Preparation, Optimization, and Evaluation of Methoxy Poly(ethylene glycol)- co-Poly(?-caprolactone) Nanoparticles Loaded by Rivastigmine for Brain Delivery

Article abstract of DOI:10.1021/acschemneuro.9b00691

The objective of this study was to formulate and investigate the neuropharmacokinetics and pharmacodynamics of rivastigmine (Riv) loaded methoxy poly(ethylene glycol)-co-poly(?-caprolactone) (MPEG-PCL) nanoparticles (Riv-NPs) in rats after IV administration. The MPEG-PCL was synthesized via ring-opening polymerization of ?-caprolactone by MPEG and used to prepare Riv-NPs by the nanoprecipitation method. Response surface D-optimal design was applied to optimize Riv-NPs drug delivery system. The optimized formulation showed a particle size (PS) of 98.5 ± 2.1 nm, drug loading (DL) of 19.2 ± 1.1%, and sustained release behavior of the drug. Moreover, the optimized Riv-NPs were characterized by AFM and DSC analyses. A simple and sensitive HPLC-DAD method for bioanalysis was developed and successfully applied to the pharmacokinetic study. The neuropharmacokinetic study in rats indicated that the integration plot was linear, and the brain uptake clearance of the drug-loaded in MPEG-PCL NPs was significantly higher than the free drug. Furthermore, results of pharmacodynamic studies using the Morris water maze test demonstrated faster regain of memory loss with Riv-NPs when compared to the free drug solution. The results revealed that the mentioned biodegradable nanoparticle holds promise as a suitable drug carrier for brain drug delivery.

Full text of DOI:10.1021/acschemneuro.9b00691

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Article
Preparation, Optimization, and Evaluation of Methoxy
Poly (ethylene glycol)-co-Poly (#-caprolactone)
Nanoparticles Loaded by Rivastigmine for Brain Delivery
Hamed Mohamadpour, Amir Azadi, Kobra Rostamizadeh, Sina
Andalib, Mohammad Reza Saghatchi Zanjani, and Mehrdad Hamidi
ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.9b00691 • Publication Date (Web): 11 Feb 2020
Downloaded from pubs.acs.org on February 14, 2020
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Preparation, Optimization, and Evaluation of Methoxy Poly
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ethylene glycol)-co-Poly (ε-caprolactone) Nanoparticles Loaded by
Rivastigmine for Brain Delivery
a,b
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Hamed Mohamadpour , Amir Azadi , Kobra Rostamizadeh , Sina Andalib ,
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Mohammad Reza Saghatchi Zanjani , Mehrdad Hamidi *.
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Department of Pharmaceutical Nanotechnology, School of Pharmacy, Zanjan University of
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Medical Sciences, Zanjan, Iran.
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Pharmaceutical Nanotechnology Research Center, Zanjan University of Medical Sciences,
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Department of Pharmaceutics, School of Pharmacy, Shiraz University of Medical Sciences,
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Department of Pharmaceutical biomaterials, School of Pharmacy, Zanjan University of 12
Medical Sciences, Zanjan, Iran
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Department of Pharmacology and Toxicology, School of Pharmacy, Zanjan University of
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Medical Sciences, Zanjan, Iran
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Department of Pharmaceutics, School of Pharmacy, Zanjan University of Medical Sciences,
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Zanjan, Iran.
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*Corresponding author: hamidim@zums.ac.ir
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ABSTRACT
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The objective of this study was to formulate and investigate the neuropharmacokinetic and
pharmacodynamic of rivastigmine (Riv) loaded methoxy poly (ethylene glycol)-co-Poly (ε-
caprolactone) (MPEG-PCL) nanoparticles (Riv-NPs) in rats after IV administration. The MPEG-
PCL was synthesized via ring-opening polymerization of ε-caprolactone by MPEG and used to
prepare Riv-NPs by nanoprecipitation method. Response surface D-optimal design was
applied to optimize Riv-NPs drug delivery system. The optimized formulation showed a
particle size (PS) of 98.5 ± 2.1 nm, drug loading (DL) of 19.2 ± 1.1 %, and sustained release
behavior of the drug. Moreover, the optimized Riv-NPs were characterized by AFM and DSC
analyses. A simple and sensitive HPLC-DAD method for bioanalysis was developed and
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successfully applied to the pharmacokinetic study. The neuropharmacokinetic study in rats 11
indicated that the integration plot was linear, and the brain uptake clearance of the drug-
loaded in MPEG-PCL NPs was significantly higher than the free drug. Furthermore, results of
pharmacodynamic studies using the Morris Water Maze test demonstrated faster regain of 14
memory loss with Riv-NPs when compared to the free drug solution. The results revealed that 15
the mentioned biodegradable nanoparticle holds promise as a suitable drug carrier for brain 16
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drug delivery.
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Keywords: Alzheimer’s disease; Rivastigmine; MPEG-PCL Nanoparticle; Optimization; Brain 18
Delivery; Pharmacodynamic. 19
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INTRODUCTION
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Alzheimer’s disease (AD), which was demonstrated by Alois Alzheimer over 100 years ago, is
the most common type of dementia among the aging population, accounting for 50-60% of
all cases (1, 2). It is characterized by an insidious onset and progressive impairment, often
most of the short-term memory, visuospatial functions, and language. Some patients also
develop behavioral and personality changes(3). The two main neuropathological hallmarks of 26
AD are amyloid plaques caused by gradual deposition of beta-amyloid (Aβ) peptides(4, 5), and 27
neurofibrillary tangles that are formed by intracellular aggregation of abnormally
hyperphosphorylated tau proteins(6). An estimated 40 million people worldwide, mostly
older than 60 years, have dementia, and this figure is predicted to almost double every 20
years, at least until 2050 (7).
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The blood-brain barrier (BBB) is a highly selective and dynamic interface that separates blood 32
from the brain parenchyma and maintains brain homeostasis. However, this safety 33
mechanism comes at the cost of shielding the brain from the approximately 99% of currently 34
available small molecules (> 400 Da) and all of the large molecule drugs (8). Consequently,
several brain disorders such as AD, Parkinson’s disease, Multiple sclerosis, etc. remain
intractable. Several drug delivery strategies have been developed to overcome the BBB and
improve drug delivery of therapeutic agents to the brain (9, 10). Nanoparticulate drug delivery 38
systems are categorized under the non-invasive techniques, which have several beneficial
characteristics, like the enhancement of solubility and stability, protection from toxicity,
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low side effects, bioavailability and favorable drug release kinetics, and protection of the drug 41
from physical and chemical degradation. 42
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A wide variety of nanocarriers have been developed for brain drug delivery, including
polymeric nanoparticles (11), micelles (12, 13), liposomes (14, 15), Solid lipid nanoparticle (16,
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7) and nanostructured lipid carriers(18).
Among nanocarriers, polymeric nanoparticles composed of synthetic copolymers have
demonstrated significant advantages over other delivery systems. Diblock copolymer micelles
typically have a core-shell structure wherein the hydrophilic shell sterically stabilizes the
hydrophobic core from the aqueous milieu. The use of polymeric micelles as a drug delivery
system was first proposed by Ringsdorf and coworkers in 1984 (19). The synthetic amphiphilic
copolymers are ideal tools for drug delivery since they are highly versatile in terms of
architecture and composition, and MPEG-b-PCL block copolymers have attracted a great deal 10
of attention in drug delivery systems(20). Furthermore, PEGs have been demonstrated to 11
interact with certain transporters, particularly efflux transporters such as P-glycoprotein(21), 12
that can inhibit the efflux of drugs and lead to increased drug concentration in the brain
parenchyma. Biodegradable nanoparticles made of PEG-b-polyester are widely used to
prolong blood circulation time, reduce immunogenicity and toxicity, and change
biodistribution (22). In other words, the main advantage of the MPEG-b-PCL micelles is that
they are characterized by very low critical micelle concentrations which are indicative of high 17
circulation stability in the blood leading to sustained drug release following intravenous 18
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injection into animals(23)
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Rivastigmine (Riv) is a reversible inhibitor of both acetylcholinesterase and 20
butyrylcholinesterase approved for the use in mild to moderate dementia by regulatory 21
agencies such as the Food and Drug Administration and the European Medicines Agency (24, 22
5). However, limitations with its oral therapy include cholinergic side effects, restricted entry 23
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into the brain due to its hydrophilic nature, necessitating frequent dosing regimen because
of its short half-life (26). Therefore, we intended to investigate the MPEG-b-PCL nanoparticle
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system to develop an effective sustained-release micellar formulation containing Riv for brain 26
delivery. A novel approach was used to increase the encapsulation efficiency of the drug, 27
which relies on the interaction between oleic acid and Riv known as “hydrophobic ion-pairing” 28
27). The optimized formulation was characterized by DSC and AFM and tested further for In- 29
(
vitro release kinetics study.
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To summarize, in this work we have designed and optimized a novel amphiphilic polymer
nanocarriers system by the nanoprecipitation method for Riv, and evaluated its
neuropharmacokinetic, pharmacokinetics, and pharmacodynamic properties. The present
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work demonstrates that Riv-loaded NPs are able to increase brain uptake clearance, brain-to- 34
plasma concentration ratio, and blood terminal half-life of the Riv compared to the free drug
following intravenous administration. Moreover, the pharmacodynamic study shows that the
Riv-NPs improve spatial learning and memory in the scopolamine-induced amnesia animal
model.
