ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2019

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Pharmacy 266

Characterization of parametersinfluencing intracellularbioavailability and prediction ofintracellular drug exposure

ANDREA TREYER

ISSN 1651-6192ISBN 978-91-513-0542-4urn:nbn:se:uu:diva-369705

Dissertation presented at Uppsala University to be publicly examined in Room B41, BMC,Husargatan 3, Uppsala, Friday, 15 February 2019 at 09:15 for the degree of Doctor ofPhilosophy (Faculty of Pharmacy). The examination will be conducted in English. Facultyexaminer: Prof. Dr. rer. nat. Gert Fricker (Ruprecht-Karls University Heidelberg, Departmentof Pharmaceutical Technology and Biopharmacy).

AbstractTreyer, A. 2019. Characterization of parameters influencing intracellular bioavailability andprediction of intracellular drug exposure. Digital Comprehensive Summaries of UppsalaDissertations from the Faculty of Pharmacy 266. 59 pp. Uppsala: Acta UniversitatisUpsaliensis. ISBN 978-91-513-0542-4.

This thesis work investigates factors influencing intracellular drug disposition. An experimentalmethod for measurement of intracellular bioavailability (Fic), was used throughout. Fic isdefined as the ratio between the unbound drug concentration inside the cell and the compoundconcentration in the cell exterior.

First, the impact of transporter proteins—such as the uptake transporter OATP-1B1 and theefflux transporter P-gp—on Fic was assessed in isolation in singly transfected, well-characterizedcell models. The net impact of ADME proteins on Fic, including drug transporter proteins andmetabolic enzymes, was assessed in primary human hepatocytes. The results indicated that theFic measurement accurately reflected system-dependent functionality of these proteins.

Second, the impact of cellular lipids on Fic was studied, in particular phospholipids (a majorconstituent of cellular membranes) and neutral lipids (in the form of neutral lipid dropletsin adipocytes). Drug partitioning to phospholipids was found to be the major determinant ofintracellular fraction of unbound drug (fu,cell), while neutral lipid droplets and cellular proteinsplayed a relatively smaller role. Therefore, the importance of phospholipids, and their major foursubspecies—phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylserine(PS) and phosphatidylinositol (PI)—was investigated in a cell-free approach with purifiedphospholipids.

Finally, Fic was applied in two ways to drug discovery settings. First, Fic successfullyharmonized system-dependent CYP450 enzyme inhibition values (IC50) obtained in humanhepatocytes and human liver microsomes. Fic measured in suspended human hepatocytesalso reflected hepatic enrichment factors of CYP450 inhibitors used in physiologically-basedpharmacokinetic modelling. Second, Fic was used as a complementary tool to study the effectof cell-penetrating peptides on intracellular disposition of targeted antisense oligonucleotideconjugates.

Overall, the thesis contributes to the mechanistic understanding of Fic and demonstrates itsuse for drug compound profiling at an early stage in drug discovery settings.

Keywords: intracellular drug bioavailability, unbound drug concentration, drug disposition,ADME, drug transport, drug metabolism membrane partitioning, phospholipid, drug-druginteraction, antisense oligonucleotide, cell-penetrating peptide

Andrea Treyer, Department of Pharmacy, Box 580, Uppsala University, SE-75123 Uppsala,Sweden.

© Andrea Treyer 2019

ISSN 1651-6192ISBN 978-91-513-0542-4urn:nbn:se:uu:diva-369705 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-369705)

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List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Mateus, A.*, Treyer, A.*, Wegler, C., Karlgren, M., Matsson, P.,

Artursson, P. (2017) Intracellular drug bioavailability: a new pre-dictor of system dependent drug disposition. Scientific Reports 22;7:43047 *These authors contributed equally to this work

II Treyer, A., Mateus, A., Wiśniewski, JR., Boriss, H., Matsson, P., Artursson, P. (2018) Intracellular drug bioavailability: effect of neutral lipids and phospholipids. Molecular Pharmaceutics 15(6):2224-2233

III Treyer, A., Walday, S., Boriss, H., Matsson, P., Artursson, P. A cell free approach for determination of cell specific drug binding based on phospholipid speciation. Submitted

IV Treyer, A., Mohammed U., Parrott, N., Molitor, B., Fowler, S., Artursson, P. Impact of intracellular bioavailability on metabolic drug-drug interactions. Submitted

V Treyer, A., Viljanen, J., Dahlén, A., Meuller, J., Björkbom, A., Kihlberg, J., Andersson, S., Artursson, P. Effect of cell penetrat-ing peptides on GLP-1R-mediated intracellular delivery of anti-sense oligonucleotides. In manuscript

Reprints were made with permission from the respective publishers.

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Additional paper not included in this thesis: Mateus, A., Gordon, L. J., Wayne, G. J., Almqvist, H., Axelsson, H., Sea-shore-Ludlow, B., Treyer, A., Matsson, P., Lundback, T., West, A., Hann, M. M., Artursson, P. (2017) Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery, Proc Natl Acad Sci USA 114(30):E6231-E6239

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Contents

Abbreviations .................................................................................................. 7 

Introduction ..................................................................................................... 9 Drug-lipid interactions: nonspecific drug binding ............................... 10 Drug-protein interactions: binding, transport and metabolism ............ 12 Drug distribution into organelles ......................................................... 13 In vitro methods for determination of intracellular drug concentrations ...................................................................................... 16 Intracellular bioavailability of large drug molecules ........................... 17 

Aims of the thesis.......................................................................................... 19 

Methods ........................................................................................................ 20 Compound selection ................................................................................. 20 Cell culture ............................................................................................... 21 

Cell lines .............................................................................................. 21 Human hepatocytes .............................................................................. 22 Differentiation of 3T3-L1 cells ............................................................ 22 

Intracellular bioavailability (Fic) ............................................................... 23 Intracellular compound accumulation (Kp) ......................................... 23 Intracellular fraction of unbound compound (fu,cell) ............................. 24 

Prediction of fu,cell from fu,PL ..................................................................... 25 Fraction unbound to microsomes ............................................................. 26 Confocal imaging ..................................................................................... 26 Mass spectrometry based quantification of small molecules ................... 27 Mass spectrometry based proteomics ....................................................... 27 Mass spectrometry based phospholipid profiling ..................................... 27 Colorimetric assays .................................................................................. 28 

Results and discussion .................................................................................. 29 Role of ADME proteins in intracellular bioavailability (Paper I) ............ 29 

Impact of uptake transporter OATP-1B1 on Fic .................................. 30 Impact of efflux transporter P-gp on Fic .............................................. 31 Impact of multiple transporters and metabolism on Fic ....................... 32 

Role of lipids in intracellular bioavailability (Paper II) ........................... 34 Impact of phospholipids on Fic ............................................................ 36 Impact of neutral lipids on Fic .............................................................. 37 

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Prediction of fu,cell from cell-free assays (Paper III) .................................. 38 fu,cell in comparison to total phospholipid content in cells.................... 38 fu,cell in comparison to lipid affinities ................................................... 39 

Applications of Fic in drug discovery settings (Papers IV and V) ............ 41 Fic as correction factor in drug-drug interaction studies ...................... 41 Fic of antisense oligonucleotides .......................................................... 43 

Conclusions ................................................................................................... 46 

Populärvetenskaplig sammanfattning ........................................................... 47 

Acknowledgements ....................................................................................... 49 

References ..................................................................................................... 52 

7

Abbreviations

ABC transporters ATP-binding cassette transporters ASO antisense oligonucleotide A549 adenocarcinomic human alveolar basalepithelial cells Caco-2 human epithelial colorectal adenocarcinoma cells CPP cell penetrating peptide Cu,cell unbound drug concentration in the cell interior Cu,plasma unbound drug concentration in plasma FASP filter-aided sample preparation FBS fetal bovine serum Fic intracellular bioavailability fu,cell intracellular fraction of unbound compound fu,hom unbound fraction in cell homogenate fu,mic unbound fraction in liver microsomes fu,PL unbound fraction to phospholipid membranes GLP-1R glucagon-like peptide-1 receptor HBSS Hanks' Balanced Salt Solution HEK293 human embryonic kidney 293 cells HH human hepatocytes HLM human liver microsomes HL-60 human leukemia cell line IC50 half maximal inhibitory concentration Ki inhibitory constant Km drug affinity constant Kp compound accumulation ratio at steady-state Kpuu unbound drug accumulation ratio at steady-state K562 myelogenous leukemia cells derived from bone marrow LLC-PK1 Lilly Laboratories Cell-Porcine Kidney 1 LogD7.4 octanol-buffer coefficient at pH 7.4 MDCK Madin-Darby canine kidney cells MTBE methyl-tert-butyl ether MW molecular weight NL neutral lipid OATP-1B1 organic anion transporting polypeptide 1B1 PA phosphatic acid PBS phosphate-buffered saline PC phosphatidylcholine

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PE phosphatidylethanolamine PG phosphatidylglycerol P-gp P-glycoprotein 1 PI phosphatidylinositol PL phospholipid PS phosphatidylserine PSA polar surface area SLC transporters solute carrier transporter family TPA total protein approach UGT Uridine 5'-diphospho-glucuronosyltransferase UPLC-MS/MS Ultra performance liquid chromatography-tandem mass spectrometry Vmax maximum transport or metabolic rate

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Introduction

After administration of a drug, the disposition is dictated by its absorption, distribution, metabolism and excretion (ADME) properties. In the case of sys-temic administration, drugs are absorbed from the administration site into the blood stream, through which they are distributed throughout the body. Most drug molecules bind to serum proteins, such as albumin and the α1-acid gly-coprotein. However, according to the free drug principle, only the unbound plasma concentration (Cu,plasma) is available to partition into tissues and reach target cells.1,2 These unbound drug molecules need to cross the cellular mem-branes through passive or active processes. Once inside the cell, only a frac-tion of drug molecules are available in the unbound form (Cu,cell) to reach in-tracellular targets. It is now widely recognized that intracellular unbound con-centrations are not necessarily equal to the unbound concentration in the blood, due to active transport and clearance processes in the cell.3,4

Accurate determination of drug exposure in the cell interior is one of the big challenges in drug discovery and development. It is critical for the predic-tion of pharmacological and off-target effects (toxic effects or interactions with drug-metabolizing enzymes).5,6 Yet, while it is relatively easy to deter-mine Cu,plasma from blood samples, the determination of Cu,cell in tissues is more challenging. First, invasive methods are required to obtain tissue biopsies, and second, nonspecific drug binding in cells is less well described than nonspe-cific binding to plasma proteins.

