Journal of Biomaterials and Nanobiotechnology, 2011, 2, 181-193
doi:10.4236/jbnb.2011.22023 Published Online April 2011 (http://www.scirp.org/journal/jbnb)
Structural Rearrangements of Polymeric
Insulin-loaded Nanoparticles Interacting with
Surface-Supported Model Lipid Membranes
Rickard Frost1, Christian Grandfils2, Bernardino Cerda2, Bengt Kasemo1, Sofia Svedhem1*
1Department of Applied Physics, Chalmers University of Technology, Göteborg, Sweden; 2Interfacultary Research Centre of Bioma-
terials (CEIB), University of Liège, Liège, Belgium.
Received December 22nd, 2010; revised February 15th, 2011; accepted February 18th, 2011.
ABSTRACT
The design and screening of nanoparticles for therapeutic applications (nanodrugs) belong to an emerging research
area, where surface based analytical techniques are promising tools. This study reports on the interaction of electro-
statically assembled nanoparticles, developed for non-invasive administration of human insulin, with cell membrane
mimics. Interactions between the nanoparticles and differently charged surface-supported model membranes were
studied in real-time with the quartz crysta l microbalance with dissipa tion monitoring (QCM-D) technique, in some ex-
periments combined with optical reflectometry. Based on the experimental observations, we conclude that structural
rearrangements of the nanoparticles occur upon adsorption to negatively charged lipid membranes.The degree of
structural changes in the nanoparticles will have important implications for the induced release of the protein drug
load. The presented results provide an example of how a surface-based experimental platform can be used to charac-
terize the physico-chemical properties of nanosized drug carriers with respect to their interactions at different surfaces.
Keywords: Nanoparticle, Human Insulin, Drug Delivery, Supported Lipid Bilayer, QCM-D, Reflectom etry, DLS, Zeta
Potential
1. Introduction
Nanotechnology based methodologies are important tools
for the development of novel drug delivery systems. In
particular, model studies using mimics of cell mem-
branes, or other biological barriers (e.g. mucus layers or
extracellular matrices), will improve our understanding
of interactions in the proximity of cells [1]. This is highly
relevant for the design of nanoparticles with specific
biomedical functions [2]. It is often desired to protect
drugs before they reach their site of action and also to
minimize adverse systemic effects by targeting of active
substances and their local release in a particular tissue or
cell [3,4] In the following, we will focus on studies of a
nanoparticulate protein drug formulation and its interac-
tion with surface supported biomimetic membranes.
These membranes are well-defined model systems,
which can be controlled and manipulated at the molecu-
lar and nano-level with respect to lipid composition [5],
morphology [6], as well as with respect to the incorpora-
tion of various membrane bound [7] or membrane asso-
ciated [8] molecules. These model systems can be used
to study the physico-chemical properties of the nanopar-
ticles upon exposure to membranes. Due to the wide
range of possibilities of the membrane mimics and the
multitude of available analytical tools, we believe that
supported lipid membranes will be developed into an
early screening platform for nanodrugs. The approach
must of course be seen as just one early component in a
screening hierarchy, where earlier “upstream” methods
are material characterization and theoretical modeling,
while “downstream” methods include more complex sub-
cellular structures, in vitro cell studies, in vivo screening,
and eventually clinical trials.
An important field where nanoparticle drug formula-
tion has great potential is for the non-invasive admini-
stration of insulin. Non-invasive alternatives (e.g. oral,
nasal, or pulmonary) to injections of the drug are highly
desired.[9] For this purpose, a cationic polymer (the drug
carrier) and the anionic proteins (the insulin drug load)
were assembled into nanoparticles (referred to as NP-HI
below) following established procedures (such assem-

Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
Surface-Supported Model Lipid Membranes
182
blies are commonly referred to as polyelectrolyte com-
plexes) [10,11]. If successful, this formulation would be
an extension of the present use of non-covalent insulin
complexes. Today insulin in complex with endogenous
protamine, a formulation which reduces the solubility of
insulin at physiological pH, is used as a standard treat-
ment by basal insulin supplementation [12]. In parallel
with in vivo testing, extensive physico-chemical charac-
terization of the novel nanoparticles was performed with
the long term goal to establish relationships between the
nanoparticle properties and function.
The main method for the monitoring of the lipid mem-
brane formation and its subsequent interaction with the
nanodrug was the quartz crystal microbalance with dis-
sipation monitoring (QCM-D), in some experiments
complemented by reflectometry. The basic components
of the experiments, where a lipid membrane is formed on
a sensor surface before exposure to the nanoparticles, are
presented in Figure 1. In essence, the QCM-D and re-
flectometry measurements provide us with three com-
plementing quantities; the QCM-D frequency shift (∆f)
that measures the change in acoustically coupled mass
(including associated solvent) associated with the sensor
surface, the QCM-D dissipation shift (∆D) that measures
changes in the shear viscosity caused by the adsorbed
nanoparticles, and the reflectometry signal (∆R) that
measures changes in the optical mass (due to changes in
refractive index) near the surface. The combination of
these three signals has been shown to constitute a powerful
way of characterizing supported lipid membrane formation
and interactions/remodeling of such bilayers [13].
