A Mechanistic Framework for In Vitro–In Vivo Extrapolation of Liver Membrane Transporters: Prediction of Drug–Drug Interaction Between Rosuvastatin and Cyclosporine
- 4.6k Downloads
Background and Objectives
The interplay between liver metabolising enzymes and transporters is a complex process involving system-related parameters such as liver blood perfusion as well as drug attributes including protein and lipid binding, ionisation, relative magnitude of passive and active permeation. Metabolism- and/or transporter-mediated drug–drug interactions (mDDIs and tDDIs) add to the complexity of this interplay. Thus, gaining meaningful insight into the impact of each element on the disposition of a drug and accurately predicting drug–drug interactions becomes very challenging. To address this, an in vitro–in vivo extrapolation (IVIVE)-linked mechanistic physiologically based pharmacokinetic (PBPK) framework for modelling liver transporters and their interplay with liver metabolising enzymes has been developed and implemented within the Simcyp Simulator®.
In this article an IVIVE technique for liver transporters is described and a full-body PBPK model is developed. Passive and active (saturable) transport at both liver sinusoidal and canalicular membranes are accounted for and the impact of binding and ionisation processes is considered. The model also accommodates tDDIs involving inhibition of multiple transporters. Integrating prior in vitro information on the metabolism and transporter kinetics of rosuvastatin (organic-anion transporting polypeptides OATP1B1, OAT1B3 and OATP2B1, sodium-dependent taurocholate co-transporting polypeptide [NTCP] and breast cancer resistance protein [BCRP]) with one clinical dataset, the PBPK model was used to simulate the drug disposition of rosuvastatin for 11 reported studies that had not been used for development of the rosuvastatin model.
The simulated area under the plasma concentration–time curve (AUC), maximum concentration (Cmax) and the time to reach Cmax (tmax) values of rosuvastatin over the dose range of 10–80 mg, were within 2-fold of the observed data. Subsequently, the validated model was used to investigate the impact of coadministration of cyclosporine (ciclosporin), an inhibitor of OATPs, BCRP and NTCP, on the exposure of rosuvastatin in healthy volunteers.
The results show the utility of the model to integrate a wide range of in vitro and in vivo data and simulate the outcome of clinical studies, with implications for their design.
As the proportion of candidate drugs within the Biopharmaceutical Drug Disposition Classification System (BDDCS) class 2–4 entering development increases, it is becoming evident that transporter-mediated drug–drug interactions (tDDIs) may pose challenges for regulatory approval and clinical practice [1, 2]. So it is not surprising that many regulatory agencies are now requesting investigation of transporter effects in vivo whenever they are likely to be clinically relevant [3, 4]. Considering the number of transporters in the body, including the gut, liver, kidney, heart and brain, investigation of tDDIs can be complicated, difficult to interpret and costly.
The contribution of the organic anion-transporting peptide (OATP) family of solute carriers (SLCs) to the hepatic elimination of many drugs and associated drug–drug interactions (DDIs) is significant [5, 6]. This may lead to an increase in the susceptibility of a drug to a tDDI. This is particularly the case for statins (HMG-CoA reductase inhibitors), as many patients taking these drugs for lipid lowering have co-morbidities and are co-prescribed a number of other medications . Rosuvastatin is a relatively hydrophilic statin (Class 3 according to the BDDCS) with a low oral bioavailability of ~20 % . With limited metabolism (~10 %) occurring mainly via cytochrome P450 (CYP) 2C9 and uridine diphosphate glucuronosyltransferase (UGT) 1A1 [9, 10, 11, 12], the main excretion pathways of rosuvastatin are mediated via the biliary and renal routes [8, 13]. Despite a low passive diffusion into hepatocytes , rosuvastatin is extensively distributed into the liver, its site of action [15, 16]. This is mainly due to active uptake of rosuvastatin by OATP1B1, OATP1B3 and OATP2B1, as well as the sodium-dependent taurocholate co-transporting polypeptide (NTCP) [17, 18]. On the canalicular side, rosuvastatin is excreted into the bile via the breast cancer resistance protein (BCRP) [17, 19]. An increase in the plasma area under the concentration–time curve (AUC) of rosuvastatin was observed in patients pre-treated with gemfibrozil, an inhibitor of OATPs . Similarly, rosuvastatin AUC and the maximum plasma concentration (Cmax) were increased by 7- and 10-fold, respectively, in heart transplant recipients (compared with healthy volunteers [HVs]) on an anti-rejection regimen including the immunosuppressant cyclosporine (ciclosporin), which is an inhibitor of OATPs and NTCP .
