Compounds
A dataset of 146 compounds was used for the investigation. A list of suitable candidates was compiled from a model dataset for transporter interaction studies (21), and this list was extended with compounds known to interact with OATP1B1 and/or MRP2 (21), bile acids and three therapeutic groups of interest for OATP1B1: statins, protease inhibitors and anti-diabetic compounds. The substances were obtained from Sigma-Aldrich (St. Louis, MO), International Laboratory USA (San Bruno, CA), 3B Scientific Corporation (Libertyville, IL) and AstraZeneca R&D Mölndal (Sweden). Radiolabeled estradiol-17β-glucuronide (E17βG) was obtained from PerkinElmer (Waltham, MA).
Construction of an OATP1B1 Expression Vector
The SLCO1B1/OATP1B1 open reading frame was obtained using restriction digestion with KpnI/XhoI from an SLCO1B1/OATP1B1-pcDNA3.1 expression vector (kindly provided by Dr Lena Gustavsson, AstraZeneca R&D Lund, Sweden). The resulting DNA fragment was cloned into the corresponding restriction sites of the expression vector pcDNA5/FRT (Invitrogen, Carlsbad, CA). The inserted sequence was verified by DNA sequencing analysis (Uppsala Genome Center, Uppsala, Sweden).
Establishment of Stable Clones and Cell Cultivation
Human embryonic kidney cells Flp-In-293 (Invitrogen, Carlsbad, CA) were transfected with the pOG44 vector (Invitrogen, Carlsbad, CA) and with the constructed OATP1B1-pcDNA5/FRT expression vector or empty pcDNA5/FRT vector (mock) using Lipofectamine 2000 (Invitrogen, Carlsbad, CA) according to the manufacturer’s recommendations. Stable clones were obtained by selection using Flp-In-293 medium (Dulbeccos’s modified eagle’s medium supplemented with 10% fetal bovine serum (FBS) and 2 mM L-glutamate) supplemented with 75 μg/ml of Hygromycin B (Invitrogen, Carlsbad, CA). For continued culturing, the stable clones were cultivated in Hygromycin B supplemented Flp-In-293 medium.
All cells were cultured at 37°C in an atmosphere of 95% air and 5% CO2 and sub-cultured twice a week. Passage numbers 5 to 30 were used throughout the study. All cell culture media and reagents were obtained from Invitrogen (Carlsbad, CA) or Sigma-Aldrich (St.Louis, MO).
Two to 3 days prior to performing the transport experiments, cells were seeded in black 96-well poly-D-lysine coated plates (Greiner, Frickenhausen, Germany) or CellBind plates (Corning, Amsterdam, Netherlands) (experiments using E17βG as substrate), or 24-well CellBind plates (Corning, Amsterdam, Netherlands) (experiments conducted using atorvastatin as the substrate) at a density of 30,000–60,000 (96-well plates) or 600,000 cells/well (24-well plates). The cell density was optimized using computer-assisted experimental design conducted with MODDE 7.0 (Umetrics, Umeå, Sweden) as described below.
Transport and Inhibition Studies
In the transport studies, described below, all experiments were performed in at least triplicate. Common to all experimental protocols was the following procedure: before starting the experiment, cells were washed twice with pre-warmed HBSS with pH 7.4, followed by incubation at 37°C with pre-warmed test solutions. The transport experiments were terminated by adding ice-cold buffer, followed by four washing steps. Total protein content was measured using the BCA Protein Assay Reagent Kit (Pierce Biotechnology, Rockford, IL) according to the manufacturer’s instructions. In all experiments, mock-transfected cells were included on each plate to correct for the passive permeability.
Characterization of the OATP1B1 Transport
The cells grown in the 96-well plates were incubated with a solution containing 1 μCi/ml (24 nM) 3H-estradiol-17β-glucuronide (3H-E17βG) and 1–200 μM of unlabeled E17βG in HBSS, and then analyzed using a liquid scintillation counter as described below to determine Km and Vmax of the model substrate E17βG, which was used as a substrate in single point inhibition experiments. The Km and Vmax of atorvastatin, the specific substrate used in the in vitro–in vivo extrapolation experiments, were determined using cells grown in 24-well plates. The cells were incubated with a solution containing 0.2–50 μM atorvastatin in HBSS and analyzed using UPLC-MS/MS as described below. Uptake kinetics were assessed by plotting the initial uptake rate (uptake after 1 min) against the substrate concentration [S]; apparent Km and Vmax were determined by non-linear regression (using Prism v.4.02 from GraphPad, San Diego, CA) fitted to Eq. 1:
$$ v = \frac{{{V_{{\max }}}[S]}}{{{K_M} + [S]}} + Pdif \times [S] $$
(1)
where Pdif is the passive permeability of the substrate. Substrate concentrations well below or close to the Km were selected for future studies using E17βG or atorvastatin, respectively.
