Abcd

pharmaceutics
Article
Development and Characterization of New Miconazole-Based
Microemulsions for Buccal Delivery by Implementing a Full
Factorial Design Modeling
Marina-Theodora Talianu 1 , Cristina-Elena Dinu-Pîrvu 1,2 , Mihaela Violeta Ghica 1,2, * , Valentina Anuţa 1,2 ,
Răzvan Mihai Prisada 1 and Lăcrămioara Popa 1,2
1 Department of Physical and Colloidal Chemistry, Faculty of Pharmacy, “Carol Davila” University of Medicine
and Pharmacy, 6 Traian Vuia Str., 020956 Bucharest, Romania;
marina-theodora.talianu@drd.umfcd.ro (M.-T.T.); cristina.dinu@umfcd.ro (C.-E.D.-P.);
valentina.anuta@umfcd.ro (V.A.); razvan.prisada@umfcd.ro (R.M.P.); lacramioara.popa@umfcd.ro (L.P.)
2 Innovative Therapeutic Structures R&D Center (InnoTher), “Carol Davila” University of Medicine and
Pharmacy, 6 Traian Vuia Str., 020956 Bucharest, Romania
* Correspondence: mihaela.ghica@umfcd.ro; Tel.: +40-74-448-6250
Abstract: This research aimed to develop miconazole-based microemulsions using oleic acid as a
natural lipophilic phase and a stabilizer mixture comprising Tween 20 and PEG 400 to solubilize
miconazole as an antifungal agent known for its activity in oral candidiasis and to improve its
bioavailability. The formulation and preparation process was combined with a mathematical ap-
proach using a 23 -full factorial plan. Fluid and gel-like microemulsions were obtained and analyzed
considering pH, conductivity, and refractive index, followed by extensive analyses focused on droplet
size, zeta potential, rheological behavior, and goniometry. In vitro release tests were performed to
assess their biopharmaceutical characteristics. Independent variables coded X1 -Oleic acid (%, w/w),
X2 -Tween 20 (%, w/w), and X3 -PEG 400 (%, w/w) were analyzed in relationship with three main
Citation: Talianu, M.-T.; Dinu-Pîrvu, outputs like mean droplet size, work of adhesion, and diffusion coefficient by combining statistical
C.-E.; Ghica, M.V.; Anuţa, V.; Prisada, tools with response surface methodology. The microemulsion containing miconazole base–2%, oleic
R.M.; Popa, L. Development and acid–5%, Tween 20–40%, PEG 400–20%, and water–33% exhibited a mean droplet size of 119.6 nm,
Characterization of New a work of adhesion of 71.98 mN/m, a diffusion coefficient of 2.11·10−5 cm2 /s, and together with
Miconazole-Based Microemulsions for remarked attributes of two gel-like systems formulated with higher oil concentrations, modeled the
Buccal Delivery by Implementing a
final optimization step of microemulsions as potential systems for buccal delivery.
Full Factorial Design Modeling.
Pharmaceutics 2024, 16, 271.
Keywords: miconazole base; solubilization; UV-spectroscopy; phase diagrams; stabilizers;
https://doi.org/10.3390/
microemulsions; in vitro drug release; factorial design; critical quality attributes
pharmaceutics16020271
Academic Editors: Hassan Almoazen

and Claire Monge
Received: 5 January 2024

1. Introduction
Revised: 31 January 2024 Oral candidiasis represents a complex pathology involving imbalances of the oral mi-
Accepted: 9 February 2024 crobiota, governed by the presence of Candida albicans as the main opportunistic pathogen
Published: 14 February 2024 settled in immunocompromised patients [1]. Multiple factors trigger the pathogenic mech-
anisms of C. albicans division [2–5], setting off local inflammatory processes, epithelial
dysfunction, and the appearance of mild to severe oral thrush [6]. The development of
pseudomembranous and erythematous plaques induces local pain, sensitivity, burning
Copyright: © 2024 by the authors.
sensation, and eating dysfunction, compromising the health status of patients [1,7,8].
Licensee MDPI, Basel, Switzerland.
It is known that buccal tissue is an attractive drug delivery site in both local and
This article is an open access article
systemic targets, being appreciated for some advantages such as the presence of the non-
distributed under the terms and
keratinized vascularized epithelium [9,10], ease of administration [11], the avoidance of the
conditions of the Creative Commons
hepatic first-pass effect [10], and being tolerated by patients with dysphagia and pediatric
Attribution (CC BY) license (https://
groups [12]. Topical treatment in oral diseases focuses on the active ingredient and its
creativecommons.org/licenses/by/
4.0/).
maintenance in active concentration at the level of the buccal mucosa [13,14].
Pharmaceutics 2024, 16, 271. https://doi.org/10.3390/pharmaceutics16020271 https://www.mdpi.com/journal/pharmaceutics

Pharmaceutics 2024, 16, 271 2 of 38
Over time, it was proven a safe handle and incorporation of active pharmaceutical
ingredients (API) for the treatment of oral candidiasis in various buccal dosage forms like
suspensions [15], medical mouthwashes [16,17], gels and in situ forming gels [18,19], var-
nishes and troches [20], mucoadhesive tablets [21], and versatile hydrogels and polymeric
films [22–24]. Furthermore, novel nanotherapeutic systems proposed progress in increasing
API bioavailability and therapeutic efficacy [25]. Vesicular systems [26], nanoparticles [27],
and micro-/nanoemulsified systems [28–30] are remarked as the main advances meant to
improve the solubility and retention of the antifungals in oral mucosa [6].
Miconazole, an azole-based model drug with clinical efficacy in the treatment of
oral candidiasis, acts by inhibition of 14-α-demethylase (CYP51) implied in the ergosterol
biosynthesis and alter the integrity of the fungi cell membrane [31]. Additional positive
effects repurpose miconazole as an anti-inflammatory agent in skin disorders [32] and a
promising cytotoxic via molecular antitumor pathways in particular forms of cancer [33,34].
Miconazole, belonging to the BCS II class, underwent extensive research to improve sol-
ubility and sustain an efficient release, thereby enhancing therapeutic efficacy [35,36]. To
this debate, Table 1 briefly presents a concise overview of the development of miconazole-
based systems designed to improve mucoadhesion and achieve a controlled release. In
the most recent findings, polymeric materials can provide excellent support for nanosized-
based system inclusion, as seen in the case of nanogels [37,38] and hydrogels loaded with
nanoemulsifyable systems [39].
Hosny K. M. et al. defined complex polymeric structures for delivering miconazole
with the aid of nanoemulsions, which can be generated in situ after inclusion in a polymeric
base [39]. Nanoemulsions are nanocolloids known to provide good solubilization ability
for APIs but they are characterized as thermodynamically unstable systems, with drop
diameters ranging up to 500 nm [40,41]. The use of small amounts of surfactants (<10%)
interferes with the stabilization process and affects the systems’ thermodynamic properties.
High-energy methods and a meticulous selection of the cosolvents are required to obtain
stable formulations with low droplet size [42].
Microemulsions are versatile nanocolloidal carriers able to sustain drug solubilization
and release at the level of buccal mucosa, as it was reported for several natural and
synthetic antifungal agents like clove oil in the form of spray-able liquid formulations [28],
clotrimazole [43], and itraconazole [30], designed as mucoadhesive structures.
The formation of thermodynamically stable and isotropic systems with droplets with
small diameters up to 100–200 nm by the use of optimized levels of surfactants and
cosurfactants is considered affordable in designing new platforms for buccal delivery as it
was demonstrated for actives from different pharmacological groups like carvedilol [44],
prednisolone [45], or triamcinolone acetonide [46]. The common achievements followed in
designing microemulsions for buccal delivery are based on improved solubilization and
drug release by delivering the medicine in a nano-sized form.
Recent studies revealed new insights into designing polymer-free microemulsions
at particular concentration domains of oil, surfactant, cosurfactant, and water [47]. The
concept was well researched for itraconazole being included in self-microemulsifying
systems made of polyoxyl 35-castor oils, a mixture of triglycerides, and water. The gel-like
microemulsions assured a sustained release of the drug in 360 min [48]. The formation of
gel-like structures was observed by building phase diagrams and explained by the oil–oil
droplet interactions. Moreover, the ability of surfactants to create lamellar structures was
linked to the powerful hydration effects established between hydrophilic chains, observed
in the case of some microemulsions with monolaurin [49] and gel-like microemulsions
for topical delivery of vitamins C and E [47].To understand how the formulation factors
impact the critical quality attributes of the microemulsions, a full factorial design can be
used to initially screen and process the main effects but also the interactions between
input factors [50–53], creating a promising approach in combining concepts in colloid
formulation with mathematical analysis to characterize new architectures with improved
therapeutic action.
Table 1. Timeline evolution in developing essential miconazole-based formulations designed for buccal application in oral candidiasis.
Year Pharmaceutical Formulation API Content Excipients Observation Ref.

