical to conduct clinical DDI studies frequently, because they are time-consuming,
expensive, and pose certain degree of risk to volunteers [3]. Consequently several approaches have been developed to predict potential DDIs. These approaches include in silico models [1, 4-6], in vitro data based static models [7-11], in vitro data based dynamic models [12-15], and the in vivo information-guided prediction (IVIP) approach [16, 17]. So far, compared with the other methods which have been extensively documented in the literature, the IVIP approach received less attention. The purpose of this review aims to introduce the principle, pros and cons of this method, as well as its applications in the prediction of DDI potentials. BASIC CONCEPT OF THE IVIP APPROACH Prediction of Enzyme Inhibition Mediated DDIs As defined by the name, usually the IVIP approach is only based on in vivo data and does not require in vitro data. When predicting DDIs involving enzyme inhibition, this modeling framework utilizes two parameters: the contribution ratio (CR) defined as the contribution of the target metabolizing enzyme to the clearance of the substrate drug, and the the inhibition ratio (IR) which is the apparent inhibitory effect of the inhibiting drug towards the target metabolizing enzyme. The drug interaction potential, expressed as the magnitude of changes in the area under the plasma drug concentration-time curve (AUC) caused by the inhibitor, can be described as Eq. 1, where 0 CR 1, 0 IR 1, and AUC and AUCI are the AUC values of the victim drug at the the absence and presence of the inhibitor, respectively. If CR and IR are known values, the drug interaction potential can be evaluated.
*Address correspondence to this author at the Mount Sinai School of Medicine, 1428 Madison Ave, New York, NY 10029, USA; Tel:/Fax: +1-917-2549869; E-mail:
[email protected]
1875-5453/14 $58.00+.00
(1)
© 2014 Bentham Science Publishers
762 Current Drug Metabolism, 2014, Vol. 15, No. 8
Chen et al.
The derivation of the model were described in the publications by Ohno [16] and Tod [17]. Their demonstrations were based on several assumptions: the fractional absorption of the victim drug after oral dose is not changed by the inhibitor; the gastric metabolism of the victim drug occurs in proportion to the hepatic metabolism as if the gut is a part of the liver; the victim drug is cleared almost exclusively by metabolism; the inhibitor is highly selective to the target metabolizing enzyme and does not affect the clearance of the victim drug through other pathways; well-stirred model is valid in describing the metabolism of the substrate drug; the inhibitor is either competitive or noncompetitive; and in vivo concentration of the substrate drug is much less than KM (substrate concentration at which half of the maximum metabolism rate is achieved), etc. Actually, this model can be deduced in an alternative way which maintains the same essential concept but necessitates fewer assumptions, as shown in Appendix. Some explanations of the model (Eq. 1) are given as below. Firstly, the essential assumption of the IVIP approach is that the IR for a given inhibitor is the same for any substrate, i.e. at a given dose level, the effect of the inhibitor (and its inhibitory metabolites, if existing) on the target enzyme is the same whatever the victim drug is [16-18]. Thus, once the IR for an inhibitor is known, it can be used to predict the interaction between the same inhibitor and any substrate whose CR is available. Secondly, IR is an apparent value reflecting the effect of the inhibitor and its inhibitory metabolites. It may accommodate different inhibitory mechanisms of different molecule species. Thirdly, IR depends on the concentration profile of the inhibitor, which can be influenced by its dosage. Upon higher dose level or repeated administration of the inhibitor, the IR would be increased. Fourthly, the inhibitor should be specific to the target metabolizing enzyme. If not, the other drug metabolizing enzymes or drug transporters interacting with the inhibitor should not play a major role in determining the pharmacokinetics of the substrate drug. Finally, if the victim drug displays linear pharmacokinetics, CR may be dose independent; otherwise, CR would vary as the dose changes. Therefore, using one CR value for all dose levels of the substrate drug could compromise the prediction accuracy.
metabolizers (EM). Eq. 3 can be derived from Eq. 4, 5 and 6, where CLPM and CLEM are the clearance of the substrate drug in poor and extensive metabolizers, respectively [17].
(3)
(4)
(5)
(6) Estimation of CR through the interaction method is achieved by using Eq. 7, with the AUC ratio determined in a DDI study with an inhibitor whose IR is known [16, 17]. Eq. 7 is transformed from Eq. 1.