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RESULTS AND DISCUSSION
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.1 Characterization of MPEG–PCL copolymer
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The H-nuclear magnetic resonance ( H-NMR) spectrum of the MPEG-PCL block copolymer
showed characteristic signals for PCL (1.4, 1.66, 2.19, 2.33, and 4.05 ppm) and MPEG (3.4 and 42
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.66 ppm) segments. The Fourier transform infrared (FTIR) spectra of the copolymers
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exhibited the characteristic absorption band for C = O stretching at ~1730 cm , C–H stretching
at 2948 and 2873 cm and at 1108 cm for C–O–C stretching vibration, which all confirmed
the existence of PCL and PEG segments (28). In other words, FTIR and H-NMR results
indicated that MPEG-PCL diblock copolymer synthesis was successful (28, 29).
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The four sets of MPEG-PCL copolymers were synthesized by ring-opening polymerization
method. The M and PCL/MPEG block ratio of various compositions of the copolymer was
calculated from H-NMR spectra with slight modifications, according to the previously
published method (30). The degree of polymerization (DP) of PCL was determined by Eq.1:
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PCL
peak area
PCL
H ― number
H ― number
DPPCL = DPPEG × PEGpeak area
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PEG
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Where PCLpeak area and PEGpeak area are the areas of the H-NMR peak of PCL and PEG moiety 11
respectively; PCLH ― number and PEGH ― number are the number of protons of the Ɛ-CL and m-
PEG respectively; DPPEG is the number of repeating units of PEG moiety which was
determined approximately by dividing M of MPEG by the molecular mass of repeating ethoxy 14
unit. Having obtained DPPCL, the M of PCL moiety and copolymer was determined. The M
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of the copolymer with feeding ratio (Ɛ-CL/m-PEG, w/w) of 1, 2, 3, and 5 were 9525, 15721,
0660, and 28625, respectively (Table S1).
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.2 Rivastigmine free-base preparation
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Riv free base, a viscous, clear, colorless to yellow oily liquid, was prepared. Successful
preparation of Riv free base was confirmed by gas chromatography-mass spectrometry
(Agilent 7890A GC coupled to an Agilent 5975C MS, USA) analysis according to the method
described by Yunfei et al. (31). The Gas chromatography-mass spectrometry spectra of the 22
obtained Riv (not shown here) indicated that characteristic ion peak at m/z 235 was its base
peak.
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.3 Characterization of rivastigmine-loaded nanoparticles
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2.3.1 Particle size distribution and zeta potential
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The Z-average diameter (intensity weighted mean hydrodynamic diameter) of the drug-
loaded MPEG-PCL NPs was measured by photon correlation spectroscopy. The size of the
optimized nanoparticles was in the range of 98.5 ± 2.1nm with narrow and monodisperse size
distribution(0.11) (Fig. S1), which were smaller in comparison to previous studies (32-34). All
of the prepared Riv/MPEG-PCL NPs exhibited negative polarity and the zeta potential value in 31
the range of 18.5–38.1 mV. It was previously reported that the zeta value of ±30 mV assures 32
the colloidal stability of nanoparticles in a dispersion (35). The PS, PDI and zeta potential of 33
drug-free nanoparticles were 84.0 ± 5.3nm, 0.18, and -23.9± 1.1mV, respectively.
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.3.2 Drug loading parameters
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Achieving high encapsulation efficiency (EE) for water soluble drugs like Riv in the
nanoparticles with the lipophilic core is a challenging issue. In the present study, the EE and
DL amount of the optimized formulation were about 40.4 and 19.2, respectively. Therefore,
with the aid of forming the Riv-OA complex, the lipophilicity of Riv was enhanced; hence, the 39
higher DL has been achieved. MPEG-PCL NPs have been reported to give high DL for poorly
water-soluble drugs (36, 37), but the present investigation reveals that the new formulation
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method can lead to NPs with high DL with the water-soluble drug. It would be beneficial for
incorporating the required dose of the drug in a minimum amount of delivery system to be
used as a dosage unit.
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2.3.3 AFM study of NPs morphology
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The morphology of the optimized drug-loaded NPs was studied by using AFM. It was observed
that the particles had a uniform spherical shape and narrow size distribution (Fig. 1a). The
cross-sectional height profile (Fig. 1b), taken at the white line in Fig. 1a, revealed the disc-like
shape of the particles. With an average diameter of 120 nm and an average height of 10 nm,
the NPs shown in the image are spherical structures that have flattened out on the mica
surface upon casting. AFM 3D image reconstruction is also illustrated in Fig. 1c. The PSs
determined by analysis of AFM images were in good agreement with PSs that were obtained
by dynamic light scattering (DLS) measurements.
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DSC thermograms (Fig. 2a) of the physical mixture, lyophilized drug-loaded, and unloaded NPs
showed a characteristic sharp endothermic peak at around 56˚C, indicating the melting point
of the polymer (38). The DSC thermogram of the Riv showed a transition glass endothermic
peak at -66.8°C. Like the pure drug, the physical mixture of polymer-drug had a glass transition 17
temperature (T ) of about -63°C. The results indicate the absence of any significant chemical
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interaction between drug and polymer in the physical mixture, while no T of Riv in the
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the other hand, DSC thermogram of NPs exhibited an endothermic transition peak at 6.1°C
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(in the range of -2.3 to 11.3°C) that was attributed to the melting point (T ) of OA (39). 23
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confirms the interaction between Riv and OA.
2.3.5 In vitro drug release
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The in vitro cumulative drug release profile of the Riv-MPEG-PCL NPs and the free drug are
shown in Fig. 2b. A biphasic release pattern of drug release was observed from nanoparticles
was observed: initial fast release and subsequent slow release phase. The fast release can be 29
attributed to the unloaded drug and the release from the surface of the nanoparticles. The
slower release rate of Riv in the second phase is thought to be due to the dispersion of Riv in
the polymer matrix and also its interaction with OA to form a less water-soluble complex,
which led to a gradual dissolution and release of the drug. Free drug, was released completely 33
at around 2 h, while its release from NPs was completed within 8 h.
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In order to determine the kinetics of drug release, the in vitro release data was fitted to
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various mathematical models using the Microsoft Excel Add-Ins DDSolver. The model with the 36
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highest R adjusted value, highest MSC value, and lowest AIC value (the most popular criteria) 37
was considered as the best-fit model (40). The order of suitable model was Weibull > First-
order > Korsmeyer-Peppas > Hixson-Crowell > Higuchi > Zero-order (Table S2).
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of Riv-NPs (R adj= 0.996, AIC= 45.58, MSC=5.41, a= 27.68, b= 0.755). The value of the
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Values of b in the range of 0.75–1.0 demonstrate a combined mechanism (Fickian diffusion
and case-II transport), which is frequently encountered in release studies(41). The Weibull
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model is an empirical model that has been widely applied to release data of both rapid and
extended release drug delivery systems.
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.4 Formulation optimization
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2.4.1 Preliminary studies of formulation
Due to the advantages of the nanoprecipitation method, such as a fast production process
without technically sophisticated equipment, simplicity, and the use of pharmaceutically
acceptable organic solvents, it was considered for further studies. Since Riv is a hydrophilic
drug, the influence of various formulation additives on drug EE was investigated. Based on
the previously published works (42-44) and our preliminary studies, OA was selected as an
additive. Based on the preliminary experiments, three influential factors were chosen as the
variables to be investigated (Table 4). Other parameters, i.e., type of polymer (PCL/MPEG
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block ratio of 5), rate of organic phase addition (1mL/min), stirring speed (1600 rpm), and 12
stirring time (12h) were set at fixed levels.
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2.4.2 Experimental design and optimization
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techniques and is used for modeling and analysis of problems in which a response of interest 16
is influenced by several variables, and the objective is to optimize the response. A multi- 17
objective optimization approach was used throughout the present study in order to optimize 18
the nanoparticle preparation method. This approach involves optimization problems with
multiple objectives. As mentioned earlier, in the subsection 2-6, three independent variables 20
including the drug/polymer ratio (A), aqueous/organic phase ratio (B) and OA concentration
in acetone (C), along with five response variables consist of EE, DL, PS, polydispersity index
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(
PDI) and zeta potential were selected for optimization studies. After preparing and analyzing 23
the formulations, the distribution of each response was checked for normality and outliers. It 24
was determined that all data points were normally distributed (data not shown). Analysis of 25
variance (ANOVA) was applied to determine the significance and the magnitude of the effects 26
of the main variables and their interaction.
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The results of ANOVA (Table 1) indicated that all of the responses were fitted with the
quadratic models, which are shown in Eqs. 2-5, respectively. The P-value of the model (<0.05) 29
and the P-value of the lack of fit (>0.05) imply that these models were significant, i.e., the
probability of the model being incorrect was less than 5%. In the polynomial regression
equations, the positive sign indicates a synergistic effect, while a negative sign represents an
antagonistic effect of the factor on the selected response.
