Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect
ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
CONTENT TYPES

Figure 1Loading Img

In Vitro and in Silico Tools To Assess Extent of Cellular Uptake and Lysosomal Sequestration of Respiratory Drugs in Human Alveolar Macrophages

View Author Information
Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.
Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, U.K.
§ Computational Modeling Sciences, DDS, GlaxoSmithKline, Upper Merion, Pennsylvania 19406, United States
Pharmaceutical Sciences, pRED, Roche Innovation Center, Basel, Switzerland
Cite this: Mol. Pharmaceutics 2017, 14, 4, 1033–1046
Publication Date (Web):March 2, 2017
https://doi.org/10.1021/acs.molpharmaceut.6b00908

Copyright © 2017 American Chemical Society. This publication is licensed under CC-BY.

  • Open Access

Article Views

2766

Altmetric

-

Citations

LEARN ABOUT THESE METRICS
PDF (2 MB)
Supporting Info (1)»

Abstract

Accumulation of respiratory drugs in human alveolar macrophages (AMs) has not been extensively studied in vitro and in silico despite its potential impact on therapeutic efficacy and/or occurrence of phospholipidosis. The current study aims to characterize the accumulation and subcellular distribution of drugs with respiratory indication in human AMs and to develop an in silico mechanistic AM model to predict lysosomal accumulation of investigated drugs. The data set included 9 drugs previously investigated in rat AM cell line NR8383. Cell-to-unbound medium concentration ratio (Kp,cell) of all drugs (5 μM) was determined to assess the magnitude of intracellular accumulation. The extent of lysosomal sequestration in freshly isolated human AMs from multiple donors (n = 5) was investigated for clarithromycin and imipramine (positive control) using an indirect in vitro method (±20 mM ammonium chloride, NH4Cl). The AM cell parameters and drug physicochemical data were collated to develop an in silico mechanistic AM model. Three in silico models differing in their description of drug membrane partitioning were evaluated; model (1) relied on octanol–water partitioning of drugs, model (2) used in vitro data to account for this process, and model (3) predicted membrane partitioning by incorporating AM phospholipid fractions. In vitroKp,cell ranged >200-fold for respiratory drugs, with the highest accumulation seen for clarithromycin. A good agreement in Kp,cell was observed between human AMs and NR8383 (2.45-fold bias), highlighting NR8383 as a potentially useful in vitro surrogate tool to characterize drug accumulation in AMs. The mean Kp,cell of clarithromycin (81, CV = 51%) and imipramine (963, CV = 54%) were reduced in the presence of NH4Cl by up to 67% and 81%, respectively, suggesting substantial contribution of lysosomal sequestration and intracellular binding in the accumulation of these drugs in human AMs. The in vitro data showed variability in drug accumulation between individual human AM donors due to possible differences in lysosomal abundance, volume, and phospholipid content, which may have important clinical implications. Consideration of drug–acidic phospholipid interactions significantly improved the performance of the in silico models; use of in vitroKp,cell obtained in the presence of NH4Cl as a surrogate for membrane partitioning (model (2)) captured the variability in clarithromycin and imipramine Kp,cell observed in vitro and showed the best ability to predict correctly positive and negative lysosomotropic properties. The developed mechanistic AM model represents a useful in silico tool to predict lysosomal and cellular drug concentrations based on drug physicochemical data and system specific properties, with potential application to other cell types.

Introduction

ARTICLE SECTIONS
Jump To

Increased uptake of drugs by AMs may result in suboptimal efficacy for drugs with extracellular targets and potential adverse effects including phospholipidosis that may compromise patients’ safety. The latter is associated with lysosomal sequestration of lipophilic amine drugs in AMs which may contribute significantly to their total intracellular concentrations and consequently distribution in tissues rich in lysosomes such as lungs, liver, and kidneys. (1) In addition to the induction of phospholipidosis, the role of this process in drug efficacy and potential involvement in drug resistance and drug–drug interactions has been highlighted. (2-7) Although total intracellular drug concentrations can be measured and related to external media drug concentrations, determination of unbound cytosolic and lysosomal concentrations is experimentally challenging. While there is an increased interest in understanding intracellular distribution of drugs, investigation of lysosomal sequestration in AMs has remained limited despite their anticipated important contribution to the overall lung drug disposition. Recently, lysosomal sequestration has been proposed to lead to the retention and prolonged duration of action of a number of inhaled beta-adrenergic bronchodilators in the lung (8) where lysosome rich AMs are likely to be the key contributors. Consequently, use of in silico mechanistic models accounting for system and drug properties may be useful in providing dynamic assessment of intracellular drug concentrations and several processes occurring in cells such as transporter-mediated uptake/efflux, passive diffusion, metabolism, intracellular binding, and lysosomal sequestration. While no mechanistic model has incorporated all of these processes for a particular system so far, a number of studies have successfully captured one or more processes in a specific cell type or tissue. (9-15) The earlier work of Trapp and colleagues (16) demonstrated the prediction of subcellular (mitochondria and lysosomes) distribution of acidic, basic, and zwitterionic compounds in a generic cell model, based on passive permeation of neutral and ionized species, consideration of membrane potentials and intracellular pH gradients. (16, 17) However, these models have not been adapted for a specific cell type and have not captured the interactions of the ionized forms of basic drugs with the membrane acidic phospholipids (AP), highlighted as an important contributor to intracellular accumulation of basic drugs and their partitioning in membranes. (13, 18)
Although a number of studies have investigated uptake and lysosomal sequestration of drugs in rat primary AMs and cell lines such as NR8383, (19-25) there has been no attempt to characterize this process in human AMs. One of the aims of this study was to investigate intracellular accumulation of a range drugs with respiratory indication and both extra- and intracellular targets in human AMs. Drugs included in the study were clarithromycin, formoterol, terbutaline, fenoterol, rifampicin, budesonide, ipratropium, and tiotropium bromide, as analyzed previously by our group in NR8383. (25) Furthermore, lysosomal sequestration of clarithromycin and the prototypical lysosomotropic drug imipramine (positive control) (26) was investigated in human AMs freshly isolated from different donors to evaluate the interindividual variability in this process. The in vitro data in human AMs were compared to those previously obtained in NR8383 cells (25) to assess the validity of this cell line as an in vitro tool for the prediction of drug accumulation in human AMs. In addition to the in vitro assessment, the generic in silico cell model reported by Trapp et al. (16) was modified by incorporating cell parameters specific for human AMs (e.g., lysosomal volume, pH of different organelles) and relevant drug physicochemical properties (logP, pKa); lipid partitioning and electrostatic interactions were also accounted for. The developed in silico AM model was subsequently assessed as a tool to predict drug accumulation in human AMs and particularly in lysosomes using the data set of investigated respiratory drugs. Three subtypes of the in silico AM model were evaluated; the models only differed in their description of drug interaction with membrane phospholipids. Cellular accumulation (as assessed by cell-to-unbound medium concentration ratio, Kp,cell) and extent of lysosomal sequestration of drugs predicted by the in silico AM models were compared to the experimental data obtained in human AMs and the NR8383 cell line using indirect methods.

Materials and Methods

ARTICLE SECTIONS
Jump To

Chemicals and Reagents

1-Aminobenzotriazole, ammonium chloride, ciprofoxacin, clarithromycin, dimethyl sulfoxide, imipramine, lactate dehydrogenase activity assay kit, Trypan blue 0.4%, and verapamil hydrochloride were all from Sigma-Aldrich Ltd., Dorset, U.K. Budesonide, ipratropium bromide, fenoterol, formoterol, terbutaline, and tiotropium bromide were all supplied by GlaxoSmithKline, U.K. Chloroform and formaldehyde 37–41% were from Fisher Scientific, Loughborough, U.K. Further chemicals include diazepam (Tocris Bioscience, Bristol, U.K.), methanol (VWR, U.K.), midazolam (Hoffman La Roche, Switzerland), Pierce BCA protein assay kit (Thermo Scientific, Loughborough, U.K.), and Lysotracker Red DND-99 (Life Technologies, Paisley, U.K.). Reagents include bovine serum albumin, penicillin–streptomycin, and Roswell Park Memorial Institute (RPMI)-1640 medium (Sigma-Aldrich Ltd., Dorset, U.K.), collagen type I rat tail (BD Biosciences, Oxford, U.K.), Dulbecco’s phosphate buffered saline, heat-inactivated fetal bovine serum, and 200 mM l-glutamine (Life Techonolgies, Paisley, U.K.), Ficoll-paque (GE Healthcare, Buckinghamshire, U.K.), and Kaighn’s modification of Ham’s F12 (Ham’s F12K) medium (American Tissue Culture Collection (ATCC), Mannasas, VA, USA).

Source and Preparation of Human Alveolar Macrophages

The human alveolar macrophages were obtained from patients undergoing lung surgery at the Respiratory and Allergy Clinical Research Facility in the University Hospital of South Manchester NHS Foundation Trust. The study was approved by South Manchester Research Ethics Committee, and all subjects gave written informed consent to participate. Nine patients undergoing surgical resection for suspected or confirmed lung cancer were recruited for human alveolar macrophage assays. Patients were categorized as either smokers (≥1 pack year history) or nonsmokers (<1 pack year history). All patients with the exception of one had normal lung function as predicted forced expiratory volume in 1 s FEV1, >80%, and FEV1/forced vital capacity (FVC) ratio, >70% (Table S1). All patients were known to be free from any medications at the time of surgery except one patient who had salbutamol in medication history; details of medication prior to surgery were unknown.
The isolation of the cells from the lung surgery sections was performed as described briefly herein: Areas of lung distant from the tumor were perfused with 0.1 M sodium chloride. The resulting cell suspension was centrifuged (400g, for 10 min, at room temperature), and the cell pellet was resuspended in RPMI-1640 medium. The cells were layered over a Ficoll-paque gradient. The mononuclear cells at the Ficoll interface were extracted, washed, and resuspended in the complete growth medium (CGM): RPMI-1640 medium containing 10% v/v FBS, 1% v/v 200 mM l-glutamine, 1% v/v 100 units/mL penicillin, and 100 μg/mL streptomycin. Following isolation, the cells were maintained in this CGM in the fridge before the start of the experiments. All experiments were initiated within 24 h of surgery. The growth medium covering the macrophage cell monolayers was replaced with fresh medium before the start of the accumulation experiments in order to remove any remaining red blood cells. Further details on the AM isolation method and characterization of AM phenotype and activation state from resected lung tissue have been reported previously. (27-29)

In Vitro Accumulation and Lysosomal Sequestration of Drugs in Human AMs

In the current study, the accumulation of 9 drugs was assessed in human AMs using the method adapted from previous study in NR8383 cells. (25) Following the isolation and resuspension of the cells in 1 mL of CGM, the cell count and viability were assessed with Trypan-blue exclusion method using a hemocytometer under the light microscope (Leica Microsystems ATC2000, Milton Keynes, U.K.). The cells were then further supplemented with the CGM, and 0.4 mL of the cell suspension was dispensed into collagen-I coated 24-well plates (BD Biosciences, Oxford, U.K.), seeding the wells at a density of 0.4 × 106 cells (0.3 × 106 in cases of insufficient number of cells). This seeding density was selected to accommodate the experimental setup that included several washing steps before accumulation studies and detachment of a substantial number of cells from the wells (∼30% of the initial seeding density). After 2 h of plating the cells at 37 °C, 5% CO2 incubator (CO2 incubator, MCO-17AIC, Sanyo Biomedical, Loughborough, U.K.), the existing medium was replaced with the prewarmed fresh medium to remove any unattached cells other than AMs which remained after their isolation. The plates were returned to the 37 °C incubator and cultured for another 2 h before the start of the uptake experiments. At this culture time, the cells were >70% confluent in wells and almost completely attached to the collagen support. The accumulation experiments at 37 °C were performed on a single occasion for each drug investigated except for clarithromycin and imipramine (multiple donors). The investigation of lysosomal sequestration of basic drugs using ammonium chloride (NH4Cl) was performed as described previously. (25) Among the drug data set, clarithromycin was selected for the assessment of lysosomal sequestration in human AMs, following positive findings in NR8383 cells. In addition, imipramine was included as a positive control, consistent with experiments in NR8383. All cell lysates and medium samples were kept at −20 °C overnight before analysis by LC–MS/MS. The amount of protein in each well was measured to determine the cell number using BCA protein assay.

Sample Preparation and Mass Spectrometry Analysis

Preparation and analysis of the cell lysate and medium samples and quantification of drug concentrations by LC–MS/MS were the same as in the previous study. (7) A calibration standard, containing the drug of investigation at a concentration range covering that of the experimental samples with an additional zero blank, was prepared in the same matrix of the experimental samples when a sufficient number of human AMs was provided. When this was not the case, NR8383 cell lysates were used; a matrix made of NR8383 cells was considered to be the closest to human AMs due to the use of the same cell type.