Application of in vitro–in vivo extrapolation (IVIVE), a “bottom-up” approach, in conjunction with physiologically based pharmacokinetic (PBPK) modelling under a mechanistic systems biology approach can help to predict complex DDIs and also inform the design of clinical studies in HVs or patient populations [22, 23]. The model that is incorporated within the Simcyp Population Based Simulator  allows investigation of metabolism and transport interplay within the liver and can also be used for quantitative prediction of metabolism-mediated drug–drug interactions (mDDIs) and tDDIs. In this study, we present an IVIVE framework for scaling in vitro liver transporter kinetic data to in vivo. We account for the impact of drug ionisation on extra- and intracellular water (EW and IW) concentrations within the permeability-limited liver (PerL) model and present equations to estimate the unbound concentration fractions in EW and IW compartments based on tissue composition and drug physicochemical data. We demonstrate application of the approach by describing the development of a PBPK model for rosuvastatin, incorporating active uptake into the liver via OATP1B1, OATP1B3, OATP2B1 and NTCP, in addition to excretion of the drug into the bile by BCRP. The impact of co-administration of cyclosporine is also investigated.
2.1 Model Theory and Development
2.1.1 Physiologically Based Pharmacokinetic Models
2.1.2 Perfusion Versus Permeability-Limited Models
The vascular and extracellular compartments are in instantaneous equilibrium, although the total concentration in these compartments can be different .
Only un-ionised and unbound species can passively permeate through the plasma membrane  and transporters act only on unbound drug.
The movement of the unbound un-ionised species from the VS to the EW is not a rate-limiting process.
Passive permeability at the canalicular side of the liver plays a negligible role in biliary secretion.
These assumptions underpin the need to determine unbound extracellular and intracellular concentrations. Unbound fractions in these milieus are usually unknown or challenging to measure in vitro. Thence, having models to predict these fractions based on the physicochemical properties of the compound and tissue compositions is desirable. The development of mechanistic equations incorporating compound lipophilicity, binding of compound to plasma and tissue macromolecules, and levels of phospholipids and neutral lipids in plasma and tissues has improved prediction of the tissue distribution of many compounds [31, 32]. These in silico models have been further developed to account for both protein binding in the EW and binding of strong bases (drug acid-ionisation constant [pKa] >7.0) to acidic phospholipids [30, 33, 34]. Steady-state conditions and instantaneous equilibrium of the unbound drug at membranes are assumed. Using the same approach [30, 33, 34], and based on the aforementioned assumptions, equations have been developed to estimate unbound concentrations of drug in IW and EW and to predict transporter functionality in the liver (see the Electronic Supplementary Materials for the derivations).
2.1.3 Liver Compartmental Concentrations
The differential equations for the liver compartments are developed in a general form and can consider any number of efflux and/or update transporters at each of the liver membranes. Assuming the transporter functionality can be described using a Michaelis–Menten equation, Jmax (in vivo maximum rate of transporter-mediated efflux or uptake) and Km (Michaelis–Menten constant) are the required parameters to determine the transport rate of the drug across membranes. The drug passive permeation is determined by the passive diffusion parameter, CLint,PD, which is equal to the permeability surface product.
2.1.4 In Vitro–In Vivo Extrapolation of Transporter Kinetic Parameters
2.1.5 Transporter-Mediated Drug–Drug Interactions
2.2 Development of the Rosuvastatin Model
The sub-models and differential equations described in the previous sections were used to develop a full PBPK model, including permeability-limited diffusion into the liver to describe the disposition of rosuvastatin. The absorption of rosuvastatin from solution was defined using the ADAM model.
