Article pubs.acs.org/molecularpharmaceutics
In Vitro Intrinsic Permeability: A Transporter-Independent Measure of Caco‑2 Cell Permeability in Drug Design and Development Linda Fredlund,*,† Susanne Winiwarter,‡ and Constanze Hilgendorf§ †
Molecular Screening and Profiling, Discovery Sciences, ‡Predictive Compound ADME and Safety, Discovery Safety, Drug Safety and Metabolism, and §ADME and Biotransformation, DMPK Cardiovascular and Metabolic Diseases, Innovative Medicines Biotech Unit, AstraZeneca R&D Gothenburg, Mölndal 431 83, Sweden S Supporting Information *
ABSTRACT: In vitro permeability data have a central place in absorption risk assessments in drug discovery and development. For compounds where active efflux impacts permeability in vitro, the inherent passive membrane permeability (“intrinsic permeability”) gives a concentration-independent measure of the compound’s permeability. This work describes the validation of an in vitro intrinsic permeability assay and application of the data in a predictive in silico model. Apparent intrinsic permeability (Papp) across Caco-2 cell monolayers is determined in the presence of an optimized cocktail of chemical inhibitors toward the three major efflux transporters ABCB1, ABCC2, and ABCG2. The intrinsic Papp value gives an estimate of passive permeability, which is independent of transporter expression levels and not limited by solubility or cell toxicity. An in silico model has been established to predict the Caco-2 intrinsic permeability and shown to consistently identify highly permeable compounds. The new intrinsic permeability assay is useful for early absorption estimates and suitable for absorption risk assessment in DMPK and pharmaceutical development. KEYWORDS: drug discovery, Caco-2, intestinal absorption, intrinsic permeability, active transport, passive diffusion, in vitro−in vivo correlation, in silico modeling
1. INTRODUCTION The Caco-2 cell line was isolated in 1974 from a human colon adenocarcinoma and has since then been widely used as an in vitro system in pharmaceutical and food research.1−3 The cells exhibit many of the functional and morphological properties of the human intestinal enterocytes.4 They express a large part of the nutrient and drug transporter systems, as well as a portion of the metabolic enzymes that are expressed in the human intestinal epithelium. The Caco-2 cell model is a wellestablished drug discovery screening tool to study a molecule’s ability to cross the intestinal epithelial membrane, to investigate transport mechanisms, and to predict oral absorption3,5,6 and has attained a central place in preclinical human absorption risk assessments.7 Apart from the dosage form, the absorption behavior of a compound is largely governed by physiological and physicochemical factors. On the physiological side, transit time, luminal pH, fasted and fed conditions as well as transporter and enzyme expression and capacity in different regions of the gastrointestinal-tract can impact rate and extent of absorption.2 Compounds that are substrates to uptake or efflux transporters can display a concentration- or region-dependent variability in their absorption behavior.8 Dose-dependent permeation profiles, due to saturation or nonsaturation of transporters, often go along with increased interindividual differences in absorption caused by variations in expression levels or polymorphisms.9,10 © 2017 American Chemical Society
Nonpassive absorption mechanisms lead also to challenges in the conduct and interpretation of in vitro studies. Time in culture and initial seeding density are confounding factors since they influence the tightness of the cell layer and affect drug transporter expression and activity patterns.8,11 For instance, high passage number was related to the selection of cell subpopulations promoting a certain transporter profile,12 and permeability data was reported to vary with culture time, transporter expression, and between laboratories.4,12−14 For passively absorbed drugs, the Caco-2 in vitro permeability measurements correlate well with the human in vivo data for absorption.13 For actively transported drugs though, Caco-2 permeability values may under- or overpredict the human in vivo situation, depending on the type of transporter involved, i.e. uptake or efflux mechanism, and the expression level in the Caco-2 cell culture used.15 Measuring the compound’s inherent passive cell membrane permeability instead can be a way to overcome the variations in the carrier-mediated transport in vitro. This intrinsic permeability is not constrained by active transport processes and therefore a concentration-independent and less variable measure of permeability. Intrinsic permeability can be Received: Revised: Accepted: Published: 1601
November 22, 2016 March 7, 2017 March 21, 2017 March 22, 2017 DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics
and 37 °C with an apical volume of 200 μL and a basolateral volume of 800 μL. The samples were collected in Nunc microtiter 96-well V-bottom PP plates (Thermo Fisher Scientific, Waltham, MA USA). The transepithelial electrical resistance (TEER)4 of each monolayer was measured before and after the experiment to obtain an indication of cell viability and monolayer formation. To ensure high quality, only cell monolayers with TEER greater than 150 Ω·cm2 prior to the experiment and with a decrease in TEER less than 30% during the experiment were used in the studies. Additionally, after the permeability experiment cell plates were incubated with 100 μM LY in the apical compartment to monitor the integrity of the cell monolayer.4 After 30 min, the fluorescent signal (excitation wavelength 428 nm and emission wavelength 543 nm) was read using the Tecan Safire 2 plate reader (Salzburg, Austria). Monolayers with greater than 1% LY-leakage were excluded from evaluations. 2.3.1. Caco-2 Bidirectional (ABBA) Assay. After the TEERmeasurement cell media on both sides of the cell monolayer were replaced with prewarmed HBSS (pH 7.4) with or without the inhibitor cocktail. After 30 min equilibration time the test compounds were added on the respective donor side and a sample from the same side was withdrawn. In the apical to basolateral (AB) direction, samples were collected from both the donor and receiver side at 45 and 120 min. In the basolateral to apical (BA) direction, samples were collected after 30 and 90 min. The difference in sampling time was chosen to maintain sink conditions for efflux substrates also in the BA direction. The volume of the donor samples was 2 μL (and diluted 1:100 with 198 μL of HBSS), and volume of the receiver samples 200 and 100 μL in AB and BA direction, respectively. After sampling from the receiver side, an equal volume of fresh HBSS with or without the cocktail was added. 