(7) The third method (Eq. 8) was based on the contribution of the target drug metabolizing enzyme to the clearance of the victim drug by metabolism (fm,TDME), as well as the contribution of the clearance through metabolism to the total clearance of the drug (fm). The fm,TDME can be estimated by in vitro studies which may employ specific chemical inhibitor or inhibitory antibody to the target metabolizing enzyme, or relative abundance or activity methods [21, 22]. The fm value can be estimated by the recovery of excreted metabolites in urine and bile/feces, post intravenous dose of the substrate drug [17, 20]. This method is best suitable for the victim drug which is extensively metabolized in a single organ, otherwise it may be not straightforward to obtain the fm,TDME value. (8)
Prediction of Enzyme Induction Mediated DDIs Likewise, as shown in Eq. 2, two parameters, CR and IndR, were used by the IVIP approach to predict the DDI potentials mediated by induction of drug metabolizing enzyme. IndR is the apparent increase in clearance of a substrate caused by induction of the target metabolizing enzyme [19]. IndR should be a value not less than 0. Interpretation of IR is largely similar to that of IndR. AUCInd is the AUC value of the victim drug at the presence of the inducer.
It is worthy of noting that the pharmacogenetic method, the interaction method and the extrapolation method should be used in order of decreasing priority, to minimize the possibility of introducing misleading CR values [17]. IR can be assumed to be 1.0 for a very strong inhibitor. Generally IR is determined by the interaction method (Eq. 9), using the AUC ratio determined in a DDI study with a substrate whose CR is known [16, 17]. IndR can be estimated in a similar manner (Eq. 10).
(2) (9) Estimation of CR, IR and IndR Thus far, CR can be calculated by three approaches: the pharmacogenetic method, the interaction method and the extrapolation method [16, 17, 20]. The first method allows determination of CR from Eq. 3, where AUCPM is the AUC of the substrate drug in poor metabolizers and AUCEM is the AUC in extensive metabolizers [17]. Poor metabolizers (PM) are the individuals with no or little activity of the enzyme responsible for the metabolism of the substrate drug; while the same enzyme functions normally in extensive
(10) APPLICATION AND EXTENSION OF THE IVIP APPROACH The IVIP approach was firstly proposed by Ohno and coworkers [16] to predict the CYP3A4 mediated DDIs. It was fur-
Quantitative Evaluation of Drug-Drug Interaction Potentials
Current Drug Metabolism, 2014, Vol. 15, No. 8
ther applied to assess DDIs mediated by other CYP enzymes [19, 20, 23] (Table 1). Of note, Hu and co-workers evaluated the IVIP approach for predicting the DDIs involving inhibitors which can be transformed to inhibitory metabolites [20]. Their study showed that the prediction accuracy of the IVIP approach was more accurate than the in vitro based methods. This may indicate that some important confounding factors (inhibitory metabolites, difference mechanisms of inhibition, and intestinal inhibition) in the prediction of DDIs, which are difficult to handle using the in vitro based methods, could be accommodated by the IVIP method. As the model parameters (CR and IR) may be calculated with bias and error, an orthogonal regression approach was introduced to obtain both the point estimate and the 90% confidence interval of CRs and IRs [17, 24, 25]. This method was able to accommodate the uncertainty in estimation of model parameters and to obtain predictive distributions of DDI magnitude. The early version of the IVIP approach (Eq. 1 and 2) was only validated for predicting DDIs occurring in extensive metabolizers. For a drug that is a substrate of a polymorphic CYP, such as 2D6, 2C9, or 2C19, it is desirable to predict the magnitude of drug interactions in humans with different genotypes. Recently, an equation was proposed to quantitatively predict the DDIs caused by CYP inhibition in subjects with any genotype (XM) (Eq. 11) [24].
(11) where IR is still the apparent inhibition ratio; XM refers to extensive, intermediate, poor, or ultrarapid metabolizers (EM, IM, PM, or UM, respectively); AUCXM is the AUC of the substrate drug in subjects with genotype XM (EM, PM, IM, or UM), AUCXM-I is the AUC in subjects with genotype XM who administered the inhibitor; CREM is the contribution ratio of the substrate drug in homozygote EM subjects; FA is the fraction of the CYP enzyme activity in subjects with any genotype XM, relative to that in the reference genotype EM. For EM subjects, FA is 1, making Eq. 11 simplified as Eq. 1. FA would be lower than 1 in IM and PM subjects and greater than 1 in UM subjects. Tod and co-workers further extended Eq. 11 to predict the magnitude of a drug interaction mediated by two CYP enzymes [25], as shown in Eq. 12 and 13.