2.2.1 Data Used for Simulations of Rosuvastatin Pharmacokinetics
Parameter values used for the rosuvastatin simulations
Molecular weight (g/mol)
Blood-to-plasma ratio (B:P)—experimental
Log of the octanol:water partition coefficient (logPo:w)—experimental
Marvin Sketch 22.214.171.124
Main plasma binding protein
HSA (human serum albumin)
Caco-2 permeability [Papp,caco-2(7.4:7.4) (10−6 cm/s)]
Reference Papp,caco-2(7.4:7.4) (10−6 cm/s)
Based on Caco-2 data
Based on Caco-2 data
Rodgers and Rowland method; see text for details
CLint (μL/min/mg protein)
Calculated using the retrograde model
Transport (active and passive)
Intestinal efflux intrinsic clearance
Intestinal BCRP REF (User)
Hepatic efflux intrinsic clearance
CLint,T,OATP1B1 (μL/min/million hepatocytes)
See text for details; 
Hepatic OATP1B1 REF (User)
CLint,T,OATP1B3 (μL/min/million hepatocytes)
See text for details; 
Hepatic OATP1B3 REF (User)
CLint,T,NTCP (μL/min/million hepatocytes)
Hepatic NTCP REF (User)
CLint,T,BCRP (μL/min/million hepatocytes)
Hepatic BCRP REF (User)
Using above data
Passive intrinsic clearance at sinusoidal membrane
CLint,PD (mL/min/million hepatocytes)
2.2.2 Whole-Organ Metabolic Clearance
2.2.3 Permeability Data
Permeability data obtained using Caco-2 cells (Papp) for rosuvastatin (3.395 × 10−6 cm/s) was calibrated using propranolol (20 × 10−6 cm/s) and then extrapolated to an effective permeability in human (Peff,man) of 0.855 × 10−4 cm/s using the default regression equation within the Simcyp Simulator. It was assumed that Peff,man was consistent across all segments of the intestine including the duodenum, jejunum, ileum and colon.
2.2.4 Kinetic Data for Rosuvastatin Transport
Active and passive kinetic transport data for the hepatic sinusoidal uptake (OATP1B1, OATP1B3 and NTCP) and canalicular efflux (BCRP) of rosuvastatin were available. It is not always possible to recover observed drug plasma concentration–time profiles accurately purely from the ‘bottom-up’ using physicochemical, in vitro permeability and metabolism/transport data. To accommodate inherent uncertainty, the parameter estimation (PE) module within the Simcyp Simulator may be used to optimise key parameter values. This provides a link between the ‘bottom-up’ and ‘top-down’ pharmacokinetic modelling paradigms by utilising available in vivo data to modify parameters based purely on in vitro studies. The simulations then move forward more effectively to predict the impact of specific perturbations of drug kinetics as a consequence of a DDI.
Preliminary simulations indicated that the experimental transporter kinetic data were not able to recover the observed profiles of rosuvastatin. Thus, a global intrinsic clearance for the active hepatic uptake (global CLint,T) of rosuvastatin was obtained using the PE module within the Simcyp Simulator and the observed plasma concentration–time data from Martin and co-workers  following intravenous administration of rosuvastatin in HVs. Other parameters were fixed to the in vitro values (Table 1), and optimisation was done using the Nelder–Mead minimisation method and weighted least squares algorithm. A global CLint,T of 222 μL/min/million cells was obtained for hepatic uptake of rosuvastatin. This was apportioned to OATP1B1, OATP1B3 and NTCP, based on the relative contributions of each of the transporters to the active uptake of rosuvastatin in vitro. According to the study of Kitamura and co-workers , the contribution of OATP1B1 to the active hepatic uptake of rosuvastatin in human hepatocytes is on average about 49 %. Using sandwich culture human hepatocyte (SCHH), Ho and co-workers  estimated that the percentage contribution of NTCP is about 35 %. It was assumed that the remaining 16 % represented the transporter-mediated uptake of rosuvastatin into the liver via OATP1B3. Thus, CLint,T values of 109, 36 and 78 μL/min/million cells were assigned to hepatic uptake of rosuvastatin by OATP1B1, OATP1B3 and NTCP, respectively (Table 1). It has been reported that OATP2B1 also contributes to the uptake of rosuvastatin. However, since modelling this transporter requires more sophisticated models that account for ion gradients and multiple binding sites, its contribution was combined with that of OATP1B3. As the data were derived from in vivo data, no REFs were applied (the REFs/RAFs were set to 1). The hepatic canalicular efflux contribution was recalculated from in vitro data in SCHH (CLint biliary 3.39 mL/min/kg ) using 107 × 106 HPGL and 25.7 g of liver per kg bodyweight and assigned to BCRP. A value of 1.23 ((3.39/25.7)/107) × 1,000) μL/min/million hepatocytes was obtained.