Screening for Inhibition of OATP1B1-Mediated Transport
Screening for inhibition of OATP1B1-mediated transport was achieved by performing single point inhibition measurements. Experimental design, as implemented in MODDE 7.0 (Umetrics, Umeå, Sweden), was used for optimizing the assay with regard to the substrate concentration, amount of labeled substrate, incubation method, cell seeding density, and number of days in culture before the experiments (18). Within the experimental design, the results from the OATP1B1 transport characterization were considered for the optimization of the substrate concentration and incubation time. In summary, in the screening assay, cells that were grown in 96-well plates were incubated for 5 min with a solution containing 20 μM of the test compound, 1 μCi/ml (24 nM) 3H-E17βG and 0.5 μM E17βG in HBSS. The strong inhibitor estrone-3-sulphate (E3S) was included on each plate as a control. OATP1B1 cells incubated without a potential inhibitor were used as the reference for the calculations of the inhibitory percentage of the compounds under investigation. A compound was classified as an OATP1B1 inhibitor if it inhibited the uptake of E17βG by more than 50% (18,21).
Establishment of IC50 Curves
Cells grown in 24-well plates were incubated for 2 min with a test solution containing 1 μM atorvastatin to enable inhibition curves to be derived for the six selected in vivo OATP1B1 inhibitors and non-inhibitors: cyclosporin A (0.01–25 μM), gemfibrozil (0.01–500 μM), fenofibrate (0.1–100 μM), atazanavir (0.01–100 μM), amprenavir (0.01–500 μM) or lopinavir (0.01–10 μM). The intracellular atorvastatin content was analyzed using UPLC-MS/MS as described below. The passive uptake in mock cells was subtracted from the total uptake in the OATP1B1 expressing cells at each inhibitor concentration. IC50 was determined and the apparent Ki (assuming the kinetics appropriate for competitive inhibition) calculated using Prism version 4.02 (GraphPad, San Diego, CA).
Liquid Scintillation Analysis
Immediately after the final washing steps in the transport experiments, the cells incubated with radioactive E17βG were trypsinized, lysed using 1 M NaOH, and then neutralized using 1 M HCl. Thereafter, the intracellular concentrations were analyzed with an Ultima Gold scintillation cocktail (PerkinElmer, Shelton, CT) using a Beckman LS6000IC liquid scintillation counter (Beckman Coulter, Fullerton, CA).
UPLC-MS/MS Analysis
After the final washing steps, the cells incubated with atorvastatin were dried, and extracted using 0.2 mL AcN:H2O 60:40 spiked with 50 nM warfarin as the internal standard, which was followed by centrifugation at 3,500 rpm for 20 min using a 5810R centrifuge from Eppendorf (Hamburg, Germany). Thereafter, the supernatants were subjected to UPLC-MS/MS analysis of intracellular atorvastin concentrations using the following analytical system: UPLC (Waters, Milford, MA) coupled to a Thermo Quantum Discovery triple quadrupole with ESI interface, with a reversed phase C18 column (particle size of 1.7 μm) (Waters, Milford, MA) and a mobile gradient consisting of acetonitrile, formic acid and MQ-water.