763
It is worthy of noting that the introduction of IX can combine Eq. 1 and Eq. 2 into a more generalized equation (Eq. 14). (14) where AUCDDI is the AUC value of the victim drug when a DDI occurs. IX would be less than 0 for an inhibitor and higher than 0 for an inducer. The prediction performances of the IVIP approach were summarized in Table 1. The bias of the prediction is expressed as the mean prediction error (MPE), which is the average of the error (predicted value minus the observed value) [17, 19], as shown in Eq. 15, where n is the number of predictions; the imprecision is represented as the mean absolute prediction error (MAPE, Eq. 16), which is the mean of the absolute error between the predicted value and the observed value [17, 19], as shown in Eq. 16; prediction accuracy is evaluated as the percentage of outliers [17]. A prediction is regarded as an outlier if the predicted AUC ratio is not in the range 50-200% of the observed value [17]. It appears that the IVIP approach performed well in all the studies (Table 1). (15) (16) The DDI magnitude of each substrate-inhibitor or substrateinducer pair can be forecasted, as long as the CR and IX are known. The IVIP model was used to forecast the magnitude of a large number of possible drug interactions, of which only a small portion have been studied (Table 1). PERSPECTIVES Advantages of the IVIP Approach Several advantages of the IVIP approach have been demonstrated [16-20, 23-27]. Firstly, the IX relies on in vivo data, thereby avoiding the confounding issues due to extrapolation from in vitro parameters. These confounding factors, such as the inhibitor or inducer existing as enantiomers in vivo, the metabolites of the perpetrator also affecting the PK of the victim drug, and the perpetrator acting as an inhibitor both reversibly and irreversibly, are often difficult to handle by the in vitro based methods and thus contribute to inaccurate prediction. The ability of the IVIP approach to accommodate these confounding factors is probably the reason for its good prediction accuracy.
(12)
(13)
where AUCXM-I, AUCXM, AUCEM, FA and CR maintain the same meaning stated as above, IX denotes the impact of the inhibitor or the inducer on the activity of the CYP enzymes involved in the drug interaction. Apparently, IX is similar to IR of inhibitors and IndR of inducers, but offers a higher level of generality. Under the criteria of acceptable prediction that the predicted AUC ratio is in the range 50-200% of the observed ratio, Eq. 12 successfully predicted 79 of 80 observed DDIs, and Eq. 13 predicted 67 of 72 observed DDIs [25].
The other advantage of the IVIP approach is that it is simple and easy to use. Dynamic modeling approach, especially physiologically based pharmacokinetic (PBPK) modeling, is becoming more widespread in DDI predictions [28, 29]. PBPK models are capable of simulating the concentration-time profiles of the victim drug and the perpetrator, even their metabolites, in different tissues especially in liver where DDIs involving CYP enzyme occur. Thus PBPK models are able to quantitatively predict the change of CYP inhibition or induction along with time. However, development of a
764 Current Drug Metabolism, 2014, Vol. 15, No. 8
Table 1.
Chen et al.