Bioadhesive slow-release Modified starch, Carbopol 934 The tablet formulation exhibited a pronounced antifungal effect at
1992 10 mg [54]
buccal tablet Sodium benzoate, SiO2 a lower dose compared to commercial gel.
1 Patches had more accurate dosing than the gel form, and the
SCMC, Chitosan, 2 PVA, 3 HEC, 4 HPMC ± 5
2003 Mucoadhesive buccal patches 2% formulation based on PVA 10% and PVP 5% had the best 6 MIC [55]
PVP (0–5%)
release.
Buccal mucoadhesive films
HPMC increased adhesion and improved the mechanical
2017 based on polyelectrolyte 2% Chitosan combined with pectin or HPMC [56]
properties of the chitosan-based films. Improved drug release.
complexes
Buccal mucoadhesive films Chitosan + Carbopol, Arabic gum, gelatin, or
Chitosan increases antifungal activity of miconazole. Gelatin and
2017 based on polyelectrolyte 2% alginate; 7 PEG 400 (30%) used as solubilizer [57]
Carbopol were appropriate polymers to form chitosan films.
complexes and plasticizer
Phospholipon 90 H, Polysorbate 80, beeswax, Nanogels with SLN improved the antifungal activity of MIC
2017 Mucoadhesive lipid nanogels 0.25–1% [37]
Polycarbophil, sorbitol compared to commercial gel.
Composite The combination of chitosan-gelatin protects MIC and determines
2018 200 mg Chitosan, gelatin and HPMC [35]
microparticle-based discs a controlled release. Therapeutic activity can be better improved.
λ-carrageenan (λ-c) was suitable combined with chitosan. The
Chitosan and three types of carrageenan (κ, λ, ι),
2018 Buccal films 8% orientation of sulfate groups in λ-c influenced the interactions with [23]
and PEG 400 as a solubilizer and plasticizer
chitosan, but also those with mucin and salivary medium.
8 Labrasol and PG, used as surfactants and cosurfactants in

HA 2%, crosslinked with Gantrez S-97 0.5%
Hydrogels loaded with nanoemulsion preparation, determined a high residence of MIC at
which was treated with a NE containing MIC,
2019 self-nanoemulsifying drug 250 mg the mucosal area, enhancing drug permeation. Hydrogel-loaded [39]
and clove oil 10–25%, Labrasol 18–70%, and 9
delivery systems NE enhanced miconazole release and its contact with the oral
PG 10–30%
mucosa.
The optimal gel contained Carbopol 0.84% and sodium hydroxide
Carbopol 940 and sodium hydroxide, glycerol
2022 Oral gels 2% 0.32%. Miconazole can be prepared in a gel base, influencing [18]
as a plasticizer, and adjuvants
texture, spreadability, viscosity and adequate antifungal activity.
The gel base sustained the delivery of the nanoparticles to the oral
Mucoadhesive Bos indicus fat, Phospholipon 90 H, Tween 80, mucosa. The nanoparticles’ high surface area increased the contact
2023 0.25%, 0.5%, 1% [38]
nanoparticulate lipospheres sorbitol, Polycarbophil with the mucosa, while the hydrogel matrix improved
mucoadhesion and controlled release.
1 SCMC represents sodium carboxy methyl cellulose; 2 PVA—polyvinyl alcohol; 3 HEC—hydroxyethyl cellulose; 4 HPMC—hydroxy propyl methyl cellulose; 5 PVP—polyvinyl
pyrrolidone; 6 MIC—miconazole; 7 PEG 400—Polyethylene glycol 400; 8 HA—hyaluronic acid; 9 PG—propylene glycol.
To the best of our knowledge, no literature reports are available to consider the for-
mulation of microemulsions with miconazole for buccal applications. Thus, the objectives
of the study relied on the development of miconazole-based microemulsions using two
levels, three factors full factorial design. The analyses performed in this study focused on
increasing miconazole solubility using microemulsions. The systems were preliminarily
characterized by considering the analysis of pH, conductivity, and refractive index. In a
further stage, the study of droplet size distribution, zeta potential, rheological behavior,
superficial properties, and drug kinetic release aimed to explain the internal behavior
of microemulsions and their biopharmaceutical performance as potential drug delivery
systems. To succeed in the mathematical modeling, three critical quality attributes (CQA,
as defined in the Quality by Design QbD approach) (drop diameter, work of adhesion,
and diffusion coefficient) were studied by applying response surface methodology. The
mathematical analysis was valuable in optimizing a model system that can be tailored in
further studies as a biocompatible topical system with mucoadhesive properties.
2. Materials and Methods

2.1. Materials
The actives and excipients used in the study were of analytical grade. The miconazole
base was purchased from Fagron (Rotterdam, Holland). Isopropyl myristate was purchased
from Merck Schuchardt (Merck, Hohembrunn, Germany), Oleic acid vegetable from Merck
(Merck KgaH, Darmstadt, Germany), Tween 20 from Sigma-Aldrich (Sigma Aldrich Chimie,
L’ lsle D’Abeau Chesnes, France) and Tween 80 was purchased from Carl Roth GmbH +
CoKG (Karlsruhe, Germany), Kolliphor P407 was acquired from Sigma (Sigma Aldrich,
St. Louis, MO, USA) and propylene glycol was supplied from Sigma Aldrich (Steinheim,
Germany). Polyethylene glycol 200 and polyethylene glycol 400 were acquired from
Scharlau (Scharlab S.L., Sentmenat, Spain). Di-sodium hydrogen phosphate heptahydrate
(Merck, Darmstadt, Germany), Potassium dihydrogen phosphate, and ethanol (Chemical
Company, Ias, i, Romania) were selected to prepare the in vitro release medium. Ultrapure
Milli-Q water with a specific resistance of 18.2 MΩ/cm and total organic carbon (TOC) of
less than 5 µg/L was generated from a Milly-Q® Direct 8 Water Purification System (Merck
Millipore, Bedford, MA, USA), and used as the aqueous phase.
2.2. Solubility Studies for Miconazole

The miconazole base solubility was tested in eight excipients using the shake flask
method [58,59]. The calibration curve of miconazole was projected by preparing standard
methanolic solutions of known concentration, as was previously reported [39]. The solu-
tions were prepared by dilution, beginning with a methanolic solution of miconazole of
800 ppm. Each solution was spectrophotometrically measured in UV at λmax = 272 nm,
as a function of methanol as the blanc solution, by using Perkin Elmer Lambda 2 UV-VIS
spectrophotometer (PerkinElmer Inc., Waltham, MA, USA). The protocol succeeded with
the preparation of the miconazole samples of unknown concentration. In 2 mL Eppendorf
mini tubes (Eppendorf, Hamburg, Germany), 1 mg miconazole was weighed at the analyt-
ical balance (Sartorius MC210P, Sartorius AG, Gottingen, Germany) in 1.5 mL excipient.
Miconazole was added until saturation occurred. After weighing each, the tubes were
shaken for 5 min at 3500 rotations per minute (rpm). After saturation, the samples were
shaken for 24 h at 1000 rpm, at 25 ± 0.5 ◦ C, using the Eppendorf ThermoMixer C (Eppen-
dorf, Hamburg, Germany). After each cycle, the samples were kept for equilibration and
centrifuged for 5 min at 15,000 rpm, at 25 ± 0.5 ◦ C, using the centrifuge Micro 200 (Hettich
North America, MA, USA). The supernatant was collected and adequately prepared in
volumetric flasks using methanol. The samples were spectrophotometrically measured in
triplicate at λmax = 272 nm to calculate the solubility.
2.3. Screening Study to Design Pseudo-Ternary Phase Diagrams

To explore the stability area for micro-/nanoemulsions formation, four pseudo-ternary
phase diagrams were built by applying the water titration method under continuous
stirring [60]. Based on the solubility data, oleic acid, a mixture of Tween 20/PEG 400 in four
ratios of 2:1, 3:1, 3:2, and 4:1, and water were considered as the three main factors implied
in the definition of various coarse dispersions, of which several targeted concentration
domains were fixed to build up the factorial plan. Furthermore, for each ratio of S/CoS
mix, several ratios of Oil: S/CoS mix (1:9, 2:8, 3:7, 4:6, 5:5, 6:4, 7:3, 8:2, 9:1) were selected to
cover a large domain of points and inspect the transitions from stable through unstable
formulations. The systems were visualized as transparent or translucent to opalescent
systems in the case of micro-/nanoemulsion domain, opaque systems defined as emulsions,
particular lamellar phases with a gel-like appearance in the area where the water and the oil
phase equally contributed to the generation of dispersions, and unstable dispersions, where
creaming and phase separation acted as unfavorable phenomena, as previously found [61].
The plotting was performed using Triplot software, version 4.1.2 (Todd Thompson Software,
LA, USA), as reported [62].
2.4. Preparation of the O/W Miconazole-Based Microemulsions Using a 23 Full Factorial Plan
The microemulsions with miconazole were obtained under formulation data process-
ing using Design Expert statistical software, version 13. The chosen model to acquire
potential microemulsions was a two-level, three-factor full factorial design augmented with
two lack of fit points. Firstly, miconazole was accurately weighed at the analytical balance
and solubilized in a calculated amount of oleic acid. The lipophilic mixture was placed on
a thermostated stirrer (DLAB MS-H380Pro, DLAB Scientific, Beijing, China) for 10 min to
attain a clear oily phase. Tween 20 was further weighed, mixed with the lipophilic phase,
and subjected to stirring for another 10 min. PEG 400 was weighed and added in the next
step to obtain a homogeneous and clear composition. A water titration method with dis-
tilled water was applied, and ten microemulsions with various aspects were obtained and
placed in equilibration at room temperature. Various structures of microemulsions resulted,
from fluid-type to gel-like microemulsions. The fluid microemulsions were prepared using
magnetic stirring. For the last case, the homogenization of the samples was assured by
trituration during the water titration procedure.
The compositions attributed to the microemulsions designed through mathematical
modeling were coded as ME 1-ME 10 and are presented in Table 2. Final compositions of
20 g (%, w/w) were generated based on the presence of three formulation factors noted X1 ,
X2 , and X3 .
Table 2. Composition of the microemulsions obtained using an augmented 23 full factorial plan.
Oleic Acid (%) Tween 20 (%) 1 PEG 400 (%)

Formulation Water (%) 2 MCZ (%)
X1 X2 X3
ME 1 5 30 10 53 2
ME 2 5 40 10 43 2
ME 3 5 30 20 43 2
ME 4 5 40 20 33 2
ME 5 10 30 10 48 2
ME 6 10 40 10 38 2
ME 7 10 30 20 38 2
ME 8 10 40 20 28 2
ME 9 6.25 35 10 51.75 2
ME 10 8.75 30 15 44.25 2
1 PEG 400 represents polyethylene glycol 400, and 2 MCZ—miconazole.
Figure 1 presents the main steps involved in the preparation process of the fluid and
gel-like microemulsions.
Pharmaceutics 2024, 16, x FOR PEER REVIEW 6 of 4
Pharmaceutics 2024, 16, 271 Figure 1 presents the main steps involved in the preparation process of 6the
of 38fluid and
gel-like microemulsions.
Figure1.1.Essential
Figure steps
Essential followed
steps in theinpreparation
followed process process
the preparation of the microemulsions with miconazole,
of the microemulsions with micona
zole, consisting
consisting in 1—
in 1— drug drug solubilization
solubilization in the 2—addition
in the oil phase, oil phase, 2—addition of the3—lipophilic
of the stabilizers, stabilizers, 3—lipo
philic mixture
mixture solubilization
solubilization with thephase
with the stabilizer stabilizer
underphase understirring,
continuous continuous stirring, 4—application
4—application of water o
water titration
titration under resulting
under stirring stirring resulting
in two typesin two types of5systems:
of systems: (a)—fluid5 microemulsions,
(a)—fluid microemulsions,
and 5 (b) and 5
(b) —gel-like
—gel-like microemulsions,
microemulsions, previously
previously trituratedtriturated to uniformize
to uniformize their consistency.
their consistency.
2.5. Organoleptic Analysis