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2.4.3 Influence of independent variables on characteristics of Riv-NPs
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From the response surface result (Fig. 3a), it was observed that with aq/org (v/v) ratio of 50:1
and drug/polymer (w/w) ratio of 0.05/1, the EE was minimum and as the aq/org ratio was
decreased to 5/1 (w/w), the EE was at the maximum level. At high aq/org ratio (50/1), the
drug/polymer ratio had little effect on EE, which was more pronounced at the lowest aq/org
ratio (5/1), where a decrease in drug/polymer ratio caused an increase in EE. This can be
mainly attributed to the high concentration of drug in lower aq/org ratios. Eq. (2) is the
quadratic polynomial model for the EE (Y1):
2
Y = 16.45 + 1.68*A - 17.89*B + 11.79*C + 11.33*A*B - 4.08*A*C - 9.74*B*C - 4.22*A + 42
7.35*B + 6.48*C
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(2)
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DL% was higher at high concentrations of OA (10 mg/mL) and reached a maximum of 24.9%
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Fig. 3b) by an increasing amount of D/P from 0.05 to 1. These results suggest that interaction
between OA and Riv through a process known as hydrophobic ion pairing (27) can increase
the encapsulation of the drug in the hydrophobic core of NPs. Moreover, the positive effect
of OA on DL% was more remarkable at high D/P ratios, which could be due to increased OA-
drug interactions and DL. Eq. (3) is the quadratic model, which indicates the effect of
independent variables on DL (Y2).
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Y = 4.41 + 5.52*A - 3.90*B + 2.93*C - 0.72*A*B + 0.17*A*C - 3.24*B*C - 2.71*A + 2.95*B +
4.55*C
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(3)
Quantitative estimation of the significant models indicated that OA concentration and D/P
ratio had a direct influence on DL due to its positive coefficient. This suggests that increasing
the mentioned two variables in the formulation can lead to higher DL values. In addition, the
ANOVA test showed that both EE and DL are significantly influenced by the studied factors,
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since the P-value for the regression model was <0.0001, and the P-value for the lack of fit was 14
>0.05 in both responses, as it is seen in Table 1.
15
As shown in Fig. 3c, PS (Y ) has been increased by augmenting the OA concentration in a
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3
nonlinear pattern. Although the PS was increased slightly in higher OA concentrations,
remarkable improvements were observed in the drug loading parameters. Eq. (4) shows the
fitted model of PS (Y3).
2
2
Y = 95.23 - 4.02*A - 4.72*B + 3.82*C - 3.35*A*B + 1.63*A*C - 3.49*B*C - 2.21*A + 9.07*B -
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.44*C
(4)
The results of ANOVA test showed a significant influence of the studied factors on PS since
the P-value for the regression was lower than 0.05. However, the lack of fit for PS was also
2
significant. The goodness-of-fit of the models was inspected by using R value. The results
2
showed that the R value of the PS model was 0.9039. In addition, the adequate precision
values for PS was well above 4 (10.00). Therefore, it could be concluded that this model was 26
suitable for predicting PS. 27
The PDI values of the formulations ranged from 0.09 to 0.16, which was the indication of a 28
narrow size distribution of the nanoparticles. Statistical analysis results revealed that none of 29
the independent variables was significant (p< 0.05) in predicting the value of PDI (Y4).
30
According to the response surface plot of zeta potential (Fig. 3d), increasing OA concentration
has clearly increased the zeta potential value of NPs (p-value = 0.0004), which was expected
due to the excess amount of OA on the surface of NPs. The P-value and lack of fit of the model
were 0.001 and 0.8784, respectively, which implied that the model was significant. Eq. (5) 34
shows the quadratic model for the quantitative effect of independent variables (as well
interaction of these variables) on zeta potential (Y5).
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Y = -24.68 - 0.82*A + 2.20*B -4.30*C - 0.35*A*B + 1.95*A*C + 4.07*B*C + 3.15*A - 4.41*B - 37
5
2
2
.83*C
(5)
38
After analyzing the polynomial equations and optimizing by using Design-Expert software, the 39
parameters of an optimum formulation were determined as follows: D/P ratio of 0.93, Aq/org 40
ratio of 18.02, and OA concentration of 10 (mg/mL), which all meet the requirements for
optimization. At these levels, the predicted values of the response variables are shown in 42
Table 2. In order to confirm the validity of the suggested model, three new batches of the
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formulation were prepared under the predicted optimal conditions and compared with the
predicted values by the model. The predicted and the experimental values of the response
variables are shown in Table 2. The predicted error was less than 5%, indicating that the actual
responses were very similar to the predicted values, and the mathematical models of the D-
optimal design were well fitted (45).
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.5 HPLC quantification of rivastigmine
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Several methods have been reported in the literature for quantification of Riv and its
metabolites in biological samples including, canine plasma by gas chromatography-mass
spectrometry (31), rat brain and plasma by HPLC‑fluorescence detection (46), rat brain and
plasma by liquid chromatography–mass spectrometry (47, 48), human plasma by HPLC-
UV(49).
The internal standardization approach was applied to construct a calibration curve. 4-
Aminoacetophenon was selected as an internal standard (IS). Calibration standards were used 13
to generate calibration curves in plasma (80– 3000 ng/mL) and brain homogenate (100– 3000 14
ng/g). HPLC-UV methods are more prone to interferences from matrix constituents in
biological samples. To evaluate the potential interference of matrix constituents and
anesthetic drugs in the bioanalysis procedure, blank plasma and brain samples of rats 17
anesthetized by ketamine/xylazine cocktail were analyzed using the aforementioned method. 18
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The results revealed the interference of the xylazine with the peak response of the drug (Fig.
A-I and 4B-I).
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The peak of Riv could not be adequately separated from the interference peak by the available 21
HPLC column. Due to the results of acepromazine and ketamine, which revealed no
interference (Fig. 4A-II and 4B-II), the acepromazine/ketamine cocktail was used in the analysis
set up and pharmacokinetic studies. Representative chromatograms of blank plasma and
brain homogenate spiked with Riv (500 ng/mL) and IS (2.5 µg/mL) are depicted in Fig. 4A-III
and 4B-III. As it is shown, the retention times of Riv and IS were 8.3 and 9.3 min, respectively.
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.5.1 HPLC method validation
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2
The proposed method exhibited a good linear response (R > 0.99) over the concentration
range in plasma (80-3000 ng/mL) and brain homogenate (100-3000 ng/mL). Intra-day and
inter-day precision (RSD%) and accuracy of the method were determined from the quality
control (QC) samples (Table S3). The Intra- and inter-day precision were all below 15%
with accuracy within the range of 92.6-111.6 %. Limits of quantification (LOQ) were proven to 32
be 80 ng/mL in plasma and 100 ng/mL in the brain homogenate. The limit of detection (LOD) 33
was 30 ng/mL in both samples. The average extraction efficiency of the plasma and brain QC
samples were 83.1- 91.8% and 57.0- 63.4%, respectively. Absence of the interference peak in 35
the extracted chromatograms of blank plasma and brain samples and complete separation of 36
drug and IS in the presence of biological matrix indicated the selectivity of the method (Fig.
4A-II and 4B-II). In conclusion, the proposed method proved to be sufficiently sensitive and
highly reproducible for pharmacokinetic studies.
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39
2
.6 Neuropharmacokinetic study
40
The plasma and brain concentrations of Riv at different time points following intravenous (IV) 41
injection of Riv-NPs, as well as the equi-dose free drug are shown in Fig. 5a, b, and Table 3. Riv
was found to be present in the rat brain at high concentrations in early time points of free
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drug dosing, as reported in the previous studies (48, 50). Compared to the free drug, IV
injection of an equal dose of Riv-NPs resulted in a 1.7, 1.8, and 3.7 fold higher concentration
of Riv in the brain 15, 30, and 60 min after injection, respectively. This higher concentration
may be due to the nano-sized particles and the presence of surface PEG groups, which can
result in a “dysopsonization” effect (51, 52), uptake reduction by the reticuloendothelial
system (RES) and more prolonged drug survival in blood circulation. The pharmacokinetic
parameters were calculated from the pooled plasma concentration-time data using
noncompartmental analysis (Table 3).
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Following the administration, the free drug was distributed and disappeared rapidly from the
circulation, with plasma clearances of 37.7 ml/min per kg. In contrast, Riv-NPs extended Riv
circulation time in the blood with a 2.3-fold increase in blood terminal half-life (t ), a 1.2-
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/2
fold increase in blood AUC0–90 min, and 22% decrease in total clearance compared to free
drug. The 2.3-fold difference in t1/2 between the free drug and encapsulated drug is indicative 13
of adequate drug retention within the nanoparticles. Increasing the mean residence time
MRT) of Riv in the plasma for Riv-NPs also verified that the half-life of drug was increased.
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15
(
Integration plot analysis was performed for the calculation of brain uptake clearance of free
drug and drug loaded in NPs. The theoretical basis for this analysis was described previously
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19
(
53-55). In brief, Kp (brain-to-plasma concentration ratio) was plotted against AUC/Cp (the
ratios of the AUC in the plasma to the corresponding plasma concentrations) from zero to a
given time. The slope of the plot, which represents the apparent brain uptake clearance, was 20
calculated. Furthermore, the intercept value provides an estimate of the fractional space of
the total volume of the brain occupied by the plasma circulating in brain microvasculature. It
was seen that the trend in both cases of free Riv and Riv-NPs was approximately linear by 30
min. The linear relationship between the Kp and AUC/Cp was lost at times >30 min.