Overview of the studies applying IVIP approach for DDI prediction
Target enzyme
Mechanism of DDI
Size of external validation dataset a
3A4
inhibition
60
-
3A4
induction
32
2D6
inhibition
2C9
% of outliers d
Size of global validation dataset a
MPE
MAPE
% of outliers
-
5.0%
-
-
-
-
251
45%
16
-
-
0.0%
-
-
-
-
154
27%
20
39
0.31
1.14
7.7%
70
0.04
0.51
1.4%
615
11%
17
inhibition
19
-0.09
0.22
0.0%
-
-
-
-
180
21%
21
2C9, 3A, 2D6 e
inhibition
14
0.1
0.42
0.0%
-
-
-
-
-
-
21
2C9
inhibition and induction
46
-0.01
0.22
0.0%
-
-
-
-
~700
10%
24
2C19
inhibition
13
-1.2
1.76
23.1%
22
-0.62
1.05
13.6%
250
8.8%
25
2 CYPs f
inhibition and induction
-
-
-
-
-
-
-
-
-
-
26
MPE
b
MAPE
c
% of forNumber of casted DDIs DDIs which are forecasted reported
Reference
-, not available or not applicable. a. The model was developed based on a training dataset and then validated by an external dataset. The global validation dataset was produced by pooling the training dataset and the external dataset. b. MPE, mean prediction error c. MAPE, mean absolute prediction error d. If the predicted AUC ratio was in the range 50-200% of the observed value, the prediction was regarded as successful; otherwise, it was regarded as an outlier. e. The IVIP approach was used to predict the DDI risks of inhibitors and their circulating metabolites which are able to inhibit 2C9, 3A or 2D6 f. The IVIP approach was used to predict the DDIs mediated by two CYPs (combination of any two enzymes out of CYP3A4, CYP2D6, CYP2C9, and CYP2C19)
PBPK model is not an easy effort. It requires incorporating many in vitro and in vivo PK parameters which are not always available [28, 29]. A specifically designed PBPK modeling software or a general modeling platform is needed to construct the PBPK model. In comparison to the dynamic models, it takes less effort to build and apply the static IVIP model. An interesting feature of the IVIP approach is that it is able to forecast the DDI potential between any victim drug and perpetrator, even for those whose interactions have been rarely documented, as long as the CR and IX are available. To achieve this purpose, a database can be established to record the CR for each substrate, as well as the IX for each perpetrator. To predict the DDI potential in subjects with any genotype XM, FA values for the polymorphic CYP enzymes also need to be recorded. So far, the CRs of some substrates metabolized by CYP3A4, CYP2D6, CYP2C9, and CYP2C19, as well as IXs of some perpetrators toward these four CYPs, have been estimated [25]. The FAs have been calculated for several groups of genotypes of CYP2D6, CYP2C9, and CYP2C19 [25]. Based on the known CRs, IXs and FAs, a website (http://www.ddi-predictor.org) has been developed by Tod and coworkers and is freely available for the quantitative prediction of DDIs [25]. Limitations of the IVIP Approach A number of limitations are associated with the IVIP approach [16, 17, 19, 25]. Firstly, if the victim drug displays nonlinear pharmacokinetics, CR should be dose dependent. Thus, for this type of drug, use of CR disregarding dose levels would result in misleading prediction. Secondly, the identical perpetrator may have different
IX values at different dose levels and schedules. An IX value which is obtained from one DDI study may only be valid in predicting DDIs which involve similar dosing regimen of the perpetrator as the DDI study, limiting the utilization of the IVIP method. The third, in most cases, the CRs are obtained from studies conducted in healthy adults, so it should be cautious to apply the IVIP model for DDI prediction in children, elders, as well as adults with severe liver impairment. The fourth, it is not recommended to use the IVIP method to predict DDIs mediated by both CYP enzymes and drug transporters. Also this method is currently restricted for the prediction drug interactions involving two CYP enzymes, with the assumption that the two CYP enzymes work independently and thus their CRs are additive. Finally, this approach aims at predicting the mean DDI potential in human population or sub-population, but is not designed to accurately predict the change in drug exposure for each individual patient, due to the inter-subject variability caused by disease, food effect, ethnicity, etc. When and How to Apply the IVIP Approach When is the right timing to apply the IVIP approach for DDI prediction in drug discovery and development settings? If an investigational new drug is a potential victim drug, its CR can be estimated by the extrapolation method after the first human PK study, and then the IVIP approach can be used to roughly predict the DDI potentials of this victim drug when it is coadministered with any inhibitor whose IX has been calculated at certain dose level. At later stage, one clinical DDI study in which this victim drug is coadministered with a strong inhibitor may be needed to investigate the potential DDI risk, according to the FDA Draft Guidance for Industry
Quantitative Evaluation of Drug-Drug Interaction Potentials
(http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulat oryInformation/ Guidances/ucm292362.pdf). Based on the result of this clinical study, CR of the victim drug can be more accurately determined by the interaction method. Subsequently, the IVIP model can be applied to evaluate the DDI risk between the victim drug and any other inhibitor. The evaluation helps to avoid unnecessary following clinical studies or provide instruction in designing these clinical studies. On the other hand, if a new drug is a strong perpetrator of one CYP enzyme, its IX can be determined by the interaction method after one single clinical study involving the perpetrator and a probe substrate of the target CYP enzyme. Then the DDIs between this perpetrator and any substrate whose CR is known can be predicted by the IVIP model. In clinical practice, the IVIP model also has important implications. When two drugs are required to be coadministered under certain scenarios, the predicted AUC ratio may be used directly by clinicians for prior dose adjustment on the basis of efficacy and tolerance.