2.5. Organoleptic Analysis
The microemulsions were visually observed, and their aspect, color, odor, and the
Theormicroemulsions
presence werephenomena
absence of instability visually observed, and based
were described their aspect,
on their color, odor, and the
composition.
presence or absence of instability phenomena were described based on their composition
2.6. pH Determination
2.6. pH
TheDetermination
pH of microemulsions was determined using a Mettler–Toledo SevenCompact pH
meterThe
(Mettler–Toledo GmbH, Greifensee,
pH of microemulsions Switzerland).using
was determined A silver-based pH glass electrode
a Mettler–Toledo SevenCompac
that can be used for fluid and gel-like dispersions was connected to the apparatus. Before
pH meter (Mettler–Toledo GmbH, Greifensee, Switzerland). A silver-based pH glass elec
each measurement, calibration was performed using buffer solutions of pH 4 and pH 7,
trode
and that
then thecan
pH be
wasused for fluid
determined in and gel-like
distilled waterdispersions was connected
[63]. The measurements wereto the apparatus
recorded
Before
in eachatmeasurement,
triplicate 24 ± 0.5 ◦ C. calibration was performed using buffer solutions of pH 4 and
pH 7, and then the pH was determined in distilled water [63]. The measurements were
2.7. Conductivity
recorded Determination
in triplicate at 24 ± 0.5 °C.
Conductivity determinations were performed to describe the type of microemulsions
and the phase behavior
2.7. Conductivity for each sample at 24 ± 0.5 ◦ C. A Corning 441 bench conductivity
Determination
meter (Cole Parmer Instrument Company, LLC, Vernon Hills, IL, USA) was used, and the
Conductivity determinations were performed to describe the type of microemulsions
measurements were recorded in triplicate.
and the phase behavior for each sample at 24 ± 0.5 °C. A Corning 441 bench conductivity
meter
2.8. (Cole Parmer
Refractive Instrument Company, LLC, Vernon Hills, IL, USA) was used, and the
Index Determination
measurements were
The refractive indexrecorded in triplicate.
was studied to inspect the isotropic nature of the samples. The
analysis was made using a Krüss DR 201-95 digital refractometer (Krüss Optronic GmbH,
2.8. Refractive
Hamburg, Index Determination
Germany). Distilled water with a refractive index of 1.3330 was used as a
reference standard for calibration before each test. The measurements were recorded in
The refractive index was studied to inspect the isotropic nature of the samples. The
triplicate at 24 ± 0.5 ◦ C.
analysis was made using a Krüss DR 201-95 digital refractometer (Krüss Optronic GmbH
Hamburg,
2.9. DynamicGermany). Distilled
Light Scattering water with a refractive index of 1.3330 was used as a ref
Determination
erence standard for calibration before each test.
Droplet size distribution and polydispersity The
index measurements
(PDI) were studiedwere
at 25 recorded
± 0.5 ◦ C, in trip
licate at 24
applying ± 0.5 °C.light scattering (DLS) technique on diluted microemulsions on a ratio
a dynamic
of 1:100. Accurate measurements were performed in triplicate using a VascoKin particle

analyzer (Cordouan Technologies, Pessac, France), equipped with a 638-nanometer laser, as
it was previously reported [63,64]. The estimation of mean droplet diameter was performed
considering the presence of spherical particles dispersed in a Newtonian fluid by applying
the Stokes–Einstein equation, as can be seen in Equation (1):
dHapp = kB T/3πηDapp , (1)
where dHapp represents the hydrodynamic diameter, kB —Boltzmann constant, T—the

absolute temperature, η—the viscosity of the medium, and Dapp —the apparent diffusion
coefficient estimated from the autocorrelation function.
The droplet size profiles projected using the Cumulant model were visualized com-
paratively to find homogeneous systems with a narrower domain of droplet dimensions,
with peaks displaced through a 100 nm size zone.
2.10. Zeta Potential Analysis

The Doppler Laser electrophoresis principle was applied to study the droplet charge
of the microemulsions. The Wallis Zeta potential analyzer (Cordouan Technologies, Pessac,
France) uses a 20 mW diode laser source with a wavelength of 635 nm [65]. A 50% aqueous
suspension of colloidal silica—Ludox TM-50 (Sigma Aldrich, St. Louis, MO, USA), diluted
in a ratio of 1:100, with a zeta potential of 40 mV was used as a reference standard. The
results fitted with the Smoluchowski model were presented as the mean values of ten
consecutive determinations for each tested sample.
2.11. Rheological Evaluation

The flow behavior of the microemulsions was tested at 37 ± 0.5 ◦ C using a Lamy
RM100 CP2000 Plus rheometer equipped with a cone-plate stage (Lamy Rheology Instru-
ments, Champagne au Mont d’Or, France) [66]. The cone-plate coded CP6020 was used,
and the measurements were performed by applying 17 rotational speeds from 0.3 rpm to
60 rpm. Shear rate (s−1 ), shear stress (Pa), and viscosity (Pa·s) were recorded, and the flow
behavior was described using mathematical modeling.
2.12. Superficial Analysis

Extensive analyses of the superficial properties of microemulsions and their wettability
behavior were based on measuring the free superficial energy and contact angle. CAM 101
Goniometer, equipped with a Hamilton syringe, a C209-30 needle, and a digital camera
(KSV Instruments Ltd., Espoo, Finland), was used as previously reported [63]. Drops of
each sample (µL) were applied on microscope slides, captured with a digital camera, and
measured throughout 64 ms by an automated curve-fitting program. The determinations
in the pendant drop model were performed in triplicate, while those in the contact angle
model were made in quintuplicate at 24 ± 0.5 ◦ C.
Young–Laplace equation (Equation (2)) was used to automatically analyze the drop
shape in the pendant drop model, while the Young equation (Equation (3)) was specific for
the contact angle model:

1 1
∆p = pint − pext = γLG + , (2)
r1 r2
γSG = γSL + γLG cos θ, (3)

where ∆p represents the pressure difference between internal and external areas of a curved
liquid, also known as Laplace pressure; r1 , r2 —the principal radii of curvature; γSG —the
interfacial tension to solid/gas (S/G) interface; γSL —interfacial tension to solid/liquid
(S/L) interface; γLG —superficial tension to liquid/gas (L/G) interface; and θ—the contact
angle made by the liquid drop with the solid surface, which gave information concerning
the wettability of microemulsions.
To better explain the drop dynamics at the level of a surface, the Young–Dupré equation
was further followed to calculate the work of adhesion (Wa ) and work of cohesion (Wc ) [63].
In a deeper understanding, the spreadability coefficient (Ws ) was linked to the parameters
mentioned above, according to the Harkins theory of spreading [67]. The equations used in
the analysis for the last three parameters are shown below (Equations (4)–(6)):
Wa = γLG (1 + cos θ), (4)
Wc = 2γLG , (5)
S = γLG (cos θ − 1), (6)
where Wa represents the work of adhesion calculated using the Dupré equation; Wc —the
work of cohesion; S—the work of spreading or spreading coefficient, which derives from
Harkins’ theory of spreading; γLG —the superficial tension to L/G interface; and θ—the
contact angle determined previously by applying the contact angle model.
2.13. In Vitro Release Studies

The in vitro release of miconazole from the designed microemulsions was performed
using a system based on six Microette vertical diffusion cells (Teledyne Hanson Research,
Hanson, USA) [68]. Hydrophilic membranes of cellulose acetate of 0.45 µm pore diame-
ter and 120 µm thickness (Sartorius Stedim Biotech GmbH, Goettingen, Germany) were
chosen according to previous reports [47,69]. The diffusion cells presented an effective
diffusional area of 1.77 cm2 and 7 mL of receptor cell capacity. Phosphate buffer with a
pH of 6.8 containing 20% ethanol (w/w) was used as a receptor medium, ensuring sink
conditions [24]. A magnetic bar was immersed in each receptor compartment, and the
stirring was set to 300 rpm. The membranes were wetted in the medium 12 h before the
experiment and were adequately placed between donor and receptor compartments of
the diffusion cells. Samples of 1 g were placed into each donor compartment using a cell
adapter for fluid formulations [70]. The operational temperature in the diffusion cells was
maintained at 37 ± 0.5 ◦ C using a thermostated Julabo Corio CD circulating water bath
(Julabo GmbH, Seelbach, Germany). An amount of 1 mL of sample from each receptor
medium was collected at predetermined times and replenished with an equal volume of
fresh medium to maintain a constant volume. The drug content was spectrophotometrically
quantified in UV at 272 nm, and the mechanism of drug release was further determined
following the Higuchi model (Equation (7)). The release profiles of miconazole from the
designed microemulsions were compared to the release of miconazole from a commercial
gel (Daktarin 2% oral gel, Esteve, Spain).
q2 π
Dm = (7)
4 C20 t
where Dm represents the diffusion coefficient of MCZ in the release medium, q—the
amount of MCZ released on the surface unit, C0 —the initial MCZ concentration in the
microemulsion, and t—the release time.
2.14. Data Analysis and Screening of the Miconazole Microemulsions Using 23 Full
Factorial Design
The screening process of the microemulsions was designed using a regular two-level
full factorial design with two levels of variation and three factors (23 ). The model was
generated using Design-Expert software, version 13 (Stat-Ease, Inc. Minneapolis, MN,
USA) [62] and was projected as a 23 factorial plan with eight experiments, augmented with
a supplementary block of experiments, specifically two lack-of-fit points. In this sense, three
independent variables were selected to design microemulsions at two levels of variation,
coded as low (−1) and high (+1): X1 : Oleic acid (%), X2 : Tween 20 (%), X3 : PEG 400 (%).
The augmentation proposed the introduction of intermediary lack of fit points. Thus, the
experimental design consisted of the preparation of 10 formulations equivalent to 10 runs

and the analysis of three responses as dependent variables, noted as Y1 : droplet size (nm),
Y2 : work of adhesion (mN/m), and Y3 : diffusion coefficient (cm2 /s).
Each response was associated with a mathematical interpretation through a linear
polynomial equation generated by software in the analysis step. To determine the signifi-
cance of the analyzed model, the independent variables, and their possible interactions, the
equations were statistically analyzed using ANOVA, assuming p < 0.05. The models were
explained by applying response surface methodology based on a graphical interpretation
using contour, surface response, and interaction plots. Valuable prototypes with specific
characteristics were identified from the results as models to follow in prospective studies
centered on buccal delivery, and predictive optimization was considered in the final step.
3. Results and Discussion