2.3.2. Caco-2 AB Intrinsic Assay. Permeability in the absorptive direction (AB) of the test compound was studied over 120 min. The cell medium was removed, and the cells were preincubated for 30 min with HBSS containing the transporter inhibitor cocktail on both the apical (pH 6.5) and the basolateral side (pH 7.4). The assay was started by adding test compound solution (10 μM) to the apical side, and samples (2 μL) were drawn immediately after addition of test compound, and after 45 and 120 min. These donor samples were diluted 1:100 with 198 μL of HBSS. From the receiver compartment a volume of 200 μL was withdrawn after 45 and 120 min and replaced with fresh HBSS pH 7.4 containing the transport inhibitors. To be able to measure the effect of the inhibitors, the permeability for each test compound without cocktail was measured (i.e., Caco-2 AB) during the validation phase. 2.4. Bioanalysis. Upon completion of the permeability experiment, samples were quenched with 67 μL of acetonitrile containing 100 nM 5,5-diethyl-1,3-diphenyl-2-iminobarbituric acid as volume marker and analyzed subsequently with LC− MS/MS, on a Waters Quattro Ultima (VC337) fast-scanning triple quadruple mass spectrometer with software MassLynx 4.0, coupled to a Waters Acquity Ultra Performance LC using an Atlantis T3, 3 μm, 2.1 × 30 mm column (Waters, Milford, MA USA). The LC−MS/MS methods were optimized for each compound, ensuring appropriate retention and specific detection of each analyte. 2.5. Calculation. The apparent permeability values (Papp) were calculated according to the following equation:
determined through studying compounds at multiple concentrations to saturate active transport.2 Often such saturation studies may require rather high compound concentrations and thus can be limited by the compound’s in vitro solubility or cell toxicity. Together with the necessity to perform multiple experiments for one compound, this results in lower suitability of the approach for routine permeability studies in (early) drug discovery. Here we show the validation of a high throughput assay to determine intrinsic permeability, based on effective chemical inhibition of the three major ABC efflux transporters ABCB1 (P-glycoprotein, P-gp), ABCG2 (breast cancer resistance protein, BCRP), and ABCC2 (multidrug resistance-associated protein 2, MRP2)16 shown to be present in the Caco-2 cell line.17 These transporters are localized on the apical membrane of the enterocyte and can limit drug absorption by pumping the substrate that reached the cell back to the luminal side.18 In a prestudy a chemical inhibitor cocktail was optimized to inhibit these main transporters, while maintaining cell viability and monolayer integrity over 2 h. The cocktail was used in an automated unidirectional, absorptive (apical to basolateral, AB) Caco-2 permeability assay, resulting in the Caco-2 AB intrinsic assay. In order to impact drug discovery early on, and knowing that membrane permeability is well correlated to a molecule’s physicochemical properties, such as lipophilicity, hydrogen bonding properties, and molecular size,19−25 a predictive in silico model based on such data was also developed.
2. MATERIALS AND METHODS 2.1. Materials and Solutions. Cell culture, buffer reagents, and Lucifer Yellow (LY) were obtained from Invitrogen/ ThermoFisherScientific (Paisley, Scotland). Solvents of analytical grade, quinidine, sulfasalazine, benzbromarone, and 5,5diethyl-1,3-diphenyl-2-iminobarbituric acid, used as volume marker compound in the analysis, were purchased from SigmaAldrich (Stockholm, Sweden). The tested compounds were synthesized at AstraZeneca (Gothenburg, Sweden) or purchased from Sigma-Aldrich. Test compounds were prepared as dimethyl sulfoxide (DMSO) stock solutions and diluted in Hanks’ Balanced Salt Solution (HBSS) to a final concentration of 10 μM, with a DMSO concentration of 1%. The inhibitor cocktail (50 μM quinidine, 30 μM benzbromarone, and 20 μM sulfasalazine) was freshly dissolved in HBSS and sonicated. Buffer used at pH 6.5 was HBSS supplemented with 25 mM 4morpholineethanesulfonic acid, and buffer at pH 7.4 was HBSS supplemented with 25 mM N-(2-hydroxyethyl)piperazine-N′(2-ethanesulfonic acid). LY was dissolved in HBSS (pH 7.4) to a concentration of 100 μM. 2.2. Cell Culture. Caco-2 cells (ATCC number HTB-37) were obtained from the American Tissue Culture Collection (Manassas, VA USA) and grown according to routine culture conditions.26 In the studies, cells from a low passage number (31−43) interval were used. The cells were grown for 14 to 18 days on 24-well polycarbonate membrane Transwell plates with a pore size of 0.4 μm (Corning Life Sciences, Tewksbury, MA USA) with medium changes every second day. 2.3. Automated Caco-2 Permeability Assays. The permeability and transport studies were carried out using a Tecan Freedom EVO 200 instrument equipped with a Te-MO 96 and a TEER Station (Tecan, Männedorf, Switzerland). All incubations in the automated assays were performed with prewarmed buffer solution in a shaking incubator at 480 rpm 1602
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics
3. RESULTS 3.1. Qualification of Inhibitor Cocktail in the Caco-2 Bidirectional (ABBA) Assay. Using a crossover minimal experimental design (not presented in this article), four different chemical inhibitors, verapamil (P-gp),33 quinidine (P-gp), 18 benzbromarone (MRP2), 18 and sulfasalazine (BCRP)34 were tested in the Caco-2 ABBA assay pH 7.4/7.4 to study their effect on bidirectional permeability of typical transporter substrates. Verapamil was excluded as its use led to increased post-TEER values in several studies, which may be attributed to the compound’s pharmacological effect as a calcium antagonist.35 As result of this prestudy, the optimized cocktail was defined to contain 50 μM quinidine, 30 μM benzbromarone, and 20 μM sulfasalazine. To validate the effect of the cocktail, known transporter substrates (rosuvastatin (BCRP),36 saquinavir (P-gp, MRP2),18 erythromycin (P-gp),33 pravastatin (MRP2)18) were compared with nonactively transported compounds (metoprolol,37 risperidone,33 propranolol38) at a drug concentration of 10 μM (Figure 1). The ER was calculated from AB and BA