Current Drug Metabolism, 2014, Vol. 15, No. 8
765
ACKNOWLEDGEMENTS The authors appreciate the financial support provided by the National Science Fund of China (Grant number 81460629). APPENDIX Herein we present an alternative way to deduce the IVIP model (Eq. 1). We assume that the fractional absorption of the victim drug after oral dose is not changed by the inhibitor; the inhibitor is specific to the target metabolizing enzyme and does not affect the clearance of the victim drug through other pathways. (1) Scenario-1: If the victim drug is intravenously dosed, the AUC ratio between the absence and presence of an inhibitor can be expressed as Eq. A1, where D denotes the dose amount of the victim drug, CLsys and CLsys-I denote the systemic clearance at the absence and presence of the inhibitor, respectively. Eq. A1 can be transformed to Eq. A2.
(A1)
Borrow the Essential of the IVIP Approach The IVIP model is a static approach, with the essential concept that at a given dose the effect of a perpetrator on the target enzyme is the same whatever the victim drug is. This concept has been implemented in dynamic modeling to evaluate the drug interaction mediated by drug transporter [30]. An approach combing PBPK modeling and “in vivo” [I]/Ki was proposed to predict Pglycoprotein (P-gp) mediated DDIs [30]. In that study, a baseline PBPK model was built for digoxin, a known P-gp substrate. The Km (Michaelis–Menten constant) of digoxin transport by P-gp in the baseline PBPK model was adjusted to Km,I (Michaelis–Menten constant at the presence of a P-gp inhibitor) to accommodate the change of digoxin pharmacokinetics caused by the P-gp inhibitor. Then the “in vivo” [I]/Ki of this P-gp inhibitor was calculated based on the difference between Km,I and Km. Here, the “in vivo” [I]/K i represents the apparent in vivo impact of one specified inhibitor on P-gp and is assumed to be the same for any P-gp substrate, which is similar to the IX in the IVIP approach. In the following step, a baseline PBPK model was developed for another P-gp substrate dabigatran etexilate (DABE) and the “in vivo” [I]/Ki was incorporated into this model to simulate the impact of the same P-gp inhibitor on DABE pharmacokinetics. This approach accurately predicted the effects of five P-gp inhibitors on DABE pharmacokinetics [30]. It is feasible to extend the method for predicting DDIs between substrates and inducers of the same drug transporter, and those between substrates and irreversible inhibitors.
Here define CRiv as (CLsys CLsys-ComI) / CLsys, where CLsysis the systemic clearance of the victim drug when the metabolism by the target metabolizing enzyme is completely inhibited. Meanwhile if define IRiv as (CLsys CLsys-I) / (CLsys CLsys-ComI), Eq. A3 which is the Eq. 1 post intravenous dose of the victim drug can be obtained. ComI
(A3) (2) Scenario-2: If the victim drug is orally dosed, the AUC ratio can be expressed as Eq. A4, where F and FI are the oral bioavailability at the absence and presence of inhibitor, respectively; CLoral is defined as CLsys / F, meanwhile CLoral-I is defined as CLsys-I / FI. Consequently, similar to the derivation of Eq. A3, Eq. A5 which is the Eq. 1 post oral dose of the victim drug can be obtained.
SUMMARY The IVIP approach has been validated in the prediction of CYP mediated DDIs in subjects with any genotype. This approach can be used in drug development after the result of the first clinical DDI study is available. Since it is capable of forecasting the magnitude of a large number of possible DDIs of which only a small portion have been studied, it should be a useful tool in drug discovery and development, to avoid unnecessary clinical studies or provide instruction for necessary clinical trials. The IVIP approach also offers a simple way for adjusting dose regimens in clinical practice. CONFLICT OF INTEREST The authors confirm that this article content has no conflict of interest.