Unbound accumulation ratios at steady-state (Kpuu) have therefore been in-troduced as a tool to describe how much compound is available in the target tissue (e.g., brain7,8) or target cells (e.g., hepatocytes9-11) (Figure 1). In this work, the nomenclature of intracellular bioavailability (Fic) is used throughout to describe the unbound drug concentration in the cell.

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Figure 1. Unbound accumulation ratios at steady-state (Kpuu) describe the ratio be-tween the unbound drug concentration in two adjacent compartments, e.g., between the unbound concentration in plasma or extracellular fluids, and the unbound concen-tration in the cell interior. Unbound concentrations are influenced, among other fac-tors, by (1) non-specific binding to proteins; (2) membrane partitioning; (3) passive permeation; or (4) transporter-mediated flux across membranes. Metabolic clearance processes further influence the unbound concentrations.

The amount of drug available in the cell interior is the result from the interac-tions of drug molecules with the major cellular components, such as lipids, proteins, nucleic acids or carbohydrates. Drug-lipid and drug-protein interac-tions and distribution of drug into subcellular compartments are discussed be-low, followed by an overview of in vitro methods for determination of Kpuu.

Drug-lipid interactions: nonspecific drug binding Drug-lipid interactions occur at all levels of the drug distribution process: in-testinal drug absorption, tissue distribution, and subcellular distribution.12

Cellular membranes are formed by amphiphilic polar lipids, the glycer-ophospholipids. Glycerophospholipids contain a diacylglycerol backbone car-rying two fatty acyl chains and a phosphate group that is an esterified head-group, which is either a choline, ethanolamine, serine, inositol, or glycerol (Figure 2). The major phospholipid subclasses in mammalian cell membranes are phosphatidylcholines (PC), followed by phosphatidylethanolamine (PE), phosphatidylinositol (PI) and phosphatidylserine (PS). PC and PS have a cy-lindrical geometry that favors assembly into bilayers. PE and other subspecies come in different shapes, influencing the surface tension and hence the curva-ture of the bilayer.13-15 Other structural lipid classes include sphingolipids, that contain a ceramide backbone, and sterols, that affect the fluidity of the mem-brane.15,16 All these lipids together form complex dynamic systems, and the lipid composition can vary between organellar membranes, and even between the inner and outer leaflet of a phospholipid bilayer.15

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Figure 2. A. Structure of phospholipid bilayers B. Scaffold structure of glycerophos-pholipids C. Head groups of the major phospholipid subclasses. Panels B and C were drawn based on information available in the online AOCS Lipid Library.

Drug partitioning into biological membranes has been suggested to be the pri-mary mechanisms of drug sequestration (i.e., nonspecific binding).17 There-fore, it is a highly relevant factor for intracellular drug disposition.

Drug-lipid interactions are described in terms of molar partition coeffi-cients (Kp) or binding- and dissociation constants (Kb or Kd).12 An easy and widely accessible partition coefficient with much experimental data is the oc-tanol:water coefficient (logD) which describes the hydrophobic interactions of drugs with octanol.18 logD is therefore widely used as a surrogate for mem-brane partitioning.19,20 However, unlike the octanol phase, biological mem-branes are not homogenous and there is clear evidence that electrostatic inter-actions of ionized compounds with biological membranes are underestimated by assumptions that are based merely on logD.12,18,21

Advanced structure-based models and modern tools such as molecular dy-namics simulations allow better understanding of these drug-lipid interactions and the positioning of drugs within phospholipid bilayers (Figure 3).22 These theoretical approaches can be complemented with a variety of experimental approaches measuring drug-lipid interactions. These include, among others, spectroscopic techniques (ultraviolet-visible, infrared), fluorescence (e.g., flu-orescence anisotropy, fluorescence resonance energy transfer), nuclear mag-netic resonance (NMR), differential scanning calorimetry (DSC), positron emission tomography (PET) or surface plasmon resonance (SPR).16 Small-scale experimental methods that allow direct measurements of drug partition-ing into intracellular membranes remain however challenging, and are one of the topics addressed in this thesis.

12

Figure 3. Predicted orientation of compounds in phospholipid membranes, based on the model by Nagar et al, 2017. a-i represent molecules with diverse polar surface properties that position preferentially in the polar headgroup region (a, b, g, i) or hy-drophobic core (c, d, e, f, h). Figure reprinted with permission from publisher.22

Besides providing a structural scaffold to the cells, lipids also store energy. For this purpose, lipids are converted into neutral lipids, mainly triacylglycer-ols and sterol esters, and are stored in highly specialized compartments, the lipid droplets.23-25 All cells contain lipid droplets, but they predominate in ad-ipose cells.24 Accumulation of drugs in these lipid reservoirs has been de-scribed for a few lipophilic drugs,26-28 but surprisingly little is known about how these lipophilic environments affect e.g., volume of distribution of drugs. Lipid droplets play a role in pathologies linked to lipid accumulation, such as obesity, diabetes and atherosclerosis,24 and can potentially affect drug dispo-sition in diseased patients.

Drug-protein interactions: binding, transport and metabolism Drug disposition and pharmacokinetic behavior are also affected by plasma protein binding.1 Specialized proteins, such as serum albumin and α1-acid gly-coprotein distribute drug-like molecules throughout the body, but at the same time they also reduce Cu,plasma (Figure 4A).

For poorly permeable compounds, transporter proteins spanning the cellu-lar membranes facilitate uptake into or efflux out of the cells (Figure 4B). Protein channels provide a hydrophilic passageway across membranes and fa-cilitate drug transport in the direction of concentration and electrochemical gradients. Active uptake or efflux transporter proteins—such as those from the solute carrier family (SLC-transporters) or the ATP-binding cassette trans-porter superfamily (ABC-transporters)—can transport drugs against these gra-dients. Prominent examples of the ABC-transporter family are the efflux transporters ABCB1 (also known as P-glycoprotein, P-gp, or multi-drug re-sistance protein 1, MDR1) and ABCG2 (also known as breast cancer re-sistance protein, BCRP)). The SLC-transporter family includes the uptake transporters in the SLC22 subfamily (e.g., organic anion transporters, OAT and organic cation transporters, OCT) and the SLCO subfamily (organic anion transporting polypeptides, OATP).29

13

Inside cells, metabolic enzymes, such as the cytochrome P450 proteins (CYP450) or uridine 5'-diphospho-glucuronosyltransferases (UGTs), contrib-ute to the clearance of the drug from the cell.

Drug uptake and clearance rates eventually result in an equilibrium in which the ratio between intracellular and extracellular free drug concentra-tions reach a steady-state. This transporter-enzyme interplay is particularly well described for hepatocytes,30-32 but occurs in any biological cell.

Figure 4. Proteins affecting intracellular drug disposition by various mechanisms. A. Nonspecific binding of drugs to serum proteins. B. Drug channels and carrier proteins altering entry- and efflux rates across cellular membranes C. Drug clearance through metabolic enzymes

Drug distribution into organelles Organelles are subcellular compartments with biochemically distinct proper-ties and specialized physiological functions. The most prominent organelles (e.g., mitochondria, endo-lysosomes, Golgi, endoplasmic reticulum) are sur-rounded by phospholipid membranes. Electrochemical and pH gradients occur across organelle membranes, which can influence drug distribution, pharma-cokinetic behavior and activity.30,33 Accumulation of drugs in the acidic lyso-somes and in the mitochondria with negative membrane potential (Figure 5) are well-described mechanisms, but accumulation can occur in all organelles (Table 1).

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Figure 5. Scheme demonstrating the processes involved in the uptake of weak bases into cells and subcellular compartments. B: weak base in its neutral form. BH+: weak base in its protonated form. Em: membrane potential. Figure modified with permis-sion, Ufuk et al., (2017).34

The low pH in lysosomes (pH ~5) is maintained through two types of transport proteins in the lysosomal membrane, proton-pumping ATPases and anion channels.35 Accumulation of weak bases in the lysosomes through pH parti-tioning is referred to as “lysosomal trapping” of drugs. These lysosomotropic drugs typically contain a weakly basic amine functional group and have a large apparent volume of distribution and a prolonged half-life in vivo.33 At neutral pH in the cytosol (pH ~7), these drugs predominate in their neutral form and easily permeate the lysosomal membrane. Due to the low pH in the lysosome, the drugs become protonated. In their ionized form, their permeability across the membrane decreases drastically and the drugs accumulate in the organelle. Tissues that are rich in lysosomes, such as lungs, liver and kidneys are partic-ularly prone to drug accumulation due to lysosomal trapping.36

Accumulation in mitochondria is driven by the negative potential differ-ence of the inner membrane (~-160 mV) and the alkaline environment inside the organelle (pH ~8). Positively charged and highly lipophilic drugs accumu-late preferentially in mitochondria, followed by negatively charged com-pounds.37

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16

In vitro methods for determination of intracellular drug concentrations Current in vitro methods for estimation of the unbound accumulation ratio at steady-state (Kpuu) are classified into the binding method, the temperature method, structure-based predictions, kinetic models and methods based on the extended clearance model (Table 2).39

The binding method, the structure-based prediction model, and the temper-ature method all determine the cell-to-medium total drug accumulation ratio at steady-state (Kp), but differ in determinations of the unbound drug fraction inside cells: In the binding method, fu,cell is determined using equilibrium dial-ysis of cell homogenates; in the structure-based prediction model fu,hep logD is

calculated from a model based on logD7.4; and in the temperature method, the unbound fraction is determined indirectly by a comparison of Kp at physio-logical temperature (37°C) and at a low temperature (4°C) at which most en-ergy-dependent processes are shut down.