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Since this plot was an ascending line, it was concluded that drug transport from blood to the
brain parenchyma across BBB was mainly unidirectional during the early 30 min, i.e., the influx 26
was predominant. 27
25
According to the Kp–time profile, the ratio of brain to plasma concentrations in both groups 28
increased by 30 min and then decreased with steep gradients (Fig. 5c). It means that the rate 29
of drug depletion in the brain is much higher than the plasma after 30 min, and there is a two- 30
way passage more rapidly from the brain to plasma after this time. Compared to the group
receiving free drug, it is clear that the Riv-NPs group displayed significantly higher Kp at 30
min, and the superiority in net brain entry of the drug remained up to 90 min. This behavior
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can be best explained by the presence of the Riv-NPs as the drug reservoirs in the brain which, 34
in turn, compensate for the drug efflux from the brain to blood circulation.
35
The integration plots of free drug and drug loaded in NPs are depicted in Fig. 5d. In the case
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2
of free drug group, R and F values were 0.99 and 111.8, and the corresponding values were
0
.97 and 29.6 for the Riv-NPs group. As illustrated graphically, the Riv-NPs remarkably
improved apparent brain uptake clearance of the Riv (about 4-times) compared to free drug. 39
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.7 Pharmacodynamic study (Morris water maze test)
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Morris Water Maze is a standard test of spatial learning for rodents, which was introduced by
Richard Morris in 1984(56). Scopolamine-induced amnesia model is one of the well-
established animal models to study memory dysfunction(57). To evaluate spatial learning and
memory during learning trial, traveled distance was investigated. The escape latencies to find
the hidden platform in all groups during the learning trial are shown in Fig. 6a. The escape
latency from all of the three trials was averaged to calculate the escape latency for each day.
On the first day of the learning trial, there were no significant differences in the mean escape
latency between the groups. In the scopolamine treated group, a characteristic swimming
behavior consisted of circling around the pool was observed (Fig. 6b), and the escape latencies
slightly shortened throughout the 4-day learning period, showing that the drug-impaired
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memory as also reported in the other works (58). However, saline-treated rats rapidly learned 13
the location of the platform, as indicated by a rapid ramp decrease in the escape latency.
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Following the administration of Riv-NPs, rats (group 4) learned to reach the platform rapidly
and exhibited behavioral patterns identical to saline-treated rats (group 1). It was quite fast
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compared to free drug-treated rats (group3). In other words, scopolamine-treated rats (group 17
) exhibited longer escape latency across the training days compared to the rats treated with 18
2
scopolamine and Riv-NPs. Thus, the results suggested faster memory regain in scopolamine-
induced memory deficit model following intraperitoneal (IP) injection of Riv-NPs, indicating
the effectiveness and efficiency of the nano formulation in spatial memory improvement in
the amnesic rats. The spatial probe test was conducted after the rats had learned how to
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locate the platform, and was assessed by analyzing the total time spent in the target quadrant. 23
A comparison of the time spent in the target quadrant on day 5 (probe test) is represented in 24
(
Fig. 6c). During the probe test, the submerged platform was removed, but its previous
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location was recorded via the monitoring software.
The results of the probe test indicated that group 4 (scopolamine and Riv-NPs receiving rats)
had greater retention of the position than did the group 2 (scopolamine receiving rats) and
group 3 (scopolamine and free drug receiving rats). Whereas, there was no significant
difference in the time spent in the target quadrant between group 4 and group 1. Moreover,
the time spent in the target quadrant relative to group 2 showed a significant increase in both 31
group 1 and group 4. The analysis of the results by 1-way ANOVA test and post hoc Tukey’s
method showed significant differences amongst the groups (P-value<0.0001), but there was
no statistically significant difference between rats in group 1 and group 4. Thus, it concludes
that the developed polymer-based nanoparticles can reduce the effect of memory
impairments induced by scopolamine.
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3 CONCLUSION
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40
In this study, biodegradable MPEG-PCL diblock copolymers with different molecular weights
and compositions were synthesized. Riv-loaded NPs were prepared using the
nanoprecipitation method. Here, a novel approach was adopted to enhance the EE, which 41
was relying on the complexation of the drug and OA, which would increase the lipophilicity of 42
the drug. The NPs were optimized and validated successfully using the systematic multi-
objective optimization approach (D-optimal design) in terms of EE%, DL%, PS, zeta potential,
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and PDI. The prepared Riv-loaded nanoparticles showed the slow release of the drug in
comparison with the free drug. The HPLC-DAD quantification method, which was developed
and presented in the current work, described a sensitive, specific, and repeatable procedure
for the determination of Riv in rat plasma and brain. Neuropharmacokinetic studies showed
that the brain uptake clearance was significantly higher in the case of the drug being loaded
in NPs than the non-encapsulated drug. Furthermore, pharmacodynamic studies revealed
that compared to free drug, Riv-NPs resulted in faster memory improvements in the
scopolamine-induced amnesia animal model. Briefly, MPEG-PCL nanoparticles may be
considered as a promising carrier to deliver Riv across the BBB to treat AD.
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MATERIALS AND METHODS
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4
.1 Materials
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Pharmaceutical grade rivastigmine hydrogen tartrate (RHT) was kindly supplied as a gift
sample by Sobhan Pharmaceutical Industry Ltd. (Rasht, Iran). 4-Aminoacetophenon (IS),
Monomethoxy-poly (ethylene glycol) (MPEG; M =5 KDa, Sigma-Aldrich), ε-caprolactone (98% 15
n
purity, Acros) and stannous 2-ethyl hexanoate (Sn(Oct) ; Sigma-Aldrich) were all purchased
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2
locally. All other reagents and solvents were of analytical or HPLC grade and were purchased
locally.
4
.2 Synthesis, purification, and characterization of MPEG–PCL copolymer
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Synthesis of MPEG–PCL copolymers was carried out with four different block lengths by a
classical ring opening polymerization method (38). Briefly, a predetermined amount of ε-
caprolactone and MPEG (with the Ɛ-CL/m-PEG feeding ratio of 1, 2, 3 and 5), and Sn(Oct)2
(
0.01 mmol) were placed in a 100 mL three-necked round bottom flask and heated for 12
hours by maintaining the temperature range of oil bath at 120-130°C. After cooling to room
temperature, the resultant copolymer was purified by dissolving it in chloroform and then
precipitating in excess cold diethyl ether. The purified copolymer was thereafter filtered and
dried under vacuum at room temperature for 24 h. The various compositions of the
1
copolymer were characterized using H-NMR analysis (Bruker AVANCE 400 MHz NMR
spectrometer, Germany) and FTIR spectroscopy (TENSOR 27 FT-IR spectrophotometer,
Bruker, Germany).
4
.3 Rivastigmine free-base preparation
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Riv free base was prepared from RHT by extracting Riv with an alkaline solution (NaOH, 1M)
and adding dichloromethane as a non-polar solvent. The appropriate amount of RHT was
dissolved in a minimum amount of distilled water (approximately 2 mL), and then NaOH (1M) 34
solution was added until the solution became milky. The aqueous phase was extracted twice
with 10 mL of dichloromethane. The organic layer was separated and evaporated using N2
stream under a ventilated fume hood. The obtained free base of the drug was stored in a 37
refrigerator at 4°C until required for use.
35
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4
.4 Preparation of blank and drug loaded nanoparticles
39
The nanoparticles were prepared via the nanoprecipitation method. The MPEG-PCL 40
copolymer and drug-free base with a predetermined optimal concentration were dissolved in 41
acetone. This solution was then added dropwise under vigorous stirring to distilled water. The
organic solvent was evaporated by magnetic stirring at room temperature for at least 12h
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until it was completely removed. The NPs were concentrated by ultrafiltration (20 min, 5500
rpm, Amicon® Ultra tubes, cut off: 10 kDa) and washed twice with distilled water to remove
the free (unentrapped) drug. Drug-free nanoparticles were prepared in a similar manner
without adding drugs. All samples were prepared in triplicate.
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.5 characterizations of rivastigmine-loaded nanoparticles
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4.5.1 Particle size distribution and zeta potential
PS (Z-average) and PDI of the NPs were determined using DLS and zeta values were obtained
by Laser Doppler Anemometry (LDA). The DLS and LDA were analyzed using zetasizer
(
Malvern Instruments, Malvern, UK) equipped with He–Ne laser (operated at 633nm). The
samples were diluted with distilled water to a suitable concentration and analyzed at 25˚C.
4.5.2 Atomic force microscopy (AFM) imaging
11
AFM was employed to investigate the morphology and size of the drug-loaded nanoparticles. 12
Imaging was carried out using Nano Wizard®II BioScience AFM (JPK Instruments Inc.,
Germany), working in intermittent contact (tapping) mode. A silicon cantilever (Acta,
APPNANO) with a resonant frequency of ∼300 kHz and a nominal spring constant of 37 N/m
was used. Scan rate and imaging resolution were 1.0 Hz and 512 × 512 pixels, respectively.
AFM samples were prepared by casting 5μL of the nano formulation (diluted 10-fold in
deionized water) onto a surface of freshly cleaved mica (1cm ×1cm) substrate.