Determination of Drug Accumulation

Accumulation of drugs in human AMs was determined by calculating Kp,cell at 5 μM drug concentration at 10 min from the cell-to-unbound medium concentration ratio. (30, 31) In cases where measured medium concentrations were not available, nominal medium concentration (i.e., 5 μM) was used. The cell volume used to calculate the total cell concentration of the drugs in human AMs was 2.2 μL/106 cells. This value represents the mean of a number of morphometric studies reporting AM cell volume in healthy smoker and nonsmoker subjects (references listed in Table 1). Previous literature reporting cell volume (Table 1) or cell diameter (32-34) indicated no signficant difference between the size of AMs from smokers (1.9 μL/106 cells) and nonsmokers (2.4 μL/106 cells), hence the average cell volume from both populations was used. The interindividual variability in clarithromycin and imipramine Kp,cell was assessed.
Table 1. Human Alveolar Macrophage Parameters Used as Input for the in Silico Mechanistic Cell Modela
    range 
parametermeanCV (%)N  source
cell
volume (pL)2.244121.424.99human AMs
surface area (m2)8.16 × 10–10 126.10 × 10–101.41 × 10–9human AMs
pH7.17 1  rabbit AMs
Em (V)–0.032 2–0.021–0.04Rat AMs
       
lysosome
vol (pL)0.204    rat AMs
surf area (m2)1.67 × 10–10     
pH4.73 24.74.75mouse peritoneal and AMs
Em (V)0.019 1  RAW264.7 mouse macrophages
       
mitochondria
vol (pL)0.082    rat AMs
surf area (m2)9.15 × 10–11     
pH7.901.457.707.98HeLa cells, rat cardiomyocytes, HEK293 cells, Jurkat cells, MDCK cells
Em (V)–0.158 2–0.155–0.161rat hepatocytes
a

Volume expressed per 106 cells; AM, alveolar macrophages; Em is membrane potential; CV, coefficient of variation; N, number of studies. References are listed in the Supporting Information.

Determination of Lysosomal Sequestration

Lysosomal sequestration of clarithromycin and imipramine in human AMs was determined using an indirect method based on the assumption that NH4Cl abolishes pH gradient between cytosol and lysosomes. The advantages and limitations of indirect methods have been discussed previously. (25)Kp,cell in the presence and absence of NH4Cl was determined in at least the same 4 donors of AMs for both drugs. The reduction in Kp,cell of each drug in the presence of NH4Cl was expressed as a percentage relative to control and used as an indicator of the extent of lysosomal sequestration in human AMs.

Statistical Analysis

The arithmetic mean, standard error, and coefficient of variation (CV) were calculated for Kp,cell where the data were determined on more than one occasion. Geometric mean fold error (gmfe) was calculated (eq 1) in order to assess the discrepancy between NR8383 and human AM Kp,cell data.(1)where N is the number of observations. When assessing lysosomal sequestration, the control and NH4Cl treated cells were compared using the two-tailed, paired t test in order to determine the existence of a statistically significant difference between the two conditions. The data were considered to be statistically significant when p < 0.05.

Assessment of Lysosomal Sequestration in Human AMs Using Lysotracker Red (LTR)

Localization of lysosomes and assessment of lysosomal sequestration in human AMs were performed as previously (25) with a minor change in the seeding density of human AMs in collagen-I coated Ibidi 8-well chamber slides (200,000 cells/well). Following the procedure, the cells were examined for the detection of LTR with a confocal laser scanning microscope (Zeiss LSM 510, Jena, Germany). The details of this system, image processing, and the quantification of LTR fluorescence intensity were as reported previously. (7)

Assessment of Cytotoxicity of NH4Cl in Human AMs

The incubation medium consisting of 5 μM clarithromycin and imipramine in the absence and presence of 20 mM NH4Cl was tested for cytotoxicity using the LDH assay kit (Sigma-Aldrich, Dorset, U.K.). Extensive assessment of cytotoxicity including the effect of NH4Cl alone on the cells could not be performed due to limited number of human AMs. The assessment of the cytotoxicity of NH4Cl presented in the incubation medium containing clarithromycin or imipramine showed that the LDH activity of the samples was <1% compared to 100% of the positive control, indicating that no cytotoxicity was associated with NH4Cl in human AMs under the conditions used.

In Silico Alveolar Macrophage Model

The mechanistic in silico AM model consists of 4 compartments, namely, extracellular medium, cytosol, mitochondria, and lysosomes. The cell compartments are associated with an aqueous and lipid fraction and surrounded by a membrane. In the model, drug accumulation in the cell and subcellular compartments occurs via passive diffusion of neutral and ionized species, with ionized species showing reduced permeability. In addition to concentration gradients, membrane potentials are taken into account, which have been suggested to drive the diffusive flux of ionized species. (16) Differences in drug ionization according to pH differences between medium, cytosol, lysosomes, and mitochondria are likewise considered (pH partitioning), along with drug partitioning of neutral and ionized species into cell and organelle membranes. The model development was supported by collation of literature reported relevant cell parameters for AMs; details of parameters are listed in Table 1. The distribution of a basic drug in AMs involving these processes is illustrated in Figure 1.

Figure 1

Figure 1. Cell model scheme demonstrates the processes involved in the uptake of a weak base into the cell and subcellular compartments. The figure was adapted from Trapp et al. (16)

Flux of Neutral and Ionized Drug Species across Membranes

The total diffusive flux of drugs across membranes was described as the sum of the flux of neutral (Fick’s first law of diffusion) and ionized (Nernst–Planck equation) species according to eq 2:(2)where P is permeability, C is concentration, N = zEmF/(RT), z is the electric charge of ionic species, Em is the membrane potential (V), F is the Faraday constant (96,484.56 C·mol–1), R is the universal gas constant (8.314 J·mol–1·K–1), and T is the absolute temperature (310.16 K). (16) The subscripts N and D represent neutral and dissociated (ionized) species, respectively.

Membrane Permeability

The membrane permeability was predicted, assuming that drug partitioning into membranes was adequately defined by partitioning into octanol. The permeability of neutral species (PN) across membranes was described by eq 3, where DC is the diffusion coefficient (10–14 m2 s–1 for all drugs), Kow is the octanol–water partition coefficient of neutral species, and dx is the membrane thickness.(3)
Regarding the permeability of ionized species, a further 3.5 log unit reduction in lipophilicity was included as a penalty term for every charge a drug molecule possessed (e.g., 3.5 and 7 log unit for mono- and diprotic drugs, respectively). (16)

Membrane Partitioning

Three subtypes of in silico AM model were explored; the physiological structure and parameters of these models were the same with the exception of the description of membrane partitioning of drugs, as summarized in Table 2. In AM model (1), drug partitioning into membranes was based on the assumption that this process is adequately defined by drug partitioning into octanol. (16) Taking the lipid content of the cells into consideration, total drug partitioning into membrane lipids was described by eq 4.(4)where L is the fractional lipid content (v/v) and Kow and Kow,D are the octanol–water partition coefficients of neutral and ionized species. The fractions of neutral (fN) and ionized (fD) species based on Henderson–Hasselbalch principles (eqs S1–S7) and membrane partitioning were subsequently used in equations describing the fractions of neutral (eq 5) and ionized (eq S8) species in cell model compartments.(5)where CN,f and CT are concentration of neutral species which can freely permeate membranes and the total concentration, respectively, and W is the fractional water content (v/v). Equation 5 provides an example for a monoprotic drug; however, it can be extended to cover drugs with multiple charges, e.g., i indicates the existence of ionized species and n is the number of ionized species.
Table 2. Parameterization of the Three in Silico AM Models in Terms of Membrane Partitioninga
 membrane partitioningdescription
AM model (1)Kp = L(Kow + Kow,D) = L(10LogP + 10LogP+3.5)octanol–water partitioning used as a surrogate; for partitioning of the ionized species, a penalty of 3.5 logP units was applied
AM model (2)Kp = Kp+NH4Clexperimental Kp+NH4Cl data used to account for partitioning of drugs into biological membranes (including also acidic phospholipids)
AM model (3)predicted membrane partitioning using Rodgers and Rowland model (1) and accounting for partitioning of drugs into neutral lipids as well as neutral and acidic phospholipids
a

Kp, cell-to-unbound medium concentration ratio; W and L,water and lipid fractions, respectively; Kow and Kow,D, octanol–water partition coefficient for neutral and dissociated (ionized) drugs, respectively; Kp+NH4Cl, Kp in the presence of NH4Cl; pHin and pHout, pH of intracellular compartments and medium, respectively; AP, acidic phospholipids; fNL and fNP are the fraction of neutral lipids and neutral phospholipids, respectively.

In contrast to AM model (1), AM model (2) takes into account interactions of cationic drug species with acidic phospholipids. Therefore, the term LKow in eq 4 was replaced with the experimentally determined cell-to-unbound medium concentration ratio obtained in the presence of NH4Cl (Kp+NH4Cl). The rationale for this approach was that, in the presence of NH4Cl, lysosome–cytosol pH gradient is diminished and the remaining intracellular accumulation was assumed to reflect membrane partitioning (i.e., minimal active uptake). Alternatively, the minimum cell-to-unbound medium partition coefficient (Kp,min), representing drug partition into membranes when active processes are saturated at high substrate concentration, (18) can be used for this purpose. This approach was not feasible here due to limited availability of human AMs. In the case of basic drugs in the data set (pKa > 8), ionized species will contribute predominantly to Kp+NH4Cl due to their extensive ionization at physiological pH. Therefore, it was assumed that Kp+NH4Cl represents the distribution of ionized species between the membrane and aqueous compartments. To account for the distribution of neutral species (equivalent of LogP), Kp+NH4Cl was extrapolated to Kp+NH4Cl,n by assigning 1.0 log unit higher distribution for the latter. This difference between the distribution of neutral and ionized species was within the range of values (0–1.8) reported for basic drugs assessed in liposomes as closer to membrane mimetic than octanol. (35-43) Once Kp+NH4Cl,n was defined, a 1.0 log unit penalty in partitioning was applied for every charge of the drug molecule. The Kp data were then used in equations describing the fractions of neutral (eq S9) and ionized (eq S8) species in each cell model compartment.
In contrast to above, membrane partitioning was predicted in AM model (3) by incorporating the cell specific AP concentration (mg/g cell) and the association constant of cationic drugs with AP (Ka) using the Rodgers and Rowland model. (1) Furthermore, the interaction of neutral drug with the fraction of neutral phospholipids (NP) and neutral lipids (NL) was considered. Consequently, the equations describing the fraction of neutral (eq S10) and ionized (eq S8) drugs in each compartment were revised (Table 2). Binding of cationic drugs to NL and NP was neglected.

Parameterization and Assumptions of the in Silico AM Model

The in silico AM model was parameterized using both AM cell and drug specific parameters. The AM cell parameters were collated from available literature; details are listed in Table 1. Most of the data were collated in human (nonsmoker and smoker) and rat AMs with the exception of pH and Em of which the majority of the data was from primary macrophages or macrophage cell lines of other species, or other cell types including rat hepatocytes. Lysosomal (Vlys) and mitochondrial (Vmit) volumes in human AMs were calculated from the information that lysosomes contribute 9.3% on average (ranged 5.8–13.6%, CV < 30%) to the total cell volume (Vcell), whereas contribution of mitochondria is 3.8% (ranged 3.0–4.9%, CV < 30%) in rat AMs (Table S2), assuming that the volume fractions were the same in both systems. The surface area of both cell and organelles was calculated from the volume of the respective compartments, assuming that they were spheres. In general, the fractional lipid and water content of the cell and organelles were 0.05 and 0.95 vol/vol, respectively. (16, 17) In AM model (3), where the volume fractions of AP (0.0123), NP (0.0549), and NL (0.0188) were considered, (44, 45) the lipid and water contents were 0.0860 (sum of lipid fractions) and 0.914 (1 – sum of lipid fractions), respectively. The AP concentration of AMs was 12.34 mg/g of cell, (44, 45) and the AP composition of lysosomal, mitochondrial, and plasma membrane was assumed to be the same. The plasma and organelle membrane thickness of AMs was assigned as 9 nm, generic to plasma membranes. (46)
The drug specific parameters were the octanol–water partition coefficient, LogP, acid–base dissociation constant(s), pKa (Table 3), Ka, and Kp+NH4Cl (Table S3 for data in human AMs and Ufuk et al. (25) for data in NR8383). As the Ka data were not readily available, this parameter was calculated for red blood cells (Ka,BC, Table S4) which also contain AP, and it was assumed that it was representative of the Ka in AMs, as done previously. (1) In cases where negative Ka,BC values were obtained, the association of the drug with blood cells was assumed to be negligible and this parameter was set to zero. In vitroKp+NH4Cl was not available for all individual AM donors; in those cases the average % lysosomal contribution to the accumulation of clarithromycin and imipramine in human AMs was used to estimate Kp+NH4Cl and for the prediction of Kp,cell in AM model (2) (Table S5). Subsequently, the interindividual variation in predicted Kp,cell of both drugs was assessed.
Table 3. Cell-to-Unbound Medium Concentration Ratio of 9 Drugs in Human Alveolar Macrophages (Kp,hAM)a
drugLogPpKa_bpKa_aacid–base propertyKp,AMs
imipramine4.89.50 base853 ± 583
clarithromycin3.168.99 base115 ± 85.2
formoterol1.998.1410.1, 11.8base11.5b
budesonide2.47  neutral30.8
rifampicin2.546.701.70zwitterion16.4b
tiotropium bromide–1.23  permanently cationic1.01
fenoterol1.098.259.40, 10.1base0.69
ipratropium bromide–1.20  permanently cationic2.65
terbutaline0.909.908.60, 11.0base0.54
a

Data are from a single experiment for all drugs except for clarithromycin and imipramine, for which data represent mean ± SD from multiple donors. References for the physicochemical properties of investigated drugs are listed in the previous study. (25)

b

Nominal medium concentration was used in the estimation of Kp,cell.