Papp = (dQ /dt )/(ACd)
where dQ/dt is the slope of the cumulative amount transported over time, A is the growth area of the cell monolayer, and Cd is the concentration on the donor side. The recovery was calculated according to the equation recovery% = ((CrVr + CdVd)/(Cd0Vd)) × 100
where Cr is the concentration in the receiver compartment at the end of experiment, Vr is the volume of the receiver compartment, Cd is the concentration in the donor compartment at the end of experiment, Cd0 is the initial concentration in the donor compartment, and Vd is the volume of the donor compartment. Permeability values in Caco-2 ABBA were calculated as described above, and the efflux ratio (ER) was determined from the permeabilities in BA and AB directions efflux ratio = Papp(BA)/Papp(AB)
where Papp(BA) denotes the apparent permeability coefficient in the basolateral to apical direction and Papp(AB) denotes the apparent permeability coefficient in the apical to basolateral direction. 2.6. In Silico Modeling. 2.6.1. Compound Selection. The initial data set comprised 111 public and in-house compounds that were selected in order to cover a diverse chemical space and a spread over intrinsic apparent permeability values (Papp values between 0.02 and ∼120 × 10−6 cm/s). Ten percent of the compounds were set aside as test set, selected to again span the Papp range. With more available data the training set was updated to include currently interesting chemical space. However, since most compounds measured were quite permeable, the data set was enriched with compounds showing low permeability. The model presented here used 2558 compounds with Papp values between 0.01 and ∼150 × 10−6 cm/s as training set and 284 compounds as test set. Selection for test set was aimed at having a 10% temporal test set, i.e., compounds that were measured after the training set compounds. However, compounds selected in earlier test sets were kept in subsequent test sets to be able to use this test set to compare models created at different times unambiguously. Additionally, the model was tested on a set of about 300 inhouse compounds measured after model development, the “validation set”. 2.6.2. Model Building and Evaluation. Papp values were used in logarithmized form (log Papp). The models were built using the in-house AutoQSAR system27 to enable easy model update over time. Both partial least-squares28 (PLS), random forest29 (RF), and support vector machine30 (SVM) methods were used, and the final model, at each date, was selected based on the resulting root mean squared error predicted (RMSEP) determined for the test set. The models used the standard AZ descriptor set31 or, in case of SVM, signature descriptors.32 Most important descriptors were given by the system for PLS and RF models. Additionally, classification statistics, such as sensitivity (true high predictions/all experimental high), specificity (true low predictions/all experimental low), positive precision (true high predictions/all predicted high), negative precision (true low predictions/all predicted low), and accuracy (true predictions/all compounds), were derived using different classification schemes.
Figure 1. Efflux ratio across Caco-2 cell monolayers in absence (gray bars) and presence of efflux inhibitor cocktail (black bars) for wellcharacterized transporter substrates and passively permeating compounds (geometric mean and geometric standard deviation, 2−10 replicates). Statistically significant difference **(p < 0.01) and ***(p < 0.001).
permeability values from the same experiment day and normalized against the passive permeability marker metoprolol.15 The cocktail showed a significant reduction in ER for the transporter substrates, even though efflux was not totally eliminated for all of them. 3.2. Caco-2 AB Intrinsic Assay. Next step was to utilize the inhibitor cocktail in the unidirectional assay, applying a pH gradient. With a pH of 6.5 on the apical donor side the buffer mimics the slightly acidic mucosal pH in the upper intestine, and pH 7.4 on the basolateral receiver side reflects the pH of the blood. The data set was expanded with both commercially available drugs and in-house project compounds (see Figure 2). The relationship between intrinsic Caco-2 Papp values with published human fraction absorbed data (fabs)7,13,39 of orally administrated drugs was investigated for 22 diverse drugs (Figure 3, Supporting Information Table 1). The resulting correlation curve can be used to categorize compounds as low, moderate, or high based on their Papp value to estimate low (80%; Papp > 1.1 × 10−6 cm/s) 1603
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics
els.22,40−46 Subsequent model updates utilized data that became available through project use of the intrinsic permeability assay. As was seen earlier, the proportion of well permeable compounds tends to be rather high in routine screening, leading to a skewed data set. In order to reduce the data set skewness as much as possible, low to intermediate Papp values (2500 data points and available within AstraZeneca’s internal data query tool. Figure 4 shows predicted Papp for 284 test set compounds compared to the experimental value. It can be perceived that most well permeating compounds are also predicted as such (sensitivity is 0.97). However, out of ∼250 compounds predicted to have good permeability, predicted Papp > 1.1 × 10−6 cm/s, ∼25 compounds showed a moderate Papp value, i.e., positive precision was 0.89. Introducing slightly higher boundaries for predicted Papp values, namely 5 and 0.5 × 10−6 cm/s as upper and lower limit, respectively (Figure 4), the positive precision could be enhanced to 0.97, whereas the sensitivity was reduced to 0.8 (see Figure 4). Three hundred additional compounds from various drug discovery projects using different chemical scaffolds were used as validation set. Most compounds (>260, i.e., >90%) show high experimental permeability (Papp > 1.1 × 10−6) likely to result in high absorption and can be identified as such from the in silico model (see Figure 5). The analysis confirms that a predicted Papp > 5 is in general a good determining factor of high experimental permeability. Out of 236 compounds with high predicted permeability less than 10 have an actual Papp < 1.1 × 10−6 cm/s. However, only 30 compounds in the data set have a measured Papp that low, so the specificity is 0.77.