2. Experimental Section
2.1. Materials
Unless otherwise stated, chemicals were obtained from
commercial sources and used without further purification.
Water was deionized (resistivity 18.2 MΩ cm–1) and pu-
rified using a MilliQ Plus unit (Millipore, France). Two
buffers were used, and referred to as Buffer 1 and Buffer
2. Buffer 1 was phosphate buffered saline prepared from
tablets (0.0015M potassium dihydrogen phosphate,
0.0081 M disodium hydrogen phosphate, 0.0027 M po-
tassium chloride and 0.137 M sodium chloride, pH 7.4,
Sigma Aldrich). Buffer 2 was a phosphate buffer with
lower ionic strength (0.010 M sodium chloride, 0.002 M
sodium dihydrogen phosphate, Ph 7.4 (adjusted using
sodium hydroxide). Buffers were filtered and degassed.
1-palmitoyl-2-oleyl-sn-glycero-3-phosphocholine (PO-
PC), 1-palmitoyl-2-oleyl-sn-glycero-3-phospho-L-serine
(POPS) and 1-palmitoyl-2-oleyl-sn-glycero-3-ethylphos-
phocholine (POEPC) were from Avanti Polar Lipids Inc.,
Figure 1. Description of the experimental platform. Differ-
ently charged supported lipid bilayers on a sensor surface
are exposed to nanoparticles, i.e. nanoassemblies of polyca-
tionic polymers and human insulin.
USA and were stored dissolved in chloroform at –20˚C.
Nanoparticles were formed by the association of human
insulin (pI = 5.4, Novo Nordisk, Denmark) and a polyca-
tionic polymer to achieve a colloidal stock solution with
a concentration of 450 μg/mL NP-HI in Buffer 2. The
drug load of the NP-HI was 33 wt%. The experiments
were performed within 4 weeks after preparation of the
nanoparticles. Size and zeta potential were measured for
all batches, and the size increased no more than 10%
when stored for 2 months.
2.2. Preparation of Liposomes
Three kinds of liposomes, of different composition and
net charge were prepared by the extrusion method [14];
POPC:POPS (3:1), yielding negatively charged lipo-
somes, POPC:POEPC (3:1), yielding positively charged
liposomes, and POPC, yielding neutral or slightly nega-
tively charged liposomes.[15] First a thin lipid film (total
lipid amount: 6 mg) was formed on the wall of a flask by
evaporation of chloroform under a flow of N2, after
which residual solvent was removed under vacuum for >
1 hour. The dried lipids were hydrated by addition of 1.2
mL of PBS. The turbid lipid solution was vortexed re-
peatedly and extruded 11 times through a 100 nm poly-
carbonate membrane and another 11 times through a 30
nm polycarbonate membrane using a mini extruder
(Avanti Polar Lipids Inc., USA). Liposomes prepared
using this method measure 80 - 100 nm.[15] The lipo-
some solution was stored at 4˚C.
2.3. Light Scattering
Size and zeta potential of nanoparticles and liposomes
were determined, at 22˚C, using a Zetasizer Nano (Mal-
vern Instruments Ltd., UK). Before measurements, lipo-
somes were diluted to 0.4 mg/mL in Buffer 1 and the
NP-HI to 225 μg/mL in Buffer 2. The Dispersion Tech-
nology Software v. 5.00 (Malvern Instruments Ltd., UK)
software was used to calculate the size distribution by
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Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
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number, using a refractive index for the NP-HI of 1.45.
The size and zeta potential of the liposomes were based
on repeated measurements on one preparation and in
accordance with literature values [15] while the results of
NP-HI are based on three independent batches.
2.4. Scanning Electron Microscopy (SEM)
For the SEM analysis, samples were prepared by the
deposition of a droplet of the nanodispersion onto a
common type of TEM grid, made of copper and in which
the holes are spanned by a thin, perforated carbon sup-
port. When adding the sample, the grid was placed on a
tissue, whereby the liquid was immediately absorbed,
and then dried at room temperature for > 1 h under nor-
mal pressure. The images were recorded under vacuum
using a LEO Ultra 55 FEG SEM with an acceleration
voltage of 5 kV. The magnification was between 20000
and 400000 times.
2.5. Substrates
Gold-coated QCM-D sensor crystals were from Q-Sense
AB, Sweden. A 10 nm adhesive layer of Ti and a 50 nm
layer of SiO2 were deposited by thermal evaporation
(pressure < 5 × 10–6 mbar) (HVC600, AVAC) onto the
sensor surface. For the combined QCM-D/reflectometry
setup, the Ti and SiO2 thicknesses were instead 100 nm
and 110 nm, respectively. Before evaporation, the sen-
sors were cleaned in an ultrasonic bath (5 min in
2-propanol and 5 min in water, followed by blow-drying
with N2), in a microwave plasma system (250 W, 2 min,
Plasma Strip TePla 300PC (TePla AG, Germany)), and,
finally, by rinsing with water and blow-drying with N2.