In Silico Cell Model Outputs

The in silico AM models were used to predict intracellular concentrations of the respiratory drugs, including also concentrations in lysosomes, mitochondria, and cytosol. Drug concentrations in all AM model compartments were predicted at steady state when net flux was zero. A differential equation describing changes in lysosomal drug concentration is illustrated in eq 6, whereas equations for remaining cellular compartments (e.g., cytosol, mitochondria) are shown in eqs S11–S14.(6)where C, F, P, SA, V, and N represent concentration, fraction, permeability, surface area, volume, and number, respectively, and the subscripts N, D, med, cell, c, l, and m represent neutral, ionized, medium, total cell, cytosol, lysosomes, and mitochondria, respectively. The total cell concentration was the sum of cytosolic, lysosomal, and mitochondrial concentrations. The N/(eN – 1) ratio represents the Nernst Planck equation. (16) The cell and organelle number was 1.
The numerical solution of the implemented equations was performed using ordinary differential equation 15 (ODE15i) solver in Matlab v7.14 (2012). The predicted concentrations were used to derive Kp for each compartment. The model predicted Kp values can be expressed relative to cytosol or the external medium; the latter was used in the current work to allow direct comparison with experimental data. The prediction of the extent of lysosomal sequestration was performed by increasing lysosomal pH to 7.2 in the in silico AM model to mimic the presence of NH4Cl and complete abolishment of cytosol–lysosome pH gradient (as done experimentally). The predicted and observed % contribution of lysosomes to intracellular accumulation (estimated from the % reduction in Kp,cell in the presence of NH4Cl) were compared.

Assessment of the in Silico AM Model Performance

Ability of the model to classify drugs as lysosomotropic was investigated by using the 30% of reduction in Kp,cell in the presence of NH4Cl as a cutoff. True positive (TP: predicted and observed, >30%) and true negative (TN: predicted and observed, <30%) indicate that the in silico model could correctly predict the extent of lysosomal sequestration observed for the investigated drugs. False positive (FP: predicted, >30%; observed, <30%) and false negative (FN: predicted, <30%, observed, >30%) indicate that the in silico model predicted incorrectly the extent of lysosomal sequestration observed for the drugs investigated. In addition, the sensitivity (TP/(TP + FN)), specificity ((TN/TN + FP), false negative ((FN/(TP + FN)) and positive ((FP/(TN + FP)) rates, and negative ((FN/(FN + TN)) and positive ((FP/(FP + TP)) errors were assessed for each model.

Results

ARTICLE SECTIONS
Jump To

In Vitro Assessment of Drug Accumulation in Human AMs

Accumulation of drugs previously investigated in NR8383 (25) was assessed in freshly isolated human AMs, and the data between the two in vitro systems were compared. Studies in human AMs showed over 1500-fold range in Kp,cell of investigated drugs, with the most extensive accumulation seen for imipramine (Kp,cell = 853, CV 63%) (Table 3). Among respiratory drugs investigated, clarithromycin accumulated the most in human AMs with Kp,cell = 115 (mean of data from 9 individual AM donors). The interindividual variation in clarithromycin Kp,cell was 74% (Figure 2), with data ranging from 36 to 322. The remaining respiratory drugs accumulated relatively less in human AMs (Kp,cell < 31). In particular, terbutaline and fenoterol intracellular concentrations remained below the extracellular medium (Kp,cell < 1) under the conditions investigated (Table 3). Comparison of the Kp,cell data between human AMs (Kp,hAM) and NR8383 (Kp,NR8383) showed a good agreement between the two systems, with an overall bias of 2.45-fold (gmfe). The ratio of Kp,NR8383 and Kp,hAM for 7 out of 9 drugs was within 3-fold error (Figure 3). Kp,cell for clarithromycin, imipramine, terbutaline, and budesonide showed a particular good agreement (within 2-fold), whereas the most pronounced outliers were fenoterol and tiotropium bromide. These two drugs had approximately 8.5-fold higher accumulation in NR8383 relative to human AMs.

Figure 2

Figure 2. Variation in clarithromycin cell-to-medium concentration ratio (Kp,cell) in human alveolar macrophages (AMs) from 9 individual donors. Numbers below bars indicate donor number. The Kp,cell was determined using mean clarithromycin media concentration for AMs from donors 1, 2, and 6.

Figure 3

Figure 3. Correlation of the cell-to-unbound medium concentration ratio (Kp,cell) between human alveolar macrophages (AMs) and NR8383 cells. The solid line represents the line of unity, and dashed lines represent 3-fold deviation from the line of unity. Error bars indicate the standard deviation. Data for NR8383 are from Ufuk et al. (25)

In Vitro Assessment of Lysosomal Sequestration in Freshly Isolated Human AMs

Among drugs studied in human AMs, clarithromycin and imipramine were selected for investigation of lysosomal sequestration using an indirect method. The concentration of NH4Cl and the incubation conditions used were the same as in NR8383 to allow direct comparison between the two systems. The pH of incubation medium containing NH4Cl was stable through the experimental setup, as a minor reduction (<0.05 unit) in the pH was observed relative to control medium.
The effect of NH4Cl on clarithromycin Kp,cell was investigated in a subset of human AM donors, as shown in Figure 4A. The reduction in clarithromycin Kp,cell in human AMs ranged from 57% to 67% between different donors (CV 7.7%). Overall, this reduction was not as pronounced as in the case of NR8383 (84%, Figure 4A), suggesting higher relative contribution of lipid partitioning for clarithromycin in human AMs. Regardless of different extent of clarithromycin accumulation observed in human AMs under the control conditions (mean Kp,cell = 81, CV = 51%), % reduction in Kp,cell in the presence of NH4Cl was comparable across donors. On average, clarithromycin accumulation in human AMs was reduced by 63% in the presence of NH4Cl (Table S5), resulting in mean Kp,cell of 32 under those conditions (CV = 62%). Despite the variability in clarithromycin accumulation observed in the absence and presence of NH4Cl, the Kp,control/Kp+NH4Cl ratio was 2.7 on average (ranged between 2.3 and 3) and was consistent between AM donors (CV = 13%).

Figure 4

Figure 4. Cell-to-unbound medium concentration ratio (Kp,cell) of (A) clarithromycin and (B) imipramine in human alveolar macrophages and NR8383 in the absence and presence of 20 mM NH4Cl. Data in human AMs represent single measurements in individual human AM donors, whereas in NR8383, data represent mean ± SD of 3 separate experiments. (25) Numbers above bars indicate % reduction in Kp,cell due to NH4Cl treatment (**, p < 0.01 by t test).

In the case of imipramine, Kp,cell was reduced by 47 to 72% across 5 human AM donors investigated (Figure 4B). The mean Kp,cell in human AMs under the control and NH4Cl treatment conditions were 963 (CV 54%) and 316 (CV 38%), respectively. On average, the total cellular accumulation of imipramine was reduced by 62% (CV 20%) in the presence of NH4Cl (Table S5). The observed reduction in Kp of imipramine was similar to that observed in NR8383 (Figure 4B). The Kp,control/Kp+NH4Cl ratio ranged between 1.9 and 3.5 among AM donors, suggesting a similar magnitude of saturable process. The extent of lysosomal sequestration was the lowest for both drugs in AMs from donor 9; however, the rank order across other overlapping donors differed between clarithromycin and imipramine.
Microscopic examination of human AMs showed the presence of a large number of lysosomes, as evident by the localization of LTR in these organelles (Figure 5B). Treatment of human AMs with LTR by 20 mM NH4Cl showed a maximal reduction of 67% in the fluorescent intensity of LTR (Figure 5C). The observed effect of NH4Cl toward LTR accumulation in the lysosomes demonstrated the lysosomal targeting of this basic dye in human AMs and its accumulation by a pH gradient dependent mechanism. This reduction in LTR lysosomal accumulation was in agreement with the effect of NH4Cl on imipramine Kp,cell in the same AM sample (donor 6, Figure 4B). The imaging results previously reported in NR8383 cells (25) were in good agreement with the current findings in human AM.

Figure 5

Figure 5. Confocal microscopic images of human alveolar macrophages treated with LysoTracker Red (LTR) in the absence and presence of NH4Cl. (A) A phase contrast image of human AMs treated with 200 nM LTR; (B) the same cells being excited to detect LTR localized in lysosomes under control conditions; (C) the localization of LTR in the lysosomes of human AMs was reduced in the presence of 20 mM NH4Cl.

In Silico Prediction of Drug Accumulation in NR8383 Cells

Using the drug and AM specific cellular parameters (Table S2), the accumulation of 10 drugs previously assessed in NR8383 in vitro was predicted using the developed in silico AM models. The comparison of the predicted Kp,cell by all 3 models and the Kp,cell obtained in NR8383 cells is shown in Figure 6; individual predicted Kp,cell and Kp for other cellular model compartments (lysosomes, mitochondria, and cytosol) are listed in Table S6. In the case of AM model (1), overall predictive bias was 7.8-fold (gmfe). The accumulation of basic terbutaline and formoterol and neutral budesonide was predicted within 3-fold of the observed data. In contrast, overprediction of cellular accumulation was observed for basic fenoterol (3.8-fold) and permanently charged ipratropium and tiotropium bromide (>19-fold). In the case of basic clarithromycin and imipramine, the predicted Kp,cell was <10% of the observed data. For zwitterions rifampicin and ciprofloxacin, the predicted Kp,cell was <1% and ∼14% of the observed Kp,cell, respectively. Use of the in silico AM model (2) reversed this underprediction trend, resulting in reduced bias (5.7-fold) and successful prediction of the intracellular accumulation for 40% of drugs, including clarithromycin and imipramine. However, for the remaining drugs in the data set, the Kp,cell was overpredicted by this model (between ∼4- and 26-fold for terbutaline and tiotropium bromide, respectively). Using AM model (3), the accumulation of imipramine, clarithromycin, budesonide, and rifampicin was predicted within 3-fold of the observed data, whereas predicted Kp,cell of ciprofloxacin was ∼14% of the observed data. For the remaining drugs, a general trend of overprediction of intracellular accumulation was evident, with most pronounced overprediction of Kp,cell observed for formoterol and fenoterol. The overall predictive bias of this model (8.4-fold) was larger than that observed for the other two models.

Figure 6

Figure 6. Comparison of observed and predicted Kp,cell of drugs in NR8383. Solid and dashed lines represent the line of unity and 3-fold prediction error, respectively. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively.

In Silico Prediction of Lysosomal Sequestration in NR8383 Cells

In addition to the prediction of Kp,cell, the ability of the in silico models to predict the extent of lysosomal sequestration of investigated drugs in NR8383 (25) was evaluated. A 30% reduction in Kp,cell cutoff and categorical prediction (TP, TN, FP, and FN) with probability assessment (Table S7) were used to evaluate the prediction success of each of the models. Among the drugs studied, clarithromycin, imipramine, and fenoterol were the three drugs for which lysosomal sequestration was correctly predicted as TP by both models (2) and (3) (Figure 7). In contrast, model (1) significantly underestimated the extent of lysosomal sequestration of imipramine (lysosomal Kp of 38, <5% of the overall cellular accumulation). Using the 30% cutoff, the sensitivity of AM models (2) and (3) was 75%, whereas 50% was achieved by model (1) (Table S7). All of the in silico models showed high ability to correctly assign nonlysosomotropic drugs, resulting in good specificity (>83% for model (2) and 67% for the other models). In the current data set, terbutaline, rifampicin, budesonide, ipratropium, and tiotropium bromide were predicted by all in silico models as nonlysosomotropic. The model predictions were inconsistent with the reported 46% reduction in ipratropium bromide Kp,cell (25) (FN); however, the physicochemical properties of this drug and the variability in the in vitro data support its nonlysosomotropic classification. Models (2) and (3) had a comparable false negative rate of 25% relative to model (1) (50%).

Figure 7

Figure 7. Comparison of observed and predicted % contribution of lysosomal sequestration to cellular accumulation of drugs in alveolar macrophages (AM). Solid lines represent 30% categorical cutoff to indicate true positive (TP), true negative (TN), false positive (FP), and false negative (FN) data. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively.

The lysosomal sequestration of 80% of investigated drugs (TN and TP) was correctly predicted only by AM model (2). This model also had the lowest incidence of false positives (10%), resulting in the lowest PPE of 25% (Table S7). The most prominent FP was formoterol, as all 3 models predicted its substantial accumulation in lysosomes (>80%), as opposed to <30% observed in vitro. In the case of ciprofloxacin, only model (2) correctly predicted its nonlysosomal cellular accumulation.

In Silico Prediction of Drug Accumulation and Lysosomal Sequestration in Human AMs

Imipramine and clarithromycin Kp,cell and lysosomal Kp were also predicted in human AMs (parameterized accordingly as in Table 1) due to availability of in vitro data in multiple donors for these two drugs. The intracellular accumulation of both drugs predicted by all 3 in silico AM models is shown in Figure 8. Analogous to evaluation against NR8383 data, performance of AM model (1) was poor, as predicted accumulation of clarithromycin and imipramine was approximately 7 and 4% of their observed Kp,hAM, respectively. Using the in vitroKp+NH4Cl data to account for the membrane partitioning (AM model (2)), intracellular accumulation of both drugs was predicted within 3-fold of the observed data, with an overall bias of 1.83-fold. Furthermore, model (2) captured successfully the variation in the observed Kp,cell of clarithromycin (CV 74%) and imipramine (CV 63%), as the predicted Kp,cell ranged between 84 and 788 (CV 74%) and 390 and 1439 (CV 48%), respectively. In the case of AM model (3), imipramine Kp,cell was predicted within 3-fold, whereas clarithromycin predicted Kp,cell was on the borderline 3-fold error.