Although rosuvastatin has been shown to be a substrate of intestinal BCRP in vitro , no quantitative data were available. Therefore, a sensitivity analysis was performed to obtain estimates of the intestinal CLint,T of BCRP using observed rosuvastatin time to Cmax (tmax) and Cmax for a given dose of 40 mg [21, 54]. A value of 35 μL/min/cm2 was able to recover observed tmax and Cmax values and was thus applied in further simulations.
2.2.5 Data Used for Simulations of Cyclosporine Pharmacokinetics
Parameter values used for the cyclosporine simulations
Molecular weight (g/mol)
Blood/plasma ratio (B:P)—experimental
Log of the octanol:water partition coefficient (logPo:w)—experimental
Main plasma binding protein
HSA (human serum albumin)
Effective permeability (Peff,man) (10−4 cm/s)
Effective colonic permeability (Peff,man,colon) (10−4 cm/s)
Permeability in the colon was set to ~0 in order to achieve an fa of <1 (consistent with observed data)
Based on observed Peff,man
Based on observed Peff,man
Rodgers and Rowland method; see text for details
When applying Kp liver and Kp spleen
Liver partition coefficient (Kp)
Spleen partition coefficient (Kp)
Intrinsic clearance (CLint CYP3A4) (μL/min/pmol CYP)
Calculated using the retrograde approach
Applying fe (fraction of drug excreted) of 0.001 (Sandimmune Prescribing Information) to a systemic clearance of 24.07 L/h
Ki—intestinal BCRP (μmol/L)
Ki—hepatic OATP1B1 (μmol/L)
Ki—hepatic OATP1B3 (μmol/L)
Ki—hepatic NTCP (μmol/L)
Ki—hepatic BCRP (μmol/L)
Interacting concentrations used depending on the selected physiologically based pharmacokinetic models for perpetrators
Liver transporter function
- Ten virtual trials of HVs (subject number, age range, % female according to Table 4) receiving a single oral dose of rosuvastatin 10, 20, 40 or 80 mg were generated. The simulated profiles for rosuvastatin were compared with observed data from 11 independent pharmacokinetic studies (Table 4).Table 4
Details on the single-dose clinical studies used for rosuvastatin performance verification
Ten virtual trials of ten HVs aged 30–65 years (0.11 % female; one female subject) receiving multiple oral doses of rosuvastatin 10 mg co-administered with oral cyclosporine (200 mg twice daily) for 10 days were generated. The simulated profiles for rosuvastatin were compared with observed data [21, 54].
4.1 Model Development and Unbound Fractions
To model pravastatin pharmacokinetics in humans, Watanabe and co-workers used a PBPK model where the liver was divided into five units consisting of the extracellular and subcellular compartments [71, 72]. The units were connected in tandem by blood flow rate to mimic the liver dispersion model. They argued that, especially for high extraction drugs, the well-stirred model underestimates the hepatic clearance compared with the parallel tube and dispersion models, and the difference becomes greater when active uptake increases. The unbound fraction in the extracellular compartment and subcellular compartments was assumed to be the same as the unbound fraction in blood and the tissue unbound fraction (measured in rats), respectively.
Jones and co-workers  also used PBPK models with a liver permeability model to predict the pharmacokinetics of seven OATP substrates using in vitro data generated from SCHH. As per Watanabe et al. , they divided the liver into five units and used measured unbound fractions from in vitro experiments. Measuring unbound extracellular and intracellular fractions is a laborious and complex process and, thus, it is highly desirable to predict these values at early stage of drug development. Recently, the advent of more mechanistic approaches to assess drug distribution into tissues has led to the development of methods for prediction of unbound fractions using physicochemical properties [30, 31, 32, 34]. The Rodgers and Rowland equations improved Kp predictions largely due to the incorporation of distribution processes related to drug ionisation, an issue that was not addressed in earlier equations . These methods were mainly developed assuming steady-state conditions and instantaneous equilibrium of the unbound drug at membranes. However, researchers have tried modifying these equations to account for non-equilibrium conditions to develop models that describe transporter functionality in different organs such as the liver, brain and heart [73, 74]. Fenneteau and co-workers  proposed mechanistic transport-based models to investigate the impact of P-glycoprotein-meditated efflux in mouse brain and heart. Their model assumed that transport occurs at the capillary membrane. They developed an equation for estimating the tissue unbound fraction where they assumed CuEW and Cup (unbound concentration in plasma) are equal and CuIW and Cup only differ by an ionisation factors. Nevertheless, these assumptions are only valid for passive permeability and instantaneous equilibrium, which are not applicable in this case. Poirier and co-workers  used a similar approach to simulate the plasma concentration of napsagatran and fexofenadine in rats and that of valsartan in humans . They predicted fuIW using the method by Poulin and Theil [31, 32] and fuEW using the method by Rodgers and Rowland [30, 34]. However, their fuIW equation for the permeability-limited model was independent of fIW.