Calculation of R-Values and In Vitro to In Vivo Drug–Drug Interaction Predictions
For the five selected compounds (including three inhibitors and two non-inhibitors in vivo) where an IC50 and Ki value could be obtained, an in vitro–in vivo extrapolation was conducted by calculating the changes in drug exposure, i.e. the R-values, with or without these five selected compounds, through the use of both Eqs. 2 and 3:
$$ R = 1 + \frac{{{F_u} * {I_{{in,\max }}}}}{{I{C_{{50}}}}} $$
(2)
$$ R = 1 + \frac{{{F_u} * {I_{{in,\max }}}}}{{{K_i}}} $$
(3)
in which Fu is the fraction unbound, obtained from the maximal inhibitor concentration at the inlet of the liver, Iin,max, which was calculated using Eq. 4 (3,22):
$$ {I_{{in,\max }}} = {I_{{\max }}} + \frac{{{F_a} * Dose * {k_a}}}{{{Q_h}}} $$
(4)
where Fa is the fraction absorbed (equal here to the maximum reported oral bioavailability (23–25), or set to 1 for the purpose of comparison with previous in vitro–in vivo extrapolations (3)). For lopinavir, no data could be identified for the bioavailability, so only a value of Fa = 1 could be used. The dose is the maximum oral dose given, Imax is the maximal systemic plasma concentration (obtained from (24–28)), ka is the absorption constant (here, set to 0.03 or 0.1 (22,29)), and Qh is the hepatic blood flow (1.5 l/min (3)). Equation 3, using Ki for R-extrapolation, was used by Hirano and co-workers in a paper from 2006 (22), in which the authors recommended setting the Fa equal to 1 and using a value of ka = 0.1 to estimate the maximum Iin,max. In contrast, the recent paper from the International Transporter Consortium uses Eq. 2, IC50 and ka = 0.03 for the R-extrapolation (3). In the latter publication, no recommendation is made regarding Fa, although Fa = 1 is used for the examples provided by the authors. Using these approaches and equations, as well as combining different values for the fraction absorbed (Fa) and the absorption rate constant (ka), as indicated above, a total of eight R-values were obtained for each compound. A mean R-value was calculated for each compound for use in comparisons with clinical data.
Molecular Descriptors
Three-dimensional molecular structures were generated from SMILES representations using Corina, version 3.0 (Molecular Networks, Erlangen, Germany), and were used as the input for molecular descriptor calculations performed with DragonX, version 1.4 (Talete, Milan, Italy), ADMETPredictor, version 5.0 (SimulationsPlus, Lancaster, CA), and SELMA (AstraZeneca R&D, Mölndal, Sweden). A total of 91 molecular descriptors representing the molecular size, flexibility, connectivity, polarity, and hydrogen bonding potential, all of which had previously been used for predictions of transport protein interactions (20,21), were used in the computational modeling procedure.
Computational Modeling
Every third compound when the compounds were listed alphabetically was included in the test set and kept out of the model development. The remaining compounds were included in the training set used for in silico modeling. A multivariate discriminant analysis was performed to separate inhibitors from non-inhibitors and to identify the critical molecular properties causing transporter inhibition. The two resulting datasets consisted of 98 compounds (including 44 inhibitors) in the training set and 48 compounds (of which 21 were inhibitors) in the test set. This resulted in a test set that was well covered by the training set used with regard to inhibitory effect and chemical structure, as shown by a principal component analysis using SIMCA-P+ version 11.0 (Umetrics, Sweden). However, in the five first principal components of the PCA of the chemical space, which together described 78% of the chemical variation of the dataset, bromosulfalein, cholecystokinin octapeptide and levothyroxine were identified as outliers. These compounds were, therefore, excluded from the training set to avoid biasing the model. Orthogonal partial least squares projection to latent structures, discriminant analysis (OPLS-DA), as implemented by SIMCA-P+ version 11.0, was used to obtain computational models for the separation of OATP1B1 inhibitors from non-inhibitors. Inhibitors were given the value 1 and non-inhibitors the value −1. The descriptors generated by DragonX were used as the input for the computational modeling. Charge was not included as a descriptor, since the charge descriptor, generated by different software, will be largely dependent on the accuracy of the pKa prediction, a property for which the predictions vary greatly from software to software. Instead, we took the simple approach of using only generally available DragonX descriptors (that do not cover charge descriptors) as the input for the model. A variable selection procedure was used in which groups of molecular descriptors that did not contain information relevant to the problem (i.e. noise) or which overlapped with other descriptors in their information content (as identified through proximity in the OPLS loading plots of the resulting models) were removed in a stepwise manner to optimize the model performance and to ensure that the final model would be transparent. If the molecular descriptors remaining in the model resulted in a prediction >0, the compound was classified as an inhibitor, whereas a negative value predicted the compound to be a non-inhibitor of OATP1B1. We excluded descriptors from the model if their removal resulted in improved or unaltered discrimination between inhibitors and non-inhibitors in the training set.