The other two methods are optimised for hepatocytes and are based on clearance kinetics. The extended clearance model takes into account the indi-vidual processes involved in hepatic clearance, such as active and passive he-patic uptake and efflux, metabolism and biliary secretion; and the kinetic mod-elling method is based on determinations of kinetic parameters, including the maximum transport or metabolic rate (Vmax) and the drug affinity constant (Km).

Table 2. Summary of methods for determination of Kpuu.39

Method Predictive equation References

Binding method ∙ , 9-11,40,41

Structure-based prediction ∙ ,

, 09161 0.2567 ∙ .

20,40

Temperature method 37° 4°

42,43

Extended clearance model ,

, 40,44

Kinetic modelling 1 , ; , 20

, ,membrane permeation clearance of unbound drugs for uptake across the sinusoidal membrane via transporter(s); , , membrane permeation clearance of unbound drugs for efflux across the sinusoidal membrane via transporter(s); , membrane permeation clearance of unbound drugs for uptake or ef-flux across the sinusoidal membrane by passive diffusion; , hepatic intrinsic clearance (of unbound drugs) for biliary excretion of unchanged drugs; , hepatic intrinsic clearance (of unbound drugs) for metabolism of unchanged drugs; Km, drug affinity constant; Vmax, maximum transport or metabolic rate.

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To date, data are too limited to allow direct comparisons of these different methods and therefore no recommendations or gold-standard have been de-fined as yet. The method used in this thesis is the binding method. It was de-veloped in analogy with measurements of Kpuu in brain,7,45 and was first pub-lished in our laboratory by Mateus et al.9,46 This method was further charac-terized and developed in the present work.

This method makes use of equilibrium dialysis with cell homogenates for determination of the fraction of drug unbound to cell (fu,cell) and steady-state cellular uptake measurements (Kp). One of the advantages of this method is its small-scale and high-throughput format, compared to the extended clear-ance model and kinetic modelling method that need extensive collection of experimental data.47 The dialysis approach for the determination of fu,cell al-lows cassette-mode measurement of drug binding in a 96-well format, and is suitable for high-throughput screens.46 Moreover, no prior knowledge of com-pound elimination kinetics or metabolic pathways is required.

The method relies on the assumption that binding of drugs is not altered upon homogenization of the cells for equilibrium dialysis. Alternative meth-ods to equilibrium dialysis, is to estimate the binding of drug to cells by inhi-bition of all active processes with chemical inhibitors48,49 or reduction of tem-perature.50 The use of chemical inhibitors assumes that all known transport and metabolism mechanisms can be inhibited, while lowering the temperature assumes that active processes are sufficiently slowed down at the low temper-ature and changes in membrane fluidity do not affect drug binding.39,47

A limitation of determination of Kpuu via the binding method is the sub-cellular resolution of drug distribution. In standard conditions, Kpuu only gives the average bioavailability in the cell, but drug concentrations can vary in dif-ferent organelles. This can be overcome by elevation of intra-lysosomal pH by the use of chemical inhibitors, or by mathematical modelling of pH and electrochemical gradients.47

Intracellular bioavailability of large drug molecules All of the abovementioned methods have been optimized for determination of intracellular concentrations of small molecules. New challenges are arising, as drug discovery programs focus on so called new drug modalities. These comprise peptides, peptidomimetics, nucleic acids, hybrid macrocycles and molecular conjugates.51 To address potential challenges with these new drug classes, the last chapter of this thesis studied intracellular bioavailability of antisense oligonucleotide-peptide-conjugates.

Therapeutic antisense oligonucleotides (ASO) are used for selective knock-down of target genes.52 Due to the negative charges residing on the phos-phodiester or phosphorotioate backbone, ASOs are repelled by negative charges in phospholipid membranes. For efficient drug uptake into cells,

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ASOs therefore need to adsorb to cell surface proteins, which induces inter-nalization via adsorptive endocytosis.52,53 A proven strategy to increase cellu-lar uptake is to conjugate a high-affinity ligand for a cell surface receptor to the ASO, such as a ligand for integrins, G-protein-coupled receptors or scav-enger receptors.52,54,55 After internalization by endosomes, the ASOs are cleared in lysosomes, unless they escape to the cytosol (Figure 6).

Figure 6. Main pathway for cellular uptake of ASOs. 1. Cell-surface adsorption to receptor proteins. 2. Internalization via endocytotic pathway. 3. Maturation into lyso-somes and breakdown of ASOs. 4. Endolysosomal escape of ASO to reach the cytosol and nucleus and act on target mRNA.

Both internalization and endo-lysosomal escape can be enhanced through con-jugation of ASOs to cell penetrating peptides (CPP).56 The effect of a cyclic CPP on intracellular bioavailability of ASO conjugates was evaluated by de-termination of Fic in presence and absence of CPP. These studies were com-plemented with confocal imaging to apprehend subcellular distribution of the ASO conjugates.

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Aims of the thesis

The main objective of this thesis was to further characterize and develop our method for the determination of intracellular drug concentrations. The specific aims were: to study the impact of ADME proteins, such as transport proteins and met-

abolic enzymes, on system-dependent intracellular bioavailability (Pa-per I)

to study the impact of neutral lipids and phospholipids on system-depend-ent intracellular bioavailability (Paper II)

to assess the contribution of individual phospholipid subspecies to unspe-cific intracellular binding of drugs, and to improve predictions of cellular drug disposition from cell-free assays (Paper III)

to apply the concept of intracellular bioavailability within drug discovery

and development programs, as exemplified by metabolic drug-drug inter-action studies (Paper IV) and by the use of cell penetrating peptides in combination with antisense oligonucleotide-conjugates (Paper V).

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Methods

Compound selection The compound sets in papers I to IV were chosen to represent the chemical space of small-molecule drugs. Special focus was given to the main physico-chemical properties molecular weight (MW), lipophilicity (logD7.4), polar sur-face area (PSA), and charge (Figure 7).

Atorvastatin and lopinavir were used as standards in most of the experi-ments, independently of whether these compounds were part of the studied data-set or not. These compounds have been used to assess reproducibility of the data during method development. The average deviation from the median fu,cell over all experiment was 1.3-fold for both compounds, and the average deviation from the median Kp over all experiments was 1.6-fold for atorvas-tatin and 1.3-fold for lopinavir.57

Figure 7. Distribution of the main physicochemical properties molecular weight (MW), lipophilicity (logD7.4) and polar surface area (PSA) of compound sets used in papers I to IV. Compound set I comprised 34 compounds with a major focus on trans-porter substrates (OATP-1B1 and/or P-gp). Compound set II comprised 23 com-pounds, selected after principal component analysis of predicted ADME-related mo-lecular descriptors (lower panel). Six compounds overlapped with compound set I. Compound set III was a subset of compound set II. In compound set IV, both literature compounds and a series of an industrial discovery program (Roche Laboratories, high-lighted in green) with similar physicochemical properties was included. Three com-pounds in this set overlapped with compound set I.

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In paper I, the compounds were selected to overlap with compounds in pre-vious studies measuring intracellular unbound drug concentrations.9 Com-pounds that were substrates of OATP1B1 (n=11) and/or P-gp (n=22) were included to allow the study of transporter-mediated drug uptake and efflux, respectively.

In paper II, a selection from the compounds in the Prestwick Library (n=1197) was made. First, 334 ADME-related molecular properties for the complete library were calculated using ADMET Predictor (Simulations Plus, version 7.2). These properties were the basis for a principal component anal-ysis in Simca-P (Umetrics, version 12.0), to separate the compounds by their ADMET properties. 23 compounds representative of the chemical space were selected.

Paper III was a continuation of paper II using a subset of 18 out of the 23 compounds. The standard atorvastatin was included in this data-set, giving a total of 19 compounds.

In paper IV, compounds with known CYP450-mediated DDI potential were selected. Seven compounds from the literature, and a series of nine com-pounds of a drug discovery series from Roche Laboratories (Basel, Switzer-land) were included; the latter nine contained a common core structure.

In paper V, conjugates from a drug development setting at AstraZeneca (Mölndal, Sweden) were used, containing an antisense oligonucleotide (ASO), a peptidic targeting sequence (targeted at GLP-1R) and a cell pene-trating peptide (CPP).

Cell culture Cell lines Various cellular systems of different complexity were used for the different projects (Table 3). HEK293 cells and MDCK cells are commonly used cell models for uptake or efflux studies, and for transfection of single proteins.58 The mouse 3T3-L1 fibroblast are committed to the adipose lineage and widely used to study adipogenesis in vitro.59 A549, K562, HL60, Caco-2 and LLC-PK1 cells were used as tissue-specific cell lines (lung, bone marrow, blood cells, colon and kidney, respectively).

Cells lines were cultured according to standard procedures in a cell culture facility. Cells were maintained at 37°C in a humidified 5% CO2 atmosphere (10% CO2 for Caco-2 cells). Whenever possible, the cells were maintained without use of antibiotics. Hygromycin B was used as selection antibiotic for transfected clones.

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Table 3. Overview of cell types used in Papers I to V.

Paper Cell type Purpose

I HEK293 - mock transfected - OATP1B1 transfected MDCK - CRISPR-Cas9 canine P-gp knockout - human P-gp transfected Freshly isolated human hepatocytes - suspended - plated

Impact of uptake transporter on Fic

Impact of efflux transporter on Fic

System–dependent net impact of multiple transporters and meta-bolic enzymes on Fic

II 3T3-L1 - wild-type - PL induced - differentiated into adipocytes

Impact of neutral lipids and phos-pholipids on Fic

III A549, K562, HL-60, Caco-2, HEK293, LLC-PK1, MDCK, human hepatocytes

Phospholipid profiling and comparative study of fu,cell

IV Cryopreserved human hepatocytes Fic as scaling factor for apparent IC50 values

V

HEK293 - wild-type - GLP-1R transfected

Fic of GLP1R-mediated uptake of antisense-oligonucleotide conju-gates in presence of a cell-pene-trating peptide

Human hepatocytes The hepatocytes in Paper I were isolated from liver samples using a two-step collagenase procedure.60,61 Liver samples were obtained from the Department of Surgery, Uppsala University Hospital, with ethical approval no. 2009/028 and 2011/037. All donors gave informed consent and all studies were per-formed in accordance with the current national regulations and ethical guide-lines.