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4.5.3 Drug loading parameters
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The EE and DL of Riv-NPs were determined indirectly, separating the non-entrapped drug
from drug-loaded NPs by centrifugal ultrafiltration method. Briefly, 1mL of freshly prepared
nano formulation was centrifuged (4000×g, 15 min) in ultrafiltration tubes. The Riv
concentration of ultrafiltrate, free drug, was analyzed by the HPLC method. The EE (%) and
DL (%) of Riv-NPs was calculated using Eqs.6 and 7, respectively:
푇퐷 ― 퐹퐷
푇퐷
퐄퐄% =
퐃퐋% =
× 100
× 100
(6)
(7)
푇퐷 ― 퐹퐷
푇퐷 + 푃
Where TD is the total amount of drug added initially, FD is the amount of free drug in the
ultrafiltrate, and P is the amount of feeding polymer.
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4.5.4 Differential scanning calorimetry (DSC)
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Thermal characteristics of the freeze-dried nano formulation, freeze-dried unloaded
nanoparticles, Riv, and the physical mixture of drug and copolymer were determined by DSC
(
METTLER TOLEDO, Switzerland). About 12 mg of the samples were completely sealed in the
aluminum standard pans. The samples were scanned at 10°C/min from -100 to 200°C.
4.5.5 Fourier transform infrared spectroscopy (FT-IR)
34
The FTIR spectra of the drug, drug-polymer physical mixture and the freeze-dried samples of 35
nanoparticles and drug-loaded nanoparticles were acquired on a Bruker TENSOR-27 infrared 36
−1
spectrometer in the spectral range of 500–4000 cm .
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.6 Formulation optimization
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Since the number of experiments would increase exponentially with
the number of independent variables tested, some preliminary tests were conducted in order
to find the most influential factors and thus keep the number of experiments affordable.
Primary independent variables including type of copolymer (ratio of hydrophilic to lipophilic
moieties), type of organic solvent, the organic to aqueous ratio (v/v), drug to polymer ratio
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w/w), addition speed of the organic phase, stirring time and PVA concentration were
assessed using one factor at a time design.
4.6.2 Experimental design and optimization
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Experimental design and formulation optimization were performed by D-optimal RSM using
Design-Expert software (version 7.0.0, Stat-Ease, Inc., Minneapolis, MN, USA). D-optimal
design reduces the number of experiment runs compared to standard factorial design and 13
gives the most accurate estimation of the affecting factors(59). D-optimal design is referred 14
to as a computer-aided design, where the determinate information matrix is maximized, and 15
the generalized variance is minimized (60).
16
Upon choosing the influential factors from the preliminary studies and literature review, the
formulation was further optimized in terms of EE, DL, PS, polydispersity index (PDI), and zeta
potential as dependent variables by D-optimal design. Three formulation variables,
drug/polymer ratio (A), aqueous/organic phase ratio (B), and OA concentration in acetone
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(
C), were included in the experimental design. The factors (independent variables) and their
levels investigated together with the responses (dependent variables), and their constraints
are tabulated in Table 4.
A total of 20 experimental runs were carried out throughout the D-optimal design and 24
responses were determined. All response values are the mean of triplicate measurements.
25
The best-fitting model to the data was analyzed by the software and its suitability was
evaluated by the correlation coefficients (R ) and adjusted R as well as the “lack of fit” test.
The significance of the relationship between independent and dependent variables was 28
evaluated by ANOVA test (P-value < 0.05). Finally, in order to validate the model, three 29
replicates of the optimized response were prepared, and the obtained values were compared 30
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with the predicted values of the model.
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4
.7 Quantification of rivastigmine in rat plasma and brain by HPLC-DAD
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33
To the best of our knowledge, a validated HPLC-DAD method has not been reported for the
determination of Riv in rat plasma and brain. Thus, we have developed and validated a simple 34
and sensitive HPLC-DAD method for the determination of Riv in rat plasma and brain using a
liquid-liquid extraction technique.
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.7.1 Chromatographic conditions
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HPLC was performed using a Knauer Smartline chromatographic System (Knauer, Germany)
that consisted of a four-channel pump, an on-line degasser, a column oven, and the diode
array detector (DAD). The separation was performed on a C18 (Eurospher 100-5, 250 x 4.6
mm, Knauer, Germany) column equipped with a guard column (Eurospher 100-5 C18, 5 × 4
mm). The mobile phase, composed of 20 mM acetate buffer (pH 4.0, adjusted with 42
triethylamine, TEA)/acetonitrile (80:20, v/v), was eluted isocratically with the flow-rate of 1.2
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mL/min. The column temperature was set at 25˚C, and the injection volume was 50 µl. The
detector wavelength was set at 211 nm.
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Stock solutions (1000 µg/mL) of Riv and IS were prepared by dissolving the compounds in
acetonitrile and deionized water, respectively. The stock solutions were further serially
diluted with the mobile phase to appropriate working solutions.
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.7.3 Sample collection
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Whole-brain was removed from each animal, quickly-frozen, and kept frozen at −20°C till
analysis. Blood samples were collected in heparinized tubes, plasma separated by
centrifugation at 4400 rpm for 5-min, and handled as described for the brain.
4.7.4 Calibration curves and quality control samples
Calibration standards were prepared by spiking different samples of 450 µl blank plasma or
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00 µl blank brain homogenate with the 50 µl and 100 µl of appropriate working solutions 13
respectively. To each sample, a 50 µl aliquot of working IS solution was added prior to
14
extraction. Calibration curves were constructed by plotting the peak area ratio (Riv/IS) against 15
Riv concentrations. The standard curves range in rat plasma and brain were 80 to 3000 ng/mL 16
and 100 to 3000 ng/mL, respectively. The QC samples (for determining the precision,
17
accuracy, and recovery) were prepared with the same procedure at three concentration levels 18
(LQC= 0.1, MQC=1, and HQC=3 ug/ml).
19
4.7.5 Extraction procedure
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Liquid-liquid extraction was used to prepare the biological samples for HPLC analysis. The
frozen samples were thawed at room temperature for 20 minutes. Whole-brain was
homogenized in a volume of deionized water equal to the brain weight using homogenizer
(
10000 rpm, 1 min). For the extraction, 1000 µl of the homogenized brain or 500 µl of plasma 24
was transferred to a 13x10 cm screw cap glass tubes. A 50 µl aliquot of working IS solution
was added to each tube. The mixture was then made alkaline by adding 1 mL of 0.7 N NH3
solution and vortex mixed for 10 s. In the next step, 2 mL of dichloromethane was added to
each tube. The tubes were shaken (1000 rpm) for 30 min on an orbital shaker and then
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centrifuged for 5 min at 4400 rpm. The extraction procedure was performed in triplicate. The 29
lower organic phase was collected in a clean glass tube and evaporated to dryness (24h) in a 30
fume hood. The resulting residue was reconstituted with 200 μL of the mobile phase, vortexed 31
(30s), and centrifuged (20,000 g, 5 min). Then, 100 μL of the supernatant was injected for
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HPLC analysis.
4.7.6 Method validation
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The method validation included calibration model, accuracy, precision, selectivity, the LOQ,
and extraction efficiency. For the determination of linear dynamic range, linear regression
calibration curves based on 8 data points (non-zero standards) were constructed. The linearity 37
of the assay was tested using the linear regression, and the acceptance criterion for the
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2
correlation coefficient (R ) was ≥0.99. Accuracy and precision were validated using QC
samples for both intra-day (n = 3) and inter-day (3 days) analysis. Accuracy was calculated as 40
deviation (%) of the mean observed concentrations from the nominal values. The RSD (%) of 41
the concentration was taken as the measures of precision. To show the specificity of the
method, blank plasma and blank brain homogenate were analyzed, and the absence of
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interference peaks was checked. The extraction efficiency (absolute recovery) of Riv was 44
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calculated by comparing the peak areas of unextracted (in mobile phase) and extracted QC
samples. The average extraction efficiency (n=3) was reported for QC samples. The LOQ was
defined as the lowest concentration with precision <15% and accuracy within ±15% of
percentage bias (relative error).
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.8 In vitro drug release
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The in vitro release profile of the Riv from the MPEG-PCL nanoparticles was evaluated by the
dialysis method under the sink condition. Briefly, free Riv aqueous solution or nano
formulation (equivalent to 1mg of Riv) were placed into dialysis bags (MWCO=12 kDa,
Millipore). The bags were then immersed in 50 mL phosphate-buffered saline, PBS, (pH 7.4)
in a shaker incubator set at 37˚C and 100 rpm. Aliquots of the samples (0.5mL) withdrawn at
predetermined time intervals (with volume replacement) were analyzed for the drug content
using the HPLC method. The release assay was performed in triplicate over an 18-hour period. 12
4.9 Neuropharmacokinetic study
Male Wistar albino rats weighing 200 ± 20 g (Zanjan University of Medical Sciences, Iran) were 14
13
used in this study. The animals were housed two per cage in environmentally controlled
conditions (22 ± 2°C, 40–70% relative humidity, 12-h light-dark cycle), with free access to
water and standard pellet diet. The animals (N=48) were divided into two groups comprising
24 rats each. Then each group was further randomly divided into six subgroups according to
the time intervals. Each group received either Riv-NPs or free drug at a dose equivalent to 2
mg/kg body weight(48, 50). The right jugular vein was cannulated (polyethylene-silicone
rubber cannula) in all rats to administer the drug and acquire blood samples. Details of the
surgery and cannulation protocol have been described previously (61, 62). The rats were
anesthetized before cannulation by IP injection of the ketamine-acepromazine mixture
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(ketamine 100 mg/kg and acepromazine 10 mg/kg).