Figure 8

Figure 8. Comparison of observed and predicted Kp,cell of drugs in human alveolar macrophages (AMs). Solid and dashed lines represent the line of unity and 3-fold prediction error, respectively. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively. Kp+NH4Cl data were used to predict Kp,cell in individual human AM donors using in silico AM model (2).

In addition to the Kp,cell prediction, all 3 models were evaluated for their ability to predict the extent of lysosomal sequestration of both drugs in human AMs (Table 4). The predicted contribution of lysosomes to clarithromycin accumulation in human AMs ranged between 56 and 74% by AM models (1) and (3), respectively. This was in good agreement with the observed data for clarithromycin both in individual donors (ranged between 57 and 67%) and the mean data (63 ± 4.8%). In the case of imipramine, model (1) significantly underpredicted the extent of its lysosomal sequestration; predicted contribution of lysosomes was 4.5% of the observed data. In contrast, models (2) and (3) predicted the contribution of lysosomal sequestration to imipramine accumulation in human AMs as 48 and 47%, respectively, relative to the mean observed data (62%).
Table 4. Predicted Contribution of Lysosomes in Imipramine and Clarithromycin Accumulation in Human Alveolar Macrophages Using in Silico AM Models (1), (2), and (3) and Comparison to the Observed Data (Individual Donors and the Mean ± Standard Deviation)
 % contribution of lysosomes in accumulation in human alveolar macrophages
 donor 5donor 6donor 7donor 8donor 9mean ± SD
Clarithromycin     
observed57n/aa60676763 ± 4.8
predicted: AM model (1)  56   
predicted: AM model (2)71 717172 
predicted: AM model (3)  74   
       
Imipramine      
observed727152477162 ± 12
predicted: AM model (1)  3   
predicted: AM model (2)4848484848 
predicted: AM model (3)  47   
a

Not available.

Discussion

ARTICLE SECTIONS
Jump To

As increased accumulation of drugs by alveolar macrophages may have important therapeutic and safety implications, it is of key interest to assess drug uptake and lysosomal sequestration of respiratory drugs in human AMs during preclinical development. The current study characterized the intracellular accumulation and lysosomal sequestration of a range of drugs with respiratory indication by both in vitro and in silico approaches.

In Vitro Drug Accumulation and Lysosomal Sequestration in Human AMs

Among respiratory drugs investigated, clarithromycin showed the most extensive accumulation in human AMs. Data obtained in AMs from multiple donors showed no clear trend between age, gender, smoking history, the time to initiate the experiments, and extent of accumulation. High accumulation of clarithromycin in human AMs was previously documented in vivo where several hundred fold higher concentrations in AMs relative to plasma were reported (AM-to-plasma concentration ratios between 400 and 1300 at 24 h following the last dose). (47-50) High clarithromycin concentrations in human AMs are important for its therapeutic efficacy against intracellular bacteria resistant to biocidal mechanisms of AMs. (22) In contrast to clarithromycin, Kp,cell remained <1 for a number of respiratory drugs investigated (e.g., terbutaline, fenoterol), suggesting that equilibrium between intracellular and extracellular drug concentrations was not achieved. These findings are supported by limited literature reported for hydrophilic, predominantly ionized drugs at physiological pH, with Kp,hAM < 1 even after 60 min. (51) Although low accumulation of these drugs in human AMs in vitro implies less likely accumulation in AMs before they interact with extracellular targets in the airways, the translation of the present findings to the in vivo situation requires also consideration of drug formulation, particle size, physiological, and disease conditions.
The paucity of in vitro drug accumulation studies in human AMs does not permit extensive comparison of the data presented here with the literature (Table S8). However, it highlights more pronounced accumulation of a number of drugs in AMs from smokers. The existence of greater number of lysosomes (enlarged in some cases) containing cellular lipids in the AMs of smokers (52-54) compared to nonsmokers may explain increased cellular partitioning of drugs in AMs. In addition, cigarette smoke may have an effect on AM phenotype, phagocytic ability, and expression of several membrane-associated proteins. (27-29, 55-57)
Analysis of lysosomal sequestration of imipramine and clarithromycin in human AMs highlighted the important contribution of this process to their accumulation in AMs. The unaffected accumulation of clarithromycin and imipramine by NH4Cl (Kp+NH4Cl) can be attributed to their partitioning into membranes (plasma and organelle). While some additional unaccounted contribution of pH partitioning may be feasible (if lysosomal pH did not equilibrate with cytosolic pH by NH4Cl), the contribution of this process is expected to be relatively minor compared to membrane partitioning. A change in the extent of membrane partitioning (e.g., due to alteration in membrane potential in the presence of NH4Cl) cannot be completely excluded, which would result in over- or underestimation of the extent of lysosomal sequestration. Data for clarithromycin and imipramine here suggest minimal variation in the extent of lysosomal sequestration between individuals. However, relatively large variation in control Kp,cell (assuming no transporter mediated active uptake) and Kp+NH4Cl suggests potential differences in the lysosomal abundance, volume, and/or extent of membrane partitioning of both drugs between individuals, in particular in smokers, all of which may have important clinical implications. Potential contamination with red blood cells in the experimental setup cannot be excluded, but all efforts were made to minimize this aspect.
The extent of lysosomal accumulation of both drugs in human AMs was comparable. However, it was evident that in all AM samples in which lysosomal sequestration of both drugs was assessed, the baseline accumulation of imipramine (in the presence of NH4Cl) was much higher compared with clarithromycin. The variability associated with clarithromycin Kp+NH4Cl (62%) was larger relative to imipramine (38%). These results can be rationalized by differences in lipophilicity and amphiphilicity of these drugs and their interactions with membrane acidic phospholipids. Macrolides (including clarithromycin) have been shown to bind close to the surface of dodecylphosphocholine micelles as a membrane mimetic, where predominantly electrostatic interactions between the polar lipid head groups and positively charged amino groups of macrolides occur. (58) In the case of imipramine, partitioning into membranes is expected to be much greater, due to both electrostatic interactions with phosphate head groups and hydrophobic interactions with the fatty acid chains in the core of the membrane bilayer. (59, 60)
While the comparison of the Kp,cell data from NR8383 with those from human AMs showed an overall good agreement between the two systems, the human AM data should be viewed as preliminary, highlighting the necessity for further studies. Nevertheless, the presented data provide important information regarding drug accumulation in both systems and emphasize potential of NR8383 as a surrogate in vitro tool for screening of respiratory drugs and their cellular accumulation, in particular in view of limited access to freshly isolated cells.

In Silico Assessment of Intracellular Drug Accumulation and Lysosomal Sequestration in AMs

In the current study the in silico AM model was developed accounting for specific cell properties of alveolar macrophages (e.g., lysosomal contribution to the cellular volume). Three subtypes of the model were considered with respect to their description of membrane partitioning of drugs, and these were evaluated for predicting Kp,cell and the extent of lysosomal sequestration in AMs. It was evident that AM model (1), which assumed membrane partitioning of drugs to occur as it would in octanol, underpredicted the intracellular accumulation of a number of drugs, including clarithromycin and imipramine. In addition, the performance of this model in predicting the contribution of lysosomal sequestration to the cellular accumulation of drugs investigated was poor (50% FN rate) (Figure 7). The 30% reduction in Kp,cell selected as a cutoff provided a clear distinction between the performance of three models. In AM model (2), experimentally determined Kp+NH4Cl data (when lysosomal pH gradient and sequestration was abolished) were used as a surrogate parameter for membrane partitioning. Overall predictive performance of this AM model was the best across in silico models investigated, with the good ability to identify lysosomotropic drugs (lowest PPE) while keeping the low NPE of 17%. This in silico model showed a clear advantage to the use of the simple Henderson–Hasselbalch equation which predicted high lysosomal sequestration and showed marginal distinction in the % contribution of this process to cellular accumulation of five basic drugs in the current data set (data not shown), in contrast to the experimental observations. It is important to note that the use of Kp+NH4Cl data assumed that active transport in AMs is negligible under the experimental conditions used. While this may be the case for basic drugs such as imipramine for which uptake into cell and subcellular compartments is likely to be driven by passive permeation, for some of the remaining drugs, the involvement of transporters has been highlighted in primary airway epithelial cells and OCT-transfected cells. (61-65) Therefore, if these transporters were present in AMs (sparse data so far in healthy and smoker subjects (57, 66)), the Kp+NH4Cl would also reflect the contribution of transporter-mediated active processes and may overestimate the extent of membrane partitioning for drugs like terbutaline or formoterol.
In order to overcome the requirement for an experimentally determined data to describe drug membrane partitioning, AM model (3) was assessed; this model accounted for the fractions of neutral lipids and neutral and acidic phospholipids in AMs. Despite its mechanistic nature and promising results obtained for the prediction of membrane partitioning in hepatocytes (unpublished data), the in silico prediction of membrane partitioning in AMs resulted in a general Kp,cell overprediction (Figure 6). The reason for this trend is unclear; however, a number of factors may contribute. One important consideration is the uncertainty in the composition of lysosomal and mitochondrial membranes in AMs relative to cellular membrane. Due to lack of data it was assumed that the amount of acidic phospholipids in plasma and organelle membrane was the same. Another source of uncertainty comes from the Ka,BC parameter, estimated from drug LogP, pKa, blood-to-plasma partition coefficients, and fraction unbound in plasma. A number of these parameters were predicted due to lack of experimental data; hence any uncertainty in the parameters would propagate to the Ka,BC estimates. Subsequently, any uncertainty in the AP and Ka,BC data would reflect in the predicted drug membrane partitioning in cell and respective compartments. One approach to reduce the prediction error would be to use measured membrane partitioning data in a system that closely resembles the composition of the cellular/tissue membrane of interest. To date, a number of mechanistic tissue distribution models have applied measured phospholipid (phosphatidylcholine):water partitioning to describe neutral phospholipid binding while calculating the acidic phospholipid partitioning from the data obtained. (67, 68) The estimation of the association constant for acidic phospholipids (Ka,BC as surrogate) is appropriate for the basic as well as zwitterionic drugs with basic pKa ≥ 7 (ciprofloxacin) in the data set, (69) whereas for zwitterions with basic pKa < 7 (rifampicin) and neutral drugs, it would be beneficial to consider also association with albumin and lipoprotein, respectively. (69) The goal of the current study was to develop a model which would predict primarily the extent of lysosomal sequestration of basic drugs; therefore, appropriate modifications in equations related to neutral, zwitterionic, and permanently charged drugs have not been explored. The extension of the present data set to include a larger number of basic drugs and the validation of the predicted lysosomal drug concentrations in AMs with measured cell composition data are currently under investigation.
One of the key advantages of the in silico AM model developed in the current work is its ability to predict drug concentrations in the cytosol and lysosomes which are experimentally challenging to obtain. In addition, the in silico cell models presented here for AMs can be adapted to any cell type by modifying the system specific input parameters (volume, membrane electrical potential, pH). This is particularly important for identifying compounds with tendency to accumulate in lysosome-rich tissues such as lungs, liver, and kidneys and to what extent this process may affect drug efficacy or potential adverse effects (e.g., phospholipidosis). Furthermore, the presented in silico cell models provide a basis for investigation of untested scenarios and improved understanding of the impact of changes in pH gradient and in cell morphology (due to environmental factors, disease) on drug intracellular and/or intraorganelle concentration. A number of processes including the possible changes in lysosomal volume, membrane potential, and permeability by the accumulated drug, potential transporter-mediated uptake/efflux, and membrane internalization by endocytosis can be considered for further development of the in silico model when appropriate supporting data become available. In silico cell models that are applicable to many different cell types and have the ability to account for both system and drug properties are valuable in facilitating compound selection in drug discovery and to guide experimental design. Likewise, the in vitro generated data are used to evaluate the performance of these models and inform further refinement.
In conclusion, the cellular accumulation of a wide range of respiratory drugs was investigated in human AMs for the first time. In addition, lysosomal sequestration of basic drugs and its interdonor variability was elucidated using clarithromycin and imipramine as representative examples. The preliminary results from the current study highlighted an overall good agreement in the extent of cellular accumulation between human AMs and NR8383 cells, although differences were evident in the partitioning to acidic phospholipids. The in silico mechanistic model was developed for AMs with the aim to predict intracellular and intraorganelle drug concentrations, including lysosomes. At present, the model performs the best when experimental Kp+NH4Cl data are used as input parameter for membrane partitioning, highlighting the gaps in existing cellular data (e.g., membrane composition). The current experimental approach combined with modeling represents a novel strategy in predicting cellular and lysosomal drug concentrations.

Supporting Information

ARTICLE SECTIONS
Jump To

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.molpharmaceut.6b00908.

  • Patient demographics, experimental data, and equations (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

ARTICLE SECTIONS
Jump To

  • Corresponding Author
  • Authors
    • Ayşe Ufuk - Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.
    • Frauke Assmus - Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.
    • Laura Francis - Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.
    • Jonathan Plumb - Respiratory and Allergy Clinical Research Facility, University Hospital of South Manchester, Manchester, U.K.
    • Valeriu Damian - Computational Modeling Sciences, DDS, GlaxoSmithKline, Upper Merion, Pennsylvania 19406, United States
    • Michael Gertz - Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.Pharmaceutical Sciences, pRED, Roche Innovation Center, Basel, Switzerland
    • J. Brian Houston - Centre for Applied Pharmacokinetic Research, School of Health Sciences, University of Manchester, Manchester, U.K.
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

ARTICLE SECTIONS
Jump To

A.U. was supported by a PhD studentship from the Biotechnology and Biological Sciences Research Council, UK (BB/1532488/1) and GlaxoSmithKline, Stevenage, U.K. The authors acknowledge Sue Murby and Dr. David Hallifax (University of Manchester) for assistance with the analytical assays and useful discussions on data analysis, Dr. Alain Pluen (University of Manchester) for his assistance with confocal imaging, and Dr. Peter Eddershaw (GlaxoSmithKline) for his support and useful discussions. Part of this work was presented at the 13th European ISSX Meeting in Glasgow, U.K., June 22–25, 2015.