Experiments in paper IV were performed with a cryopreserved hepatocyte batch pooled from ten donors (BioreclamationIVT).

Differentiation of 3T3-L1 cells For differentiation of 3T3-L1 fibroblasts into a steatotic, adipocyte-like phe-notype, confluent cells were incubated in differentiation medium supple-mented with a hormonal cocktail containing insulin, dexamethasone and 3-butyl-1-methylxanthine (IBMX). IBMX inhibits a phosphodiesterase and hence the breakdown of lipids.59 From day 3 post-induction, the differentiation

23

medium was replaced with adipocyte maintenance medium containing insulin. This medium was replaced every second day until full maturation of the adi-pocytes (typically reached after 8-10 days).

To induce accumulation of phospholipids (PL), propranolol was added to the culture medium for 48 h. Amphiphilic cationic drugs, such as propranolol, increase PL and cholesterol biosynthesis, and reduce lysosomal phospholipase activity, and thereby induce phospholipidosis.62,63 The extent of PL induction in the 3T3-L1 cell model was time- and concentration dependent. Induction for 48 h with 20 µM propranolol was deemed ideal, because in these condi-tions there was a clear increase in PLs, but without morphological changes characteristic of manifest phospholipidosis. This allowed the study of the im-pact of PLs in isolation.

Intracellular bioavailability (Fic) Intracellular bioavailability (Fic) is defined as the ratio between the unbound drug concentration in the cell (Cu,cell) and the concentration in the cell exterior (Cmedium):

, , ∙ (eq. 1)

It is an extension of the traditionally used unbound drug accumulation ratio

(Kpuu), i.e., the ratio between the unbound drug concentration in the cell (Cu,cell) and the unbound drug concentration in the cell exterior (Cu,medium).9,64 Since cellular screens in drug discovery settings are often performed in the presence of serum proteins, Kpuu is not directly applicable and would require additional experiments determining Cu,medium. Fic is a more inclusive term and can be directly applied as a scaling factor to estimate the unbound concentra-tion in the cells where the concentration in the cell exterior is known. When there are no binding sites present in the medium, Cmedium equals Cu,medium and then Fic is equal to the commonly used term Kpuu.

Unlike oral bioavailability, which is defined as fraction of dose (i.e., amount), Fic is defined in concentration terms. Therefore, Fic can take any pos-itive value and is not limited to the range between zero and one.47

Intracellular compound accumulation (Kp) The intracellular compound accumulation ratio at steady-state (Kp) is de-

fined as

⁄ eq.2

where Acell is the amount of compound in the cell and Vcell is the cell volume.

24

Kp was measured in confluent cells on 24- or 96-well plates, generally 24 to 48 h after cell seeding or after full maturation of the cells. Cells were washed twice with pre-warmed buffer (Hank's Balanced Salt Solution (HBSS), pH 7.4) and solutions of test compounds were added for 45 minutes at 37 °C to reach equilibrium. A sample of the extracellular compound solu-tion was taken and diluted 10x in acetronitrile:water solutions (60:40) con-taining internal standard for UPLC-MS/MS quantification of Cmedium.

Immediately after removal of the remaining compound solution the cells were washed twice with ice-cold phosphate-buffered saline (PBS). Intracellu-lar compound was extracted from the cells with acetronitrile:water solutions (60:40) containing internal standard for determination of Acell.

Vcell was determined by quantification of cellular proteins in representative wells using the Pierce BCA Protein Assay Reagent Kit (Thermo Fisher Sci-entific) and assuming 6.5 µL/mg protein.65,66

The Cy3 labelled ASO-GLP1 conjugates (Paper V) were not compatible with classic LC-MS/MS analysis and were therefore quantified based on flu-orescence. For this purpose, cells were grown on black 96-well plates with transparent-bottom.

Intracellular fraction of unbound compound (fu,cell) Intracellular fraction of unbound compound (fu,cell) was determined by equi-

librium dialysis on cell homogenates. Frozen cell pellets were thawed on ice and suspended at 10 million cells/ml. The suspensions were homogenized by sonication (VCX 750 Sonicator; Papers I-III) or a mini-bead beater (Precellys; Paper IV). The cell homogenate was spiked with up to eight compounds for cassette-dosing.46 The spiked homogenate was dialyzed against blank buffer for 4 h in a Rapid Equilibrium Dialysis Device (Thermo Fisher Scientific). After equilibration, cell and media samples were collected and diluted with acetronitrile:water solution (60:40) containing internal standard for UPLC-MS/MS analysis. Matrices of these samples were matched by adding blank cell homogenate to the buffer samples and blank buffer to the cell samples.

The unbound fraction in the cell homogenate (fu,hom) was determined as fol-lows:

, (eq. 3)

where Cbuffer is the concentration in the buffer chamber and Chom is the con-

centration in the chamber containing the cell homogenate. fu,cell was derived from fu,hom as follows:

25

,∙

,

(eq. 4)

where DP was determined for each homogenate preparation:

(eq. 5)

Cellular volume (Vcell) was calculated from protein content in the cell homog-enate as described above.

Prediction of fu,cell from fu,PL

The fraction of unbound drug to pure phospholipid membranes (fu,PL) was de-rived from membrane affinity measurements to PL bilayers immobilized on silica beads (TRANSIL Intestinal Absorption Kit).67

Membrane affinity is defined as:

(eq. 6)

and fu,PL is derived as follows:

, (eq. 7)

fu,PL was used for prediction of fu,hom using eq. 4, but by replacing DP (which accounts for cellular volume) with DL. DL was interpreted as a dilution factor accounting for the reduced binding capacity in homogenates as compared to the pure PL membranes, and thereby allowed scaling of fu,hom,pred from fu,PL. DL was optimized for each cell type to minimize the sum of the squared errors between experimental and predicted fu,hom. The full procedure is illustrated in Figure 8.

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Figure 8. Determination of fu,cell using cell homogenates (left side) or cell-free PL bi-layers immobilized on silica beads (right side). DP and DL are dilution factors used in eq. 4, to account for cellular volume (6.5 µL/mg protein) and binding capacities of a given matrix, respectively. Once DP and DL are established for a given cell line, the cell-free assay can be used to predict fu,cell in large screening processes. Figure adapted from Paper II.

Fraction unbound to microsomes The fraction of compound unbound to human liver microsomes (fu,mic) was determined by performing a membrane dialysis in analogy to fu,hom. Human liver microsomes diluted to 0.2 mg/ml and spiked with compound were used instead of cell homogenate, and fu,mic was calculated in analogy with eq. 3.

Confocal imaging Confocal images of cells were acquired on a Zeiss LSM 700 confocal micro-scope with a Plan-Apochromat 20x/0.8 objective or LSM710 confocal micro-scope with a Plan-Apochromat 40x/1.3 Oil DIC M27 objective.

NLs were stained with AdipoRed Adipogenesis Assay Reagent (Lonza), a dye based on the hydrophilic stain Nile Red. The fluorescent emission spectra of Nile Red is dependent on whether the environment consists of neutral or polar lipids.68

PLs were visualized by adding a fluorescently labelled phospholipid (N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)-1,2-dihexadecanoyl-sn-glycero-3-phos-phoethanolamine; NBD-PE) to the culture medium. NBD-conjugated phos-pholipids are specific reporters of vacuoles rich in polar lipids.68

Lysosomes were stained using LysoTracker Red DND-99 (Thermo Fisher Scientific). This fluorescent dye is a weak base that is only partially protonated

27

at neutral pH, which allows free permeation across cell membranes, and pref-erentially partitions into acidic cellular compartments.

Nuclei were stained using the standard nuclear stains DAPI (fixed cells) or Hoechst33342 (living cells).

The conjugates used in paper V were covalently linked to the cyanine dye Cy3 and no additional labelling was required.

Mass spectrometry based quantification of small molecules Compound quantification was performed using UPLC-MS/MS. The system consisted of a Waters Xevo TQ MS with electrospray coupled to a Waters Acquity UPLC. Compounds were chromatically separated on a Water BEH C18 column (2.1 x 50 mm; 1.7 µm) with a 2 min gradient elution at a flow rate of 0.5 ml/min, unless otherwise stated.

Mass spectrometry based proteomics For UPLC-MS/MS based quantification of proteins, the cell pellets were lysed with detergents and the proteins denatured by heating. After sonication, the lysates were clarified by centrifugation and subjected to the filter-aided sam-ple preparation protocol (FASP) that uses enzymatic digestion with trypsin and/or Lys-C.69,70

For targeted proteomics analysis, the samples were spiked with stable iso-tope labelled peptides, analyzed on a QTRAP6500 mass spectrometer (Sciex), and quantified against the standard.

For global proteomics, label- and standard-free samples were analyzed on a QExactive mass spectrometer (Thermo Fisher Scientific) and quantified us-ing the “Total Protein Approach”.70

Mass spectrometry based phospholipid profiling The proportional content of PL subclasses in the cells was determined using a mass spectrometric shotgun approach. The lipids were extracted from the cel-lular homogenates with a modified Bligh and Dyer extraction approach that uses methyl-tert-butyl ether (MTBE) and gives high recoveries—approxi-mately 90%—for several PL subclasses.71 The samples were dissolved in anal-ysis buffer (consisting of isopropanol, methanol and water (5:1:4, v/v/v) con-taining 0.2% formic acid and 0.028% ammonium acetate72), then infused into

28

the ion source of a Sciex QTRAP 6500 mass spectrometer. Individual PL sub-classes were identified simultaneously using specific fragments of each PL subclass: these used a precursor ion scan of 184 Da m/z for PC and SM; or a neutral loss scan of 141 Da m/z for PE; 185 Da m/z for PS; 98 Da m/z for PA; 277 Da m/z for PI; and 172 Da m/z for PG.73,74 A standard mixture of known concentration of each PL class (LIPIDOMIX kit, Avanti) was used as refer-ence and for normalization of ion intensities.

Colorimetric assays The protein content in cell samples of Kp and fu,cell experiments was quantified using the colorimetric bicinchoninic acid (BCA) assay (Pierce, Thermo Fisher Scientific) according to the manufacturer’s instructions. The assay is based on reduction of Cu2+ ions by proteins. The total PL content in cell homogenates was quantified using the colorimetric enzymatic WAKO LabAssay Phospholipid-Choline Oxidase/DAOS method (Nordic Biolabs) according to the manufacturer’s instructions. The assay is based on hydrolysis and oxidation of the choline subunit of PLs.