Figure 3. Relationship in vitro intrinsic permeability in Caco-2 cell monolayers to fraction absorbed (fabs) in humans for 22 mostly passively absorbed compounds.
fraction absorbed in humans. These in-house limits correspond essentially to the boundaries of the linear part in the sigmoidal relationship between 20 and 80% fraction absorbed (Figure 3). Therefore, they differ slightly from the values published in the FDA guidance,7 defining a high fraction absorbed as at least 85% of the administered dose. 3.3. In Silico Modeling. The intrinsic permeability data was correlated to compound structure using a machine learning approach. The aim was to have an in silico model for intrinsic Caco-2 permeability available at the time of assay launch for compound screening. Thus, the first model was built based on 111 diverse compounds only. Eleven compounds (10% of the data set) were selected as test set as outlined in methods. The data for the 100 training set compounds was quite evenly spread over four log units (logPapp values between −2 and 2.1). Nevertheless, the resulting PLS model had rather low predictivity (RMSEP, log Papp, was 0.76, see Table 1). Important descriptors were associated with hydrogen bond donor properties and lipophilicity, but also with positive charge, which is in line with many published permeability mod-
4. DISCUSSION For drugs aimed at the oral administration route it is important to understand how well they will be absorbed from the gastrointestinal tract. Several factors need to be considered including a drug’s permeability, solubility, and presystemic metabolism. The present work addresses one of these critical factors with the aim to define whether absorption may be limited by a low intrinsic permeability early on in the drug discovery process. 1604
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics
human situation. The rat fetal intestinal epithelial cell line 2/4/ A1 was particularly developed to resemble the passive paracellular permeability of the human small intestine. In contrast, it lacks the functional expression of the ABC transport and efflux systems. In our experience, drug discovery compounds are more likely to use the transcellular route, and the human origin of the Caco-2 cell line was an additional reason for our choice. It was observed and described that the impact of drug efflux transporters may be exaggerated in the Caco-2 system, while in vivo a high dose and high passive permeability in many cases can outweigh an active transport component.51,52 High oral doses leading to high compound concentrations in the gastrointestinal lumen may saturate and thereby inhibit intestinal epithelial transporters. Poorly or moderately permeable substrates, however, are more likely to be affected by active transport.53 In order to reduce the confounding factor of transporter interaction in these cell lines, the FDA guidance on BCS permeability determinations requires experimental evidence of in vitro efflux properties of a test compound in the Caco-2 cell line alongside the permeability values.7 In other context the Caco-2 model is established as transporter expression system and accepted to investigate transporter interactions with P-gp or BCRP.54 Accordingly, one should aim at determining a compound’s inherent, passive permeability over a cell membrane, nonconfounded by transporter expression and activity in the in vitro system. Passive permeability has been suggested to be readily assessable via PAMPA (Parallel Artificial Membrane Permeability Assay).55 However, this artificial systems mimics a homogeneous lipid membrane and can only capture permeability across a single membrane instead of the transport across a cell layer. Therefore, the Caco-2 AB assay was here expanded by adding chemical inhibition of important efflux transport proteins to achieve a Caco-2 AB intrinsic permeability assay. The assay was validated in two steps. First, the proposed inhibitor cocktail targeting the three major efflux transporters P-gp, MRP2, and BCRP was evaluated in a bidirectional manner using the Caco-2 efflux assay setup. Here a systematic attenuation of efflux of well-known substrates to the efflux transporters, i.e., rosuvastatin (BCRP),36 saquinavir (P-gp, MRP2),18 and erythromycin (P-gp),33 was demonstrated. Pravastatin had been reported as a substrate to MRP2;18 however, in our Caco-2 cell assays we could not demonstrate high efflux ratios, and an increasing body of evidence points to OATP2B1 being of greater importance for pravastatin absorption.56 Pravastatin has a low intrinsic permeability, which may result in a too low compound concentration at the transporter level in our system or that competing transporters from the OATP-family17 would counteract MRP2-mediated efflux. Second, the routine application of the same inhibitor cocktail was established in a unidirectional Caco‑2 permeability assay with pH gradient, thus applying physiological conditions as found in the upper intestine. Comparing the results to Caco-2 permeability values obtained in the traditional manner without chemical efflux inhibition, it can be seen that the majority of efflux substrates shows clearly higher permeability values in the presence of the inhibitor cocktail, whereas passively permeating compounds remain unaffected (Figure 2, Supporting Information Table 1). This behavior is fully in line with theory and published observations: largely passively permeating compounds do not show trans-
Figure 4. Model test: predicted Papp vs experimental Papp; 284 test set compounds, solid lines indicate limits between moderate and high (Papp = 1.1 × 10−6 cm/s for experiment and 5 × 10−6 cm/s for predictions), dotted lines indicate limits between low and moderate Papp (Papp = 0.1 × 10−6 cm/s for experiment and 0.5 × 10−6 cm/s for prediction), dashed line indicates experimental limit (Papp = 1.1 × 10−6 cm/s), and small dashed line indicates line of unity; shaded areas indicate misclassified compounds, number of compounds in each area of the plot stated.
Figure 5. Model validation: predicted Papp vs experimental Papp; >300 validation set compounds, black lines indicate the boundary between moderate and high permeability (experimental Papp = 1.1 × 10−6 cm/s; predicted Papp = 5 × 10−6 cm/s) and light gray line shows line of unity; seven specific drug discovery projects with at least 10 compounds marked as squares, circles, diamonds, triangles, reversed triangles, pentagons, and stars, respectively; crosses indicate remaining projects; shaded areas indicate misclassified compounds, number of compounds in each area of the plot stated.