References

ARTICLE SECTIONS
Jump To

This article references 69 other publications.

  1. 1
    Rodgers, T.; Leahy, D.; Rowland, M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases J. Pharm. Sci. 2005, 94 (6) 1259 76 DOI: 10.1002/jps.20322
  2. 2
    Reasor, M. J.; Hastings, K. L.; Ulrich, R. G. Drug-induced phospholipidosis: issues and future directions Expert Opin. Drug Saf. 2006, 5 (4) 567 83 DOI: 10.1517/14740338.5.4.567
  3. 3
    Duvvuri, M.; Krise, J. P. A novel assay reveals that weakly basic model compounds concentrate in lysosomes to an extent greater than pH-partitioning theory would predict Mol. Pharmaceutics 2005, 2 (6) 440 8 DOI: 10.1021/mp050043s
  4. 4
    Chen, V. Y.; Rosania, G. R. The great multidrug-resistance paradox ACS Chem. Biol. 2006, 1 (5) 271 3 DOI: 10.1021/cb600215q
  5. 5
    Logan, R.; Funk, R. S.; Axcell, E.; Krise, J. P. Drug-drug interactions involving lysosomes: mechanisms and potential clinical implications Expert Opin. Drug Metab. Toxicol. 2012, 8 (8) 943 58 DOI: 10.1517/17425255.2012.691165
  6. 6
    Logan, R.; Kong, A.; Krise, J. P. Evaluating the roles of autophagy and lysosomal trafficking defects in intracellular distribution-based drug-drug interactions involving lysosomes J. Pharm. Sci. 2013, 102 (11) 4173 80 DOI: 10.1002/jps.23706
  7. 7
    Daniel, W. A.; Wojcikowski, J. Lysosomal trapping as an important mechanism involved in the cellular distribution of perazine and in pharmacokinetic interaction with antidepressants Eur. Neuropsychopharmacol. 1999, 9 (6) 483 91 DOI: 10.1016/S0924-977X(99)00034-6
  8. 8
    Bäckström, E.; Boger, E.; Lundqvist, A.; Hammarlund-Udenaes, M.; Friden, M. Lung Retention by Lysosomal Trapping of Inhaled Drugs Can Be Predicted In Vitro With Lung Slices J. Pharm. Sci. 2016, 105 (11) 3432 3439 DOI: 10.1016/j.xphs.2016.08.014
  9. 9
    Chu, X.; Korzekwa, K.; Elsby, R.; Fenner, K.; Galetin, A.; Lai, Y.; Matsson, P.; Moss, A.; Nagar, S.; Rosania, G. R.; Bai, J. P.; Polli, J. W.; Sugiyama, Y.; Brouwer, K. L. Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver Clin. Pharmacol. Ther. 2013, 94 (1) 126 41 DOI: 10.1038/clpt.2013.78
  10. 10
    Zhang, X.; Shedden, K.; Rosania, G. R. A cell-based molecular transport simulator for pharmacokinetic prediction and cheminformatic exploration Mol. Pharmaceutics 2006, 3 (6) 704 16 DOI: 10.1021/mp060046k
  11. 11
    Friden, M.; Bergstrom, F.; Wan, H.; Rehngren, M.; Ahlin, G.; Hammarlund-Udenaes, M.; Bredberg, U. Measurement of unbound drug exposure in brain: modeling of pH partitioning explains diverging results between the brain slice and brain homogenate methods Drug Metab. Dispos. 2011, 39 (3) 353 62 DOI: 10.1124/dmd.110.035998
  12. 12
    Ménochet, K.; Kenworthy, K. E.; Houston, J. B.; Galetin, A. Simultaneous assessment of uptake and metabolism in rat hepatocytes: a comprehensive mechanistic model J. Pharmacol. Exp. Ther. 2012, 341 (1) 2 15 DOI: 10.1124/jpet.111.187112
  13. 13
    Nagar, S.; Tucker, J.; Weiskircher, E. A.; Bhoopathy, S.; Hidalgo, I. J.; Korzekwa, K. Compartmental models for apical efflux by P-glycoprotein--part 1: evaluation of model complexity Pharm. Res. 2014, 31 (2) 347 59 DOI: 10.1007/s11095-013-1164-7
  14. 14
    Ghosh, A.; Maurer, T. S.; Litchfield, J.; Varma, M. V.; Rotter, C.; Scialis, R.; Feng, B.; Tu, M.; Guimaraes, C. R.; Scott, D. O. Toward a unified model of passive drug permeation II: the physiochemical determinants of unbound tissue distribution with applications to the design of hepatoselective glucokinase activators Drug Metab. Dispos. 2014, 42 (10) 1599 610 DOI: 10.1124/dmd.114.058032
  15. 15
    Korzekwa, K. R.; Nagar, S.; Tucker, J.; Weiskircher, E. A.; Bhoopathy, S.; Hidalgo, I. J. Models to predict unbound intracellular drug concentrations in the presence of transporters Drug Metab. Dispos. 2012, 40 (5) 865 76 DOI: 10.1124/dmd.111.044289
  16. 16
    Trapp, S.; Rosania, G. R.; Horobin, R. W.; Kornhuber, J. Quantitative modeling of selective lysosomal targeting for drug design Eur. Biophys. J. 2008, 37 (8) 1317 28 DOI: 10.1007/s00249-008-0338-4
  17. 17
    Trapp, S.; Horobin, R. W. A predictive model for the selective accumulation of chemicals in tumor cells Eur. Biophys. J. 2005, 34 (7) 959 66 DOI: 10.1007/s00249-005-0472-1
  18. 18
    Hallifax, D.; Houston, J. B. Saturable uptake of lipophilic amine drugs into isolated hepatocytes: mechanisms and consequences for quantitative clearance prediction Drug Metab. Dispos. 2007, 35 (8) 1325 32 DOI: 10.1124/dmd.107.015131
  19. 19
    Heyneman, C. A.; Reasor, M. J. Role of the alveolar macrophage in the induction of pulmonary phospholipidosis by chlorphentermine. II. Drug uptake into cells in vitro J. Pharmacol. Exp. Ther. 1986, 236 (1) 60 4
  20. 20
    Antonini, J. M.; Reasor, M. J. Accumulation of amiodarone and desethylamiodarone by rat alveolar macrophages in cell culture Biochem. Pharmacol. 1991, 42 (Suppl. 1) S151 S156 DOI: 10.1016/0006-2952(91)90405-T
  21. 21
    Togami, K.; Chono, S.; Morimoto, K. Distribution characteristics of clarithromycin and azithromycin, macrolide antimicrobial agents used for treatment of respiratory infections, in lung epithelial lining fluid and alveolar macrophages Biopharm. Drug Dispos. 2011, 32 (7) 389 97 DOI: 10.1002/bdd.767
  22. 22
    Togami, K.; Chono, S.; Morimoto, K. Subcellular Distribution of Azithromycin and Clarithromycin in Rat Alveolar Macrophages (NR8383) in Vitro Biol. Pharm. Bull. 2013, 36 (9) 1494 9 DOI: 10.1248/bpb.b13-00423
  23. 23
    Togami, K.; Chono, S.; Seki, T.; Morimoto, K. Distribution characteristics of telithromycin, a novel ketolide antimicrobial agent applied for treatment of respiratory infection, in lung epithelial lining fluid and alveolar macrophages Drug Metab. Pharmacokinet. 2009, 24 (5) 411 7 DOI: 10.2133/dmpk.24.411
  24. 24
    Togami, K.; Chono, S.; Seki, T.; Morimoto, K. Intracellular pharmacokinetics of telithromycin, a ketolide antibiotic, in alveolar macrophages J. Pharm. Pharmacol. 2010, 62 (1) 71 5 DOI: 10.1211/jpp.62.01.0007
  25. 25
    Ufuk, A.; Somers, G.; Houston, J. B.; Galetin, A. In Vitro Assessment of Uptake and Lysosomal Sequestration of Respiratory Drugs in Alveolar Macrophage Cell Line NR8383 Pharm. Res. 2015, 32 (12) 3937 51 DOI: 10.1007/s11095-015-1753-8
  26. 26
    Nadanaciva, S.; Lu, S.; Gebhard, D. F.; Jessen, B. A.; Pennie, W. D.; Will, Y. A high content screening assay for identifying lysosomotropic compounds Toxicol. In Vitro 2011, 25 (3) 715 23 DOI: 10.1016/j.tiv.2010.12.010
  27. 27
    Higham, A.; Booth, G.; Lea, S.; Southworth, T.; Plumb, J.; Singh, D. The effects of corticosteroids on COPD lung macrophages: a pooled analysis Respir. Res. 2015, 16, 98 DOI: 10.1186/s12931-015-0260-0
  28. 28
    Southworth, T.; Metryka, A.; Lea, S.; Farrow, S.; Plumb, J.; Singh, D. IFN-gamma synergistically enhances LPS signalling in alveolar macrophages from COPD patients and controls by corticosteroid-resistant STAT1 activation Br. J. Pharmacol. 2012, 166 (7) 2070 83 DOI: 10.1111/j.1476-5381.2012.01907.x
  29. 29
    Plumb, J.; Robinson, L.; Lea, S.; Banyard, A.; Blaikley, J.; Ray, D.; Bizzi, A.; Volpi, G.; Facchinetti, F.; Singh, D. Evaluation of glucocorticoid receptor function in COPD lung macrophages using beclomethasone-17-monopropionate PLoS One 2013, 8 (5) e64257 DOI: 10.1371/journal.pone.0064257
  30. 30
    Hallifax, D.; Houston, J. B. Uptake and intracellular binding of lipophilic amine drugs by isolated rat hepatocytes and implications for prediction of in vivo metabolic clearance Drug Metab. Dispos. 2006, 34 (11) 1829 36 DOI: 10.1124/dmd.106.010413
  31. 31
    Yabe, Y.; Galetin, A.; Houston, J. B. Kinetic characterization of Rat Hepatic Uptake of 16 Actively Transported Drugs Drug Metab. Dispos. 2011, 39, 1808 DOI: 10.1124/dmd.111.040477
  32. 32
    Harris, J. O.; Swenson, E. W.; Johnson, J. E., 3rd Human alveolar macrophages: comparison of phagocytic ability, glucose utilization, and ultrastructure in smokers and nonsmokers J. Clin. Invest. 1970, 49 (11) 2086 96 DOI: 10.1172/JCI106426
  33. 33
    Reynolds, H. Y.; Newball, H. H. Analysis of proteins and respiratory cells obtained from human lungs by bronchial lavage J. Lab. Clin. Med. 1974, 84 (4) 559 73
  34. 34
    Territo, M. C.; Golde, D. W. The function of human alveolar macrophages J. Reticuloendothelial Soc. 1979, 25 (1) 111 20
  35. 35
    Pauletti, M.; Wunderli-Allenspach, H. Partition coefficients in vitro: artificial membranes as a standardized distribution model Eur. J. Pharm. Sci. 1994, 1 (5) 273 282 DOI: 10.1016/0928-0987(94)90022-1
  36. 36
    Austin, R. P.; Davis, A. M.; Manners, C. N. Partitioning of ionizing molecules between aqueous buffers and phospholipid vesicles J. Pharm. Sci. 1995, 84 (10) 1180 3 DOI: 10.1002/jps.2600841008
  37. 37
    Krämer, S. D.; Wunderli-Allenspach, H. The pH-dependence in the partitioning behaviour of (RS)-[3H]propranolol between MDCK cell lipid vesicles and buffer Pharm. Res. 1996, 13 (12) 1851 5 DOI: 10.1023/A:1016089209798
  38. 38
    Ottiger, C.; Wunderli-Allenspach, H. Partition behaviour of acids and bases in a phosphatidylcholine liposome–buffer equilibrium dialysis system Eur. J. Pharm. Sci. 1997, 5 (4) 223 231 DOI: 10.1016/S0928-0987(97)00278-9
  39. 39
    Avdeef, A.; Box, K. J.; Comer, J. E.; Hibbert, C.; Tam, K. Y. pH-metric logP 10. Determination of liposomal membrane-water partition coefficients of ionizable drugs Pharm. Res. 1998, 15 (2) 209 15 DOI: 10.1023/A:1011954332221
  40. 40
    Fruttero, R.; Caron, G.; Fornatto, E.; Boschi, D.; Ermondi, G.; Gasco, A.; Carrupt, P. A.; Testa, B. Mechanisms of liposomes/water partitioning of (p-methylbenzyl)alkylamines Pharm. Res. 1998, 15 (9) 1407 13 DOI: 10.1023/A:1011953622052
  41. 41
    Krämer, S. D.; Braun, A.; Jakits-Deiser, C.; Wunderli-Allenspach, H. Towards the predictability of drug-lipid membrane interactions: the pH-dependent affinity of propanolol to phosphatidylinositol containing liposomes Pharm. Res. 1998, 15 (5) 739 44 DOI: 10.1023/A:1011923103938
  42. 42
    Balon, K.; Riebesehl, B. U.; Muller, B. W. Drug liposome partitioning as a tool for the prediction of human passive intestinal absorption Pharm. Res. 1999, 16 (6) 882 8 DOI: 10.1023/A:1018882221008
  43. 43
    Marenchino, M.; Alpstag-Wohrle, A. L.; Christen, B.; Wunderli-Allenspach, H.; Kramer, S. D. Alpha-tocopherol influences the lipid membrane affinity of desipramine in a pH-dependent manner Eur. J. Pharm. Sci. 2004, 21 (2–3) 313 21 DOI: 10.1016/j.ejps.2003.10.022
  44. 44
    Fletcher, K.; Wyatt, I. The composition of lung lipids after poisoning with paraquat Br. J. Exp. Pathol. 1970, 51 (6) 604 10
  45. 45
    Sahu, S.; Lynn, W. S. Lipid composition of human alveolar macrophages Inflammation 1977, 2 (2) 83 91 DOI: 10.1007/BF00918670
  46. 46
    Korn, E. D. Structure of biological membranes Science 1966, 153 (3743) 1491 8 DOI: 10.1126/science.153.3743.1491
  47. 47
    Honeybourne, D.; Kees, F.; Andrews, J. M.; Baldwin, D.; Wise, R. The levels of clarithromycin and its 14-hydroxy metabolite in the lung Eur. Respir. J. 1994, 7 (7) 1275 80 DOI: 10.1183/09031936.94.07071275
  48. 48
    Conte, J. E., Jr.; Golden, J. A.; Duncan, S.; McKenna, E.; Zurlinden, E. Intrapulmonary pharmacokinetics of clarithromycin and of erythromycin Antimicrob. Agents Chemother. 1995, 39 (2) 334 8 DOI: 10.1128/AAC.39.2.334
  49. 49
    Patel, K. B.; Xuan, D.; Tessier, P. R.; Russomanno, J. H.; Quintiliani, R.; Nightingale, C. H. Comparison of bronchopulmonary pharmacokinetics of clarithromycin and azithromycin Antimicrob. Agents Chemother. 1996, 40 (10) 2375 9
  50. 50
    Rodvold, K.; Gotfried, M.; Danziger, L.; Servi, R. Intrapulmonary steady-state concentrations of clarithromycin and azithromycin in healthy adult volunteers Antimicrob. Agents Chemother. 1997, 41 (6) 1399 1402
  51. 51
    Hand, W. L.; Corwin, R. W.; Steinberg, T. H.; Grossman, G. D. Uptake of antibiotics by human alveolar macrophages Am. Rev. Respir. Dis. 1984, 129 (6) 933 7
  52. 52
    Cohen, A. B.; Cline, M. J. The human alveolar macrophage: isolation, cultivation in vitro, and studies of morphologic and functional characteristics J. Clin. Invest. 1971, 50 (7) 1390 8 DOI: 10.1172/JCI106622
  53. 53
    Pratt, S. A.; Smith, M. H.; Ladman, A. J.; Finley, T. N. The ultrastructure of alveolar macrophages from human cigarette smokers and nonsmokers Lab. Invest. 1971, 24 (5) 331 8
  54. 54
    Hocking, W. G.; Golde, D. W. The pulmonary-alveolar macrophage (first of two parts) N. Engl. J. Med. 1979, 301 (11) 580 7 DOI: 10.1056/NEJM197909133011104
  55. 55
    Taylor, A. E.; Finney-Hayward, T. K.; Quint, J. K.; Thomas, C. M.; Tudhope, S. J.; Wedzicha, J. A.; Barnes, P. J.; Donnelly, L. E. Defective macrophage phagocytosis of bacteria in COPD Eur. Respir. J. 2010, 35 (5) 1039 47 DOI: 10.1183/09031936.00036709
  56. 56
    Hodge, S.; Hodge, G.; Ahern, J.; Jersmann, H.; Holmes, M.; Reynolds, P. N. Smoking alters alveolar macrophage recognition and phagocytic ability: implications in chronic obstructive pulmonary disease Am. J. Respir. Cell Mol. Biol. 2007, 37 (6) 748 55 DOI: 10.1165/rcmb.2007-0025OC
  57. 57
    van der Deen, M.; de Vries, E. G.; Visserman, H.; Zandbergen, W.; Postma, D. S.; Timens, W.; Timmer-Bosscha, H. Cigarette smoke extract affects functional activity of MRP1 in bronchial epithelial cells J. Biochem. Mol. Toxicol. 2007, 21 (5) 243 51 DOI: 10.1002/jbt.20187
  58. 58
    Kosol, S.; Schrank, E.; Krajacic, M. B.; Wagner, G. E.; Meyer, N. H.; Gobl, C.; Rechberger, G. N.; Zangger, K.; Novak, P. Probing the interactions of macrolide antibiotics with membrane-mimetics by NMR spectroscopy J. Med. Chem. 2012, 55 (11) 5632 6 DOI: 10.1021/jm300647f
  59. 59
    Joshi, U. M.; Kodavanti, P. R.; Coudert, B.; Dwyer, T. M.; Mehendale, H. M. Types of interaction of amphiphilic drugs with phospholipid vesicles J. Pharmacol. Exp. Ther. 1988, 246 (1) 150 7
  60. 60
    Fisar, Z.; Fuksova, K.; Velenovska, M. Binding of imipramine to phospholipid bilayers using radioligand binding assay Gen. Physiol. Biophys. 2004, 23 (1) 77 99
  61. 61
    Vavricka, S. R.; Van Montfoort, J.; Ha, H. R.; Meier, P. J.; Fattinger, K. Interactions of rifamycin SV and rifampicin with organic anion uptake systems of human liver Hepatology (Hoboken, NJ, U. S.) 2002, 36 (1) 164 72 DOI: 10.1053/jhep.2002.34133
  62. 62
    Nakanishi, T.; Haruta, T.; Shirasaka, Y.; Tamai, I. Organic cation transporter-mediated renal secretion of ipratropium and tiotropium in rats and humans Drug Metab. Dispos. 2011, 39 (1) 117 22 DOI: 10.1124/dmd.110.035402
  63. 63
    Nickel, S.; Clerkin, C. G.; Selo, M. A.; Ehrhardt, C. Transport mechanisms at the pulmonary mucosa: implications for drug delivery Expert Opin. Drug Delivery 2016, 13 (5) 667 90 DOI: 10.1517/17425247.2016.1140144
  64. 64
    Ong, H. X.; Traini, D.; Bebawy, M.; Young, P. M. Ciprofloxacin is actively transported across bronchial lung epithelial cells using a Calu-3 air interface cell model Antimicrob. Agents Chemother. 2013, 57 (6) 2535 40 DOI: 10.1128/AAC.00306-13
  65. 65
    Nakamura, T.; Nakanishi, T.; Haruta, T.; Shirasaka, Y.; Keogh, J. P.; Tamai, I. Transport of ipratropium, an anti-chronic obstructive pulmonary disease drug, is mediated by organic cation/carnitine transporters in human bronchial epithelial cells: implications for carrier-mediated pulmonary absorption Mol. Pharmaceutics 2010, 7 (1) 187 95 DOI: 10.1021/mp900206j
  66. 66
    Moreau, A.; Le Vee, M.; Jouan, E.; Parmentier, Y.; Fardel, O. Drug transporter expression in human macrophages Fundam. Clin. Pharmacol. 2011, 25 (6) 743 52 DOI: 10.1111/j.1472-8206.2010.00913.x
  67. 67
    Ruark, C. D.; Hack, C. E.; Robinson, P. J.; Mahle, D. A.; Gearhart, J. M. Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability J. Pharm. Sci. 2014, 103 (7) 2189 98 DOI: 10.1002/jps.24011
  68. 68
    Schmitt, W. General approach for the calculation of tissue to plasma partition coefficients Toxicol. In Vitro 2008, 22 (2) 457 67 DOI: 10.1016/j.tiv.2007.09.010
  69. 69
    Rodgers, T.; Rowland, M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions J. Pharm. Sci. 2006, 95 (6) 1238 57 DOI: 10.1002/jps.20502