The day after, a dose of Riv dissolved in normal saline or equivalent amount of filtered and
concentrated formulation was injected to a pre-defined number of rats through the cannula. 26
Blood was sampled (1mL) at time intervals of 5, 15, 30, 60, 90, and 120 min. Immediately after 27
25
the blood samples were collected, the animals were anesthetized with CO , decapitated, and
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brain tissues were collected. The blood samples were immediately centrifuged at 4400 rpm
for 5 min, and plasma samples were collected. All samples were stored at −20°C until being
analyzed by the validated HPLC-DAD method, as indicated above. The protocol of the animal
study was approved by the Ethics Committee of Zanjan University of Medical Sciences.
Pharmacokinetic parameters were calculated using noncompartmental analysis.
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.10 Pharmacodynamic study
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To evaluate the effect of the developed nano formulation on spatial learning and memory
capacities, Morris Water Maze Test was performed as described (63-65). Animals (Wistar-
Albino male rats) were assigned to four groups (n=5): i) saline-treated group, ii) scopolamine 37
treated group, iii) group treated with scopolamine + free drug and iv) group treated with
scopolamine + nano formulation. The apparatus consisted of a circular pool (120 cm in
diameter and 45 cm deep) that was filled with water (23 ± 1°C) and a circular black escape
platform (10 cm in diameter, 2 cm below the water surface). The rats could escape from the
water on to the platform. The location of the platform remained the same throughout the
training. Four unique visual cues were placed around the inside wall of the pool in a cross 43
pattern. There were two unique visual cues around the inside wall of the pool to provide
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spatial cues. Escape latencies (time to reach the platform) and probe trial measures were
recorded with the video tracking system and monitoring software.
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Amnesia was induced by IP injection of scopolamine hydrochloride (2 mg/kg of body weight)
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0 min prior to the experiments while nano formulation (containing Riv equivalent to 2
mg/kg), free drug aqueous solution (2 mg/kg) and normal saline were administrated
intraperitoneally 30 min prior to the experiment on each day. The hidden platform was
removed for the probe test, and the rats were allowed to swim for 60ꢀsec. The parameter
measured during the probe trial was the time spent in the quadrant of the target platform.
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.11 Statistical analysis
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Quantitative data are presented as mean ± standard deviation. For multiple comparisons, 13
one-way ANOVAs were followed by post hoc Tukey’s test when appropriate. An independent
t-test was used for comparison of two groups. In all measurements, a P-value < 0.05 was
considered statistically significant.
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ACKNOWLEDGMENTS
18
This study was financially supported by Zanjan Pharmaceutical Nanotechnology Research 19
Center (ZPNRC; Grant No: A-12-233-22). We would like to acknowledge Sobhan Daru Co. for
kindly providing the rivastigmine tartrate as a gift sample.
20
21
Conflict of interest
The authors declare no conflicts of interest.
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REFERENCES
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34
35
36
37
38
39
40
41
42
43
1.
Qaseem A, Snow V, Cross JT, Jr., Forciea MA, Hopkins R, Jr., Shekelle P, Adelman A, Mehr D,
Schellhase K, Campos-Outcalt D, Santaguida P, Owens DK, American College of Physicians/American
Academy of Family Physicians Panel on D. Current pharmacologic treatment of dementia: a clinical
practice guideline from the American College of Physicians and the American Academy of Family
Physicians. Ann Intern Med. 2008;148(5):370-8.
2
.
Qiu C, De Ronchi D, Fratiglioni L. The epidemiology of the dementias: an update. Curr Opin
Psychiatry. 2007;20(4):380-5.
3.
Shin J, Kepe V, Small GW, Phelps ME, Barrio JR. Multimodal Imaging of Alzheimer
Pathophysiology in the Brain's Default Mode Network. Int J Alzheimers Dis. 2011;2011:687945.
4
.
Derakhshankhah H, Hajipour MJ, Barzegari E, Lotfabadi A, Ferdousi M, Saboury AA, Ng EP,
Raoufi M, Awala H, Mintova S. Zeolite nanoparticles inhibit Aβ–fibrinogen interaction and formation
of a consequent abnormal structural clot. ACS Appl Mater Interfaces. 2016;8(45):30768-79.
5
.
Lotfabadi A, Hajipour MJ, Derakhshankhah H, Peirovi A, Saffar S, Shams E, Fatemi E, Barzegari
E, Sarvari S, Moakedi F. Biomolecular Corona Dictates Aβ Fibrillation Process. ACS Chem Neurosci.
2
6
7.
018;9(7):1725-34.
Morgan D. Immunotherapy for Alzheimer’s disease. J Intern Med. 2011;269(1):54-63.
Scheltens P, Blennow K, Breteler MMB, de Strooper B, Frisoni GB, Salloway S, Van der Flier
.
WM. Alzheimer's disease. The Lancet. 2016;388(10043):505-17.
ACS Paragon Plus Environment

Page 17 of 33
ACS Chemical Neuroscience
1
2
3
4
5
6
7
8
9
8
.
Downs ME, Buch A, Sierra C, Karakatsani ME, Chen S, Konofagou EE, Ferrera VP. Long-term
1
safety of repeated blood-brain barrier opening via focused ultrasound with microbubbles in non-
human primates performing a cognitive task. PloS one. 2015;10(5):e0125911.
9
RK, Ahmad FJ. CNS drug delivery systems: novel approaches. Recent Pat Drug Deliv Formul.
2009;3(1):71-89.
10.
E Barreto G, G Klochkov S, O Bachurin S. Nanotechnology for Alzheimer disease. Curr Alzheimer Res.
2
1
2
3
.
Pathan SA, Iqbal Z, Zaidi SM, Talegaonkar S, Vohra D, Jain GK, Azeem A, Jain N, Lalani JR, Khar
4
5
6
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
6
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
Leszek J, Md Ashraf G, Tse WH, Zhang J, Gasiorowski K, Fidel Avila-Rodriguez M, V Tarasov V,
7
8
017;14(11):1182-9.
1. Rao KS, Reddy MK, Horning JL, Labhasetwar V. TAT-conjugated nanoparticles for the CNS
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
delivery of anti-HIV drugs. Biomaterials. 2008;29(33):4429-38.
2. Kanazawa T, Akiyama F, Kakizaki S, Takashima Y, Seta Y. Delivery of siRNA to the brain using a
1
combination of nose-to-brain delivery and cell-penetrating peptide-modified nano-micelles.
Biomaterials. 2013;34(36):9220-6.
13.
Chekhonin VP, Nazarova IR, Kabanov VA. A new class of drug carriers: micelles of poly (oxyethylene)-
poly (oxypropylene) block copolymers as microcontainers for drug targeting from blood in brain. J
Controlled Release. 1992;22(2):141-57.
Kabanov AV, Batrakova EV, Melik-Nubarov NS, Fedoseev NA, Dorodnich TY, Alakhov VY,
1
4.
brain using thermosensitive liposome and local hyperthermia. Int J Hyperthermia. 1996;12(1):157-65.
5. Mori N, Kurokouchi A, Osonoe K, Saitoh H, Ariga K, Suzuki K, Iwata Y. Liposome-entrapped
Kakinuma K, Tanaka R, Takahashi H, Sekihara Y, Watanabe M, Kuroki M. Drug delivery to the
1
phenytoin locally suppresses amygdaloid epileptogenic focus created by db-cAMP/EDTA in rats. Brain
Res. 1995;703(1-2):184-90.
16.
doxorubicin incorporated in solid lipid nanospheres (SLN). Pharmacol Res. 1999;40(3):281-6.
7. Zara GP, Cavalli R, Bargoni A, Fundarò A, Vighetto D, Gasco MR. Intravenous administration to
Zara GP, Cavalli R, Fundaro A, Bargoni A, Caputo O, Gasco MR. Pharmacokinetics of
1
rabbits of non-stealth and stealth doxorubicin-loaded solid lipid nanoparticles at increasing
concentrations of stealth agent: pharmacokinetics and distribution of doxorubicin in brain and other
tissues. J Drug Targeting. 2002;10(4):327-35.
1
8.
Sadegh SM, Izadi Z, Azadi A, Kurd M, Derakhshankhah H, Sharif MZ, Akbari HJ, Hamidi M.
Neuroprotective Potential of Curcumin-Loaded Nanostructured Lipid Carrier in an Animal Model of
Alzheimer's Disease: Behavioral and Biochemical Evidence. J Alzheimer's Dis. 2019.
19.
1
2
Bader H, Ringsdorf H, Schmidt B. Watersoluble polymers in medicine. Angew Makromol Chem.
984;123(1):457-85.
0.
Grossen P, Witzigmann D, Sieber S, Huwyler J. PEG-PCL-based nanomedicines: A
biodegradable drug delivery system and its application. J Control Release. 2017;260:46-60.
1. Chen M-L, Sadrieh N, Yu L. Impact of osmotically active excipients on bioavailability and
bioequivalence of BCS class III drugs. The AAPS journal. 2013;15(4):1043-50.
2. Zhao B, Wang XQ, Wang XY, Zhang H, Dai WB, Wang J, Zhong ZL, Wu HN, Zhang Q. Nanotoxicity
2
2
comparison of four amphiphilic polymeric micelles with similar hydrophilic or hydrophobic structure.