Our proposed liver model (PerL) and the unbound fraction prediction equations account for the processes involved in drug transport at the liver membranes. Obviously, their validity should be assessed for a range of compounds. It should be noted that these equations are mainly developed to provide predictions during the discovery and early stages of drug development when measured values are not available. Generally, to increase confidence in pharmacokinetic predictions it is recommended these values be obtained from in vitro experiments whenever possible .
4.2 Development of the Rosuvastatin Model
In this study, we have presented a PBPK model for rosuvastatin, incorporating gut efflux by BCRP and active uptake into the liver via OATP1B1, OATP1B3 (OATP2B1) and NTCP, in addition to excretion of the drug into the bile by BCRP. Although the model was able to recover the observed data in most cases, for some studies the absorption delay was not captured. The delay may be a result of increasing solubility along the intestine due to pH changes or, alternatively, rosuvastatin may be a substrate of other efflux transporters that have higher expression in the proximal part of the intestine, e.g. MRP2 (multi-drug resistance protein 2). However, as there was considerable variability in clinical data across doses and the rosuvastatin model was able to recover observed data in most cases, the model was considered to be reasonably robust for assessment of the magnitude of interaction with cyclosporine, an inhibitor of OATPs and NTCP.
Predicted median AUC and Cmax ratios of rosuvastatin (10 mg multiple dose) following co-administration of cyclosporine (200 mg twice daily) for 10 days ranged from 1.45 to 1.68 and 2.73 to 3.62, respectively, for the ten trials. Although clinical data from a crossover study design in HVs were not available for direct comparison, rosuvastatin pharmacokinetic parameters had been assessed in an open-label trial involving stable heart transplant recipients (>6 months after transplant) on an anti-rejection regimen including cyclosporine . In these patients taking rosuvastatin 10 mg for 10 days, geometric mean values and percentage coefficient of variation for steady-state AUC24 and Cmax were 284 ng·h/mL (31.3 %) and 48.7 ng/mL (47.2 %), respectively. In controls (historical data AUC24 and Cmax from 21 HVs taking rosuvastatin 10 mg), these values were 40.1 ng·h/mL (39.4 %) and 4.58 ng/mL (46.9 %), respectively . Thus, compared with control values, AUC24 and Cmax values were increased 7.1- and 10.6-fold, respectively, in transplant recipients. The predicted increase in exposure of rosuvastatin following co-administration of cyclosporine in virtual HV subjects is lower than observed in vivo.
Recently, Gertz and co-workers  developed a PBPK model to predict the inhibitory effects of cyclosporine and its mono-hydroxylated metabolite on intestinal CYP3A4 metabolism and uptake and efflux transporters. Their model indicated that cyclosporine had the highest impact on the liver uptake transporters and minimal impact on hepatic efflux and metabolism. Inclusion of the cyclosporine metabolite had little impact on the predicted interaction with liver uptake transporters. Thus, the fact that we did not consider the impact of the cyclosporine metabolite does not account for the under-prediction of the tDDIs with rosuvastatin seen in our study. However, it should be noted that other disease-related factors may contribute to increased exposure of rosuvastatin in patients. In addition, the exposure of cyclosporine itself may differ between heart transplant patients and the virtual HV subjects. Despite the limitations and lack of clinical data for direct comparison, the PBPK model presented here for rosuvastatin is sensitive to inhibition of the transporters OATP1B1, OATP1B3, OATP2B1, NTCP and BCRP by cyclosporine. The developed PBPK model also predicts the intracellular liver concentration of rosuvastatin in the presence and absence of the inhibitor. As Fig. 4 shows, the predicted magnitude of interaction within the tissue can be different to that obtained in the plasma. Depending on the drug characteristics, the difference can be significant. Such knowledge can be valuable, especially when liver toxicity is an issue for some drugs or when the site of drug action is within the liver, in the case of statins. Indeed, it can only increase our understanding of the drug response and its variability .