Cited By

ARTICLE SECTIONS
Jump To

This article is cited by 20 publications.

  1. Richard Zang, Aline Barth, Harvey Wong, Jan Marik, Jie Shen, Julie Lade, Kerri Grove, Matthew R. Durk, Neil Parrott, Patrick J. Rudewicz, Sylvia Zhao, Tao Wang, Zhengyin Yan, Donglu Zhang. Design and Measurement of Drug Tissue Concentration Asymmetry and Tissue Exposure-Effect (Tissue PK-PD) Evaluation. Journal of Medicinal Chemistry 2022, 65 (13) , 8713-8734. https://doi.org/10.1021/acs.jmedchem.2c00502
  2. Carla F. Newman, Rasmus Havelund, Melissa K. Passarelli, Peter S. Marshall, Ian Francis, Andy West, Morgan R. Alexander, Ian S. Gilmore, and Colin T. Dollery . Intracellular Drug Uptake—A Comparison of Single Cell Measurements Using ToF-SIMS Imaging and Quantification from Cell Populations with LC/MS/MS. Analytical Chemistry 2017, 89 (22) , 11944-11953. https://doi.org/10.1021/acs.analchem.7b01436
  3. Aishvarya Tandon, Anna Santura, Herbert Waldmann, Axel Pahl, Paul Czodrowski. Identification of Lysosomotropism using Explainable Machine Learning and Morphological Profiling Cell Painting Data. RSC Medicinal Chemistry 2024, https://doi.org/10.1039/D4MD00107A
  4. Venkatesh Pilla Reddy, Eman El‐Khateeb, Heeseung Jo, Natalie Giovino, Emily Lythgoe, Shringi Sharma, Weifeng Tang, Masoud Jamei, Amin Rastomi‐Hodjegan. Pharmacokinetics under the COVID‐19 storm. British Journal of Clinical Pharmacology 2023, 89 (1) , 158-186. https://doi.org/10.1111/bcp.14668
  5. Linda B. S. Aulin, Sebastian T. Tandar, Torben van Zijp, Etienne van Ballegooie, Piet H. van der Graaf, Mohammed A. A. Saleh, Pyry Välitalo, J. G. Coen van Hasselt. Physiologically Based Modelling Framework for Prediction of Pulmonary Pharmacokinetics of Antimicrobial Target Site Concentrations. Clinical Pharmacokinetics 2022, 61 (12) , 1735-1748. https://doi.org/10.1007/s40262-022-01186-3
  6. Aditya R. Kolli, Florian Calvino-Martin, Julia Hoeng. Translational Modeling of Chloroquine and Hydroxychloroquine Dosimetry in Human Airways for Treating Viral Respiratory Infections. Pharmaceutical Research 2022, 39 (1) , 57-73. https://doi.org/10.1007/s11095-021-03152-3
  7. Elizabeth Hann, Karine Malagu, Andrew Stott, Huw Vater. The importance of plasma protein and tissue binding in a drug discovery program to successfully deliver a preclinical candidate. 2022, 163-214. https://doi.org/10.1016/bs.pmch.2022.04.002
  8. Aditya R. Kolli, Tanja Zivkovic Semren, David Bovard, Shoaib Majeed, Marco van der Toorn, Sophie Scheuner, Philippe A. Guy, Arkadiusz Kuczaj, Anatoly Mazurov, Stefan Frentzel, Florian Calvino-Martin, Nikolai V. Ivanov, John O’Mullane, Manuel C. Peitsch, Julia Hoeng. Pulmonary Delivery of Aerosolized Chloroquine and Hydroxychloroquine to Treat COVID-19: In Vitro Experimentation to Human Dosing Predictions. The AAPS Journal 2022, 24 (1) https://doi.org/10.1208/s12248-021-00666-x
  9. Zachary Enlo-Scott, Magda Swedrowska, Ben Forbes. Epithelial permeability and drug absorption in the lungs. 2021, 267-299. https://doi.org/10.1016/B978-0-12-814974-4.00004-3
  10. Laura Francis, Andrew Harrell, David Hallifax, Aleksandra Galetin. Utilising Magnetically Isolated Lysosomes for Direct Quantification of Intralysosomal Drug Concentrations by LC-MS/MS Analysis: An Investigatory Study With Imipramine. Journal of Pharmaceutical Sciences 2020, 109 (9) , 2891-2901. https://doi.org/10.1016/j.xphs.2020.05.026
  11. Jasleen K. Sodhi, Shuaibing Liu, Leslie Z. Benet. Challenging the Relevance of Unbound Tissue-to-Blood Partition Coefficient (Kpuu) on Prediction of Drug-Drug Interactions. Pharmaceutical Research 2020, 37 (4) https://doi.org/10.1007/s11095-020-02797-w
  12. Christine C. Orozco, Karen Atkinson, Sangwoo Ryu, George Chang, Christopher Keefer, Jian Lin, Keith Riccardi, Robert K. Mongillo, David Tess, Kevin J. Filipski, Amit S. Kalgutkar, John Litchfield, Dennis Scott, Li Di. Structural attributes influencing unbound tissue distribution. European Journal of Medicinal Chemistry 2020, 185 , 111813. https://doi.org/10.1016/j.ejmech.2019.111813
  13. Donglu Zhang, Cornelis E.C.A. Hop, Gabriela Patilea-Vrana, Gautham Gampa, Herana Kamal Seneviratne, Jashvant D. Unadkat, Jane R. Kenny, Karthik Nagapudi, Li Di, Lian Zhou, Mark Zak, Matthew R. Wright, Namandjé N. Bumpus, Richard Zang, Xingrong Liu, Yurong Lai, S. Cyrus Khojasteh. Drug Concentration Asymmetry in Tissues and Plasma for Small Molecule–Related Therapeutic Modalities. Drug Metabolism and Disposition 2019, 47 (10) , 1122-1135. https://doi.org/10.1124/dmd.119.086744
  14. Yi‐Lin Zhang, Jian‐Chang Feng, Li‐Jiao Ke, Jia‐Wen Xu, Ze‐Xin Huang, Jiehong Huang, Yun‐Xin Zhu, Wen‐Liang Zhou. Mechanisms underlying the regulation of intracellular and luminal pH in vaginal epithelium. Journal of Cellular Physiology 2019, 234 (9) , 15790-15799. https://doi.org/10.1002/jcp.28237
  15. Yingying Guo, Xiaoyan Chu, Neil J. Parrott, Kim L.R. Brouwer, Vicky Hsu, Swati Nagar, Pär Matsson, Pradeep Sharma, Jan Snoeys, Yuichi Sugiyama, Daniel Tatosian, Jashvant D. Unadkat, Shiew‐Mei Huang, Aleksandra Galetin, . Advancing Predictions of Tissue and Intracellular Drug Concentrations Using In Vitro , Imaging and Physiologically Based Pharmacokinetic Modeling Approaches. Clinical Pharmacology & Therapeutics 2018, 104 (5) , 865-889. https://doi.org/10.1002/cpt.1183
  16. Peter Strong, Kazuhiro Ito, John Murray, Garth Rapeport. Current approaches to the discovery of novel inhaled medicines. Drug Discovery Today 2018, 23 (10) , 1705-1717. https://doi.org/10.1016/j.drudis.2018.05.017
  17. Mark Pryjma, Ján Burian, Charles J. Thompson. Rifabutin Acts in Synergy and Is Bactericidal with Frontline Mycobacterium abscessus Antibiotics Clarithromycin and Tigecycline, Suggesting a Potent Treatment Combination. Antimicrobial Agents and Chemotherapy 2018, 62 (8) https://doi.org/10.1128/AAC.00283-18
  18. Per Bäckman, Sumit Arora, William Couet, Ben Forbes, Wilbur de Kruijf, Amrit Paudel. Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled drugs. European Journal of Pharmaceutical Sciences 2018, 113 , 41-52. https://doi.org/10.1016/j.ejps.2017.10.030
  19. Rahul Maheshwari, Kaushik Kuche, Ankita Mane, Yashu Chourasiya, Muktika Tekade, Rakesh K. Tekade. Manipulation of Physiological Processes for Pharmaceutical Product Development. 2018, 701-729. https://doi.org/10.1016/B978-0-12-814423-7.00020-4
  20. Frauke Assmus, J. Brian Houston, Aleksandra Galetin. Incorporation of lysosomal sequestration in the mechanistic model for prediction of tissue distribution of basic drugs. European Journal of Pharmaceutical Sciences 2017, 109 , 419-430. https://doi.org/10.1016/j.ejps.2017.08.014
  • Abstract