Part Fibre Toxicol. 2013;10(1):47.
2
3.
Savić R, Azzam T, Eisenberg A, Maysinger D. Assessment of the integrity of poly (caprolactone)-
b-poly (ethylene oxide) micelles under biological conditions: a fluorogenic-based approach. Langmuir
:
2
the ACS journal of surfaces and colloids. 2006;22(8):3570-8.
4. U.S. FOOD & DRUG ADMINISTRATION. Drugs@FDA: FDA Approved Drug Products 2018,
November 2 [Available from: https://www.accessdata.fda.gov/scripts/cder/daf/.
2
26.
5.
EUROEIAN MEDICINES AGENCY. Medicines. 2018, October 31.
Eskander MF, Nagykery NG, Leung EY, Khelghati B, Geula C. Rivastigmine is a potent inhibitor
of acetyl-and butyrylcholinesterase in Alzheimer's plaques and tangles. Brain Res. 2005;1060(1-
):144-52.
2
ACS Paragon Plus Environment

ACS Chemical Neuroscience
Page 18 of 33
1
2
3
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5
6
7
8
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1
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4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
6
2
7.
Miller AC, Bershteyn A, Tan W, Hammond PT, Cohen RE, Irvine DJ. Block copolymer micelles
1
as nanocontainers for controlled release of proteins from biocompatible oil phases.
Biomacromolecules. 2009;10(4):732-41.
2
3
2
8.
Cogent Medicine. 2016;3(1):1142411.
29. Tran TQ, Hsieh MF, Chang KL, Pho QH, Nguyen VC, Cheng CY, Huang CM. Bactericidal Effect of
Danafar H, Schumacher U. MPEG-PCL copolymeric nanoparticles in drug delivery systems.
4
5
6
0
1
2
3
4
5
6
7
8
9
0
1
2
3
4
5
6
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0
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0
1
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3
4
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0
1
2
3
4
5
6
7
8
9
0
Lauric Acid-Loaded PCL-PEG-PCL Nano-Sized Micelles on Skin Commensal Propionibacterium acnes.
Polymers (Basel). 2016;8(9):321.
7
8
3
0.
Chem Educ. 2011;88(8):1098-104.
1. Sha Y, Deng C, Liu Z, Huang T, Yang B, Duan G. Headspace solid-phase microextraction and
Izunobi JU, Higginbotham CL. Polymer Molecular Weight Analysis by1H NMR Spectroscopy. J
9
10
11
12
13
14
15
16
17
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48
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51
3
capillary gas chromatographic-mass spectrometric determination of rivastigmine in canine plasma
samples. J Chromatogr B Analyt Technol Biomed Life Sci. 2004;806(2):271-6.
3
2.
Variable Optimization. scientia pharmaceutica. 2013;81.
33. Dubey N, Varshney R, Shukla J, Ganeshpurkar A, Hazari PP, Bandopadhaya GP, Mishra AK,
Rivastigmine-Loaded L-Lactide-Depsipeptide Polymeric Nanoparticles: Decisive Formulation
Trivedi P. Synthesis and evaluation of biodegradable PCL/PEG nanoparticles for neuroendocrine tumor
targeted delivery of somatostatin analog. Drug Deliv. 2012;19(3):132-42.
3
rivastigmine liposomes for delivery into the brain through intranasal route. Acta pharmaceutica.
2
3
4.
Arumugam K, Subramanian G, Mallayasamy S, Averineni R, Reddy M, Udupa N. A study of
008;58(3):287-97.
5. Rahman Z, Zidan AS, Habib MJ, Khan MA. Understanding the quality of protein loaded PLGA
nanoparticles variability by Plackett-Burman design. Int J Pharm. 2010;389(1-2):186-94.
36. Formulation of a Geldanamycin Prodrug in mPEG-b-PCL Micelles Greatly Enhances Tolerability
and Pharmacokinetics in Rats. J Control Release. 2008;129(1).
7. Peng W, Jiang XY, Zhu Y, Omari-Siaw E, Deng WW, Yu JN, Xu XM, Zhang WM. Oral delivery of
capsaicin using MPEG-PCL nanoparticles. Acta pharmacologica Sinica. 2015;36(1):139-48.
8. Danafar H, Davaran S, Rostamizadeh K, Valizadeh H, Hamidi M. Biodegradable m-PEG/PCL
3
3
Core-Shell Micelles: Preparation and Characterization as a Sustained Release Formulation for
Curcumin. Adv Pharm Bull. 2014;4(Suppl 2):501-10.
39.
DEVELOPMENT OF POLYMERIC DELIVERY SYSTEM. MJAS. 2017;21(2):496-511.
40. Zhang Y, Huo M, Zhou J, Zou A, Li W, Yao C, Xie S. DDSolver: an add-in program for modeling
and comparison of drug dissolution profiles. AAPS J. 2010;12(3):263-71.
1. Papadopoulou V, Kosmidis K, Vlachou M, Macheras P. On the use of the Weibull function for
the discernment of drug release mechanisms. Int J Pharm. 2006;309(1-2):44-50.
2. Guo Y, Liu X, Sun X, Zhang Q, Gong T, Zhang Z. Mannosylated lipid nano-emulsions loaded with
lycorine-oleic acid ionic complex for tumor cell-specific delivery. Theranostics. 2012;2(11):1104-14.
3. Varshosaz J, Sadeghi-Aliabadi H, Ghasemi S, Behdadfar B. Use of magnetic folate-dextran-
Tajau R, Rohani R, Isahak WNRW, Salleh MZ. PALM OIL-BASED PRECURSORS FOR
4
4
4
retinoic acid micelles for dual targeting of doxorubicin in breast cancer. BioMed research
international. 2013;2013.
4
evaluation of follicular delivery of arginine via nanostructured lipid carriers: a novel potential approach
for the treatment of alopecia. Artif Cells Nanomed Biotechnol. 2017;45(7):1379-87.
4
Sun KX. Lower irritation microemulsion-based rotigotine gel: formulation optimization and in vitro and
in vivo studies. Int J Nanomedicine. 2015;10:633-44.
46.
Chamallamudi M. High performance liquid chromatographic fluorescence detection method for the
quantification of rivastigmine in rat plasma and brain: application to preclinical pharmacokinetic
studies in rats. J Young Pharm. 2011;3(4):315-21.
4.
Yazdani-Arazi SN, Ghanbarzadeh S, Adibkia K, Kouhsoltani M, Hamishehkar H. Histological
5.
Wang Z, Mu HJ, Zhang XM, Ma PK, Lian SN, Zhang FP, Chu SY, Zhang WW, Wang AP, Wang WY,
Mullangi R, Ranjithkumar A, Arumugam K, Mallayasamy S, Ganesan S, Jamadar L, Udupa N,
ACS Paragon Plus Environment

Page 19 of 33
ACS Chemical Neuroscience
1
2
3
4
5
6
7
8
9
4
7.
Enz A, Chappuis A, Dattler A. A simple, rapid and sensitive method for simultaneous
1
determination of rivastigmine and its major metabolite NAP 226-90 in rat brain and plasma by
reversed-phase liquid chromatography coupled to electrospray ionization mass spectrometry. Biomed
Chromatogr. 2004;18(3):160-6.
2
3
4
4
8.
Yang ZZ, Zhang YQ, Wu K, Wang ZZ, Qi XR. Tissue distribution and pharmacodynamics of
5
rivastigmine after intranasal and intravenous administration in rats. Curr Alzheimer Res.
2012;9(3):315-25.
4
rivastigmine in human plasma for application in pharmacokinetic studies. Iran J Pharm Res.
2
5
pharmacodynamics of rivastigmine by liposomes following intranasal administration. Int J Pharm.
2
5
uptake of phosphatidylserine and poly(ethylene glycol) liposomes by mouse macrophages. Biochim
Biophys Acta. 2001;1513(1):25-37.
5
6
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5
6
7
8
9
0
1
2
3
4
5
6
7
8
9
0
7
9.
Amini H, Ahmadiani A. High-performance liquid chromatographic determination of
8
9
010;9(2):115-21.
0. Yang ZZ, Zhang YQ, Wang ZZ, Wu K, Lou JN, Qi XR. Enhanced brain distribution and
10
11
12
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15
16
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20
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013;452(1-2):344-54.
1. Johnstone SA, Masin D, Mayer L, Bally MB. Surface-associated serum proteins inhibit the
2.
Xu L, Anchordoquy T. Drug delivery trends in clinical trials and translational medicine:
challenges and opportunities in the delivery of nucleic acid-based therapeutics. J Pharm Sci.
2
5
8
5
011;100(1):38-52.
3.
.
Hamidi M. Role of P-glycoprotein in tissue uptake of indinavir in rat. Life Sci. 2006;79(10):991-
4.
Sadegh Malvajerd S, Azadi A, Izadi Z, Kurd M, Dara T, Dibaei M, Sharif Zadeh M, Akbari Javar
H, Hamidi M. Brain delivery of curcumin using solid lipid nanoparticles and nanostructured lipid
carriers: Preparation, optimization, and pharmacokinetic evaluation. ACS Chem Neurosci.
2
5
Brain Drug Delivery: Optimization, Characterization and Neuropharmacokinetic Evaluation. Curr Drug
Deliv. 2019;16(4):341-54.