(A5) (3) Strictly speaking, CRoral may not equate to CRiv and IRoral may not equate to IRiv. Under some scenarios, i.e. there is no remarkable difference between F and FI, CRiv may be similar to CRoral and IRiv may be regarded as interchangeable as IRoral [27]. If the difference between F and FI cannot be neglected, it may lead to inaccurate prediction when using CRiv and IRiv to predict interactions between the inhibitor and the orally dosed victim drug, or using CRoral and IRoral to predict interactions between the inhibitor and the intravenously dosed victim drug.
766 Current Drug Metabolism, 2014, Vol. 15, No. 8
Chen et al.
REFERENCES [1]
[2]
[3] [4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
Cami, A.; Manzi, S.; Arnold, A.; Reis, B. Y. Pharmacointeraction network models predict unknown drug-drug interactions. PLoS One, 2013, 8(4), e61468. Yeo, K. R.; Jamei, M.; Rostami-Hodjegan, A. Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach. Expert Rev. Cli.n Pharmacol., 2013, 6(2), 143-57. Greenblatt, D. J., Introduction to drug-drug interactions. In Drug Interactions in Infectious Diseases, Springer: 2011; pp 1-10. Butina, D.; Segall, M. D.; Frankcombe, K. Predicting ADME properties in silico: methods and models. Drug Discov. Today, 2002, 7(11), S83-8. Ekins, S.; Wrighton, S. A. Application of in silico approaches to predicting drug--drug interactions. J. Pharmacol Toxicol. Methods, 2001, 45(1), 65-9. Hudelson, M. G.; Ketkar, N. S.; Holder, L. B.; Carlson, T. J.; Peng, C. C.; Waldher, B. J.; Jones, J. P. High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods. J. Med. Chem., 2008, 51(3), 648-54. Obach, R. S.; Walsky, R. L.; Venkatakrishnan, K.; Gaman, E. A.; Houston, J. B.; Tremaine, L. M. The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions. J. Pharmacol. Exp. Ther., 2006, 316(1), 336-48. Obach, R. S.; Walsky, R. L.; Venkatakrishnan, K. Mechanismbased inactivation of human cytochrome p450 enzymes and the prediction of drug-drug interactions. Drug Metab. Dispos., 2007, 35(2), 246-55. Ito, K.; Brown, H. S.; Houston, J. B. Database analyses for the prediction of in vivo drug-drug interactions from in vitro data. Br. J. Clin. Pharmacol., 2004, 57(4), 473-86. Yoshida, K.; Maeda, K.; Sugiyama, Y. Transporter-mediated drug-drug interactions involving OATP substrates: predictions based on in vitro inhibition studies. Clin. Pharmacol. Ther., 2012, 91(6), 1053-64. Shou, M.; Hayashi, M.; Pan, Y.; Xu, Y.; Morrissey, K.; Xu, L.; Skiles, G. L. Modeling, prediction, and in vitro in vivo correlation of CYP3A4 induction. Drug Metab. Dispos., 2008, 36(11), 235570. Watanabe, T.; Kusuhara, H.; Maeda, K.; Shitara, Y.; Sugiyama, Y. Physiologically based pharmacokinetic modeling to predict transporter-mediated clearance and distribution of pravastatin in humans. J. Pharmacol. Exp. Ther., 2009, 328(2), 652-62. Bois, F. Y. Physiologically based modelling and prediction of drug interactions. Basic Clin. Pharmacol. Toxicol., 2010, 106(3), 15461. Barton, H. A.; Lai, Y.; Goosen, T. C.; Jones, H. M.; El-Kattan, A. F.; Gosset, J. R.; Lin, J.; Varma, M. V. Model-based approaches to predict drug-drug interactions associated with hepatic uptake transporters: preclinical, clinical and beyond. Expert Opin. Drug Metab. Toxicol., 2013, 9(4), 459-72. Kato, M.; Shitara, Y.; Sato, H.; Yoshisue, K.; Hirano, M.; Ikeda, T.; Sugiyama, Y. The quantitative prediction of CYP-mediated drug interaction by physiologically based pharmacokinetic modeling. Pharm. Res., 2008, 25(8), 1891-901. Ohno, Y.; Hisaka, A.; Suzuki, H. General framework for the quantitative prediction of CYP3A4-mediated oral drug interactions