Shortly before experiments, the sensor crystals were
treated with UV/O3 for > 30 minutes, rinsed with water,
and blow-dried with N2 to minimize hydrocarbon con-
tamination from the ambient. The sensor crystals were
used repeatedly and stored in a 10 mM sodium dodecyl
sulphate (SDS) solution between measurements.
2.6. Nanoparticle—Lipid Membrane Interaction
Experiments
In a typical interaction experiments, the model lipid
membranes were first prepared on the sensor surface
using liposomes diluted in Buffer 1 to a concentration of
0.1 mg/mL at a flow rate of 100 μl/min. For POPC:POPS
(3:1) liposomes, 5 mM MgCl2 was added to Buffer 1 to
enhance the kinetics of the bilayer formation process [16].
After the formation of the lipid bilayer, the buffer was
exchanged from Buffer 1 to Buffer 2 at a flow rate of 100
μl/min (QCM-D) or 300 μl/min (QCM-D/reflectometry),
generating shifts in the recorded signals. The bilayer was
exposed to NP-HI diluted to a concentration of 45 μg/mL
in Buffer 2 at a flow rate of 50 μl/min (QCM-D) or 100 μl/
min (QCM-D/reflectometry). For the NP-HI concentra-
tion series, the three concentrations used were: 45, 5.6
and 1.1 μg/mL. Higher flow rates were used in the com-
bined setup to promote a rapid exchange of the liquid. In
all other respects the QCM-D and QCM-D/reflectometry
measurements were similar. The experiments were per-
formed at 22˚C.
2.7. Quartz Crystal Microbalance with
Dissipation (QCM-D).
QCM-D measurements were performed at several har-
monics (z = 3, 5, 7, 9, 11 and 13) using a Q-Sense E4
system (Q-Sense AB, Sweden). All presented frequency
shifts (∆fz = 9) of the nanoparticle-lipid bilayer interac-
tion processes were recorded at the 9th overtone, and
normalized by division with 9. Frequency shifts were
translated to adsorbed mass (macoustic) through the
Sauerbrey equation (Equation (1)):
9


acoustic Z
mCf (1)
where C, the mass sensitivity constant, is -17.7 ng/(cm2·Hz)
for the type of crystal used (an AT cut crystal operated in
shear mode with a fundamental frequency of 5 MHz).
The Sauerbrey equation holds if the adsorbed mass is
rigidly coupled to the sensor surface and follows the
shear oscillations of the crystal (i.e. there is no dissipa-
tion shift (∆D) upon adsorption). For more dissipative
layers, the Sauerbrey mass is less accurate (overesti-
mated and different for different harmonics), and if the
dissipation is high, viscoelastic modeling should be used
to determine the adsorbed mass. The dissipation signal is
due to energy dissipation during shear deformation of the
oscillating sensor crystal. The magnitude of the dissipa-
tion signal depends both on the viscoeleastic properties
of the adsorbed material and on its thickness. Thick-
nesses of the adsorbed nanoparticle layers were deter-
mined by modeling of the QCM-D data using the Q-tools
software (Q-sense AB, Sweden), assuming a density of
all layers of 1000 kg/m3 and a viscosity of the buffer of
0.001 kg/ms.
2.8. Combined QCM-D/Reflectometry
Optical reflectometry measurements were performed in a
novel experimental setup combining QCM-D and reflec-
tometry in simultaneous measurements on the same sur-
face [13,17]. The wavelength of the incident light is 635
nm, and the angle of incidence is 70.53˚. [17] As op-
posed to QCM-D, which measures acoustically coupled
mass (macoustic, see above), reflectometry measures
changes in refractive index near the surface due to ad-
sorption of material on the sensor surface (moptic). The
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Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
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184
optical result is given as ΔR (reflectometry shift), the
relative change of the intensity ratio of the two polariza-
tions of the reflected light (which is different from ellip-
sometry, which takes as well the phase shift of the re-
flected light into account). The relation between ΔR and
the amount of adsorbed mass on the sensor surface is
given by Equations (2) and (3):

0
 RdnnA (2)
where d and n are the thickness and the refractive index
of the adsorbed layer, respectively, n0 is the refractive
index of the buffer, and A is the sensitivity factor (see
below). By substituting equation 2 in De Feijter’s for-
mula an approximation of the optical mass (moptic) is
obtained:

0


optic
dnn
mdn dc (3)
where the refractive index increments (dn/dc) used in the
calculations was 0.169 mL/g for lipids [18] and 0.278
mL/g for NP-HI. The latter was determined using a
PN3120dndc instrument (Postnova Analytics), using five
different concentrations (9.4, 16, 32, 48, and 62 μg/mL)
of NP-HI. The formation of the lipid bilayer is a very
reproducible process (here: Δf = –26 ± 1 Hz (z = 9), ΔD =
0.2 ± 0.1, ΔR = 0.0250 ± 0.0009) and has a well defined
mass (here: macoustic = 457 ± 13 ng/cm2, Equation 1),
with a low degree of hydration (< 10%) [13]. Therefore,
the sensitivity factor (A) of the crystal was determined to
be 0.0323 nm–1 (Equations (2,3)) by assuming, for these
high quality membranes, macoustic = moptic. For thin
layers (< 5 nm), the sensitivity factor can be assumed to
be constant (the error will be < 10%). [17] The depend-
ency of the thickness of the adsorbed material on the
sensitivity factor was studied by optical modeling using
the Wvase 32 software (J.A. Woollam Co. Inc., USA). A
slight oxidation of the Ti adhesion layer was taken into
account by assuming a mixture of Ti and TiO2.