018;10(1):728-39.
5. Nasiri M, Azadi A, Zanjani MRS, Hamidi M. Indinavir-Loaded Nanostructured Lipid Carriers to
5
6.
Neurosci Methods. 1984;11(1):47-60.
57. Konar A, Shah N, Singh R, Saxena N, Kaul SC, Wadhwa R, Thakur MK. Protective role of
Morris R. Developments of a water-maze procedure for studying spatial learning in the rat. J
Ashwagandha leaf extract and its component withanone on scopolamine-induced changes in the brain
and brain-derived cells. PloS one. 2011;6(11):e27265.
5
8.
cholinesterase activity and cognitive function in rats. Neuropharmacology. 2000;39(3):497-506.
9. Xu X, Costa AP, Khan MA, Burgess DJ. Application of quality by design to formulation and
processing of protein liposomes. Int J Pharm. 2012;434(1-2):349-59.
0. Holm R, Jensen IH, Sonnergaard J. Optimization of self-microemulsifying drug delivery systems
SMEDDS) using a D-optimal design and the desirability function. Drug Dev Ind Pharm.
2006;32(9):1025-32.
Wang RH, Bejar C, Weinstock M. Gender differences in the effect of rivastigmine on brain
5
6
(
61.
Azadi A, Rouini MR, Hamidi M. Neuropharmacokinetic evaluation of methotrexate-loaded
chitosan nanogels. Int J Biol Macromol. 2015;79:326-35.
6
2.
sampling in the unrestrained conscious rat. Brain Res Protoc. 2002;10(2):84-94.
3. Vorhees CV, Williams MT. Morris water maze: procedures for assessing spatial and related
forms of learning and memory. Nat Protoc. 2006;1(2):848-58.
4. Bejar C, Wang RH, Weinstock M. Effect of rivastigmine on scopolamine-induced memory
impairment in rats. Eur J Pharmacol. 1999;383(3):231-40.
65. Salunkhe SS, Bhatia NM, Bhatia MS. Implications of formulation design on lipid-based
Thrivikraman KV, Huot RL, Plotsky PM. Jugular vein catheterization for repeated blood
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nanostructured carrier system for drug delivery to brain. Drug Deliv. 2016;23(4):1306-16.
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Table captions:
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Table 1. Analysis of variance (ANOVA) results for D-optimal models
Table 2. Predicted and observed responses for the optimized formulation
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Table 3. Plasma and brain concentrations of Riv and pharmacokinetic parameters following
i.v. administration of free Riv solution and Riv-NPs at a dose of 2mg/kg of Riv in the Male
Wistar albino rats.
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Table 4. Factors, responses and experimental Matrix for the D-optimal Design
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Figure captions:
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Fig. 1. AFM image of Riv-loaded MPEG-PCL NPs: (a) 2D image; (b) height cross section of the
selected nanoparticles in (a); (c) 3D image (imaging mode: intermittent contact, scan area:
2
6
×6 µm , Scan rate: 1.0 Hz, imaging resolution: 512 × 512 pixels)
Fig. 2. (a) DSC thermograms from top to bottom: pure drug (Riv), polymer-drug physical
mixture, Riv loaded and unloaded MPEG-PCL NPs (b) In-vitro drug release of rivastigmine
(scanned at 10°C/min from -100 to 200°C.)
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Fig. 3. Response surface model (RSM) showing the influence of the independent variables on
the (a) EE, (b) DL (c) PS, and (d) Zeta potential.
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Fig 4. Representative chromatograms of plasma (A) and brain (B): blank samples of rats
anesthetized with xylazine/ketamine (A-I & B-I), blank samples of rats anesthetized with
acepromazine/ketamine (A-II & B-II), blank samples of rats anesthetized with
acepromazine/ketamine spiked with Riv (500 ng/mL) and IS (2.5 µg/mL) (A-III & B-III).
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Fig. 5. Plasma (a) and brain (b) concentration-time profiles, Kp (brain/plasma concentrations– 24
time) profile (c) and integration plots (d) of Riv-NPs and free Riv following IV administration
2 mg/kg).
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Fig 6. Morris water maze test results in Wistar-Albino male rats (four groups, n=5). a) Mean
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escape latencies in the hidden-platform test. b) Representative swim paths for each group 28
during the probe test. c) Time spent in the target quadrant during the probe test.
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Supporting Information:
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Table S1. Molecular weight of diblock copolymers synthesized with different PCL/MPEG ratio.
Table S2. Kinetic analysis of drug release
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Table S3. HPLC method validation in rat plasma and brain
Fig. S1. Particle size distributions of the optimized Riv-NPs measured by Dynamic light scattering
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Tables
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2
3
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Table 1
0
1
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9
0
1
2
3
4
5
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7
8
9
0
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2
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8
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2
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0
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2
3
4
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9
0
Mean
Square
665.37
41.05
68.06
14.04
Source
Sum of Squares
df
F Value
16.21
Prob > F
< 0.0001
EE
LC
Model
5988.36
410.48
340.28
70.20
6398.83
1046.83
57.38
9
10
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5
19
9
10
5
5
19
9
10
5
5
19
9
10
5
5
Residual
Lack of Fit
Pure Error
Cor Total
Model
Residual
Lack of Fit
Pure Error
Cor Total
Model
Residual
Lack of Fit
Pure Error
Cor Total
Model
Residual
Lack of Fit
Pure Error
Cor Total
4.85
0.0541
116.31
5.74
7.93
20.27
2.23
< 0.0001
0.1993
39.63
17.75
3.55
1104.21
1650.16
175.49
156.35
19.13
1825.65
530.65
66.25
Size
zeta
183.35
17.55
31.27
3.83
10.45
8.17
0.0005
0.0189
58.96
6.63
3.25
8.90
0.33
0.0010
0.8784
16.26
49.99
596.90
10.00
19
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Table 2
Responses
Predicted values
Observed values (n=3) *
EE
DL
PS
38.5
18.6
94.2
-31.8
40.4 ± 1.6
19.2 ± 1.1
98.5 ± 2.1
-29.7 ± 3.6
Zeta
Mean ± SD, n=3
*
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Table 3
Free drug
Riv-NPs
Brain Plasma
Time intervals
5
Brain
Plasma
3.95±0.50 1.55±0.13
1.72±0.38 0.59±0.05
0.92±0.11 0.27±0.06
0.23±0.06 0.21±0.04
0.21±0.08 0.23±0.07
Free drug
3.71±0.52 2.28±0.16
1
5
0
2.93±0.50 0.86±0.06
3
1.68±0.14 0.24±0.03
60
0.86±0.24 0.21±0.03
90
0.27±0.09 0.14±0.10
Parameters
AUC 0-90 min µg.min/mL
AUC 0-ꝏ min µg.min/mL
t1/2 (min)
Cl (ml/min/kg)
MRT (min)
Riv-NPs
50.7
68.1
83.7
29.4
41.1
53.0
35.8
37.7
51.4
67.8
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Table 4
Factors and their levels in the D-optimal design together with the responses
and their constraints
Factors
Units
(w/w)
Levels
Drug/Polymer ratio(A)
Aqueous/Organic phase ratio(B)
Oleic acid concentration (C)
0.05
5
1
(v/v)
50
10
(mg/mL)
1
Responses
Units
%
constraints
Max
Max
Min
Min
In rang
Y1: Encapsulation efficiency (EE)
Y2: Drug loading (DL)
Y3: Particle size (PS)
%
nm
-
Y4: Polydispersity index (PDI)
Y5: Zeta potential (zeta)
mV
Experimental Matrix for the D-optimal Design and Results
Factors
Responses
Run
A
B
C
EE
LC
PS
PDI Zeta
1
2
3
4
5
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7
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9
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5
5
10
10
49.01
50.04
15.97
0.00
24.35
24.91
8.04
0.00
12.84
7.81
13.89
0.00
18.82
5.36
8.44
11.94
0.00
6.69
8.56
1.16
10.55
1.22
2.78
1.94
106.63
110.10
83.50
96.20
80.07
88.03
90.53
96.63
109.07
80.77
82.87
87.63
89.50
89.47
86.97
106.33
94.67
91.47
108.77
97.13
0.15 -36.60
0.15 -38.10
0.11 -27.70
0.12 -29.97
0.14 -33.40
0.12 -23.83
0.12 -33.13
0.12 -25.13
0.14 -35.50
0.13 -24.30
0.12 -25.57
0.14 -25.80
0.14 -19.60
0.11 -23.70
0.10 -20.77
0.17 -24.77
0.15 -26.50
0.09 -18.53
0.13 -31.20
0.12 -25.40
1
1
50
10
0.05
1
50
10
50
1
25.75
15.71
45.21
0.00
1
23.19
23.26
50
4.64
10
0.44
0.05
0.64
0.64
1
10
5
6.71
1
48.97
13.77
16.93
23.64
0.00
10
33.43
50
11
1
12
13
14
15
1
50
10
0.05
0.76
1
33.9
36.14
23.19
21.88
5
1
7.62
4.64
4.48
1
15.45
17.40
3.57
16
17
0.42
0.67
0.05
0.05
0.43
26.21
25.54
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ACS Chemical Neuroscience
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ACS Chemical Neuroscience
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ACS Chemical Neuroscience
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ACS Chemical Neuroscience
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