    Figure 1

    Figure 1. Cell model scheme demonstrates the processes involved in the uptake of a weak base into the cell and subcellular compartments. The figure was adapted from Trapp et al. (16)

    Figure 2

    Figure 2. Variation in clarithromycin cell-to-medium concentration ratio (Kp,cell) in human alveolar macrophages (AMs) from 9 individual donors. Numbers below bars indicate donor number. The Kp,cell was determined using mean clarithromycin media concentration for AMs from donors 1, 2, and 6.

    Figure 3

    Figure 3. Correlation of the cell-to-unbound medium concentration ratio (Kp,cell) between human alveolar macrophages (AMs) and NR8383 cells. The solid line represents the line of unity, and dashed lines represent 3-fold deviation from the line of unity. Error bars indicate the standard deviation. Data for NR8383 are from Ufuk et al. (25)

    Figure 4

    Figure 4. Cell-to-unbound medium concentration ratio (Kp,cell) of (A) clarithromycin and (B) imipramine in human alveolar macrophages and NR8383 in the absence and presence of 20 mM NH4Cl. Data in human AMs represent single measurements in individual human AM donors, whereas in NR8383, data represent mean ± SD of 3 separate experiments. (25) Numbers above bars indicate % reduction in Kp,cell due to NH4Cl treatment (**, p < 0.01 by t test).

    Figure 5

    Figure 5. Confocal microscopic images of human alveolar macrophages treated with LysoTracker Red (LTR) in the absence and presence of NH4Cl. (A) A phase contrast image of human AMs treated with 200 nM LTR; (B) the same cells being excited to detect LTR localized in lysosomes under control conditions; (C) the localization of LTR in the lysosomes of human AMs was reduced in the presence of 20 mM NH4Cl.

    Figure 6

    Figure 6. Comparison of observed and predicted Kp,cell of drugs in NR8383. Solid and dashed lines represent the line of unity and 3-fold prediction error, respectively. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively.

    Figure 7

    Figure 7. Comparison of observed and predicted % contribution of lysosomal sequestration to cellular accumulation of drugs in alveolar macrophages (AM). Solid lines represent 30% categorical cutoff to indicate true positive (TP), true negative (TN), false positive (FP), and false negative (FN) data. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively.

    Figure 8

    Figure 8. Comparison of observed and predicted Kp,cell of drugs in human alveolar macrophages (AMs). Solid and dashed lines represent the line of unity and 3-fold prediction error, respectively. Black, red, and blue symbols represent predictions with AM models (1), (2), and (3), respectively. Kp+NH4Cl data were used to predict Kp,cell in individual human AM donors using in silico AM model (2).

  • References

    ARTICLE SECTIONS
    Jump To

    This article references 69 other publications.