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Results and discussion

Role of ADME proteins in intracellular bioavailability (Paper I) In this paper, the impact of transport proteins and metabolic enzymes on Fic

was assessed in apposite cell types. Two cell lines overexpressing an uptake transporter (OATP-1B1) or efflux transporter (P-gp), were used to evaluate the influence of these transporters on Fic. The effect of multiple transporters and metabolic enzymes was studied in primary human hepatocytes. An over-view of the attributed impact of these ADME proteins on Fic is presented in Figure 9.

Figure 9. Impact of drug-transporting proteins and metabolizing enzymes on intracel-lular drug bioavailability. Uptake transporters (green, e.g., OATP1B1) increase the Fic, while efflux transporters (light orange, e.g., P-gp) and metabolizing enzymes (dark orange, e.g., CYP3A4) lower the Fic. Small arrows represent diffusion over the plasma membrane (passive lipoidal transmembrane diffusion or carrier-mediated diffusion). Figure reprinted from Paper I.

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Impact of uptake transporter OATP-1B1 on Fic The effect of the uptake transporter OATP-1B1 on Fic was studied in OATP-1B1-transfected HEK293 cells. Mock-transfected HEK293 cells were used as control. A comparison of the Fic of 34 drugs for both cell lines showed that Fic for substrates was increased in the transporter-expressing cells by 2.9-fold on average (range: 1.1 to 9.8-fold, p=0.001, Wilcoxon matched-pairs signed rank test) while Fic of non-substrates was not affected (range: 0.57 to 1.9-fold) (Fig-ure 10A). Pitavastatin and atorvastatin—well-established OATP1B1 sub-strates75,76—showed the largest differences in Fic with 8.3 and 9.8-fold differ-ence, respectively.

Four compounds from the statin family were used for more detailed con-centration-dependence studies (0.01 to 100 µM). The Fic in mock-transfected cells was not concentration-dependent. This base-line Fic was below 1 for pitavastatin and atorvastatin, suggesting that these compounds have limited cell penetration in the absence of transporter proteins. On the other hand, Fic in the OATP-1B1 transfected cells decreased with increasing compound con-centrations (Figure 10B). The equilibrium at saturating concentrations reached the same levels observed across the whole concentration range in mock-transfected cells. This supported the notion that the differences in Fic were dependent on active transport. The concentrations at which Fic was half of its maximal value corresponded to reported Km values for OATP1B1-me-diated transport of the respective substrates, giving further evidence for transport mediated by this transporter.76-78

Figure 10. Impact of the uptake transporter OATP1B1 on Fic. A. Comparison between Fic in mock-transfected and OATP1B1-transfected HEK293 cells at 0.1 µM compound concentration. Negatively charged compounds at pH 7.4 are represented as triangles; neutral and zwitterionic species are represented by circles; and positively charged compounds are represented by squares. Substrates of OATP1B1 are highlighted in green. B. Concentration-dependence of Fic in OATP1B1-transfected HEK293 cells (green filled squares), fitted with a sigmoidal model (green line), and mock-trans-fected HEK293 cells (blue circles) for simvastatin acid, fluvastatin, pitavastatin and atorvastatin. For simvastatin acid, intracellular concentrations at 0.01 µM were below limit of quantification. Figure adapted from paper I.

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The maximum difference in Fic between the transporter-transfected and mock-transfected cell lines was 10-fold higher for atorvastatin and pitavas-tatin, and up to 2-fold higher for fluvastatin and simvastatin acid in the pres-ence of the transporter. This difference reflected the increase of AUC in DDI studies in human subjects: Upon co-administration with cyclosporine A— an inhibitor of the major drug transporters and metabolic enzymes42,79—the AUC increased 5- to 15-fold for atorvastatin and pitavastatin, and 3- to 4-fold for simvastatin and fluvastatin.42,77,79 This illustrates that the Fic obtained from rel-atively simple cell models is a complementary tool for assessment of trans-porter-mediated DDI potential in vivo.

Impact of efflux transporter P-gp on Fic The effect of the efflux transporter P-gp was studied in MDCK cells trans-fected with human P-gp (hP-gp). The baseline Fic was determined in wild-type MDCK cells where background endogenous canine P-gp (cP-gp) was knocked out using CRISPR-Cas9 technology (cP-gp-KO cells).80

Fic for P-gp substrates was on average 2-fold lower in cells expressing hP-gp than in cP-gp-KO cells (range 0.94 to 20-fold, p<0.001, Wilcoxon matched-pairs signed rank test) (Figure 11A). The well-established P-gp sub-strate nelfinavir showed the largest difference between the two cell lines (20-fold).

For non-substrates, the difference in Fic between the two cell lines was lower (1.1 to 3-fold), but still statistically significant (p=0.001, Wilcoxon matched-pairs signed rank test). Unexpectedly, concentration-dependence studies showed that Fic decreased with increasing compound concentrations in both the cP-gp-KO and hP-gp-transfected cells (Figure 11B). Concentration-independent baseline Fic was only observed upon addition of a general trans-porter inhibitor (cyclosporine A81). Together, these findings suggested that ad-ditional active transport mechanisms are present in these cells, counteracting the effect of P-gp. This hypothesis was strengthened in simulations using a kinetic cell model that included components for passive permeability, uptake- and efflux transport. The experimental findings were replicated when an ac-tive uptake was incorporated in addition to the P-gp-mediated efflux (Figure 11C).

For some compounds large differences in Fic in the two cell lines were ob-served, even though these compounds were not previously reported to be P-gp substrates (e.g., astemizole, ketoconazole, lovastatin and rosiglitazone). The present experiments were carried out in P-gp-KO cells rather than in sys-tems using chemical P-gp inhibitors, which often also inhibit other (uptake) transporters. Given that the P-gp-KO cells allow unbiased classification of P-gp substrates and non-substrates,82 these compounds might have been misclas-sified in previous substrate screens. Indeed, ketoconazole and lovastatin are in the meanwhile annotated as P-gp substrates in the DrugBank database.83

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Figure 11. Impact of the efflux transporter P-gp on intracellular bioavailability (Fic). A. Comparison between Fic in P-gp-KO and in P-gp-transfected MDCK cells at 0.5 µM compound concentration. Triangles: negatively charged compounds; cirlces: neutral and zwitterionic species; squares: positively charged compounds at pH 7.4. Substrates of P-gp are highlighted in yellow. Nelfinavir is represented in parentheses as its Fic is outside the range of this plot in P-gp-transfected cells. B. Relationship between simvastatin acid concentration and Fic in P-gp transfected MDCK cells (yel-low filled squares), cP-gp-KO MDCK cells (blue circles), and cP-gp-KO MDCK cells pre-incubated (15 min) followed by co-incubation with 10 µM cyclosporine A (CsA, grey triangles). C. Kinetic cell model simulations that best described experimental observations of concentration-dependence Fic for simvastatin acid (conditions: passive permeability=10 ×10-6

cm/s; Vmax,uptake=1000 pmol/min/mg protein; Km,uptake=10 µM; Vmax,efflux=1000 pmol/min/mg protein; Km,efflux=100 µM). Figure adapted from paper I.

Impact of multiple transporters and metabolism on Fic The contribution of multiple transporters and metabolism was studied in pri-mary human hepatocytes. Compared to the previous studies, these cells better represent the in vivo situation where multiple processes occur simultaneously.

Hepatocytes were used in two common culture conditions: suspension and monolayer.58 Freshly isolated cells in suspension (Figure 12A) express similar levels of transporters and metabolizing enzymes as those observed in the hu-man liver.60,84-86 In contrast, cells cultured for 24 h post-isolation in monolayer format (Figure 12B) show a significant down-regulation of many important transporters and enzymes on the transcriptome level due to cell dedifferentia-tion.84-86

Figure 12. Brightfield images of human hepatocytes in different culture conditions. A. Freshly isolated human hepatocytes in suspension. B. Freshly isolated human hepatocytes after 24 h, cultured in monolayer format.

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Fic was determined in both cellular systems for a subset of the compounds from the uptake and efflux studies (n=18). Some compounds (e.g., astemizole, indomethacine and ketoconazole) yielded the same Fic in both formats, while another group of compounds yielded lower Fic values in the suspended hepato-cytes (average difference 5.7-fold, p=0.0002 in Wilcoxon matched-pairs signed rank test) (Figure 13A). Substrates of multiple efflux transporters and metabolizing enzymes showed the largest differences. Three compounds from each group were tested in clearance studies. The compounds with similar Fic in both culture formats also had a similar clearance. In reverse, the compounds with large differences in Fic had also a significantly lower clearance in mono-layer hepatocytes than in suspended hepatocytes (p<0.05 Mann-Whitney test).

These differences were not reflected in targeted proteomics data, for which there were no statistical significant changes between suspended and monolayer hepatocytes. This could be because drug-transporting proteins are redistributed from the plasma membrane to intracellular compartments under certain culture conditions.87 Post-translational modifications88 or lower levels of co-factors89 could also influence the functionality of proteins.

Figure 13. Impact of multiple transporters and metabolizing enzymes on Fic. A. Com-parison between Fic in suspension hepatocytes and hepatocytes cultured in monolayer format. Triangles: negatively charged compounds; cirlces: neutral and zwitterionic species; squares: positively charged compounds at pH 7.4. Compound numbers indi-cate: 1) astemizole; 2) indomethacin; 3) ketoconazole; 4) atorvastatin acid; 5) pro-pranolol; 6) ritonavir. B. Differences in metabolic clearance in suspension hepatocytes and hepatocytes cultured in monolayer format (*p<0.05 Mann-Whitney test). Figure adapted from paper I.

Overall, the results underlined that Fic reflects the net impact of all cellular drug disposition processes on intracellular bioavailable drug levels. No prior knowledge of the involved drug distribution pathways is required. This allows for high-throughput determination of drug access to intracellular targets in highly defined systems (e.g., single-transporter transfectants) or in complex systems orchestrating many active processes simultaneously (including pri-mary human cells).