3. Results and Discussion
The insulin-loaded nanoparticles (NP-HI) were charac-
terized with respect to size and charge, before monitoring
their interaction with model membranes of different
charge. Based on these results, an insulin release mecha-
nism governed by structural rearrangements introduced
during the adsorption process is discussed.
3.1. Characteristics of NP-HI
According to light scattering analyses, the NP-HI hydro-
dynamic diameter was about 220 nm (219 ± 18 nm), and
the zeta potential was positive (26 ± 2 mV). Typical size
distributions (Figure 2(a) and (b)) showed that the
NP-HI dispersion was well-defined with a narrow size
distribution (PDI = 0.095 ± 0.039). Similar results were
obtained for intensity and number distribution analyses
further supporting low nanoparticle polydispersity. No
small components were detected, suggesting completion
of the particle assembly process.
In addition, SEM images of NP-HI adsorbed on a car-
bon surface (Figure 2(c)) showed much smaller particles
(< 100 nm) than the particle size as determined by DLS.
This decrease in size was not surprising in view of the
loss of water during sample preparation (for the degree of
hydration of NP-HI, see below).
3.2. Formation of Model Membranes
Model lipid membranes were formed by the adsorption
and rupture of liposomes on SiO2 at high ionic strength,
as described in detail previously [16,19] and in the sup-
porting information. In the present study, membranes of
different charge were formed using liposomes of differ-
ent lipid composition. Three types of liposomes were
used; negatively charged POPC:POPS (3:1) liposomes
(zeta potential: (–26 ± 1.2) mV), neutral/slightly nega-
tively charged POPC liposomes (zeta potential: (–0.3 ±
1.0) mV), and positively charged POPC:POEPC (3:1)
liposomes (zeta potential: (+22 ± 0.8) mV). The compo-
sition and charge of the formed membranes were as-
sumed to be close to that of the corresponding liposome.
3.3. NP-HI Interaction with Model Membranes
of Different Charge
A charge-specific interaction behavior has previously
been reported for interactions between charged lipo-
somes and charged membranes. [15] Here, differently
charged model lipid membranes were formed and ex-
posed to positively charged insulin-loaded nanoparticles.
No interaction was observed between the NP-HI and the
positively charged POPC:POEPC (3:1) membrane (Fig-
ure 3(a)), as expected in the view of the positive charge
of the NP-HI particles. In contrast, the NP-HI readily
adsorbed to the negatively charged POPC:POPS (3:1)
membrane (Figure 3(b)). The QCM-D signals obtained
when adsorbing NP-HI on this negatively charged mem-
brane varied somewhat between different experiments
(Δf = 31 ± 6.4 and ΔD = 3.7 ± 1.5, respectively (z = 9)),
at a magnitude that could not be correlated with some
particular variation in the experimental procedure. The
NP-HI adsorption was predominantly irreversible, in
contrast to our previous experiments with charged lipo-
somes where transient interactions involving lipid ex
change were observed, [15] For the NP-HI interaction
there was only a small increase in the frequency shift
upon rinsing, associated with a relatively larger degree of
reversibility in the dissipation shift. This can be inter-
preted as a higher dissipation per NP-HI for the reverse-
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(a)
(b)
(c)
Figure 2. NP-HI size measurements shown both as (a) in-
tensity and (b) number distributions by DLS, and as (c)
SEM images (20000x (inset) and 400 000x magnifications).
In the SEM images NP-HI are seen as white dots and the
larger black areas are holes in the carbon support.
bly adsorbed NP-HI compared to the irreversibly ad-
sorbed ones, i.e. the few reversibly adsorbed nanoparti-
cles are in a different structural state and contribute more
to the ∆D signal. Alternatively, structural changes in the
whole adsorbed layer upon rinsing leads to release of
associated buffer and the formation of a more rigid layer,
without the desorption of NP-HI (see also below).
Additional experiments were performed with neutral
(in practice slightly negatively charged) membranes pre-
pared from POPC liposomes (Figure 3(c)). Similarly to
the more negatively charged membranes (POCP:POPS
(3:1)), NP-HI adsorb to POPC membranes, but QCM-D
(a)
(b)
(c)
Figure 3. QCM-D data (z = 3, 5, 7, 9, 11 and 13) of the in-
teraction between NP-HI and three differently charged
model membranes. Larger frequency and dissipation shifts
were obtained for lower overtone numbers. (a) Positively
charged POPC:POEPC (3:1), (b) negatively charged POPC:
POPS (3:1) and (c) slightly negatively charged POPC. The
plots show a sequence of events including (1) baseline in
buffer, (2) addition of NP-HI and (3) buffer rinse. The pre-
ceding steps of bilayer formation (see supplementary data)
and buffer exchange were omitted in the plots.