In vitro assessment of permeability relies often on cell lines such as the Caco-2,5 MDCK,47,48 or 2/4/A1 cell lines.49,50 Caco-2 is a human derived cell line well suited for permeability assessment in the gastrointestinal tract. It has been extensively utilized for more than two decades and found regulatory recognition.7 The cell line expresses relevant human transporters and forms tight monolayers. Drawbacks are that the paracellular porosity of the Caco-2 cells is lower compared to the human small intestine and that the transporter expression depends on culture conditions and may be higher than relevant for in vivo. The MDCK cell line originated from dog kidney, it forms monolayers within a week; however, varying levels of basal canine transporter expression may be misleading for the 1605
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics Scheme 1. Permeability Decision Treea
a
Compounds are sorted according to their predicted permeability, lipophilicity, and molecular weight and treated accordingly to enhance compound quality with regard to permeation and triage measurements.
porter-dependent flux, and their permeation is therefore not affected by the chemical inhibitors.15,53,54,57 Figure 2 shows also that the Caco-2 permeability of many project compounds is limited by active processes, where estimation of fraction absorbed may be too restrictive when not based on the intrinsic permeability. Furthermore, literature values for fabs data were identified for additional 14 of the drug molecules shown in Figure 2, a majority with values above 80%.58,59 All five drugs with fabs between 20 and 80% showed Papp values below 1.1 in the Caco-2 intrinsic assay, indicating that the assay is well able to identify low fabs compounds. Thus, the presented in vitro assay can assess the permeability across a cell layer, relevant in vivo, in one single experiment that combines a generically applicable test concentration, the upper gastro-intestinal pH-gradient and minimal impact of active efflux. Selecting a generic, low concentration of 10 μM reduces the risk to encounter solubility issues or a negative effect on the cells due to the query compounds.51 Also, the present set up reduces the amount of compound required in comparison to a saturation study comprising multiple experiments over a range of concentrations. Furthermore, any potential impact of changes in transporter expression levels due to growth and culture conditions and interlaboratory differences are minimized, addressing some of the disparities associated with Caco‑2 permeability data described in the literature.11,13,51 In order to support compound design an in silico model for intrinsic permeability predictions was developed at the same time as the assay became available to projects. Data for the first model was carefully selected, regarding both structural diversity and the Papp value range covered. The model was aimed to be updated with new data on a regular schedule to easily accommodate new project chemistry within the model. The first model showed quite poor predictivity for the 11 test set compounds, despite the efforts to ensure a relevant training set. However, already the second model with just below 400 compounds in the training set reduced RMSEP for subsequent project compounds to below 0.5 log units. This shows that the quality of a training set is not necessarily dependent on its size, but on whether it contains sufficient compounds with relevant chemical structures. Overall, a predictivity as measured by
RMSEP of around 0.45 is a reasonable result for such a model. Assuming an assay variability defined by a stdev of 0.2 (1.6 fold) to 0.3 (2 fold) the error propagation alone would lead to a RMSEP of 0.3 to 0.45.60 In literature RMSEPs between 0.2 and 0.5 have been reported for permeability models; however, the number of test compounds varies significantly.44 Since it was observed that the compounds being sent for measurements were in most cases well permeating, we investigated whether the model could be used as effective prescreen: by defining a more rigorous limit for the predicted Papp value (5 × 10−6 cm/s) the positive precision was enhanced to 0.97 (see Figure 4). Using the validation set again the limit of 5 × 10−6 cm/s was found to be in general useful to determine whether a compound is likely to be permeable in the Caco-2 cell assay. However, the predictivity for specific projects may vary (e.g., compare pentagons and stars in Figure 5). Combining the predicted permeability with two easily accessible molecular properties, lipophilicity and molecular weight (MW), led to the “permeability decision tree” (Scheme 1). Considerations on lipophilicity capture additional compounds with a somewhat higher chance of good permeability, and the MW is an indicator whether a hydrophilic compound is likely to be able to follow the paracellular route.61,62 Projects are recommended to postpone the measurement of compounds that are predicted to have a high Papp value since permeability is likely not a design issue for the specific compound. When the predicted permeability is lower, but log D above 0, the recommendation is to check the compound series and, if necessary, change the compound design to move toward more permeable compounds. If the lipophilicity is lower, also the MW needs to be considered since smaller compounds might in vivo actually use the paracellular route, which cannot be assessed properly in the Caco-2 cell line.4 The paracellular porosity of the Caco-2 cells is lower compared to the human small intestine due to a smaller number of paracellular pores per cm2,63 tighter junctional resistance,61 and smaller absorptive surface area.4 Prioritizing experimental measurements of compounds with a predicted permeability below the limit of 5 was found to have the potential of significant cost savings. From the data shown 1606