3.1. Solubility Studies
The solubility of miconazole in oils, surfactants, and cosurfactants was established
through the spectrophotometric technique and presented in Table 3. The selected excipients
have been widely used in the preparation of microemulsions, being selected for solubilizing
antifungals for multiple routes of administration [71,72], including buccal delivery [39].
Table 3. Solubility values for eight excipients obtained through the spectrophotometric technique.
Components Excipient Solubility (mg/mL)

Isopropyl myristate 36 ± 3
Oils
Oleic acid 110 ± 5
Tween 80 152 ± 10
Surfactants Tween 20 236 ± 11
Kolliphor 407 27 ± 5
Propylene glycol 193 ± 7
Cosurfactants Polyethylene glycol 200 179 ± 12
Polyethylene glycol 400 209 ± 13
Among the potential two oils selected, the highest solubility was obtained for oleic
acid (110 ± 5 mg/mL). The stabilizer component with good solubilization properties
was composed of Tween 20 (236 ± 11 mg/mL) and PEG 400 (209 ± 13 mg/mL), which
contributed to the formation of microemulsions.
The use of oleic acid in pharmaceutical systems is not limited just to its role as a
lipophilic phase [73]. Oleic acid is an unsaturated fatty acid known to increase drug local-
ization into tissues, being a potent penetration and permeation enhancer for even big and
small molecules, in combination with various molecules like PEG 200, PEG 400, or poloxam-
ers [74]. In the study of Yang T-L. et al., oleic acid was used to obtain self-microemulsifying
systems with clotrimazole. Oleic acid synergistically enhanced the antifungal activity
of clotrimazole through particular mechanisms acting against filamentation and biofilm
formation [75].
From the group of surfactants, Tween 20 exhibited the highest solubilization ability
for miconazole. PEG 400 was selected as a suitable cosurfactant generally recognized as
a solubilizer, humectant, and plasticizer in various pharmaceutical formulations. Several
contributions proposed the inclusion of PEG 400 in some model nanocolloidal systems
for drug solubilization. PEG 400 1–2.5% was included in the form of gel-based nanoemul-
sions containing 6-gingerol 10%, oleic acid 1–5%, Tween 20 4–10%, and water 82.5–94%,
for wound treatment [76]. PEG 400 6.5–10% was selected in formulations designed for
intranasal delivery to increase the rate of survival in glioblastoma treatment [77], while
concentrations of 15–21% were selected for designing microemulsions for the oral delivery
of carvedilol as suitable solubilizer that acts synergistically with peppermint oil 10% and
Tween 80 15–21% [78]. Amounts of 6–9% PEG 400 were tested for antifungal buccal sprays,
emphasizing the importance of the stabilizers for microemulsion development [28].
3.2. Screening Study to Design Pseudo-Ternary Phase Diagrams

Depiction of the stability area of microemulsions is considered an important step to
proceed before the preparation process of the microemulsions. It is well known that micro-
/nanoemulsions are considered self-emulsified nanodispersions with common properties in
a matter of dimensional domain but with a different behavior concerning structural aspects,
thermodynamic and kinetic stability [79]. However, confusion persists over the subject,
and novel studies have come out to reveal particular aspects of their formation. Firstly, in
the so-called paradox of drop dimension, nanoemulsions were studied as nanodispersions
with oil drop diameters that attain 20–200 nm [80] up to 500 nm, according to several
reports [41]. On the other hand, microemulsions’ droplet size is concentrated in the domain
of nanoemulsions, restricting up to 100–200 nm, due to the presence of high concentrations
of S/CoS mix reaching up to 70% of the total composition. The high thermodynamic
stability and clear appearance of microemulsions promoted by the self-emulsification
technique continue to draw attention in the area of drug delivery and beyond [81,82].
The initial step of pseudo-ternary diagram plotting offered an indicative perspective
concerning the area in which micro-/nanoemulsions could be generated. An essential
goal of the experiment consisted of the depiction of instability points and the avoidance of
inappropriate preparation trials. This approach was considered helpful in determining the
main concentrations of the oil and stabilizers that can be integrated into a factorial design
to further succeed in the development of microemulsions with miconazole. In Figure 2
are presented pseudo-ternary phase diagrams for coarse dispersions containing stabilizer
mixtures based on (a) Tween 20/PEG 400 in a ratio of 2:1, (b) Tween 20/PEG 400 in a ratio
of 3:1, (c) Tween 20/PEG 400 in a ratio of 4:1, and (d) Tween 20/PEG 400 in a ratio of
3:2. The areas specific for each type of dispersion were projected, patterned, and noted as
follows: 1 represents the area of microemulsions (in green pattern) and 2 represents the
area of nanoemulsions (in yellow pattern) which collides with the microemulsion zone
(cases (b), (c) and (d)).
It was concluded that microemulsions could be adequately obtained if the Tween
20/PEG 400 ratio is selected in a ratio of 3:1 or 4:1, as represented in Figure 3—cases (b) and
(c). The area of microemulsions was represented with a green color, while the yellow-like
area was defined as a particular area of nanoemulsions. As the ratio of Tween 20/PEG 400
is modified through 2:1 and 3:2, the area of stability becomes narrowed, especially in the
case of the O/W systems. At the same time, the unstable area was wider in the two last
cases. Tween 20 significantly contributed to an enlargement of the microemulsion area. The
method was considered useful to appreciate potential domains of stable microemulsions
that can be proposed and studied using a factorial model.
points.
Table 5. Experimental matrix equivalent to a 23 + 2 full factorial model for microemulsion design.
Pharmaceutics2024,
2024,16,
16,271
x FOR PEER REVIEW X1 X2 X3 11
Pharmaceutics 11 of 40
of 38
Std. Block Run Oleic Acid Tween 20 PEG 400
(%) (%) (%)
1 1 1 −1 −1 −1
3 1 2 −1 +1 −1
5 1 3 −1 −1 +1
7 1 4 −1 +1 −1
2 1 5 +1 −1 −1
4 1 6 +1 +1 −1
6 1 7 +1 −1 +1
8 1 8 +1 +1 +1
9 2 9 0.625 0 −1
10 2 10 0.875 −1 0
One day after preparation, microemulsions were visually inspected. The aspect,
color, and odor were dependent upon composition. The concentration of Tween 20 influ-
enced the aspect, determining the clarity or the opalescent appearance, as can be seen in
Figure 3. The systems were predominantly opalescent in the selected lower concentration
of Tween 20 30%. An increase of 10% of Tween 20 modified their clarity. ME 2, ME 4, ME
8, and ME 10 were the clearest systems formulated with a concentration of 40% Tween 20,
but also 30% in the case of ME 10. On the other side, an increase in concentration for PEG
400 from 10% to 20% improved the aspect, obtaining clear microemulsions only in the
presence of Tween 20 40%. Variation of the oil phase from 5% up to 10% influenced the
internal structure of the samples, resulting in four systems with gel-like appearance,
namely ME 5–ME 8. In the case of the systems from Block 2, ME 9 had a similar composi-
tion
Figurewith
Figure the fluid group
2.2.Pseudo-ternary
Pseudo-ternary ofdiagrams
phase
phase MES, while
diagrams for forME 10
coarse
coarse was representative
dispersions
dispersions of aa stabilizer
containing
containing lackmixture
a stabilizer of fitmixture
point
based
derived
based on from
(a) the
Tween gel-like
20/PEG group.
400 in aThe replication
ratio of 1:1, (b) (for
Tween the last
20/PEG two
400MES)
in a was
ratio
on (a) Tween 20/PEG 400 in a ratio of 1:1, (b) Tween 20/PEG 400 in a ratio of 2:1, (c) Tween 20/PEG automatically
of 2:1, (c) Tween
20/PEG
obtained,
400 400resulting
in a ratio in
ofa3:1,
ratio of(d)
in
and 3:1,where
and (d)
viscous where as
systems 1 represents
1 represents ME 7. ME
the area ofthe
1, area
ME of3, microemulsions
and ME
microemulsions 9 were
(in green (in
thegreen
color) only
and color)
sys-
2is the
and
tems 2is the area of nanoemulsions (in yellow color)
area ofthat encountered
nanoemulsions (ininstability
yellow color).phenomena during the study.
It was concluded that microemulsions could be adequately obtained if the Tween