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(and also reflectometry, as will be seen below) reveals
important differences between the two systems. In par-
ticular, the ΔD shift is much larger for the NP-HI ad-
sorbed on the POPC membrane compared to the POPC:
POPS (3:1) membrane, indicating a viscoelastic layer,
which is more loosely coupled to the surface and there-
fore induce higher dissipation (D-value). Furthermore,
there are much larger differences between the signals at
different overtones for adsorption on the POPC bilayer,
compared to the POPC: POPS (3:1) bilayer. For “simple”
systems different overtones usually yield the same in-
formation after normalization of the absolute values. [20]
The behavior seen in the present case is characteristic for
viscoelastic layers adsorbed to the sensor surface, where
the response of the adsorbed layer to the shear, oscilla-
tory motion of the sensor, is different at different fre-
quencies. Under these conditions, the Sauerbrey equation
usually does not hold, and a Voigt-based modeling ap-
proach is required to obtain the correct adsorbed mass.
To further investigate the interaction between NP-HI
and the POPC:POPS (3:1) membranes, experiments were
performed at different NP-HI concentrations (see sup-
plementary data). It is clear that the time scale of the
binding kinetics in these QCM-D experiments changes
significantly when the NP-HI concentration is varied.
However, the data for different concentrations coincide
when plotted against exposure (exposure time x con-
centration), indicating a time-independent process, up to
certain coverage, i.e. the rearrangements and structures
formed on the surface are the same at a given coverage,
independent on the time scale during which this coverage
has been established.

The NP-HI adsorption experiments on the membranes
were repeated in a combined QCM-D/reflectometry in-
strument where the QCM-D and the corresponding re-
flectometry data were obtained simultaneously and on
the same surface (Figure 4). The above tentative conclu-
sions about certain structural differences between POPC
and POPC:POPS bilayers, makes it very valuable to
complement the QCM-D data, which yield acoustically
coupled mass (see below), with an optical method like
reflectometry that is only sensitive to mass changes not
including solvent. The optical mass is insensitive, or
relatively insensitive, to structural rearrangements.
When NP-HI were adsorbed to a POPC:POPS (3:1)
membrane the QCM-D data could readily be used to
quantify the acoustically coupled mass, macoustic, ad-
sorbed to the surface, since the D value was low and the
Sauerbrey equation is likely to hold. The optical mass,
moptic, obtained by reflectometry, was calculated at the
point where the adsorption of NP-HI had leveled out (in
Figure 4(a)-(b) at the end of section 2), i.e. just before
rinsing. The corresponding macoustic was found to be
475 ng/cm2 based on the Sauerbrey equation (Equation 1)
(confirmed by comparison with Voigt-based modeling
[21]). Assuming a homogenous adsorbed layer with a
density of 1000 kg/m3, this mass corresponds to a NP-HI
layer thickness of ~ 4 nm. For such a thin layer, the re-
flectometry data can easily be quantified (using the sen-
sitivity factor A obtained by calibration with the lipid
bilayer, see experimental section), and moptic was found
to be 133 ng/cm2 (Equations (2,3)) i.e. only about one
third of the acoustic mass. This difference is most likely
due to solvent associated with the adsorbed layer, in-
cluded in the measure of the acoustic mass but invisible
to the optical measurement. With this interpretation we
arrive at a water content in the adsorbed NP-HI layer of
72% when macoustic and moptic are compared. Similar
differences between the two (acoustic and optical) meas-
ures of mass are frequently seen in combined optical and
QCM-D experiments, e.g. for liposomes adsorbed on a
SiO2-surface during bilayer formation (~77%), and for a
layer of streptavidin, biospecifically bound to a bio-
tin-functionalized lipid bilayer (55% - 80%, depending
on surface coverage) [13].
The calculation of moptic in the above analysis was
based on the assumption of a thin, homogenous film. We
do not claim this to be exactly the case, but the measured
results combined with the calculations seem to suggest
that the adlayer is closer to a homogeneously spread out
layer, than an adlyer of intact nanoparticles. This picture
is, however, somewhat contradicted by the observation in
Figure 4(b) where, the reflectometry signal in contrast to
the frequency shift increases somewhat upon rinsing after
NP-HI adsorption. For a thin film on the surface, this
increase would be interpreted as a mass increase (which
is not supported by the QCM-D frequency response). For
a thin but inhomogeneous film, the increase in the re-
flectometry signal can also result from further structural
rearrangements, generating a thinner structure accompa-
nied by an increase of the sensitivity factor (Figure 5)
(which is, however, not supported by the dissipation re-
sponse).
These complications (with respect to a clear interpre-
tation) are more pronounced in the case where NP-HI
was adsorbed on the less charged POPC membrane (Fig-
ures 4(c),(d)). Here, the QCM-D data showed adsorption
of mass (negative Δf) and the formation of a viscoelastic
structure (high ΔD and different results for different
overtones, see above) while the corresponding optical
signal decreased (in some experiments, the descent was
preceded by a lag phase as exemplified in Figure 4(d)).