34

Role of lipids in intracellular bioavailability (Paper II) The effect of neutral lipids (NL) and phospholipids (PL) on Fic was studied in cellular systems of 3T3-L1 fibroblasts with increased NL and/or PL content. The formation of neutral lipid droplets and accumulation of PL was induced by specific culture conditions. Wild-type 3T3-L1 cells (3T3-WT), cells con-taining neutral lipid droplets and increased levels of phospholipids (3T3-NL+PL+) and cells with increased phospholipid content (3T3-PL+) were used (Figure 14).

Figure 14. Brightfield and confocal images with neutral lipid and phospholipid stains of 3T3-L1 cells. Wild-type cells (3T3-WT, panel A) had a fibroblastic phenotype, and were used to establish baseline levels of neutral lipids (panel B) and phospholipids panel C). Adipocyte-like 3T3-L1 cells (3T3-NL+PL+, panel D) contained large neutral lipid droplets (panel E) and enhanced levels of phospholipids (panel F). 3T3-L1 cells induced to accumulate phospholipids (3T3-PL+, panel G) presented normal NL levels (panel H), but increased PL content (panel I). Figure adapted from paper II.

Lysosomal integrity is a potential confounding factor and was therefore deter-mined in all cell types (Figure 15A). 3T3-WT and 3T3-PL+ cells showed char-acteristic lysosomal staining. 3T3-NL+PL+ cells showed a diffuse pattern of the lysosomal stain, indicating a loss of pH gradient over the lysosomal mem-brane.

In order to ensure that the cell types only differed regarding their lipid con-tent and not relevant ADME proteins, global proteomics analysis was con-ducted (5911 detected proteins, Figure 15B). ADME proteins were not altered significantly and were expected to contribute equally to Fic in all three cell types. 3T3-NL+PL+ had increased levels of adipocyte-specific proteins, such as fatty acid binding proteins (FABPs), which confirmed their phenotype.

35

Given the large morphological changes from fibroblast (3T3-WT and 3T3-PL+ cells) to adipocytes (3T3-NL+PL+), it was deemed necessary to character-ize the cells for their specific cellular volume per protein mass. Both the de-termination of Kp and fu,cell rely on accurate estimation of cell volume (eq. 2 and 4) and generally, the protein mass per cell volume is considered constant for mammalian cells.90,91 Indeed, the constant of 6.5 µL/mg protein was found to be applicable for all phenotypes (Figure 15C).

Figure 15. Potentially confounding factors influencing Fic measurements. A. Lyso-somes (purple) were intact in 3T3-WT and 3T3-PL+ cells, but the lysosomal stain showed a diffuse pattern in 3T3-NL+PL+ cells. Nuclei are depicted in blue. B. In the global proteomic studies, 5911 proteins were identified, of which 4902 overlapped in all three cell types. C. All phenotypes had similar cell volume per mg protein con-stants, as determined by measurement of cell diameter, cell number and protein con-tent. Figure adapted from paper II.

Intracellular bioavailability was then measured for 23 drugs in all three cell types. Fic was significantly lower in the lipid-enhanced cell types (3T3-NL+PL+ and 3T3-PL+ cells) compared to wild-type cells (p=0.04 and 0.001 for 3T3-NL+PL+ and 3T3-PL+ cells vs. 3T3-WT, respectively, Figure 16.) Changes to Fic were found to have different causes in the two cell lines, as discussed in the next sections.

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Figure 16. Fic values of the test compounds (n=23) represented for each cellular model and divided by charge of the compounds. Positively charged compounds are shown in red open circles, neutral compounds in yellow closed circles and negatively charged compounds in blue half-filled circles.*Wilcoxon matched-pairs signed rank test, ns = non-significant. Figure adapted from paper II.

Impact of phospholipids on Fic In the 3T3-PL+ model, Fic was significantly lower than in the 3T3-WT cells (p=0.001, Wilcoxon matched-pairs signed rank test, Figure 16), due to in-creased non-specific binding of the drugs (i.e., decreased fu,cell), since the total drug accumulation (i.e., Kp) was not altered. On average, fu,cell was 1.7-fold lower in 3T3-PL+ cells than in 3T3-WT cells (p=0.01). This was attributed to the 1.5-fold increase in PL content in 3T3-PL+ cells. To confirm this hypoth-esis, an intermediate phenotype, harvested at 24 h instead of 48 h, was tested, and a gradual increase in non-specific binding of drug was observed (Figure 17).

Figure 17. Correlation between cellular drug binding and PL content. 3T3-L1 cells were exposed to the phospholipidosis inducing drug propranolol for different periods of time (24h vs 48h). PL content increased with increasing incubation time, and cel-lular drug binding was proportional to the increase in PL content. Figure adapted from paper II.

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Impact of neutral lipids on Fic In the 3T3-NL+PL+ model, the differences in Fic, and notably Kp, were charge-dependent, with a reduced accumulation of cationic drugs in the 3T3-NL+PL+

cells compared to the wild-type cells. This charge dependency was not ob-served in the 3T3-PL+ cells. Lysosomal staining (Figure 15A) and control ex-periments with chloroquine—a compound that disrupts pH gradients92,93— in-dicated a lack of pH-gradient between lysosomes and the cytosol in the 3T3-NL+PL+ cells (Figure 15A). Therefore, the charge-dependent differences were not attributable to the presence of neutral lipids, but rather to altered lysosomal trapping of the cationic drugs. In vivo, hypertrophic adipocytes have permea-bilized lysosomes,94 and therefore Fic is likely to be affected similarly in in vitro studies. Therefore, the presence of neutral lipid droplets indicate physi-ological changes that affect total drug accumulation in cells.

fu,cell in 3T3-NL+PL+ was affected to the same extent as in 3T3-PL+ cells. This suggested that the additional 5-fold increase in NL content did not affect most drugs. A trend for lower fu,cell was observed for the highly lipophilic com-pounds (logD > 3). The increase in FABPs in 3T3-NL+PL+ did not result in altered fu,cell. This supports the notion that specific cellular proteins contribute negligibly to non-specific drug binding, as shown for cells overexpressing sin-gle transporter proteins (Paper I) or the FABP family (Paper II).

In summary, Fic in lipid-induced cells was significantly lower than in wild-type cells. These differences were caused by lower fu,cell. The 1.5-fold increase in PL concentrations resulted in a corresponding average 1.7-fold lower fu,cell in both cell types. In consequence, PLs are the major determinant of nonspe-cific cellular binding. The presence of neutral lipids did not additionally in-crease the binding of drugs in the 3T3-NL+PL+ model, unless the compounds were highly lipophilic (Table 4).

Table 4. Summary of the parameters studied and their observed influence on Kp, fu,cell, and Fic. *n.a. = not applicable (ruptured lysosomes upon homogenization). Table adapted from paper II.

Cell characteristic Observed influence on parameter Present in cell type

Kp fu,cell Fic WT NL+PL+ PL+

Increased PLs no yes yes no yes yes Increased NLs no yes if logD > 3 no no yes no Lysosomal pH gradient yes n.a.* yes yes no yes

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Prediction of fu,cell from cell-free assays (Paper III) The aim of this study was to investigate if predictions of cellular drug dispo-sition could be simplified by using cell-free, phospholipid-based assays. This investigation was approached from two angles: 1. Measurement and comparison of fu,cell in tissue-specific human cell types

with different phospholipid profiles. 2. Measurement of fu,PL of the major subclasses of purified phospholipids

and investigation of the specific contribution of each phospholipid sub-class to fu,cell.

fu,cell in comparison to total phospholipid content in cells The findings in the well-defined cellular systems in Paper II (accumulated PL content vs. wild-type control) were supported by the results in more diverse, tissue-derived cell types. 19 compounds tested in cells that were characterized for their PL content (Figure 18A). Of these 19 compounds, 12 had decreased fu,cell in the cell types with higher PL contents. The fu,cell for any given com-pound varied on average 9.5-fold between the maximum and the minimum values for the different cell types. This was reflected by the differences in PL content, which were 9.3-fold between the highest (human hepatocytes, HH) and lowest (HL60) values.

Compounds with an fu,cell below 0.1 (i.e., compounds that were bound more than 90%) were all negatively correlated to PL content (Figure 18B). The neg-ative correlation was statistically significant for the median fu,cell across all cell types and for four individual compounds (lovastatin, phenazopyridine, atorvastatin and repaglinide; p < 0.05, Figure 18C). Low-binding compounds (in grey) were not affected by PL content.

Figure 18. fu,cell vs. PL content in six human cell types. A. PL content (mg/million cell) determined using an enzymatic assay for each of the human cell types. B. Linear regression of fu,cell and PL content. Compounds of the highly bound class (fu,cell < 0.1, n=12) are depicted in black and low binding drugs (n=7) are depicted in grey. C. fu,cell

of significantly correlated compounds in panel B (lovastatin (■), phenazopyridine (▲), atorvastatin (▽) and repaglinide (●) in the tested human cell types, sorted according to their PL content in panel A. Figure adapted from paper III.

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fu,cell in comparison to lipid affinities A comparison of fu,PL of the three major phospholipid subclasses PC, PE and PS revealed large differences in binding affinities across the tested compounds (Figure 19). These differences were in line with previous reports that had mainly focused on amine-containing basic compounds.95,96 The affinities were distributed over a range of three orders of magnitude. However, for PE, the affinities were confined to a fairly narrow range (one log unit, Figure 19) when the two most lipophilic drugs of the data-set, lovastatin and simvastatin, were disregarded.

For the neutral compounds, fu,PL of all three subclasses were related to logD. This trend was less apparent for the anionic and cationic compounds, reflect-ing the importance of charge-dependent PL-drug interactions.21

Figure 19. Experimental fu,PC, fu,PE and fu,PS sorted by logD7.4 (increasing from left to right) and charge class of compounds. Figure adapted from paper III.