    1. 1
      Rodgers, T.; Leahy, D.; Rowland, M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases J. Pharm. Sci. 2005, 94 (6) 1259 76 DOI: 10.1002/jps.20322
    2. 2
      Reasor, M. J.; Hastings, K. L.; Ulrich, R. G. Drug-induced phospholipidosis: issues and future directions Expert Opin. Drug Saf. 2006, 5 (4) 567 83 DOI: 10.1517/14740338.5.4.567
    3. 3
      Duvvuri, M.; Krise, J. P. A novel assay reveals that weakly basic model compounds concentrate in lysosomes to an extent greater than pH-partitioning theory would predict Mol. Pharmaceutics 2005, 2 (6) 440 8 DOI: 10.1021/mp050043s
    4. 4
      Chen, V. Y.; Rosania, G. R. The great multidrug-resistance paradox ACS Chem. Biol. 2006, 1 (5) 271 3 DOI: 10.1021/cb600215q
    5. 5
      Logan, R.; Funk, R. S.; Axcell, E.; Krise, J. P. Drug-drug interactions involving lysosomes: mechanisms and potential clinical implications Expert Opin. Drug Metab. Toxicol. 2012, 8 (8) 943 58 DOI: 10.1517/17425255.2012.691165
    6. 6
      Logan, R.; Kong, A.; Krise, J. P. Evaluating the roles of autophagy and lysosomal trafficking defects in intracellular distribution-based drug-drug interactions involving lysosomes J. Pharm. Sci. 2013, 102 (11) 4173 80 DOI: 10.1002/jps.23706
    7. 7
      Daniel, W. A.; Wojcikowski, J. Lysosomal trapping as an important mechanism involved in the cellular distribution of perazine and in pharmacokinetic interaction with antidepressants Eur. Neuropsychopharmacol. 1999, 9 (6) 483 91 DOI: 10.1016/S0924-977X(99)00034-6
    8. 8
      Bäckström, E.; Boger, E.; Lundqvist, A.; Hammarlund-Udenaes, M.; Friden, M. Lung Retention by Lysosomal Trapping of Inhaled Drugs Can Be Predicted In Vitro With Lung Slices J. Pharm. Sci. 2016, 105 (11) 3432 3439 DOI: 10.1016/j.xphs.2016.08.014
    9. 9
      Chu, X.; Korzekwa, K.; Elsby, R.; Fenner, K.; Galetin, A.; Lai, Y.; Matsson, P.; Moss, A.; Nagar, S.; Rosania, G. R.; Bai, J. P.; Polli, J. W.; Sugiyama, Y.; Brouwer, K. L. Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver Clin. Pharmacol. Ther. 2013, 94 (1) 126 41 DOI: 10.1038/clpt.2013.78
    10. 10
      Zhang, X.; Shedden, K.; Rosania, G. R. A cell-based molecular transport simulator for pharmacokinetic prediction and cheminformatic exploration Mol. Pharmaceutics 2006, 3 (6) 704 16 DOI: 10.1021/mp060046k
    11. 11
      Friden, M.; Bergstrom, F.; Wan, H.; Rehngren, M.; Ahlin, G.; Hammarlund-Udenaes, M.; Bredberg, U. Measurement of unbound drug exposure in brain: modeling of pH partitioning explains diverging results between the brain slice and brain homogenate methods Drug Metab. Dispos. 2011, 39 (3) 353 62 DOI: 10.1124/dmd.110.035998
    12. 12
      Ménochet, K.; Kenworthy, K. E.; Houston, J. B.; Galetin, A. Simultaneous assessment of uptake and metabolism in rat hepatocytes: a comprehensive mechanistic model J. Pharmacol. Exp. Ther. 2012, 341 (1) 2 15 DOI: 10.1124/jpet.111.187112
    13. 13
      Nagar, S.; Tucker, J.; Weiskircher, E. A.; Bhoopathy, S.; Hidalgo, I. J.; Korzekwa, K. Compartmental models for apical efflux by P-glycoprotein--part 1: evaluation of model complexity Pharm. Res. 2014, 31 (2) 347 59 DOI: 10.1007/s11095-013-1164-7
    14. 14
      Ghosh, A.; Maurer, T. S.; Litchfield, J.; Varma, M. V.; Rotter, C.; Scialis, R.; Feng, B.; Tu, M.; Guimaraes, C. R.; Scott, D. O. Toward a unified model of passive drug permeation II: the physiochemical determinants of unbound tissue distribution with applications to the design of hepatoselective glucokinase activators Drug Metab. Dispos. 2014, 42 (10) 1599 610 DOI: 10.1124/dmd.114.058032
    15. 15
      Korzekwa, K. R.; Nagar, S.; Tucker, J.; Weiskircher, E. A.; Bhoopathy, S.; Hidalgo, I. J. Models to predict unbound intracellular drug concentrations in the presence of transporters Drug Metab. Dispos. 2012, 40 (5) 865 76 DOI: 10.1124/dmd.111.044289
    16. 16
      Trapp, S.; Rosania, G. R.; Horobin, R. W.; Kornhuber, J. Quantitative modeling of selective lysosomal targeting for drug design Eur. Biophys. J. 2008, 37 (8) 1317 28 DOI: 10.1007/s00249-008-0338-4
    17. 17
      Trapp, S.; Horobin, R. W. A predictive model for the selective accumulation of chemicals in tumor cells Eur. Biophys. J. 2005, 34 (7) 959 66 DOI: 10.1007/s00249-005-0472-1
    18. 18
      Hallifax, D.; Houston, J. B. Saturable uptake of lipophilic amine drugs into isolated hepatocytes: mechanisms and consequences for quantitative clearance prediction Drug Metab. Dispos. 2007, 35 (8) 1325 32 DOI: 10.1124/dmd.107.015131
    19. 19
      Heyneman, C. A.; Reasor, M. J. Role of the alveolar macrophage in the induction of pulmonary phospholipidosis by chlorphentermine. II. Drug uptake into cells in vitro J. Pharmacol. Exp. Ther. 1986, 236 (1) 60 4
    20. 20
      Antonini, J. M.; Reasor, M. J. Accumulation of amiodarone and desethylamiodarone by rat alveolar macrophages in cell culture Biochem. Pharmacol. 1991, 42 (Suppl. 1) S151 S156 DOI: 10.1016/0006-2952(91)90405-T
    21. 21
      Togami, K.; Chono, S.; Morimoto, K. Distribution characteristics of clarithromycin and azithromycin, macrolide antimicrobial agents used for treatment of respiratory infections, in lung epithelial lining fluid and alveolar macrophages Biopharm. Drug Dispos. 2011, 32 (7) 389 97 DOI: 10.1002/bdd.767
    22. 22
      Togami, K.; Chono, S.; Morimoto, K. Subcellular Distribution of Azithromycin and Clarithromycin in Rat Alveolar Macrophages (NR8383) in Vitro Biol. Pharm. Bull. 2013, 36 (9) 1494 9 DOI: 10.1248/bpb.b13-00423
    23. 23
      Togami, K.; Chono, S.; Seki, T.; Morimoto, K. Distribution characteristics of telithromycin, a novel ketolide antimicrobial agent applied for treatment of respiratory infection, in lung epithelial lining fluid and alveolar macrophages Drug Metab. Pharmacokinet. 2009, 24 (5) 411 7 DOI: 10.2133/dmpk.24.411
    24. 24
      Togami, K.; Chono, S.; Seki, T.; Morimoto, K. Intracellular pharmacokinetics of telithromycin, a ketolide antibiotic, in alveolar macrophages J. Pharm. Pharmacol. 2010, 62 (1) 71 5 DOI: 10.1211/jpp.62.01.0007
    25. 25
      Ufuk, A.; Somers, G.; Houston, J. B.; Galetin, A. In Vitro Assessment of Uptake and Lysosomal Sequestration of Respiratory Drugs in Alveolar Macrophage Cell Line NR8383 Pharm. Res. 2015, 32 (12) 3937 51 DOI: 10.1007/s11095-015-1753-8
    26. 26
      Nadanaciva, S.; Lu, S.; Gebhard, D. F.; Jessen, B. A.; Pennie, W. D.; Will, Y. A high content screening assay for identifying lysosomotropic compounds Toxicol. In Vitro 2011, 25 (3) 715 23 DOI: 10.1016/j.tiv.2010.12.010
    27. 27
      Higham, A.; Booth, G.; Lea, S.; Southworth, T.; Plumb, J.; Singh, D. The effects of corticosteroids on COPD lung macrophages: a pooled analysis Respir. Res. 2015, 16, 98 DOI: 10.1186/s12931-015-0260-0
    28. 28
      Southworth, T.; Metryka, A.; Lea, S.; Farrow, S.; Plumb, J.; Singh, D. IFN-gamma synergistically enhances LPS signalling in alveolar macrophages from COPD patients and controls by corticosteroid-resistant STAT1 activation Br. J. Pharmacol. 2012, 166 (7) 2070 83 DOI: 10.1111/j.1476-5381.2012.01907.x
    29. 29
      Plumb, J.; Robinson, L.; Lea, S.; Banyard, A.; Blaikley, J.; Ray, D.; Bizzi, A.; Volpi, G.; Facchinetti, F.; Singh, D. Evaluation of glucocorticoid receptor function in COPD lung macrophages using beclomethasone-17-monopropionate PLoS One 2013, 8 (5) e64257 DOI: 10.1371/journal.pone.0064257
    30. 30
      Hallifax, D.; Houston, J. B. Uptake and intracellular binding of lipophilic amine drugs by isolated rat hepatocytes and implications for prediction of in vivo metabolic clearance Drug Metab. Dispos. 2006, 34 (11) 1829 36 DOI: 10.1124/dmd.106.010413
    31. 31
      Yabe, Y.; Galetin, A.; Houston, J. B. Kinetic characterization of Rat Hepatic Uptake of 16 Actively Transported Drugs Drug Metab. Dispos. 2011, 39, 1808 DOI: 10.1124/dmd.111.040477
    32. 32
      Harris, J. O.; Swenson, E. W.; Johnson, J. E., 3rd Human alveolar macrophages: comparison of phagocytic ability, glucose utilization, and ultrastructure in smokers and nonsmokers J. Clin. Invest. 1970, 49 (11) 2086 96 DOI: 10.1172/JCI106426
    33. 33
      Reynolds, H. Y.; Newball, H. H. Analysis of proteins and respiratory cells obtained from human lungs by bronchial lavage J. Lab. Clin. Med. 1974, 84 (4) 559 73
    34. 34
      Territo, M. C.; Golde, D. W. The function of human alveolar macrophages J. Reticuloendothelial Soc. 1979, 25 (1) 111 20
    35. 35
      Pauletti, M.; Wunderli-Allenspach, H. Partition coefficients in vitro: artificial membranes as a standardized distribution model Eur. J. Pharm. Sci. 1994, 1 (5) 273 282 DOI: 10.1016/0928-0987(94)90022-1
    36. 36
      Austin, R. P.; Davis, A. M.; Manners, C. N. Partitioning of ionizing molecules between aqueous buffers and phospholipid vesicles J. Pharm. Sci. 1995, 84 (10) 1180 3 DOI: 10.1002/jps.2600841008
    37. 37
      Krämer, S. D.; Wunderli-Allenspach, H. The pH-dependence in the partitioning behaviour of (RS)-[3H]propranolol between MDCK cell lipid vesicles and buffer Pharm. Res. 1996, 13 (12) 1851 5 DOI: 10.1023/A:1016089209798
    38. 38
      Ottiger, C.; Wunderli-Allenspach, H. Partition behaviour of acids and bases in a phosphatidylcholine liposome–buffer equilibrium dialysis system Eur. J. Pharm. Sci. 1997, 5 (4) 223 231 DOI: 10.1016/S0928-0987(97)00278-9
    39. 39
      Avdeef, A.; Box, K. J.; Comer, J. E.; Hibbert, C.; Tam, K. Y. pH-metric logP 10. Determination of liposomal membrane-water partition coefficients of ionizable drugs Pharm. Res. 1998, 15 (2) 209 15 DOI: 10.1023/A:1011954332221
    40. 40
      Fruttero, R.; Caron, G.; Fornatto, E.; Boschi, D.; Ermondi, G.; Gasco, A.; Carrupt, P. A.; Testa, B. Mechanisms of liposomes/water partitioning of (p-methylbenzyl)alkylamines Pharm. Res. 1998, 15 (9) 1407 13 DOI: 10.1023/A:1011953622052
    41. 41
      Krämer, S. D.; Braun, A.; Jakits-Deiser, C.; Wunderli-Allenspach, H. Towards the predictability of drug-lipid membrane interactions: the pH-dependent affinity of propanolol to phosphatidylinositol containing liposomes Pharm. Res. 1998, 15 (5) 739 44 DOI: 10.1023/A:1011923103938
    42. 42
      Balon, K.; Riebesehl, B. U.; Muller, B. W. Drug liposome partitioning as a tool for the prediction of human passive intestinal absorption Pharm. Res. 1999, 16 (6) 882 8 DOI: 10.1023/A:1018882221008
    43. 43
      Marenchino, M.; Alpstag-Wohrle, A. L.; Christen, B.; Wunderli-Allenspach, H.; Kramer, S. D. Alpha-tocopherol influences the lipid membrane affinity of desipramine in a pH-dependent manner Eur. J. Pharm. Sci. 2004, 21 (2–3) 313 21 DOI: 10.1016/j.ejps.2003.10.022
    44. 44
      Fletcher, K.; Wyatt, I. The composition of lung lipids after poisoning with paraquat Br. J. Exp. Pathol. 1970, 51 (6) 604 10
    45. 45
      Sahu, S.; Lynn, W. S. Lipid composition of human alveolar macrophages Inflammation 1977, 2 (2) 83 91 DOI: 10.1007/BF00918670
    46. 46
      Korn, E. D. Structure of biological membranes Science 1966, 153 (3743) 1491 8 DOI: 10.1126/science.153.3743.1491
    47. 47
      Honeybourne, D.; Kees, F.; Andrews, J. M.; Baldwin, D.; Wise, R. The levels of clarithromycin and its 14-hydroxy metabolite in the lung Eur. Respir. J. 1994, 7 (7) 1275 80 DOI: 10.1183/09031936.94.07071275
    48. 48
      Conte, J. E., Jr.; Golden, J. A.; Duncan, S.; McKenna, E.; Zurlinden, E. Intrapulmonary pharmacokinetics of clarithromycin and of erythromycin Antimicrob. Agents Chemother. 1995, 39 (2) 334 8 DOI: 10.1128/AAC.39.2.334
    49. 49
      Patel, K. B.; Xuan, D.; Tessier, P. R.; Russomanno, J. H.; Quintiliani, R.; Nightingale, C. H. Comparison of bronchopulmonary pharmacokinetics of clarithromycin and azithromycin Antimicrob. Agents Chemother. 1996, 40 (10) 2375 9
    50. 50
      Rodvold, K.; Gotfried, M.; Danziger, L.; Servi, R. Intrapulmonary steady-state concentrations of clarithromycin and azithromycin in healthy adult volunteers Antimicrob. Agents Chemother. 1997, 41 (6) 1399 1402
    51. 51
      Hand, W. L.; Corwin, R. W.; Steinberg, T. H.; Grossman, G. D. Uptake of antibiotics by human alveolar macrophages Am. Rev. Respir. Dis. 1984, 129 (6) 933 7
    52. 52
      Cohen, A. B.; Cline, M. J. The human alveolar macrophage: isolation, cultivation in vitro, and studies of morphologic and functional characteristics J. Clin. Invest. 1971, 50 (7) 1390 8 DOI: 10.1172/JCI106622
    53. 53
      Pratt, S. A.; Smith, M. H.; Ladman, A. J.; Finley, T. N. The ultrastructure of alveolar macrophages from human cigarette smokers and nonsmokers Lab. Invest. 1971, 24 (5) 331 8
    54. 54
      Hocking, W. G.; Golde, D. W. The pulmonary-alveolar macrophage (first of two parts) N. Engl. J. Med. 1979, 301 (11) 580 7 DOI: 10.1056/NEJM197909133011104
    55. 55
      Taylor, A. E.; Finney-Hayward, T. K.; Quint, J. K.; Thomas, C. M.; Tudhope, S. J.; Wedzicha, J. A.; Barnes, P. J.; Donnelly, L. E. Defective macrophage phagocytosis of bacteria in COPD Eur. Respir. J. 2010, 35 (5) 1039 47 DOI: 10.1183/09031936.00036709
    56. 56
      Hodge, S.; Hodge, G.; Ahern, J.; Jersmann, H.; Holmes, M.; Reynolds, P. N. Smoking alters alveolar macrophage recognition and phagocytic ability: implications in chronic obstructive pulmonary disease Am. J. Respir. Cell Mol. Biol. 2007, 37 (6) 748 55 DOI: 10.1165/rcmb.2007-0025OC
    57. 57
      van der Deen, M.; de Vries, E. G.; Visserman, H.; Zandbergen, W.; Postma, D. S.; Timens, W.; Timmer-Bosscha, H. Cigarette smoke extract affects functional activity of MRP1 in bronchial epithelial cells J. Biochem. Mol. Toxicol. 2007, 21 (5) 243 51 DOI: 10.1002/jbt.20187
    58. 58
      Kosol, S.; Schrank, E.; Krajacic, M. B.; Wagner, G. E.; Meyer, N. H.; Gobl, C.; Rechberger, G. N.; Zangger, K.; Novak, P. Probing the interactions of macrolide antibiotics with membrane-mimetics by NMR spectroscopy J. Med. Chem. 2012, 55 (11) 5632 6 DOI: 10.1021/jm300647f
    59. 59
      Joshi, U. M.; Kodavanti, P. R.; Coudert, B.; Dwyer, T. M.; Mehendale, H. M. Types of interaction of amphiphilic drugs with phospholipid vesicles J. Pharmacol. Exp. Ther. 1988, 246 (1) 150 7
    60. 60
      Fisar, Z.; Fuksova, K.; Velenovska, M. Binding of imipramine to phospholipid bilayers using radioligand binding assay Gen. Physiol. Biophys. 2004, 23 (1) 77 99
    61. 61
      Vavricka, S. R.; Van Montfoort, J.; Ha, H. R.; Meier, P. J.; Fattinger, K. Interactions of rifamycin SV and rifampicin with organic anion uptake systems of human liver Hepatology (Hoboken, NJ, U. S.) 2002, 36 (1) 164 72 DOI: 10.1053/jhep.2002.34133
    62. 62
      Nakanishi, T.; Haruta, T.; Shirasaka, Y.; Tamai, I. Organic cation transporter-mediated renal secretion of ipratropium and tiotropium in rats and humans Drug Metab. Dispos. 2011, 39 (1) 117 22 DOI: 10.1124/dmd.110.035402
    63. 63
      Nickel, S.; Clerkin, C. G.; Selo, M. A.; Ehrhardt, C. Transport mechanisms at the pulmonary mucosa: implications for drug delivery Expert Opin. Drug Delivery 2016, 13 (5) 667 90 DOI: 10.1517/17425247.2016.1140144
    64. 64
      Ong, H. X.; Traini, D.; Bebawy, M.; Young, P. M. Ciprofloxacin is actively transported across bronchial lung epithelial cells using a Calu-3 air interface cell model Antimicrob. Agents Chemother. 2013, 57 (6) 2535 40 DOI: 10.1128/AAC.00306-13
    65. 65
      Nakamura, T.; Nakanishi, T.; Haruta, T.; Shirasaka, Y.; Keogh, J. P.; Tamai, I. Transport of ipratropium, an anti-chronic obstructive pulmonary disease drug, is mediated by organic cation/carnitine transporters in human bronchial epithelial cells: implications for carrier-mediated pulmonary absorption Mol. Pharmaceutics 2010, 7 (1) 187 95 DOI: 10.1021/mp900206j
    66. 66
      Moreau, A.; Le Vee, M.; Jouan, E.; Parmentier, Y.; Fardel, O. Drug transporter expression in human macrophages Fundam. Clin. Pharmacol. 2011, 25 (6) 743 52 DOI: 10.1111/j.1472-8206.2010.00913.x
    67. 67
      Ruark, C. D.; Hack, C. E.; Robinson, P. J.; Mahle, D. A.; Gearhart, J. M. Predicting passive and active tissue:plasma partition coefficients: interindividual and interspecies variability J. Pharm. Sci. 2014, 103 (7) 2189 98 DOI: 10.1002/jps.24011
    68. 68
      Schmitt, W. General approach for the calculation of tissue to plasma partition coefficients Toxicol. In Vitro 2008, 22 (2) 457 67 DOI: 10.1016/j.tiv.2007.09.010
    69. 69
      Rodgers, T.; Rowland, M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions J. Pharm. Sci. 2006, 95 (6) 1238 57 DOI: 10.1002/jps.20502
  • Supporting Information

    Supporting Information

    ARTICLE SECTIONS
    Jump To

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.molpharmaceut.6b00908.

    • Patient demographics, experimental data, and equations (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.