Due to the dramatic dependency in reflectometry of the
sensitivity function on the film thickness (Figure 5, see
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Figure 4. Combined QCM-D and reflectometry results where the data from the two techniques were collected simultaneously
on the same sensor surface. (A) QCM-D data (z = 9) from the interaction between the positively charged NP-HI (45 μg/ml)
and the negatively charged model membrane (POPC:POPS (3:1)). (b) Reflectometry data corresponding to (a). (c) QCM-D
data (z = 9) from the interaction between the positively charged NP-HI (45 μg/ml) and the slightly negatively charged model
membrane (POPC). (d) Reflectometry data corresponding to (c). Both plots show a sequence of events including (1) baseline
in buffer, (2) addition of NP-HI and (3) buffer rinse. The bilayer formation and buffer exchange were omitted from the plots.
Figure 5. Modeling of the reflectometry sensitivity factor A. (a) The different layers included in the model and their respec-
tive thicknesses (d) and optical properties (n, k). (b) The plot shows how the calculated sensitivity factor varies as a function
of the thickness (10-500 nm) of the NP-HI layer (grey in (a)).
3.4. Suggested Scenario for NP-HI Adsorption
and Insulin Release upon Membrane
Interaction
also work on hyaluronan films [22]), we believe that the
NP-HI layer on the less charged POPC membrane is
much thicker than on the POPC:POPS (3:1) membrane.
The sensitivity factor is negative for film thicknesses
similar to the present nanoparticle size, and Voigt-based
modeling [21] of the QCM-D data confirms thicknesses
of about 100 nm on the POPC membrane.
Based on our experimental results we attempt to formu-
late a scenario for the interaction between the insu-
lin-loaded polymeric nanoparticles and model mem-
branes. As the system is obviously complex in behavior

Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
Surface-Supported Model Lipid Membranes
188
and some of the measured signals partly contradict each
other when too simple pictures are tried, the suggested
scenarios should be taken as tentative. We also want to
emphasize that the results, although complex to interpret,
demonstrate the value of the experimental platform as
such, i.e. a multitechnique approach to study the interac-
tions between supported biomimetic membranes and
drug loaded nanoparticles.
Our first conclusion is that the cationic nanoparticles
in this study adsorb selectively to negatively charged
membranes. This observation is in line with the general
idea that the electrostatic properties of the drug carrier
are very important for its interaction with biological bar-
riers. We note however, that the observed specificity of
the interaction between NP-HI and model membranes
with respect to charge cannot always be predicted based
on the nanoparticle zeta potential alone. Cationic nano-
materials which have the ability to interact as well via
hydrophobic interactions will bind to lipid membranes
independently of membrane charge, if the interaction is
such the hydrophobic regions are exposed to each other.
Furthermore, for targeted drug carriers it is desirable that
the interaction with the membrane is only governed by
the (biospecific) targeting entity.
In our experiments with negatively charged mem-
branes we observed adsorption of cationic nanoparticles
and different interaction behaviors dependent on the de-
gree of negative charge. In all experiments the NP-HI
mass obtained on the negatively charged membranes is
much lower than would have been expected for a closely
packed layer of nanoparticles. This low surface coverage,
about 3% assuming intact particles1, cannot be explained
by a simple scenario where electrostatic repulsion be-
tween the charged nanoparticles controls the adsorption,
since the typical Debye-length in these experiments is
much smaller (nm range) than the calculated interparticle
distance (μm range).
A likely explanation for the low number of adsorbed
nanoparticles was already discussed above, and involves
conformational changes of the nanoparticles upon ad-
sorption, e.g. due to particle flattening, governed by the
membrane charge, so that each particle occupy a much
larger area (Figure 6) than it would do if it adsorbed in-
tact. Alternatively the conformational change of the
nanoparticles (partly) goes even further so that they dis-
assemble and free polycations bind to the surface. The
low number of adsorbed particles could also be related to
a more dynamic participation of the supported membrane,
than being just a 2D film surface for adsorption. For ex-
Figure 6. Schematic illustration of possible NP-HI adsorp-
tion scenarios on two model membranes. When NP-HI are
adsorbed on a POPC:POPS (3:1) membrane the particles
collapse, possibly in associated with the release of human
insulin. If instead the membrane consist of POPC lipids
only, the NP-HI adsorb fairly intact and a thicker layer is
formed. In the latter case disruption of the membrane could
occur. See text for details.
ample, fluid model lipid membranes are different from-
many other model surfaces in that the lipids within the
membrane can move rapidly on the time scale of nanopar-
ticle adsorption. Thus lateral surface rearrangements occur
in response to adsorbing material, e.g., nanoparticles could
affect the fluidity of the membrane [23] and positively
charged liposomes could move laterally when adsorbed to
a negatively charged membrane due to an induced charge
gradient [24]. In our experiments with mixed membrane
compositions (POPC:POPS), it is likely that the negatively
charged lipids (POPS) accumulate under the adsorbed
positively charged nanoparticles. In this way the adsorbed
nanoparticles deplete the membrane zone around them
from negative lipids. This lateral mobility cannot fully
explain the observed low surface coverage, since nanopar-
ticles adsorb as well (in separate experiments) readily to
membranes consisting of only POPC lipids. However it is
likely to give rise to an inhomogeneous layer which is also
suggested by the experimental data (see above). The pas-
sivation of the surface in between the particles could also
be explained by remodeling and even disruption of the
membrane and removal of the membrane from the surface.