20/PEG 400 ratio is selected in a ratio of 3:1 or 4:1, as represented in Figure 3—cases (b)
and (c). The area of microemulsions was represented with a green color, while the yellow-
like area was defined as a particular area of nanoemulsions. As the ratio of Tween 20/PEG
400 is modified through 2:1 and 3:2, the area of stability becomes narrowed, especially in
the case of the O/W systems. At the same time, the unstable area was wider in the two last
cases. Tween 20 significantly contributed to an enlargement of the microemulsion area.
The method was considered useful to appreciate potential domains of stable microemul-
sions that
Figure can
3. The be proposed coded
microemulsions and studied
ME1–ME using a factorial
10 visualized model.
after preparation at room temperature,
Figure 3. The microemulsions coded ME 1–ME 10 visualized after preparation at room temperature,
24 ± 0.5 °C.
24 ± 0.5 ◦ C.
3.3. Formulation Design and Organoleptic Analysis
3.4.
3.3. pHTheDetermination
Formulation Design
screening and was
design Organoleptic Analysis
concentrated on the generation of two levels, three factors
full factorial
The screening design was concentrated oninthe
Over theplan.
pH The three
analysis, it variables
was selected
observed that this design
values areofpresented
varied
generation between
two inthree
5.15
levels, Table
± 0.024. and
The
factors
levels
5.80 of variation
± 0.02, being
full factorial were coded
plan.influenced as low (−1)
by composition.
The three variables and high
selectedItincan (+1).
thisbedesignFor
statedare the model’s
thatpresented
both the in reliability,
stabilizers the
and
Table 4. The
factorial
the
levels planof
presence was
of variation augmented
oleic acidcoded
were with
modulated two
as low (−more
pH experiments,
variation.
1) and highTween andand
20
(+1). For here,
the PEGtwo lack
400
model’s of fit
were points
implied
reliability, the
were
in pHgenerated
increasing,
factorial plan was bywhile
the software,
the acidic
augmented resulting
with group in aexperiments,
grafted
two more 23 in
+ 2the
model.
structure of oleic
and here, two acid
lack determined
of fit points
a decrease
were in pH,
generated bywhich can be resulting
the software, seen in some 3 + 2 like
in a 2pairs ME 1 and ME 5, ME 2 and ME 6,
model.
TableME
and 4. Factorial
3 and ME plan 7.
with
The2 levels and 3 factors
maximum as independent
pH values variables.for ME 4 and ME 8,
were obtained
Table 4. Factorial plan with 2 levels and 3 factors as independent variables.
Level
Factor Variable
Low (−1) Level High (+1)
Factor
X1 OleicVariable
acid (%) 5 1)
Low (− High 10
(+1)
X2 Tween 20 (%) 30 40
X1 Oleic acid (%) 5 10
XX
3 PEG 40020
Tween (%)
(%) 3010 4020
2
X3 PEG 400 (%) 10 20
The full matrix describing the factorial model is presented in Table 5. It is composed
of the main block specific for 23 experiments and the second block with the augmented
points.
Table 5. Experimental matrix equivalent to a 23 + 2 full factorial model for microemulsion design.
X1 X2 X3
Std. Block Run Oleic Acid Tween 20 PEG 400
(%) (%) (%)
1 1 1 −1 −1 −1
3 1 2 −1 +1 −1
5 1 3 −1 −1 +1
7 1 4 −1 +1 −1
2 1 5 +1 −1 −1
4 1 6 +1 +1 −1
6 1 7 +1 −1 +1
8 1 8 +1 +1 +1
9 2 9 0.625 0 −1
10 2 10 0.875 −1 0
One day after preparation, microemulsions were visually inspected. The aspect, color,
and odor were dependent upon composition. The concentration of Tween 20 influenced
the aspect, determining the clarity or the opalescent appearance, as can be seen in Figure 3.
The systems were predominantly opalescent in the selected lower concentration of Tween
20 30%. An increase of 10% of Tween 20 modified their clarity. ME 2, ME 4, ME 8, and
ME 10 were the clearest systems formulated with a concentration of 40% Tween 20, but
also 30% in the case of ME 10. On the other side, an increase in concentration for PEG 400
from 10% to 20% improved the aspect, obtaining clear microemulsions only in the presence
of Tween 20 40%. Variation of the oil phase from 5% up to 10% influenced the internal
structure of the samples, resulting in four systems with gel-like appearance, namely ME
5–ME 8. In the case of the systems from Block 2, ME 9 had a similar composition with the
fluid group of MES, while ME 10 was representative of a lack of fit point derived from the
gel-like group. The replication (for the last two MES) was automatically obtained, resulting
in viscous systems as ME 7. ME 1, ME 3, and ME 9 were the only systems that encountered
instability phenomena during the study.
3.4. pH Determination
Over the pH analysis, it was observed that the values varied between 5.15 ± 0.02 and
5.80 ± 0.02, being influenced by composition. It can be stated that both the stabilizers and
the presence of oleic acid modulated pH variation. Tween 20 and PEG 400 were implied in
pH increasing, while the acidic group grafted in the structure of oleic acid determined a
decrease in pH, which can be seen in some pairs like ME 1 and ME 5, ME 2 and ME 6, and
ME 3 and ME 7. The maximum pH values were obtained for ME 4 and ME 8, characterized
by a maximum content of the stabilizers. The normal salivary pH varies between 6.2 and
7.6 [83], while in the pathological state, the pH of the oral cavity changes and tends to be
lowered (favorable for bacterial and fungi development) [84]. The pH of ME 4 and ME 8
were closer to 6 and may positively interfere with promoting an antifungal effect in the
affected oral mucosa. The results of pH obtained in triplicate for each microemulsion are
presented in Table 6.
Table 6. Physico-chemical parameters of the O/W microemulsions determined at 24 ± 0.5 ◦ C (n = 3).
Conductivity Refractive Zaverage D10% D50% D90% Zeta

Code pH PDI Span
(µS/cm) Index (nm) (nm) (nm) (nm) (mV)
ME1 5.29 ± 0.01 101.90 ± 0.66 1.3823 ± 0.0002 152.89 ± 2.10 0.230 ± 0.001 118.11 205.71 375.23 1.24 +10.76 ± 1.15
ME2 5.60 ± 0.01 88.70 ± 0.10 1.4047 ± 0.0001 128.90 ± 2.15 0.303 ± 0.002 98.17 187.54 375.23 1.47 +10.68 ± 1.19
ME3 5.53 ± 0.07 55.70 ± 0.30 1.4012 ± 0.0001 188.33 ± 5.03 0.296 ± 0.011 142.11 284.33 543.17 1.41 +14.00 ± 1.28
ME4 5.74 ± 0.02 47.60 ± 0.10 1.4238 ± 0.0001 119.60 ± 1.37 0.332 ± 0.002 93.73 187.54 358.28 1.41 +10.68 ± 2.19
ME5 5.15 ± 0.02 21.67 ± 0.06 1.3959 ± 0.0001 161.34 ± 4.06 0.165 ± 0.003 123.7 205.71 342.09 1.06 +12.34 ± 1.42
ME6 5.48 ± 0.01 18.07 ± 0.06 1.4154 ± 0.0001 202.29 ± 5.02 0.292 ± 0.002 155.87 297.78 568.88 1.38 +9.62 ± 3.23
ME7 5.27 ± 0.01 7.96 ± 0.01 1.4148 ± 0.0001 225.13 ± 5.20 0.280 ± 0.005 170.9 326.63 623.99 1.38 +13.30 ± 1.07
ME8 5.80 ± 0.01 8.80 ± 0.25 1.4318 ± 0.0001 250.20 ± 6.50 0.301 ± 0.007 196.41 375.23 716.84 1.38 +12.62 ± 1.12
ME9 5.57 ± 0.01 74.43 ± 0.03 1.3895 ± 0.0001 133.57 ± 3.08 0.214 ± 0.012 102.81 179.07 311.87 1.16 +9.10 ± 1.94
ME10 5.48 ± 0.00 18.89 ± 0.02 1.4058 ± 0.0001 144.36 ± 1.71 0.230 ± 0.005 112.77 196.41 358.28 1.24 +12.85 ± 2.44
3.5. Conductivity Determination

Conductivity analysis was performed to confirm the O/W type of the microemul-
sions [85]. The results obtained and presented in Table 6 showed a conductivity variation
between 7.92 ± 0.02 and 101.90 ± 0.66 µS/cm, where the maximum value was specific
for the microemulsions with the highest water level of 53%. Furthermore, by increas-
ing Tween 20 from 30% to 40% or PEG 400 from 10% to 20%, conductivity decreased to
47.60 ± 0.10 µS/cm and was well visualized for the ME 1–ME 4 group. With the addition
of the oil phase in the maximum concentration of 10%, a strong effect can be observed,
attaining minimum values around 8 µS/cm, specific for ME 7, its replications, and ME 8.
In the case of ME 9 and ME 10, intermediary results were obtained.
3.6. Refractive Index Determination

Refractive index (RI) variation between 1.3823 ± 0.0002 and 1.4318 ± 0.0001 was
specific for O/W microemulsions with isotropic characteristics [63]. The main ingredients
that influenced RI as a quality parameter were oleic acid, Tween 20, and PEG 400. The
RI values were shifted after the value of 1.3330, specifically for water as a reference over
determinations. The variation in RI values was attributed to a higher refractive index of
surfactants and the oil phase [85,86].
The microemulsions characterized by a high clarity and high RI were those prepared
with at least one increase of the three ingredients. The first increase can be seen in the pair
ME 1–ME 2, where Tween 20 increase from 30% to 40% determined a RI of 1.4047 ± 0.0001.
The same phenomenon was observed in the case of the ME 1–ME 3 pair, where only PEG
400 was modified. When Tween 20 and PEG 400 increased simultaneously, RI attained
1.4238 ± 0.0001 for ME 4. The second increase was obtained when oleic acid concentration
was fixed at 10%. Together with the modulation in the concentration of the previously
mentioned stabilizers, the refractive index variation was similar and attained the maximum
value of 1.4318 ± 0.0001 for ME 8. Intermediate values of RI were obtained for ME 9 and
ME 10 systems, where the maximum value was attributed to ME 10.
3.7. Rheological Evaluation