Received: June 27, 2014
Revised: August 30, 2014
Accepted: September 16, 2014
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
based on the AUC increase by coadministration of standard drugs. Clin. Pharmacokinet., 2007, 46(8), 681-96. Tod, M.; Goutelle, S.; Clavel-Grabit, F.; Nicolas, G.; Charpiat, B. Quantitative prediction of cytochrome P450 (CYP) 2D6-mediated drug interactions. Clin. Pharmacokinet., 2011, 50(8), 519-30. Hisaka, A.; Ohno, Y.; Yamamoto, T.; Suzuki, H. Theoretical considerations on quantitative prediction of drug-drug interactions. Drug Metab. Pharmacokinet., 2010, 25(1), 48-61. Ohno, Y.; Hisaka, A.; Ueno, M.; Suzuki, H. General framework for the prediction of oral drug interactions caused by CYP3A4 induction from in vivo information. Clin. Pharmacokinet., 2008, 47(10), 669-80. Hu, Z. Y.; Parker, R. B.; Laizure, S. C. In vivo information-guided prediction approach for assessing the risks of drug-drug interactions associated with circulating inhibitory metabolites. Drug Metab. Dispos., 2012, 40(8), 1487-94. Soars, M. G.; Gelboin, H. V.; Krausz, K. W.; Riley, R. J. A comparison of relative abundance, activity factor and inhibitory monoclonal antibody approaches in the characterization of human CYP enzymology. Br. J. Clin. Pharmacol., 2003, 55(2), 175-81. Youdim, K. A.; Zayed, A.; Dickins, M.; Phipps, A.; Griffiths, M.; Darekar, A.; Hyland, R.; Fahmi, O.; Hurst, S.; Plowchalk, D. R.; Cook, J.; Guo, F.; Obach, R. S. Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction. Br. J. Clin. Pharmacol., 2008, 65(5), 680-92. Castellan, A. C.; Tod, M.; Gueyffier, F.; Audars, M.; Cambriels, F.; Kassai, B.; Nony, P. Quantitative prediction of the impact of drug interactions and genetic polymorphisms on cytochrome P450 2C9 substrate exposure. Clin. Pharmacokinet., 2013, 52(3), 199-209. Goutelle, S.; Bourguignon, L.; Bleyzac, N.; Berry, J.; ClavelGrabit, F.; Tod, M. In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates. AAPS J., 2013, 15(2), 415-26. Tod, M.; Nkoud-Mongo, C.; Gueyffier, F. Impact of genetic polymorphism on drug-drug interactions mediated by cytochromes: a general approach. AAPS J., 2013, 15(4), 1242-52. Hisaka, A.; Kusama, M.; Ohno, Y.; Sugiyama, Y.; Suzuki, H. A proposal for a pharmacokinetic interaction significance classification system (PISCS) based on predicted drug exposure changes and its potential application to alert classifications in product labelling. Clin. Pharmacokinet., 2009, 48(10), 653-66. Hisaka, A.; Ohno, Y.; Yamamoto, T.; Suzuki, H. Prediction of pharmacokinetic drug-drug interaction caused by changes in cytochrome P450 activity using in vivo information. Pharmacol. Ther., 2010, 125(2), 230-48. Rowland, M.; Peck, C.; Tucker, G. Physiologically-based pharmacokinetics in drug development and regulatory science. Annu. Rev. Pharmacol. Toxicol., 2011, 51, 45-73. Zhao, P.; Zhang, L.; Grillo, J. A.; Liu, Q.; Bullock, J. M.; Moon, Y. J.; Song, P.; Brar, S. S.; Madabushi, R.; Wu, T. C.; Booth, B. P.; Rahman, N. A.; Reynolds, K. S.; Gil Berglund, E.; Lesko, L. J.; Huang, S. M. Applications of physiologically based pharmacokinetic (PBPK) modeling and simulation during regulatory review. Clin. Pharmacol. Ther., 2011, 89(2), 259-67. Zhao, Y.; Hu, Z. Y. Physiologically based pharmacokinetic modelling and in vivo [I]/K(i) accurately predict P-glycoproteinmediated drug-drug interactions with dabigatran etexilate. Br. J. Pharmacol., 2014, 171(4), 1043-53.