[25] Note however that the NP-HI was observed to adsorb
to bare SiO2 surfaces as well, and thus exposed areas of
SiO2 would not be resistant to NP-HI adsorption.
Taking all these considerations into account, it is likely
that the low surface coverage of nanoparticles is due
mainly to structural rearrangements of the nanoparticles
occurring at or just after the adsorption event. The driv-
ing force for such rearrangement is most likely domi-
1By assuming spherical nanoparticles, with a diameter of 200 nm and
an effective density of 1.1 - 1.3 g/cm3, the (saturation) surface coverage
of NP-HI (based on macoustic) was estimated to 3%, with an interparti-
cle distance of ~1
μ
m.
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Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
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189
nated, or at least initiated, by electrostatic interactions
between the components of the nanoparticles (polyelec-
trolyte and human insulin) and the fluid membrane. This
is consistent with the fact that the (suggested) structural
rearrangements are more pronounced on the more nega-
tively charged membrane (Figure 6), where the particles
collapse. We suggest that the collapse of the particles is
controlled by the membrane charge and that the struc-
tural rearrangements are accompanied by release of insu-
lin, which is negatively charged, when the polycation is
exposed to the negatively charged surface. A complete
disintegration of the nanoparticles is not likely to have
occurred, since the resulting layer would then yield, even
on the more charged membrane, a much lower dissipa-
tion shift than observed. The measured dissipation shift
instead correlates well with previously reported values,
on SiO2, for a layer of adsorbed lipid vesicles of the same
size range as the nanoparticles (~200 nm) [26].
3.5. Extensions of the Experimental Platform
Our results demonstrate how a surface-based approach,
using model membranes supported on a surface and sur-
face analytical techniques, can provide valuable struc-
ture-function relationships for novel nanoparticles. With
such studies, properties of the drug carrier can be opti-
mized to promote drug release at a target interface of a
certain charge.
The three model membranes used in this study consists
each of one or two types of lipids. This is of course far
from the complexity of a native cell membrane, and the
biological relevance might therefore be questioned.
However, firstly the results still demonstrate the value of
the current methodological approach, with which the
complexity of the membranes can be increased succes-
sively. Secondly the strategy followed for further devel-
opment of the nanoparticles used in this study does not
rely upon targeting of a specific biological receptor. There-
fore, our simple model membranes are relevant as model
membranes to address efficiency and kinetics of the
nanoparticle interaction with charged surfaces in general
and lipid membranes in particular. Native cell mem-
branes are negatively charged, and because of this an
interaction with the nanoparticles is expected. Even if
specific membrane receptors were targeted, the overall
interaction would still be influenced by the demonstrated
non-specific interactions. Note however that although we
have emphasized electrostatic interactions above, other
non-specific interactions may also be important, like van
der Waals and hydrophobic interactions, perhaps follow-
ing upon initial electrostatic interactions (which could be
one possible cause of the difficulty to reconcile the meas-
ured D and reflectometry signals in the present work).
Besides QCM-D and reflectometry, other surface sen-
sitive techniques can be added to the presented platform.
For example, AFM would be useful to study the surface
topography after nanoparticle adsorption [27], and elec-
trochemical impedance spectroscopy (EIS) can be ap-
plied to study the integrity of the membrane [28] and
how it is affected by nanoparticles. Other methods within
the scope of our future studies are FTIR and fluorescence
measurements.
Finally, the presented methodology (supported model
lipid membranes and surface-sensitive analytical tech-
niques to study nanoparticle interaction with biomimetic
membranes) is not restricted to investigation of nanopar-
ticles for drug delivery, but all nano- and micro- sized
materials are possible to evaluate. Apart from drug de-
livery, nanoparticles could for example be evaluated with
respect to toxicity, since many toxic substances specifi-
cally target the cell membrane. In particular, it has been
demonstrated that it is possible to assess the disruptive
effect that cationic nanoparticles exert on lipid bilayers
[25].
3.6. Implications for in Vivo Function
For non-invasive insulin delivery it is desirable to obtain
a release of protein drug load in close proximity to the
cell surface e.g. in the epithelial cell lining of the small
intestine or the lung. The insulin receptor is surface asso-
ciated, wherefore uptake of intact nanoparticles is not
desired. The presented data show that the nanoparticles
structurally rearrange when adsorbed to negatively
charged surfaces, a process that is also likely to occur for
nanoparticles interacting with mucosa. These structural
rearrangements are most likely associated with drug re-
lease. For therapeutic use, products offering either rapid
or slow release of the insulin will be required. The kind
of data presented here can be used to tune the drug re-
lease from its carrier with the aim to design efficient in
vivo testing and to improve the fundamental understand-
ing of the nano-bio interface. In general, the development
of advanced drug delivery systems for biopharmaceuti-
cals will require tools for in vitro characterization of their
physico-chemical properties. We foresee that surface
sensitive analytical techniques will play an important role
in the field.