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics
Oral Dosage Forms Based on a Biopharmaceutics Classification System; U.S. Food and Drug Administration, 2015. (8) Anderle, P.; Rakhmanova, V.; Woodford, K.; Zerangue, N.; Sadee, W. Messenger RNA expression of transporter and ion channel genes in undifferentiated and differentiated Caco-2 cells compared to human intestines. Pharm. Res. 2003, 20 (1), 3−15. (9) Keskitalo, J. E.; Zolk, O.; Fromm, M. F.; Kurkinen, K. J.; Neuvonen, P. J.; Niemi, M. ABCG2 polymorphism markedly affects the pharmacokinetics of atorvastatin and rosuvastatin. Clin. Pharmacol. Ther. 2009, 86 (2), 197−203. (10) Tubic, M.; Wagner, D.; Spahn-Langguth, H.; Bolger, M. B.; Langguth, P. In silico modeling of non-linear drug absorption for the P-gp substrate talinolol and of consequences for the resulting pharmacodynamic effect. Pharm. Res. 2006, 23 (8), 1712−20. (11) Hayeshi, R.; Hilgendorf, C.; Artursson, P.; Augustijns, P.; Brodin, B.; Dehertogh, P.; Fisher, K.; Fossati, L.; Hovenkamp, E.; Korjamo, T.; Masungi, C.; Maubon, N.; Mols, R.; Mullertz, A.; Monkkonen, J.; O’Driscoll, C.; Oppers-Tiemissen, H. M.; Ragnarsson, E. G.; Rooseboom, M.; Ungell, A. L. Comparison of drug transporter gene expression and functionality in Caco-2 cells from 10 different laboratories. Eur. J. Pharm. Sci. 2008, 35 (5), 383−96. (12) Behrens, I.; Kamm, W.; Dantzig, A. H.; Kissel, T. Variation of peptide transporter (PepT1 and HPT1) expression in Caco-2 cells as a function of cell origin. J. Pharm. Sci. 2004, 93 (7), 1743−1754. (13) Artursson, P.; Karlsson, J. Correlation between Oral-Drug Absorption in Humans and Apparent Drug Permeability Coefficients in Human Intestinal Epithelial (Caco-2) Cells. Biochem. Biophys. Res. Commun. 1991, 175 (3), 880−885. (14) Stewart, B. H.; Kugler, A. R.; Thompson, P. R.; Bockbrader, H. N. A Saturable Transport Mechanism in the Intestinal-Absorption of Gabapentin Is the Underlying Cause of the Lack of Proportionality between Increasing Dose and Drug Levels in Plasma. Pharm. Res. 1993, 10 (2), 276−281. (15) Larregieu, C. A.; Benet, L. Z. Drug Discovery and Regulatory Considerations for Improving In Silico and In Vitro Predictions that Use Caco-2 as a Surrogate for Human Intestinal Permeability Measurements. AAPS J. 2013, 15 (2), 483−497. (16) Szakacs, G.; Varadi, A.; Ozvegy-Laczka, C.; Sarkadi, B. The role of ABC transporters in drug absorption, distribution, metabolism, excretion and toxicity (ADME-Tox). Drug Discovery Today 2008, 13 (9−10), 379−93. (17) Hilgendorf, C.; Ahlin, G.; Seithel, A.; Artursson, P.; Ungell, A. L.; Karlsson, J. Expression of thirty-six drug transporter genes in human intestine, liver, kidney, and organotypic cell lines. Drug Metab. Dispos. 2007, 35 (8), 1333−40. (18) Takano, M.; Yumoto, R.; Murakami, T. Expression and function of efflux drug transporters in the intestine. Pharmacol. Ther. 2006, 109 (1−2), 137−61. (19) Ren, S.; Das, A.; Lien, E. J. QSAR analysis of membrane permeability to organic compounds. J. Drug Target 1996, 4 (2), 103− 7. (20) van De Waterbeemd, H.; Camenisch, G.; Folkers, G.; Raevsky, O. A. Estimation of Caco-2 Cell Permeability using Calculated Molecular Descriptors. Quant. Struct.-Act. Relat. 1996, 15 (6), 480− 490. (21) Palm, K.; Luthman, K.; Ungell, A. L.; Strandlund, G.; Artursson, P. Correlation of drug absorption with molecular surface properties. J. Pharm. Sci. 1996, 85 (1), 32−9. (22) Winiwarter, S.; Bonham, N. M.; Ax, F.; Hallberg, A.; Lennernas, H.; Karlen, A. Correlation of human jejunal permeability (in vivo) of drugs with experimentally and theoretically derived parameters. A multivariate data analysis approach. J. Med. Chem. 1998, 41 (25), 4939−49. (23) Conradi, R. A.; Hilgers, A. R.; Ho, N. F.; Burton, P. S. The influence of peptide structure on transport across Caco-2 cells. Pharm. Res. 1991, 8 (12), 1453−60. (24) Camenisch, G.; Folkers, G.; van de Waterbeemd, H. Review of theoretical passive drug absorption models: historical background,
the number of compounds to be measured can be reduced by more than 50% without loss of information. Resources can be used more efficiently when only compounds for which the prediction does not give a conclusive result are subjected to the experiment in the early stage of the drug discovery process.3,64 Thus, the in silico intrinsic permeability model can be used both on virtual compounds to select for synthesis and thereby enhance compound quality,65 and on existing compounds to triage measurements, thereby enhancing drug discovery process efficiency.
5. CONCLUSION The Caco-2 AB intrinsic assay determines the passive intrinsic permeability for a compound by chemical inhibition of major efflux transporters. The methodology combines unidirectional permeability in the presence of the upper intestinal pHgradient, lumen/blood pH 6.5/7.4, and efflux inhibition in one single measurement and thus provides a straightforward way to determine the property relevant for in vivo. It is now routinely used at AstraZeneca as a tier 2/3 assay and preceded by an in silico model as tier 1 and for virtual compounds. The method is useful for early absorption estimates and suitable for absorption simulations and modeling in DMPK and pharmaceutical development.
■
ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.molpharmaceut.6b01059. Experimental results for Caco-2 AB and Caco-2 AB intrinsic assays (PDF)
■
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Phon e: +46317762988. ORCID
Linda Fredlund: 0000-0002-2104-5617 Notes
The authors declare no competing financial interest.