Scaling of fu,PL to fu,cell indicated a dominating role of PC in unspecific drug binding, as exemplified with the results from human hepatocytes in Figure 20A-C. Scaling from PC resulted in the best correlation among the pure bead types (PC: r2=0.721 > PS: r2=0.563 > PE: r2=0.322), and it was also the major PL subclass in relative amounts (49%, 29% and 3% for PC, PE and PS, re-spectively). The dominating role of PC in drug binding is therefore explained by its role as the major constituent of cells. Consideration of other sub-classes—either by devising beads containing mixed membranes of several PL subclasses in proportions reflecting a mammalian cells (Figure 20D), or by summing the affinities of the pure systems (Figure 20E)—resulted in minor improvements for all cell types studied.

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Figure 20. Correlations of fu,cell determined from cell homogenates from human hepatocytes (y-axis), and fu,cell predicted from lipid affinities to pure phospholipid membranes (A-C), mixed phospholipid membranes (D) or from a combination of the individual lipid affinities (E). Figure adapted from paper III.

In this study, only the four major PL classes were considered. However, the lipidome in the plasma membrane comprises more than 1000 different lipid molecules,97 which opens up opportunities for refined studies. The current work indicates that the lipidomic approach is promising for future cell-free determinations of fu,cell. The use of lipid affinities instead of logD will account for more complex lipid-drug interactions, such as charge state or flexibility of the chemical structure. In large drug discovery screens the use of standardized phospholipid beads will reduce assay variability, increase high-throughput rates by reducing incubation time, and decrease the costs caused by cell cul-ture.

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Applications of Fic in drug discovery settings (Papers IV and V) In many cell-based drug discovery screens, intracellular drug concentrations need to be considered to correctly appreciate the potency of drug candidates towards intracellular targets.98 The Fic methodology has been successfully used to understand potency of small molecule drug candidates towards intra-cellular targets and has the potential to reduce attrition rates in drug discov-ery.98

First, the use of Fic in the context of metabolic drug-drug interactions was investigated.

Second, the methodology was, for the first time, adapted for measuring the Fic of large molecules, namely antisense oligonucleotides (ASOs).

Fic as correction factor in drug-drug interaction studies Assessment of drug-drug interaction (DDI) potential is an important step in early drug discovery screens. CYP enzyme inhibition values (Ki or IC50) can be measured in human liver microsomes (HLM) or human hepatocytes (HH).

In the microsomal setting, the drug concentration available to interact with the enzyme is equal to the unbound drug concentration in the incubation me-dium (i.e., the nominal incubation concentration, Cinc, corrected for the frac-tion of unbound drug in liver microsomes (fu,mic)).58 In the hepatocyte setting, compounds need to reach the cell interior where the CYP enzymes are located, a process that may involve passive permeability and active transport.58

Apparent inhibition concentrations (IC50,app) therefore need to be corrected for fu,mic or Fic as outlined in Figure 21.

Figure 21. Fic and fu,mic as correction factors for apparent inhibitory concentrations in CYP450 inhibition studies. Fic can also be used as correction factor of in vitro inhibi-tion constants in PBPK modelling. In this case, Fic is interpreted as an enrichment factor of drug in the target tissue. Figure adapted from paper IV.

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Indeed, Fic successfully reconciled literature Ki values for enoxacin, clarithro-mycin, saquinavir and nelfinavir. Before correction, the apparent Ki values de-termined in hepatocytes and microsomes differed 2-to 22-fold. Enoxacin and clarithromycin—two hydrophilic drugs that are poorly metabolized—had higher apparent Ki values in microsomes than in hepatocytes. These com-pounds also had an Fic that was greater than one, indicating accumulation in the hepatocytes. In return, nelfinavir and saquinavir—two lipophilic drugs that are highly metabolized—had lower apparent Ki values in microsomes than in hepatocytes. These compounds had an Fic lower than one, indicating lower drug exposure at the cytosolic enzymes. After applying Fic and fu,mic, the cor-rected Ki values differed less than 2-fold for three of the four compounds. These data served as proof-of-concept of the approach presented in Figure 21.

The approach was then applied to a discovery compound series that ap-peared to have large differences in CYP IC50 values in screens of hepatocytes and microsomes The nine compounds included in this study were structurally related, with a common core structure containing a sulphonamide and a sec-ondary amide, with aromatic substituents including (iso-)thiazoles, benzothi-ophenes, pyridines or furanes. All compounds were acidic at pH 7.4, with logD, PSA, MW and membrane permeability values confined in a relatively narrow range (Table 5). Despite these similarities in structure, apparent IC50 values between hepatocytes and microsomes differed to a varying degree: three out of the nine compounds had identical IC50,app values in hepatocytes and microsomes, while others differed up to 12-fold (Figure 22A). Experi-mental Fic and fu,mic values successfully reconciled these differences, reducing the root mean square error (RMSE) from 9.4 to 0.9 (Figure 22B).

Figure 22. Comparison of apparent (A) and corrected (B) IC50 values measured in hepatocytes and microsomes. Apparent IC50 in microsomes were multiplied with fu,mic, and apparent IC50 in hepatocytes were multiplied with Fic to obtain corrected IC50 val-ues. Figure adapted from paper IV.

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Table 5. Major physicochemical properties of discovery compound series used in Figure 22. Table adapted from paper IV.

Compound MW charge LogD PSA Papp

(×10-6 cm/s) RO01 385.24 Acidic 0.7 123 10 RO02 432.34 Acidic 1 153 2 RO03 410.26 Acidic -1 147 9 RO04 424.29 Acidic -0.7 147 13 RO05 452.75 Acidic 1.1 153 2 RO06 415.29 Acidic 0.4 130 3 RO07 503.41 Acidic 0.9 175 4 RO08 390.28 Acidic -0.1 125 22 RO09 454.38 Acidic -0.3 162 2

Finally, the Fic was measured in suspended human hepatocytes for three com-pounds of the azole family. These are commonly used inhibitors of CYP3A4 in preclinical and clinical DDI studies. Although drug accumulation of keto-conazole, itraconazole and posaconazole in cells has been observed and widely described, the uptake mechanisms are not fully elucidated.99-102 This concentrative uptake requires correction of Ki determined typically in micro-somes in vitro in order to match the in vivo Ki values in DDI models. The observed Fic of ketoconazole, itraconazole and posaconazole was 6.9±1.1, 7.5±6.3 and 37.6±12.8 respectively. This accurately reflected the empirically determined enrichment factors in PBPK models which ranged from 1.3 to 6 for ketoconazole103-105, 11 to 14 for itraconazole99,103,106 and 10 to 26 for posaconazole.107

The results from the three data-sets—literature compounds, drug discovery series and azole series—further support the use of Fic in drug discovery set-tings. The method can be adapted to different cellular systems and specific conditions, and it is possible to match the conditions to measurements of in vitro parameters (i.e., incubation time, concentration of the drug, cell culture model). This choice of the right conditions is essential to make accurate pre-dictions for DDI and to calibrate PBPK models.

Fic of antisense oligonucleotides The effect of incorporation of CPP in ASO-GLP1 conjugates was studied by combining Fic measurements with confocal imaging. For this purpose, uptake was investigated of the ASO itself, ASO conjugated to GLP-1 (ASO-GLP1), and ASO conjugated to both GLP-1 and CPP (ASO-GLP1-CPP) (Figure 23). Wild-type HEK293 cells and HEK293 cells overexpressing GLP-1R were used for this purpose.

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Figure 23. Schematic structure of antisense oligonucleotide-peptide conjugates. A. Cy3-labelled ASO (Cy3-ASO). B. ASO conjugated to targeting peptide GLP-1 and fluorescent label (Cy3-ASO-GLP1). C. Cyclic CPP was used in combination with Cy3-ASO-GLP1 in its free form and in a conjugated form (Cy3-ASO-GLP1-CPP). All conjugates were also synthesized without the Cy3 label. The phosphorotioate backbone of the ASO is negatively charged and the CPP is positively charged at phys-iological pH. Figure adapted from paper V.

Both Fic and confocal images indicated that the effect of GLP-1 was cell-type specific, and that CPP increased uptake into cells (Figure 24). Conjugates con-taining the targeting peptide GLP1 had significantly higher Fic in cells express-ing GLP-1R than the wild-type HEK293 cells (Bonferroni-Dunn method, p<0.001). The effect of the GLP-1R targeting moiety and the CPP moiety on Fic was cumulative.

Figure 24. A. Fic of the Cy-ASO, Cy3-ASO-GLP1 and Cy-ASO-GLP1-CPP conjuag-tes in wild-type and GLP-1R expressing HEK293 cells. B. Confocal images overlaid on bright-field images of fixed wild-type and GLP-1R expressing HEK293 cells, after incubation with Cy-ASO, Cy3-ASO-GLP1 and Cy-ASO-GLP1-CPP. Conjugates containing the Cy3 stains are shown in red and nuclei stained with Hoechst33342 are shown in blue. Figure adapted from paper V.

For determination of Fic, two adaptations were made in the method for small molecules. (i) The LC-MS/MS-based quantification used for small molecules was not possible due to a lack of suitable extraction methods for oligonucleo-tide-peptide conjugates from cell matrixes. Therefore, fluorescence-based

45

quantification was used. (ii) ASOs were not compatible with equilibrium di-alysis (for determination of fu,cell) due to the high negative charge of the phos-phorotioate backbone. The negative charge resulted in poor permeability across the dialysis membranes despite sufficient pore size. Therefore, the pre-dictive approach based on immobilised PL membranes on beads (presented in Paper III) was used.

The resulting Fic values were relatively high in all cases (>10), suggesting high drug exposure in the cell interior. This was not confirmed in the confocal images, which showed barely detectable levels of ASO conjugates in wild-type cells. The high Fic values were most likely due to unspecific binding ef-fects and clusters of conjugates that were not associated to the cells, which interfered with the fluorescence-based quantification. Nevertheless, the cur-rent Fic methodology enabled, for the first time, relative comparison of intra-cellular bioavailability for a series of drug conjugates. In the current setting, the methodology is adaptable to a large variety of new drug modalities. There is further potential for refinement of the method by using pH-sensitive probes that indicate subcellular localization of the drug,56 or by using subcellular frac-tionation strategies.11,41

46

Conclusions

In this thesis work, Fic was introduced as an approach to overcome the chal-lenge of assessing intracellular drug disposition in drug discovery and devel-opment. The thesis contributes to the mechanistic understanding of how ADME proteins and cellular lipids influence Fic.