Following rheological evaluation, two distinctive groups of microemulsions were
assessed and described under four rheological models. The fluid-type microemulsions,
namely ME 2, ME 3, and ME 4, exhibited a Newtonian flow, with viscosities placed in the
narrow domain of 0.192 Pa·s and 0.346 Pa·s. In this case, the increase of Tween 20 and
PEG 400 concentration diminished the viscosity. Graphical representations of shear stress
(Pa) as a function of shear rate (s−1 ) are presented in Figure 4—case (a). The linear model
was mathematically described by Newton’s Law and validated by adequate rheological
descriptors, with correlation coefficients between 0.9993 and 0.9998. Newtonian behavior
remains a classical model for fluid microemulsions with a laminar flow, keeping a constant
viscosity with the increase in the shear stress and shear rate [87,88]. In this case, the
rheological parameters assessed from the regression equations are presented in Table 7.
Figure
Figure 4.
4.Plots
Plotsofofshear
shearstress
stress(Pa)
(Pa)asasa afunction
functionofofshear
shearrate
rate(s(s−
−1) following specific
1 ) following specificrheological
rheologicalmod-
els: linear
models: profiles
linear for for
profiles MEME 2–ME
2–ME 4 systems
4 systemsdescribed
described by theNewton’s
by the Newton’smodel,model,andand
MEME 9 described
9 described
by
by Bingham model—case(a);
Bingham model—case (a);flow
flowcurves
curves fitted
fitted byby
thethe Ostwald–de
Ostwald–de WaeleWaele model
model for1ME
for ME and1ME
and8 ME
8systems,
systems, and Herschel–Bulkley model specific for ME 5–ME 7 and ME 10 gel-like
and Herschel–Bulkley model specific for ME 5–ME 7 and ME 10 gel-like systems—cases (b,c).
systems—cases
(b,c).
Table 7. Rheological descriptors describing flow behavior for the microemulsions tested at
At the increase in the oil phase from 5% to 10%, the microemulsions were character-
37 ± 0.5 ◦ C.
ized by various gel-like structures with shear-thinning behavior. In the case of ME 5, the
maximum
Code
consistency
Viscosity index was 30.89
(Pa·s)/Consistency IndexPa·s, obtained
1n 2 R at the minimum level of Tween 20
Rheological Model
and PEG 400, which signifies (Pa·sn ) the establishment of strong interactions between compo-
nents.
ME 1The Herschel–Bulkley 1.001 model assumed 0.49 in this0.9974
case a flowOstwald–de
profile starting
Waele from τ0 of
ME 2 0.346 1.0 0.9993
19.60 Pa. A similar behavior was seen for ME 10, where τ0 was equal to 28.17 Pa. When Newton
ME 3 0.192 1.0 0.9998 Newton
Tween
ME 4
20 increased to 40%, 0.304
the consistency coefficient
1.0
decreased atNewton
0.9997
21.81 Pa·sn for ME 6,
andMEit5continued to diminish 30.89 by 2.6 folds in the0.22 case 0.9956
of ME 7. MEHerschel–Bulkley
8 had a lower consistency
coefficient
ME 6 of 2.213 Pa·sn21.81at the maximum concentrations
0.22 0.9654 of Tween 20 andWaele
Ostwald–de PEG 400, these
ME 7
excipients being implied 8.390 0.50 0.9973
in fluidifying the microemulsions. Ostwald–de Waele
Considering the rheograms
ME 8 2.213 0.45 0.9993 Ostwald–de Waele
presented in Figure 4—cases (b) and (c), as the amount of the stabilizer increased, a tran-
ME 9 0.285 1.0 0.9994 Bingham
sition
ME from the Herschel–Bulkley model to the Ostwald–de Waele flow type was assessed.
20.85 0.30 0.9986 Herschel–Bulkley
10The rheological behavior in microemulsions is viewed in the literature in perfect har-
mony with the
1 n represents theflow
internal transitions,
index and which
2 R represents dependcoefficient.
the correlation on the water and oil content, triggering
significant changes in viscosity [91]. Secondly, the nature of the surfactants and cosurfac-
tants On modulates
the otherthe viscosity,
side, as was seenflow
the non-Newtonian in abehavior
previouswas report centered
specific for theon the effect of
microemul-
glycols
sions forinwhich
the microemulsion
viscosity variedformation [88].rate
with the shear It was stated that
at constant viscosity can
temperature. ME 9befollowed
influenced
by
an the
idealdrug solubilization
plastic flow described in by
thethe
oilBingham
phase. At lowerThe
model. oillinearity
concentrations, drug
is kept, but the molecules
regres-
sion straight line was designed with a start point from an initial
can be solubilized nearest the interface area, determining a lack of droplet yield stress (τ 0 ) of 3.207 Pa,
interactions
as presented
and reduced in Figure 4—case
availability (a). Bingham
to create model hydrogen
intermolecular was described bondsin [89].
a previous study by
Djekic,
WhenL. etthe
al. oil
forphase
some model microemulsions
is increased at 10%, thewithdrugibuprofen
molecules atare
particular composition
solubilized in a deeper
core, determining the creation of organized networks that modify the rheological proper-
ties of the systems. Suggestive findings describe the rheological properties of liquid
with isopropyl myristate 5.50%, Labrasol 19.80–29.70%, cosurfactant 19.80–29.70% and

water 45% [89]. In a recent report, Bingham-type microemulsions were associated with the
formation of flocculated systems characterized by frictional forces as determinants for the
initial yield stress recorded [90], being applicable in the case of ME 9, likewise.
In contrast, pseudoplastic behavior is well defined in ME 1 and the gel-like type ME
5, ME 6, ME 7, ME 8, and ME 10. Rheological plots of shear stress (Pa) as a function of
shear rate (s−1 ) are represented in Figure 4—case (b) and (c) for the model systems. The
Ostwald–de Waele and Herschel–Bulkley models fitted the non-Newtonian pseudoplastic
flow with correlation coefficients between 0.9654 and 0.9993. In this case, the graphical
representations show ascendent rheograms outlined by rheological descriptors, namely
consistency index—K (Pa·sn ) and flow index values under 1 (n < 1), which were depicted
from regression equations, as were presented above in Table 7.
A particular behavior was noticed in this group as a function of the influence of the
critical formulation factors upon the internal structure of the microemulsions. Firstly, ME 1
had the lowest consistency index, with a fluid-like appearance, and it was characterized by
minimum levels of oil, surfactant, and cosurfactant.
At the increase in the oil phase from 5% to 10%, the microemulsions were character-
ized by various gel-like structures with shear-thinning behavior. In the case of ME 5, the
maximum consistency index was 30.89 Pa·s, obtained at the minimum level of Tween 20
and PEG 400, which signifies the establishment of strong interactions between components.
The Herschel–Bulkley model assumed in this case a flow profile starting from τ0 of 19.60 Pa.
A similar behavior was seen for ME 10, where τ0 was equal to 28.17 Pa. When Tween 20
increased to 40%, the consistency coefficient decreased at 21.81 Pa·sn for ME 6, and it con-
tinued to diminish by 2.6 folds in the case of ME 7. ME 8 had a lower consistency coefficient
of 2.213 Pa·sn at the maximum concentrations of Tween 20 and PEG 400, these excipients
being implied in fluidifying the microemulsions. Considering the rheograms presented in
Figure 4—cases (b) and (c), as the amount of the stabilizer increased, a transition from the
Herschel–Bulkley model to the Ostwald–de Waele flow type was assessed.
The rheological behavior in microemulsions is viewed in the literature in perfect har-
mony with the internal transitions, which depend on the water and oil content, triggering
significant changes in viscosity [91]. Secondly, the nature of the surfactants and cosurfac-
tants modulates the viscosity, as was seen in a previous report centered on the effect of
glycols in the microemulsion formation [88]. It was stated that viscosity can be influenced
by the drug solubilization in the oil phase. At lower oil concentrations, drug molecules
can be solubilized nearest the interface area, determining a lack of droplet interactions and
reduced availability to create intermolecular hydrogen bonds [89].
When the oil phase is increased at 10%, the drug molecules are solubilized in a
deeper core, determining the creation of organized networks that modify the rheological
properties of the systems. Suggestive findings describe the rheological properties of liquid
crystals containing oleic acid 10–20%, a mixture of esters of phosphoric acid and the
polyoxypropylene, polyoxyethylene ether of cetyl alcohol (PPG-5-CETETH)-20 50%, and
water 30–40%, as intermediary systems discovered in the ternary phase diagram. Herschel–
Bulkley model was representative of systems with a consistency index of 0.04–1.17 Pa·sn ,
with values that were raised to 3.63–29.34 Pa·sn after adding ursolic acid [92]. The gel-
like microstructure of some microemulsions with itraconazole for buccal application was
highlighted, resulting in systems with K values between 0.0156–2.895 Pa·sn and flow index
values of 0.361–1.1817. In this case, the pseudoplastic behavior was emphasized using the
Power Law model [48]. Rozman B. et al. analyzed the viscosity of a gel-like microemulsion
prepared with isopropyl myristate 9.86%, Tween 40 14.79%, Imwitor 308 14.79%, and
water 59.16% to incorporate vitamin C 0.4% and vitamin E 1%. In this case, at a testing
temperature of 20 ◦ C, the pseudoplastic behavior was preserved. In comparison, in the
case of a modified temperature of 32 ◦ C, the system suffered structural changes following
the Newtonian flow model [47].
Notably, the structural attributes in microemulsions depend upon the composition

and govern the flowability and spontaneous changes in the internal behavior. This way,
the rheological modeling creates a pathway to assess the strong connections between
viscosity and the droplet size distribution impacting the biopharmaceutical properties of
the designed microemulsions.
3.8. Dynamic Light Scattering Analysis