4. Conclusion
The interaction of nanoassemblies of a polycationic
polymer and human insulin with model lipid membranes
was investigated. The cationic nanoparticles (hydrody-
namic diameter about 220 nm) readily interacted with
negatively charged membranes, while no adsorption oc-
curred on a positively charged membrane. Based on a
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190
combined QCM-D/reflectometry analysis it was con-
cluded that the insulin-loaded nanoparticles undergo
structural rearrangements when adsorbed to a negatively
charged membrane, likely also releasing the drug load.
On a less negatively charged membrane, the structural
collapse was less apparent. These results motivate the
further use and development of this experimental plat-
form to guide the design and development of novel
nano-sized drug carriers.
5. Acknowledgements
This work was financially supported by the EU FP6 IP
NanoBioPharmaceutics. Novo Nordisk is acknowledged
for supplying human insulin. The authors thank Stefan
Gustafsson for performing the SEM analysis and Laurent
Feuz for modeling of the sensitivity factor. Additional
support was obtained from the Swedish Research Council.
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Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
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192
Supplementary Data
Results and discussion
Formation of Supported Lipid Bilayers
The formation of supported lipid bilayers serves as a
good illustration of the type of information that can be
obtained from the two measured QCM-D responses (the
resonance frequency shift (Δf) and the dissipation shift
(ΔD)) and the combination with an optical surface sensi-
tive technique (Figure S1). The frequency shift is a
measure of mass adsorbed to the sensor surface (de-
creasing frequency means increasing mass), and the dis-
sipation shift is a measure of the viscoelastic properties
of the adsorbed layer (increasing dissipation means more
viscoelastic or more loosely bound structures on the sur-
face). The frequency shift/mass change includes medium
(buffer) which is associated with and acoustically cou-
pled to the adsorbed material, e.g. inside or between ad-
sorbed vesicles. This is in contrast to optical techniques,
which measure effective changes in refractive index.
When liposomes (under the present conditions) are ad-
sorbed to a SiO2 surface, they first attach, temporarily, as
intact liposomes, causing a large decrease in frequency
(i.e. mass increase at the surface) and a high dissipation
(indicating a viscoelastic structure) (Figure S2(a)). At a
(a)
(b)
Figure S1. (a) QCM-D (z = 7) and (b) reflectometry data
measured simultaneously during formation of a POPC:
POPS (3:1) bilayer from vesicles on a SiO2-surface.
(a)
(b)
Figure S2. (a) QCM-D results (frequency shift and dissipa-
tion shift as functions of time) from experiments where a
negatively charged bilayer (POPC:POPS (3:1)) was exposed
to NP-HI of different concentrations (45 (x10), 5.6 (x80) and
1.1 (x400) μg/ml). The plot shows a sequence of events in-
cluding (1) basekine in buffer, (2) addition of NP-HI and (3)
buffer rinse. The bilayer formation and buffer exchange are
omitted from the plot. (b) shows a different representation
of the results in (a); the vertical axis is the same but the time
axis has been replaced by exposure, i.e. time multiplied by
concentration and flow rate. Since higher concentrations
allow higher exposure values to be reached in a reasonable
time, the curve for the lowest NP-HI concentration termi-
nates at the lowest exposure and the highest concentration
curve terminates at the highest exposure value.
certain surface coverage, the liposomes will, due to a
combination of their surface interaction and inter-vesicle
interactions, start to rupture and fuse, leading to release
of encapsulated liquid medium (buffer). This causes an
increase in frequency (mass loss) and a decrease in dis-
sipation. After a few minutes, a completed supported
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Structural Rearrangements of Polymeric Insulin-loaded Nanoparticles Interacting with
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193
lipid bilayer has formed. Under the present conditions,
characteristic values of Δf and ΔD for a completed lipid
bilayer of high quality are –26 Hz and 0.2, respectively.
The reflectometry signal shows, in contrast to QCM-D,
just a monotonic increase in mass throughout the forma-
tion of the lipid bilayer (Figure S2(b)), and it is insensi-
tive to changes in the amount of the lipid-associated sol-
vent, i.e. the optical signal monitors only the amount of
lipid on the surface, not its supramolecular structure (e.g.
it does not discriminate between lipid molecules in vesi-
cles or in a surface-confined planar bilayer).
Various NP-HI Concentrations on POPC: POPS (3:1)
Membranes
From Figure S2(a), it is clear that the time scale of the
binding kinetics in these QCM-D experiments changes
significantly when the NP-HI concentration is varied.
The initial NP-HI adsorption is linear in time, and the
rate is proportional to the concentration of NP-HI, in
accordance with a mass transport limited process. In
Figure S2b the data are plotted as adsorbed mass versus
NP-HI exposure (exposure time x concentration). In
this way, it is possible to test if there are differences in
e.g., structural rearrangements that influence the ob-
served signals at the different time scales of the experi-
ment. The main feature is that the data for different con-
centrations coincide when plotted against exposure, in-
dicating a time-independent process, up to certain cov-
erage, i.e. the rearrangements and structures formed on
the surface are the same at a given coverage, independent
on the time scale during which this coverage has been
established. However, there is a tendency for a slightly
higher uptake, at a given exposure, for lower concentra-
tions.

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