In paper I, Fic was shown to be suitable for the study of the effect of single or multiple ADME proteins on intracellular drug disposition. The expression and functionality of these proteins vary depending upon the culture conditions. Fic is a system-dependent factor that can be used to reconcile observed differences in drug disposition in different assay set-ups. This, together with the versatility and high-throughput format of the method, makes Fic suitable for drug com-pound profiling at an early stage in a drug discovery setting. In paper II, fu,cell was shown to be dominated by partitioning into PL bilayers. Thus, neutral lipids and cellular proteins play a minor role in nonspecific drug binding in cell types treated to have differing lipid contents. The results from paper III indicated that drug affinities to the major PL subclasses PC, PE and PS, can be used as a basis for a cell-free alternative to predict fu,cell. In paper IV, the Fic obtained from suspended human hepatocytes proved to be an easy-to-use in vitro parameter to reconcile divergent IC50 values from CYP450 enzymes obtained in experimental systems such as liver microsomes and hepatocytes. Further, by reconciling in vivo and in vitro Ki values, Fic pro-vided a mechanistic understanding of the empirically obtained scaling factors in PBPK modelling of drug-drug interactions. In paper V, Fic was applied for the first time to large molecules, to study the effect of cell-penetrating peptides (CPP) on antisense oligonucleotide (ASO) conjugates. The results indicated that CPPs increase the Fic of ASOs in HEK293 cells. Incorporation of the CPP in the ASO-GLP1 conjugate resulted in a targeted, receptor-mediated uptake.

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Populärvetenskaplig sammanfattning

Efter att ett läkemedel tagits upp i kroppen måste läkemedelsmolekylerna fär-das långväga innan de når sitt mål, vilket ofta befinner sig inuti vävnadens celler. Man har länge känt till att många läkemedelsmolekyler binder till plas-maproteiner under färden i blodomloppet, men vad som händer med moleky-lerna när de kommit in i en cell har varit mer okänt. Det här är en viktig fråga, eftersom endast läkemedelsmolekyler som inte fastnat på proteiner eller mem-bran kan interagera med sitt mål och utöva önskad effekt.

I den här avhandlingen utforskas en nyligen utvecklad metod för att mäta hur mycket av ett läkemedel som är fritt tillgängligt inuti en cell i mer detalj. Metoden mäter den så kallade intracellulära biotillgängligheten, som beskri-ver hur mycket obundet läkemedel som finns inuti en cell jämfört med mäng-den läkemedel utanför cellen.

Många mekanismer bidrar till skillnader i läkemedelskoncentrationer mel-lan cellens in- och utsida. Till exempel finns specialiserade transportproteiner som hjälper läkemedlet att ta sig över cellens membran, vilket därmed påver-kar den intracellulära biotillgängligheten. Beroende på i vilken riktning trans-porten går kan dessa proteiner antingen minska eller öka mängden läkemedel inuti cellen. Det här utreddes systematiskt genom att jämföra intracellulär bi-otillgänglighet i celler som innehåller ett givet transportprotein med celler som saknar detsamma. En annan typ av proteiner, metaboliska enzymer, kan bryta ned läkemedelsmolekylerna och därmed också bidra till minskad läkemedels-koncentration inuti cellen.

Härnäst undersöktes cellmembranens främsta beståndsdelar, de så kallade fosfolipiderna. Fosfolipider visade sig vara en ”fälla” för många läkemedels-molekyler, som minskade mängden fritt tillgängligt läkemedel på cellens in-sida. Det här studerades genom att jämföra intracellulär biotillgänglighet för läkemedel i celler med olika stort fosfolipidinnehåll. Genom att bättre förstå fosfolipidernas funktion var det möjligt att förutse intracellulär biotillgänglig-het utifrån experiment med artificiella fosfolipidkulor istället för celler. Det här tillvägagångssättet minskar det experimentella behovet av celler och gör mätningarna mycket snabbare.

I industriella läkemedelsutvecklingsprogram måste man testa om läkeme-delskandidater inhiberar metaboliska enzymer. Om sådan inhibition föreligger finns risk för att läkemedelskandidaten kommer att påverka effekten av ett annat läkemedel som administreras samtidigt. Inhibition kan testas genom att tillsätta läkemedlet direkt till enzymerna (i form av så kallade mikrosomer),

48

eller genom att tillsätta läkemedlet till intakta celler (oftast hepatocyter) där enzymerna finns på insidan. Dessa olika tester ger inte alltid samma resultat: ett läkemedel kan verka aktivt i ett test, men mindre aktivt eller till och med inaktivt i det andra. Mätningar av intracellulär biotillgänglighet bidrog till att förstå skillnaderna mellan testmetoderna, eftersom denna parameter beskriver hur mycket läkemedel som finns inuti cellen.

En annan utmaning i läkemedelsutvecklingsprogram är att det med klassisk design av läkemedelsmolekyler inte är möjligt att påverka alla mål som kan orsaka sjukdom. Därför har mycket större och kemiskt varierade läkemedels-molekyler utvecklats de senaste åren. Metoden för att mäta intracellulär bio-tillgänglighet behöver viss anpassning för att vara kompatibel med dessa nya molekyler. Ett första steg i den riktningen togs i den här avhandlingen, genom att mäta intracellulär biotillgänglighet för läkemedelsmolekyler bestående av peptider (proteiners byggstenar) och oligonukleotider (DNA:s byggstenar).

Sammanfattningsvis bidrar arbetet i den här avhandlingen till bättre förstå-else för mekanismerna som påverkar hur mycket läkemedel som hamnar i en given celltyp efter administrering. Avhandlingen illustrerar hur metoden kan implementeras i industrin för att bättre beskriva effekten av ett läkemedel, dels för klassiska läkemedelsmolekyler, men också för nya typer av läkemedel. Metoden kan således hjälpa till i upptäckten av nya, effektiva läkemedel.

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Acknowledgements

The work presented in this thesis was carried out at the Department of Phar-macy, Faculty of Pharmacy, Uppsala University, Sweden. The work was financially supported by an Initial Training Network (ITN FP7) founded by the Marie-Skłodowska Curie actions, called Analytical Research in ADME (ARIADME). Apotekarsocieteten provided travel grants that al-lowed me to present my work at international conferences.

I thank my supervisor and co-supervisors:

Per Artursson, for your continuous support during these five years. I appre-ciate your challenging, but fair mind-set. I am grateful for that you pushed me to investigate scientific questions in detail, and for your ambitions to explain many aspects in university life that are necessary besides conducting science. Thank you for giving me the freedom for continuing my second passion, the music.

Pär Mattson, I admire your calm personality and your capability to solve any problem on the spot whenever I turned to you.

Manfred Kansy, for your support in the background by paving the way for the secondment period of the ARIADME project. I also thank Mohammed Ullah, my supervisor at Roche. It was truly inspiring for me as a young re-searcher to immerge into the industrial context.

I want to thank all collaborators in- and outside BMC, in the ARIADME net-work, at Roche and AstraZeneca for your support and inspirations. I learned something from each one of you and without you, the thesis work would not have been possible. It was great pleasure meeting you all.

I thank my fellow PhD students:

André – you taught me more than what I could have expected from any senior PhD student. Your passion for science, your helpfulness and your ana-lytical mind are impressive. Thank you for helping me settling down at BMC and in the Drug Delivery group, and for your patience to explain everything as many times as needed. I will never forget the lunch excursions and the Fri-day experiments.

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Magnus – your capability for combining science with fun is amazing. Thank you for all these moments. There is no proof for that, but the gnomes in my office might have some connection to you.

Christine – you always strive to support anyone whenever you can, thank you for everything!

Niklas, Signe and Anna, even if our time together was a bit shorter, I really appreciate every moment we shared! All of you are amazing research buddies.

All PhD students in Kickis group - you opened up my mind to so many different aspects of science, by presenting your own work or by giving feed-back on my work.

Special thanks also to my master students Sandra and Valeria – I enjoyed working with both of you.

And not to forget all senior researchers in our group for sharing your sci-entific knowledge and life experience!

I also want to thank all other PhD students, postdocs and staff members at the department. We had so many unforgettable fika and lunch conversations. Not to forget the after works, dissertation parties and other fun events. I really ap-preciate the helpful spirit of all of you, which makes this department a great place to be in!

I thank my previous and current office mates:

Richard, you are the one who introduced me to salted liquorice. Maria, we were a good team at the decorate-your-office contest! Vicky, it was nice having you for the short visit during summer this year. Khadijah, my long-term room buddy: Thank you for having an open ear to

science-related and non-science-related topics. Thank you for providing some survival food during long evenings in the lab. And for making us a winning team at another edition of the decorate-your-office contest!

Fabienne and Ursula, the Fondue- and Marroni-events we shared brought homeland a bit closer to us. Merci et danke pour alles! Patrik, it was fun seeing you walking around in your Swiss T-Shirt.

Theodosia, our life started just a few hours apart, but we first met when our ARIADME journey begun. I could not imagine a better ‘twin sister’ and I am thankful for that we could support each other and grow together into our role as scientists!

Tomás, the moment you joint the department you changed my life. Thank you for all your support, travelling adventures and patience in the last three years!

I thank everyone at Musicum for giving me the feeling of belonging to a music family. You reminded me during the past five years that life is not only about science. You helped me to feel home in Sweden and to become part of

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Swedish traditions. Emil, I will never forget when you first opened the door to Musicum for me and I want to thank you for your countless efforts for the cello section.

Barbara and Torbjörn, I loved staying at the little cottage in your backyard that was home to me for many years.

Finally, I thank my family and friends back home for supporting me in the decision to start this journey, and for making me feel welcome whenever I had a chance to return.

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A doctoral dissertation from the Faculty of Pharmacy, UppsalaUniversity, is usually a summary of a number of papers. A fewcopies of the complete dissertation are kept at major Swedishresearch libraries, while the summary alone is distributedinternationally through the series Digital ComprehensiveSummaries of Uppsala Dissertations from the Faculty ofPharmacy. (Prior to January, 2005, the series was publishedunder the title “Comprehensive Summaries of UppsalaDissertations from the Faculty of Pharmacy”.)

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