Dynamic light scattering technique was applied to determine the mean droplet size
distribution in miconazole-based microemulsions using the Cumulant algorithm. The
samples were diluted with distilled water in a ratio of 1:100, and the resulting dispersions
were analyzed at 24 ± 0.5 ◦ C. According to the Rayleigh phenomenon of light dispersion,
diffusion coefficients were depicted and then integrated into the Stokes–Einstein equation.
The mean hydrodynamic diameter was estimated as Zaverage and was considered one of
the main critical quality attributes included as a response in the statistical analysis and
optimization process.
The values of mean droplet size (Ds) obtained in this stage varied between
119.60 ± 1.37 nm and 250 ± 6.50 nm and are presented in Table 6 as means of three different
acquisitions. The results are accompanied by polydispersity index (PDI) values, which
varied between 0.165 ± 0.003 and 0.301 ± 0.007, being used to appreciate the homogeneity
of the systems and the distribution of the droplets. Over the analysis, the data assessed in
this analysis present the Ds distribution of microemulsion droplets using the D10% , D50% ,
and D90% domains presented in Table 6, being extrapolated to the profiles generated by the
software and expressed as intensity (a.u.) as a function of diameter (nm). The Span values
were calculated to evaluate the size distribution width. In the case of the microemulsions
with miconazole, a limited variation between 1.06 and 1.47 was found. It is unanimously
accepted that the droplet size is not part of a single narrowed domain but may detect some
extended droplet sizes that can be evaluated from the peak distribution, as was reported.
In the study of Monton C. et al., the incorporation of clove oil 2% into microemulsions
stabilized with Tween 80 9–18% and PEG 400 6–9% was quantified by sized distributions
in a maximum range of D10%, D50%, D90% of 20.7–686.6 nm, with Span values between
1.3 ± 0.0 and 16.3 ± 1.3 [28].
Herein, Figures 5–7 present cumulative profiles of microemulsion droplet distribution
Pharmaceutics 2024, 16, x FOR PEER REVIEW
in three groups to observe the peak shifts from 200 nm to 100 nm as a function17 ofoftheir
40
composition.
Figure 5. Distribution profiles obtained in the DLS study and represented as intensity (a.u.) as a
functionofofdiameter
diameter(nm)
(nm)for
forME
ME1,1,ME
ME3,3,ME
ME5,5,and
andME
ME7,7,evaluated 24±±0.5
evaluatedatat24 ◦ C.
0.5°C.
function
Pharmaceutics 2024, 16, 271 Figure 5.ofDistribution profiles obtained
function diameter (nm) for ME 1, ME 3,inME
the5,DLS
and study
ME 7, and represented
evaluated at 24 ± as
0.5intensity
°C. (a.u.) as38
17 of a
function of diameter (nm) for ME 1, ME 3, ME 5, and ME 7, evaluated at 24 ± 0.5 °C.
Figure
Figure 6.
6. Distribution
Distributionprofiles
profilesobtained
obtainedin inthe
theDLS
DLS study
study and
and represented
represented asas intensity
intensity (a.u.)
(a.u.) as
as aa
Figure 6.ofDistribution
function profilesME
obtained in the6,DLS study and represented as intensity (a.u.) as a
function ofdiameter
diameter (nm)
(nm) for
for ME 2,
2, ME
ME 4,
4, ME
ME 6, and
and ME
ME 8,
8, evaluated
evaluated at
at 24
24 ±±0.5
0.5°C.
◦ C.
function of diameter (nm) for ME 2, ME 4, ME 6, and ME 8, evaluated at 24 ± 0.5 °C.
function7.ofDistribution
Figure diameter (nm) for ME
profiles 9 and ME
obtained 10, evaluated
in the 24 ± 0.5 ◦ C.
atand
DLS studyat
function of diameter (nm) for ME 9 and ME 10, evaluated 24 ±represented
0.5 °C. as intensity (a.u.) as a
function of diameter (nm) for ME 9 and ME 10, evaluated at 24 ± 0.5 °C.
From a statistical point of view, it was observed that oleic acid and PEG 400 were
significant afactors
From statistical point
implied in of view,size
droplet it was observed
variation. thatsame
At the oleictime,
acid the
andsurfactant
PEG 400 hadwerea
From factors
significant a statistical point
implied in of view,size
droplet it was observed
variation. At that
the oleictime,
same acidtheand PEG 400had
surfactant were a
behavior less pronounced and partially understood (p < 0.05). By associating the Zaverage
significant
behavior factors
less implied
pronounced in
anddroplet size
partially variation.
understood At(p the
< same
0.05). time,
By the surfactant
associating the Zhad a
results with the microemulsion compositions, particular dynamics were observed in droplet average
behavior
results withless pronounced andcompositions,
partially understood (p dynamics
< 0.05). Bywere associating the Z average
behavior as the microemulsion
a function of the three particular
main formulation factors. As such, at observed
a constantinlevel
drop- of
results
let with
behavior the microemulsion compositions, particular dynamics were observed in drop-
oleic acid ofas a function
5%, of the threecan
some observations main beformulation
made in thefactors.
ME 1–ME As such, at a constant level
4 group:
letoleic
of behavior ofas 5%,
a function of the three main formulation factors. As such, at a constant level
• Anacid increase ofsome
Tweenobservations can be
20 content from made
30% in the
to 40% ME 1–ME
determined
of oleic acid of 5%, some observations can be made in the ME 1–ME 4 group:
a4 decrease
group: in Ds from
152.89 nm to 128.90 nm in the case of the ME 1–ME 2 pair and from 188.33 nm to
119.60 nm in the case of the ME 3–ME4 pair; an additive effect of PEG 400 was observed
in the ME 2–ME 4 pair where Ds decreased from 128.90 nm to 119.60 nm.
• At the minimum concentration of Tween 20 of 30%, the increase of PEG 400 determined
an increase in droplet size, as can be observed in the case of the ME 1–ME 3 pair, acting
oppositely as it was proposed in the mathematical modeling of the Ds response.
In the second group, the presence of oleic acid in the maximum concentration involved
an increase in droplet diameter, with a high impact on the broadening of the distribution
domain:
• An increase of Tween 20 from 30% to 40% determined an increase of Ds from 161.34 nm
to 202.29 nm in the case of ME 5–ME 6 pair, but also from 225.13 nm to 250.20 nm for
ME 7–ME 8 pair.
• When Tween 20 is maintained as constant, PEG 400 variation from 10% to 20% pro-
moted growth in droplet size, and it can be very well emphasized in the case of the
ME 5–ME 7 pair and ME 6–ME 8 pair, where the maximum droplet size of 250.20 nm
was attributed for ME 8.
In the case of the ME 9 and ME 10, intermediate values were obtained.

Considering the above observations, a similar behavior was reported in the literature
for PEG 400 chosen in the concentration of 6–9%, which determined an increase in droplet
size in microemulsions prepared with clove oil 2% and Tween 80 9–18% as potential oral
sprays for buccal application in oral candidiasis [28]. Using its high solubilization ability,
Tang H. et al. observed an increase in droplet size of self-microemulsifying drug delivery
systems (SMEDDS) with resveratrol when PEG 400 was selected between 5–35%. The maxi-
mum concentration of PEG associated with a lower surfactant concentration contributes to
the expansion of the interfacial area, affecting the system stability [93]. Moreover, Badawi
N.M. et al. reported that in some cases, the surfactant molecules can increase droplet size
by forming micellar aggregates that may hinder their normal deposition at the oil/water
interface [94]. On the other hand, the microemulsions prepared with oleic acid 10%, but
also the ME 10 containing oleic acid 8.75% have a different structure compared to those
containing a lower oil concentration. The oil phase promoted an increase in viscosity and
generation of gel-like systems with particular interactions between oil, the stabilizers, and
water, which will be further relevant for the adhesion properties and the drug release
mechanism.
3.9. Zeta Potential Analysis

Zeta potential was specific for positive-charged microemulsion droplets, with experi-
mentally recorded values between +9.10 mV and +14.00 mV, as presented in Table 6. The
parameter was important in appreciating stability and explaining phenomena implied in
droplet stabilization [95]. Zeta potential was studied in various micro-/nanoemulsified
systems [96,97], including microemulsions with antifungal agents [28,98]. It was clarified
that stabilization can be influenced not only by electrostatic repulsions but also by steric
stabilization, promoted by polysorbate-based surfactants [98] and dispersion forces [59].
It was stated that non-ionic surfactants and PEG 400 are not charged contributors, and
therefore, zeta potential is influenced by other molecules [99]. In the present study, the
electrostatic repulsions between positively charged droplets can be justified by the presence
of miconazole base, which at a weakly acidic pH is found in a protonated state [100]. The
results can be correlated with recent findings for solid nanoparticles created with stearic
acid and Tween 20 to entrap miconazole nitrate. The pH dependence of the systems was
proved using indomethacin as an API with an acidic group that modifies zeta potential
through negative charging. A negative charge was also recorded for the blank system,
where stearic acid behaves as an adsorption component implied in electrostatic stabiliza-
tion [101]. In the present study, steric stabilization was more pronounced than electrostatic
stabilization. Carrying a lower positive charge, the studied microemulsions may interact
with negatively charged glycoprotein groups in the oral mucosa, as previously observed for
positively charged nanoparticles [102]. However, prospective research must be considered
to demonstrate such a mechanism that could be beneficial in providing therapeutic efficacy.
3.10. Superficial Analysis

The study of superficial properties in colloid science offers an advanced perspective
by understanding fundamental parameters like superficial tension, adhesive and cohe-
sive interactions, or spreading implications in the behavior of multicomponent systems
as microemulsions. The equilibrium established between adhesive and cohesive forces
governs the displaying and wettability of microemulsions at a given surface, being strongly
associated with a variation in the formulation factors. The goniometric technique offers
a reliable solution in assessing surface characteristics for nanocolloids, by dealing with
the pendant drop and contact angle models, which were mathematically described using
Young-Laplace and Young equations, as previously reported [63,103,104].
In the case of miconazole-based microemulsions, superficial tension (ST) was tested
using both analysis models, and the results obtained are presented in Table 8. According
to the pendant drop model, the shape of a drop dispensed through a Hamilton syringe
needle tip was analyzed considering the pressure difference at the liquid/air interface, the
radii of curvature, and the surface tension opposing the gravitational force. Thus, the mean
values of ST varied between 17.97 ± 0.62–25.80 ± 0.03 mN/m. A slight reduction in γLG
was observed in the group of viscous ME 5–ME 8 microemulsions, and the minimum value
was obtained for ME 10.
Table 8. Mean values of superficial parameters of the microemulsions determined at 24 ± 0.5 ◦ C.
Parameters Tested through Goniometric Technique