Given the complexity of processes involved in the transport of drugs into cells, obtaining accurate kinetic parameters from in vitro assays is very challenging. Recently, model-based data analysis of in vitro assays has attracted more attention and new methods and data analysis techniques are proposed; for more detail see previous publications [76, 78, 79, 80]. The functional translation of some SLC effects using IVIVE approaches is challenging due to the existence of several binding sites , the possible time-dependent inhibition for some SLCs [57, 77] and the limitations of apparent in vitro kinetic parameter measurements . Adding to this complexity, in order to have a robust PBPK model, the relative contributions of both active and passive components need to be assigned correctly. In addition, the active component itself can be a sum of different uptake and efflux transporters working in parallel or against each other on the same membrane. Thus, the fractional contribution of each transporter (ft) to the global transport is as important as the ‘fm’ (the fraction of a drug metabolized by an enzyme) for CYP enzymes in the prediction of interactions involving metabolism. For our PBPK model of rosuvastatin, a global hepatic uptake was fitted using intravenous clinical data and the relative contributions of OATP1B1, OATP1B3 (OATP2B1), NTCP and BCRP were assigned based on in vitro data. Our model has also successfully been applied to predict the disposition and DDIs of other drugs such as repaglinide and pravastatin [40, 58, 83, 84].
Transporters’ ‘ft’ values are used to predict tDDIs in a manner similar to that used for CYP enzymes. These fraction values have been used in static equations with different degrees of success depending on the applied assumptions [84, 85]. In some cases it is assumed that the transport process occurs exclusively via a particular uptake transporter, ignoring the potential contribution of passive diffusion or other transporters or involvement of metabolism. The static equations cannot account for the time-varying nature of the substrate and inhibitor concentrations and assume constant values. Further, since these models cannot estimate the relevant concentrations at the transport site, surrogate concentrations such as plasma or average gut concentration (highest oral dose diluted in 250 mL) are used. These assumptions often increase the possibility of encountering false positive predictions and may lead to unnecessary clinical studies being conducted. Given the complexity of the processes involved in metabolism and transport interplay, dynamic models for determining DDIs are preferable. However, in some cases during the early stages of drug development, static equations, if applied with correct assumptions, may provide a reasonable estimate of tDDIs.
Incorporation of transporters within PBPK models can provide some insight into their role in drug disposition and lead to improved understanding of the drug response and its variability. In particular, when these models account for mDDIs and tDDIs simultaneously for multiple moieties they become very powerful tools for investigating complex cases that can occur in clinical practice. Although there are still gaps in our knowledge regarding physiological data such as reliable absolute abundance/activity data for transporters, observed clinical data can be used to estimate the unknown model parameters and, once validated, the model can then be applied prospectively, to predict tDDIs.
Application of these models can aid in making informed decisions on the design of clinical studies and give an indication of whether such studies are needed. Indeed, successful application of these models (e.g. Varma et al. [58, 83]) demonstrates the value and impact of model-based drug development in the pharmaceutical industry.
This work was funded by Simcyp Limited (A Certara Company). The Simcyp Simulator is freely available, following completion of the training workshop, to approved members of academic institutions and other not-for-profit organisations, for research and teaching purposes. Parts of this work have been published as posters at ASCPT (American Society for Clinical Pharmacology and Therapeutics) in 2011 and AAPS (American Association of Pharmaceutical Scientists) in 2012. The help of James Kay and Eleanor Savill in preparing the manuscript is appreciated.
Conflicts of interest
Masoud Jamei, Sibylle Neuhoff, Zoe Barter and Karen Rowland-Yeo are employees of Simcyp Limited (A Certara Company). Amin Rostami-Hodjegan is an employee of the University of Manchester and part-time secondee to Simcyp Limited (A Certara Company). Jiansong Yang is an employee of GSK, China. Fania Bajot is an employee of British American Tobacco.
MJ, YJ, SN, KR-Y and AR-H designed the research, developed the PBPK model and algorithms and wrote the manuscript. MJ, FB, SN, ZB and KR-Y designed the research, developed the compound files, ran simulations, analysed the data and wrote the manuscript. All authors read and approved the final manuscript.
- 3.Committee for Human Medicinal Products. Guideline on the investigation of drug interactions. European Medicines Agency; 2012. http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf. Accessed 1 Apr 2013.