■
REFERENCES
(1) Fogh, J.; Fogh, J. M.; Orfeo, T. One hundred and twenty-seven cultured human tumor cell lines producing tumors in nude mice. J. Natl. Cancer Inst 1977, 59 (1), 221−6. (2) Angelis, I. D.; Turco, L. Caco-2 cells as a model for intestinal absorption. In Current Protocols in Toxicology; Wiley, 2011; Chapter 20, Unit 20.6; DOI: 10.1002/0471140856.tx2006s47. (3) Ballard, P.; Brassil, P.; Bui, K. H.; Dolgos, H.; Petersson, C.; Tunek, A.; Webborn, P. J. The right compound in the right assay at the right time: an integrated discovery DMPK strategy. Drug Metab. Rev. 2012, 44 (3), 224−52. (4) Shah, P.; Jogani, V.; Bagchi, T.; Misra, A. Role of Caco-2 cell monolayers in prediction of intestinal drug absorption. Biotechnol. Prog. 2006, 22 (1), 186−98. (5) Hubatsch, I.; Ragnarsson, E. G.; Artursson, P. Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers. Nat. Protoc. 2007, 2 (9), 2111−9. (6) Ungell, A. L. Caco-2 replace or refine? Drug Discovery Today: Technol. 2004, 1 (4), 423−30. (7) Food, U. S.; Drug, A. Guidance for Industry: Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate Release Solid 1607
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics recent developments and limitations. Pharm. Acta Helv. 1996, 71 (5), 309−27. (25) Clark, D. E. Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption. J. Pharm. Sci. 1999, 88 (8), 807− 14. (26) Neuhoff, S.; Ungell, A. L.; Zamora, I.; Artursson, P. pHDependent passive and active transport of acidic drugs across Caco-2 cell monolayers. Eur. J. Pharm. Sci. 2005, 25 (2−3), 211−20. (27) Davis, A. M.; Wood, D. J. Quantitative Structure-Activity Relationship Models That Stand the Test of Time. Mol. Pharmaceutics 2013, 10 (4), 1183−1190. (28) Dunn, W. J.; Wold, S.; Edlund, U.; Hellberg, S.; Gasteiger, J. Multivariate structure-activity relationships between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method. Quant. Struct.-Act. Relat. 1984, 3 (4), 131−137. (29) Svetnik, V.; Liaw, A.; Tong, C.; Culberson, J. C.; Sheridan, R. P.; Feuston, B. P. Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1947−58. (30) Cortes, C.; Vapnik, V. Support-Vector Networks. Mach Learn 1995, 20 (3), 273−297. (31) Wood, D. J.; Buttar, D.; Cumming, J. G.; Davis, A. M.; Norinder, U.; Rodgers, S. L. Automated QSAR with a Hierarchy of Global and Local Models. Mol. Inf. 2011, 30 (11−12), 960−72. (32) Varadharajan, S.; Winiwarter, S.; Carlsson, L.; Engkvist, O.; Anantha, A.; Kogej, T.; Friden, M.; Stalring, J.; Chen, H. Exploring in silico prediction of the unbound brain-to-plasma drug concentration ratio: model validation, renewal, and interpretation. J. Pharm. Sci. 2015, 104 (3), 1197−206. (33) Broccatelli, F.; Larregieu, C. A.; Cruciani, G.; Oprea, T. I.; Benet, L. Z. Improving the prediction of the brain disposition for orally administered drugs using BDDCS. Adv. Drug Delivery Rev. 2012, 64 (1), 95−109. (34) Liang, E.; Proudfoot, J.; Yazdanian, M. Mechanisms of transport and structure-permeability relationship of sulfasalazine and its analogs in Caco-2 cell monolayers. Pharm. Res. 2000, 17 (10), 1168−74. (35) Katz, A. M. Pharmacology and mechanisms of action of calciumchannel blockers. J. Clin Hypertens 1986, 2 (3 Suppl), 28S−37S. (36) Huang, L.; Wang, Y.; Grimm, S. ATP-dependent transport of rosuvastatin in membrane vesicles expressing breast cancer resistance protein. Drug Metab. Dispos. 2006, 34 (5), 738−42. (37) Larregieu, C. A.; Benet, L. Z. Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery. Mol. Pharmaceutics 2014, 11 (4), 1335−44. (38) Zheng, Y.; Benet, L. Z.; Okochi, H.; Chen, X. pH Dependent but not P-gp Dependent Bidirectional Transport Study of Spropranolol: The Importance of Passive Diffusion. Pharm. Res. 2015, 32 (8), 2516−2526. (39) Lennernas, H. Human in vivo regional intestinal permeability: importance for pharmaceutical drug development. Mol. Pharmaceutics 2014, 11 (1), 12−23. (40) Norinder, U.; Osterberg, T.; Artursson, P. Theoretical calculation and prediction of Caco-2 cell permeability using MolSurf. parametrization and PLS statistics. Pharm. Res. 1997, 14 (12), 1786− 1791. (41) Cruciani, G.; Pastor, M.; Guba, W. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur. J. Pharm. Sci. 2000, 11, S29−39. (42) Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43 (20), 3714−3717. (43) Stenberg, P.; Luthman, K.; Artursson, P. Prediction of membrane permeability to peptides from calculated dynamic molecular surface properties. Pharm. Res. 1999, 16 (2), 205−12. (44) Wang, N. N.; Dong, J.; Deng, Y. H.; Zhu, M. F.; Wen, M.; Yao, Z. J.; Lu, A. P.; Wang, J. B.; Cao, D. S. ADME Properties Evaluation in
Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting. J. Chem. Inf. Model. 2016, 56 (4), 763−73. (45) Sherer, E. C.; Verras, A.; Madeira, M.; Hagmann, W. K.; Sheridan, R. P.; Roberts, D.; Bleasby, K.; Cornell, W. D. QSAR Prediction of Passive Permeability in the LLC-PK1 Cell Line: Trends in Molecular Properties and Cross-Prediction of Caco-2 Permeabilities. Mol. Inf. 2012, 31 (3−4), 231−45. (46) Broccatelli, F.; Salphati, L.; Plise, E.; Cheong, J.; Gobbi, A.; Lee, M. L.; Aliagas, I. Predicting passive permeability of drug-like molecules from chemical structure: where are we? Mol. Pharmaceutics 2016, 13, 4199. (47) Di, L.; Whitney-Pickett, C.; Umland, J. P.; Zhang, H.; Zhang, X.; Gebhard, D. F.; Lai, Y.; Federico, J. J., 3rd; Davidson, R. E.; Smith, R.; Reyner, E. L.; Lee, C.; Feng, B.; Rotter, C.; Varma, M. V.; Kempshall, S.; Fenner, K.; El-Kattan, A. F.; Liston, T. E.; Troutman, M. D. Development of a new permeability assay using low-efflux MDCKII cells. J. Pharm. Sci. 2011, 100 (11), 4974−4985. (48) Irvine, J. D.; Takahashi, L.; Lockhart, K.; Cheong, J.; Tolan, J. W.; Selick, H. E.; Grove, J. R. MDCK (Madin-Darby canine kidney) cells: A tool for membrane permeability screening. J. Pharm. Sci. 1999, 88 (1), 28−33. (49) Tavelin, S.; Taipalensuu, J.; Hallbook, F.; Vellonen, K. S.; Moore, V.; Artursson, P. An improved cell culture model based on 2/ 4/A1 cell monolayers for studies of intestinal drug transport: characterization of transport routes. Pharm. Res. 2003, 20 (3), 373− 381. (50) Tavelin, S.; Taipalensuu, J.; Soderberg, L.; Morrison, R.; Chong, S.; Artursson, P. Prediction of the oral absorption of low-permeability drugs using small intestine-like 2/4/A1 cell monolayers. Pharm. Res. 2003, 20 (3), 397−405. (51) Press, B.; Di Grandi, D. Permeability for intestinal absorption: Caco-2 assay and related issues. Curr. Drug Metab. 2008, 9 (9), 893− 900. (52) Sugano, K.; Kansy, M.; Artursson, P.; Avdeef, A.; Bendels, S.; Di, L.; Ecker, G. F.; Faller, B.; Fischer, H.; Gerebtzoff, G.; Lennernaes, H.; Senner, F. Coexistence of passive and carrier-mediated processes in drug transport. Nat. Rev. Drug Discovery 2010, 9 (8), 597−614. (53) Wu, C. Y.; Benet, L. Z. Predicting drug disposition via application of BCS: Transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm. Res. 2005, 22 (1), 11−23. (54) Elsby, R.; Surry, D. D.; Smith, V. N.; Gray, A. J. Validation and application of Caco-2 assays for the in vitro evaluation of development candidate drugs as substrates or inhibitors of P-glycoprotein to support regulatory submissions. Xenobiotica 2008, 38 (7−8), 1140−1164. (55) Avdeef, A.; Artursson, P.; Neuhoff, S.; Lazorova, L.; Grasjo, J.; Tavelin, S. Caco-2 permeability of weakly basic drugs predicted with the double-sink PAMPA pKa(flux) method. Eur. J. Pharm. Sci. 2005, 24 (4), 333−49. (56) Proctor, W. R.; Ming, X.; Bourdet, D.; Han, T. K.; Everett, R. S.; Thakker, D. R. Why Does the Intestine Lack Basolateral Efflux Transporters for Cationic Compounds? A Provocative Hypothesis. J. Pharm. Sci. 2016, 105 (2), 484−96. (57) Volpe, D. A. Variability in Caco-2 and MDCK cell-based intestinal permeability assays. J. Pharm. Sci. 2008, 97 (2), 712−25. (58) Zhao, Y. H.; Abraham, M. H.; Le, J.; Hersey, A.; Luscombe, C. N.; Beck, G.; Sherborne, B.; Cooper, I. Rate-limited steps of human oral absorption and QSAR studies. Pharm. Res. 2002, 19 (10), 1446− 57. (59) Zhao, Y. H.; Le, J.; Abraham, M. H.; Hersey, A.; Eddershaw, P. J.; Luscombe, C. N.; Boutina, D.; Beck, G.; Sherborne, B.; Cooper, I.; Platts, J. A. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 2001, 90 (6), 749−84. (60) Wenlock, M. C.; Carlsson, L. A. How experimental errors influence drug metabolism and pharmacokinetic QSAR/QSPR models. J. Chem. Inf. Model. 2015, 55 (1), 125−34. 1608
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
Article
Molecular Pharmaceutics (61) Kumar, K. K.; Karnati, S.; Reddy, M. B.; Chandramouli, R. Caco-2 cell lines in drug discovery- an updated perspective. J. Basic Clin Pharm. 2010, 1 (2), 63−9. (62) Tavelin, S.; Milovic, V.; Ocklind, G.; Olsson, S.; Artursson, P. A conditionally immortalized epithelial cell line for studies of intestinal drug transport. J. Pharmacol Exp Ther 1999, 290 (3), 1212−21. (63) Linnankoski, J.; Makela, J.; Palmgren, J.; Mauriala, T.; Vedin, C.; Ungell, A. L.; Lazorova, L.; Artursson, P.; Urtti, A.; Yliperttula, M. Paracellular porosity and pore size of the human intestinal epithelium in tissue and cell culture models. J. Pharm. Sci. 2010, 99 (4), 2166−75. (64) Desai, P. V.; Sawada, G. A.; Watson, I. A.; Raub, T. J. Integration of in silico and in vitro tools for scaffold optimization during drug discovery: predicting P-glycoprotein efflux. Mol. Pharmaceutics 2013, 10 (4), 1249−61. (65) Cumming, J. G.; Davis, A. M.; Muresan, S.; Haeberlein, M.; Chen, H. Chemical predictive modelling to improve compound quality. Nat. Rev. Drug Discovery 2013, 12 (12), 948−62.
1609
DOI: 10.1021/acs.molpharmaceut.6b01059 Mol. Pharmaceutics 2017, 14, 1601−1609