- 4.Food and Drug Administration. Drug interaction studies—study design, data analysis, implications for dosing, and labeling recommendations (draft). U.S. Department of Health and Human Services; 2012. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm292362.pdf. Accessed 1 Apr 2013.
- 12.McCormick A, McKillop D, Butters C. ZD4522—an HMG-CoA reductase inhibitor free of metabolically mediated drug interactions: metabolic studies in human in vitro systems. Twenty Ninth Annual Meeting, American College of Clinical Pharmacology, September 17–19 Chicago, A46. J Clin Pharmacol. 2000;40:1055.Google Scholar
- 26.Darwich AS, Neuhoff S, Jamei M, Rostami-Hodjegan A. Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: a simulation assessment using the “Advanced Dissolution, Absorption, Metabolism (ADAM)” model. Curr Drug Metab. 2010;11(9):716–29.PubMedCrossRefGoogle Scholar
- 29.Berezhkovskiy LM. A valid equation for the well-stirred perfusion limited physiologically based pharmacokinetic model that consistently accounts for the blood-tissue drug distribution in the organ and the corresponding valid equation for the steady state volume of distribution. J Pharm Sci. 2009;99(1):475–85.CrossRefGoogle Scholar
- 36.Barter ZE, Bayliss MK, Beaune PH, Boobis AR, Carlile DJ, Edwards RJ, et al. Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human microsomal protein and hepatocellularity per gram of liver. Curr Drug Metab. 2007;8(1):33–45.PubMedCrossRefGoogle Scholar
- 39.Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A. Physiologically-based mechanistic modelling to predict complex drug–drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut-the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci. 2010;39(5):298–309.PubMedCrossRefGoogle Scholar
- 44.Ahmad S, Madsen CS, Stein PD, Janovitz E, Huang C, Ngu K, et al. (3R,5S,E)-7-(4-(4-fluorophenyl)-6-isopropyl-2-(methyl(1-methyl-1h-1,2,4-triazol-5-yl)amino)pyrimidin-5-yl)-3,5-dihydroxyhept-6-enoic acid (BMS-644950): a rationally designed orally efficacious 3-hydroxy-3-methylglutaryl coenzyme-a reductase inhibitor with reduced myotoxicity potential. J Med Chem. 2008;51(9):2722–33.PubMedCrossRefGoogle Scholar
- 52.Barter ZE, Chowdry JE, Harlow JR, Snawder JE, Lipscomb JC, Rostami-Hodjegan A. Covariation of human microsomal protein per gram of liver with age: absence of influence of operator and sample storage may justify interlaboratory data pooling. Drug Metab Dispos. 2008;36(12):2405–9.PubMedCrossRefGoogle Scholar
- 55.Clarke J, Generaux G, Harmon K, Kenworthy KE, Polli JW. Predicting clinical rosuvastatin interactions using in-vitro OATP inhibition data. Drug Transporters in ADME: From Bench to Bedside, AAPS Transporters Workshop, 14–16 Mar 2011, Bethesda.Google Scholar
- 56.Clarke J. The use of in-vitro BCRP/OATP data to predict rosuvastatin DDI: a static modelling approach. 3rd International clinically relevant drug transporters, 28–29 Feb 2012, Berlin.Google Scholar
- 73.Fenneteau F, Turgeon J, Couture L, Michaud V, Li J, Nekka F. Assessing drug distribution in tissues expressing P-glycoprotein through physiologically based pharmacokinetic modeling: model structure and parameters determination. Theor Biol Med Model. 2009;6:2.PubMedCentralPubMedCrossRefGoogle Scholar
- 77.Gertz M, Cartwright CM, Hobbs MJ, Kenworthy KE, Rowland M, Houston JB, et al. Cyclosporine inhibition of hepatic and intestinal CYP3A4, uptake and efflux transporters: application of PBPK modeling in the assessment of drug–drug interaction potential. Pharm Res. 2013;30(3):761–80.PubMedCrossRefGoogle Scholar
- 84.Varma MVS, Lin J, Bi YA, Rotter CJ, Fahmi OA, Lam J, et al. Quantitative prediction of repaglinide–rifampicin complex drug interactions using dynamic and static mechanistic models: delineating differential CYP3A4 induction and OATP1B1 inhibition potential of rifampicin. Drug Metab Dispos. 2013;41(5):966–74.PubMedCrossRefGoogle Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.