No. Pendant Drop (n = 3) Contact Angle (n = 5) Wa Wc S
Vol (µL) γLG (mN/m) Vol (µL) γLG (mN/m) θ (◦ ) (mN/m) (mN/m) (mN/m)
1 4.96 ± 0.09 26.44 ± 0.21 5.04 ± 0.003 34.78 ± 0.525 52.14 ± 0.560 56.07 ± 0.72 69.56 ± 1.05 −13.49 ± 0.52
2 4.83 ± 0.04 25.49 ± 0.08 4.72 ± 0.003 40.85 ± 0.434 57.75 ± 0.596 62.91 ± 0.24 81.69 ± 0.86 −18.78 ± 0.77
3 4.73 ± 0.05 25.80 ± 0.03 4.85 ± 0.008 44.53 ± 1.058 48.00 ± 0.557 74.77 ± 2.23 89.05 ± 2.11 −44.52 ± 0.48
4 4.66 ± 0.03 25.38 ± 0.02 4.24 ± 0.004 35.99 ± 0.634 48.49 ± 0.438 60.56 ± 1.80 71.98 ± 1.26 −11.41 ± 1.21
5 3.51 ± 0.44 21.18 ± 0.60 3.62 ± 0.008 10.11 ± 0.095 51.10 ± 0.413 16.44 ± 0.14 20.22 ± 0.19 −3.78 ± 0.09
6 4.29 ± 0.59 23.06 ± 0.89 3.96 ± 0.003 8.05 ± 0.023 42.37 ± 0.012 13.99 ± 0.04 16.09 ± 0.04 −2.10 ± 0.007
7 3.63 ± 0.10 20.90 ± 0.49 4.25 ± 0.002 6.75 ± 0.050 47.75 ± 0.106 11.28 ± 0.08 13.50 ± 0.09 −2.21 ± 0.01
8 3.54 ± 0.23 19.08 ±0.69 4.10 ± 0.003 5.92 ± 0.036 37.03 ± 0.156 10.65 ±0.05 11.84 ± 0.07 −1.19 ± 0.02
9 5.42 ± 0.24 25.98 ± 0.49 5.27 ± 0.022 26.48 ± 1.790 53.72 ± 0.309 42.23 ± 2.96 52.95 ± 3.57 −10.72 ± 0.63
10 3.50 ± 0.32 17.97 ± 0.62 2.39 ± 0.001 5.65 ± 0.014 41.87 ± 0.057 9.84 ± 0.02 11.29 ± 0.03 −1.44 ± 0.01
In contrast, by applying the sessile drop method, the mean values for γLG depended on
composition. In this case, γLG is specific for drops displayed on a solid surface, with ST val-
ues placed between 5.65 ± 0.014–44.53 ± 1.058 mN/m, which assumed different behaviors
in matters of adhesion and spreading. The highest values between 26.48–44.53 mN/m were
obtained for microemulsions described by high contact angles of 48.00–57.75◦ (ME 1–ME 4,
and ME 9) where the superficial tension opposed to an increased wetting of the surface. A
high work of adhesion was calculated in this case and varied between 42.23–74.77 mN/m.
The work of cohesion attained maximum values of 52.95–89.05 mN/m, signifying strong
interactions between microemulsion molecules. The phenomenon was also defined by high
negative values of the spreading coefficient varying between −10.72 and – 44.52 mN/m.
Secondly, it was well observed that low values of γLG were obtained in the case of
gel-like ME 5–ME 8 and ME 10 microemulsions, with a significant variation between 5.65 ±
0.014–10.11 ± 0.095 mN/m. A substantial decrease in ST was strongly correlated to the high
oil content selected in the preparation process, which may highly impact the wettability
and spreading of the formulation at the surface of administration. In this group, the contact
angle values varied between 37.03–51.10◦ , with a wettability behavior influenced by the oil
phase and also through the fluidizer effect of PEG 400 that increased the hydrophilicity of
the samples. Accordingly, the work of adhesion quantified in values of 9.84–16.44 mN/m
sustained a low adhesive effect but also a decrease in cohesion forces from 20.22 mN/m to
11.29 mN/m, and an increase in the spreadability with low negative values, closer to zero,
from −3.78 mN/m to −1.19 mN/m, as it can be viewed in Table 9.
Table 9. Kinetic descriptors specific for the miconazole release from microemulsions following the
Higuchi model.
ME 1 2 3 4 5 6 7 8 9 10 Gel (R)
D·10−5 (cm2 /s) 2.569 2.22 1.85 2.11 0.252 0.516 0.265 1.00 1.27 0.343 0.0162
R 0.9904 0.9842 0.9630 0.9812 0.9858 0.9910 0.9959 0.9861 0.9935 0.9961 0.9758
D represents the diffusion coefficient and R represents the correlation coefficient.
In the study of Wang W. et al., microemulsions formulated with fatty acid methyl esters
were explored with attention to superficial properties. The dynamic contact angle was
useful to appreciate the spreading phenomenon of diluted microemulsions at the surface
level. Moreover, it was stated that the superficial tension of the dispersion decreased when
16.44 mN/m sustained a low adhesive effect but also a decrease in cohesion forces from
20.22 mN/m to 11.29 mN/m, and an increase in the spreadability with low negative values,
closer to zero, from −3.78 mN/m to −1.19 mN/m, as it can be viewed in Table 9.
In the study of Wang W. et al., microemulsions formulated with fatty acid methyl
Pharmaceutics 2024, 16, 271 esters were explored with attention to superficial properties. The dynamic contact angle 20 of 38
was useful to appreciate the spreading phenomenon of diluted microemulsions at the sur-
face level. Moreover, it was stated that the superficial tension of the dispersion decreased
when the microemulsions
the microemulsions werewere tested
tested as concentrated
as concentrated systems
systems [104],
[104], andand
thisthis
may may explain
explain the
the
behavior of the gel-like microemulsions. A decrease in surface tension is explained by by
behavior of the gel-like microemulsions. A decrease in surface tension is explained the
the activity
activity of surfactant
of surfactant andand cosurfactant
cosurfactant in in promoting
promoting negative
negative free
free energy
energy atatthethe oil/wa-
oil/water
ter interface
interface [105,106].
[105,106].
ItItcan
canbebeappreciated
appreciatedthatthatthe
themicroemulsion
microemulsionsamples
samplesexperienced
experiencedpartial
partialwetting,
wetting,
having
havingθθvalues
valuesunder
under90°,
90◦ ,and
andthe
themost
mosthydrophilic
hydrophilicmicroemulsion
microemulsionwas wasME ME88formulated
formulated
with
witholeic
oleicacid
acid10%,
10%,Tween
Tween20 2040%,
40%,andandPEG
PEG40040020%.
20%.TheThevariation
variationfor foreach
eachparameter
parameter
analyzed
analyzed through the sessile drop method can be observed in Figure 8 comparatively.The
through the sessile drop method can be observed in Figure 8 comparatively. The
ordinate
ordinateaxisaxisisisnoted
notedwith
with”y”“y”labeling
labelingbybyturning
turningthe
theexperimental
experimentalvaluesvaluesof ofsuperficial
superficial
tension,
tension,contact
contact angle, work
work of ofadhesion,
adhesion,workworkofof cohesion,
cohesion, andand work
work of spreading.
of spreading. TheThebest
best spreading
spreading waswas observed
observed for gel-like
for the the gel-like microemulsions
microemulsions formulated
formulated withwith the maxi-
the maximum
mum concentration
concentration of oleic
of oleic acid. acid.
Variationininsuperficial
Figure8.8.Variation
Figure superficialparameters
parametersfor
forthe
theME
ME1–10
1–10microemulsions,
microemulsions,evaluated
evaluatedfollowing
following
thesessile
sessiledrop
dropmethod 24±±0.5
methodatat24 ◦ C.
0.5°C.
the
3.11. In Vitro Drug Release

The biopharmaceutical analysis of the miconazole-based microemulsions valued the
presence of significant formulation factors in the drug diffusion process. The presence of
the oleic acid and PEG 400 had an essential contribution in explaining diffusion phenomena
(p < 0.05), as the statistical analysis confirmed over 3.11.3. section. The miconazole release
profiles defined as the cumulative amount released (mg) per unit area (cm2 ) as a function
of time (min) are presented in Figure 9—case (a). The release of miconazole during 600 min
was evaluated for different behaviors that collided with the structure of the fluid and gel-
like microemulsions. In the case of the fluid ME 1–ME 4 containing oleic acid 5%, a burst
release was seen in the first 2 h, followed by a gradual liberation up to 10 h. In contrast, ME
9, an intermediary point in the present design, had a release profile resembling the gel-like
systems. In this case, its behavior can be justified by an increased oil phase content of
6.25% with a strong effect upon drug release and a modified profile allure. As can be seen
in the cases of ME 5–ME 8 and ME 10, the obtained profiles suggested a controlled drug
release. ME 5 and ME 7 presenting PEG 400 10% were characterized by an inferior release.
Meanwhile, ME 6 and ME 8 containing PEG 400 20% exceeded a drug release quantified by
a cumulative release per unit area up to 12 mg/cm2 . MCZ release from microemulsions
was superior to the commercial gel, which assumed a constant profile of MCZ diffusion.
lure. As can be seen in the cases of ME 5–ME 8 and ME 10, the obtained profiles suggested
a controlled drug release. ME 5 and ME 7 presenting PEG 400 10% were characterized by
an inferior release. Meanwhile, ME 6 and ME 8 containing PEG 400 20% exceeded a drug
release quantified by a cumulative release per unit area up to 12 mg/cm2. MCZ release
from microemulsions was superior to the commercial gel, which assumed a constant pro-
file of MCZ diffusion.
Figure 9.
Figure 9. MCZ
MCZrelease
releaseprofiles—(a)
profiles—(a)Cumulative
Cumulativeamount released
amount (mg)
released per per
(mg) unitunit
area area
(cm2)(cm
as a2 )func-
as a
tion of time (min) for the microemulsions ME 1-ME 10, and (b) Identification of Higuchi model’s
function of time (min) for the microemulsions ME 1-ME 10, and (b)2 Identification of Higuchi model’s
linearity range for cumulative MCZ release per unit area (mg/cm ) as a function of the square root
linearity range for cumulative MCZ release per unit area (mg/cm2 ) as a function of the square root
of time.
of time.
Kinetic descriptors specific for MCZ release from the ME 1–ME 10 microemulsions
Kinetic descriptors specific for MCZ release from the ME 1–ME 10 microemulsions
and the referencegel
and the reference gelwere
weredescribed
describedasas diffusion
diffusion coefficients
coefficients and and correlation
correlation coefficients
coefficients (R)
(R) that well fitted the Higuchi model. The results are presented in Table
that well fitted the Higuchi model. The results are presented in Table 9, in relationship with 9, in relationship
withindividual
the the individual profiles
profiles of in miconazole
of in vitro vitro miconazole
release release from microemulsions
from microemulsions after ap-
after applying
plying
the the Higuchi
Higuchi model.model. Generally,
Generally, the kinetic
the kinetic modelmodel was used
was used to describe
to describe and and validate
validate the
the kinetic release of various drugs from designed and studied microemulsions
kinetic release of various drugs from designed and studied microemulsions with ibupro- with ibu-
profen
fen [89],[89], quercetin
quercetin [52],
[52], oleanolic
oleanolic acid
acid [107],ororfrom
[107], frommore
morecomplex
complexsystems
systems containing
containing
microemulsions for delivery of clotrimazole from nanofibers [108], polymer-based gels,
microemulsions for delivery of clotrimazole from nanofibers [108], polymer-based gels,
microemulsion-based gels,
microemulsion-based gels,ororliposomal
liposomalgels gelswith
withcroconazole
croconazole [109]. In this
[109]. study,
In this the the
study, cu-
mulative amount
cumulative amount of of
MCZ MCZ released
releasedperper
unit area
unit (mg/cm
area (mg/cm
2 ) was
2 graphically
) was presented
graphically as a
presented
function
as of the
a function ofsquare root root
the square of time, and and
of time, the linear profiles
the linear validating
profiles the model
validating werewere
the model pro-
jected (Figure
projected 9—case
(Figure 9—caseb). b).
The diffusion coefficients varied between 0.0162·10−5 cm2 /s (in the case of the ref-
erence gel) and 2.56·10−5 cm2 /s in the case of ME 1, which exhibited the highest release
along with ME 2 and ME 4. From the group of the gel-like formulations, ME 8 had the
maximum diffusion coefficient value of 1.00 cm2 /s.
The structural complexity of the microemulsions governs the drug release, being
composition-dependent. Thereby, the release was superior from all the microemulsions
compared to the gel system. The observation is closely related to some reports where
the reference was a miconazole suspension tested using dialysis membranes [71,72] or
a cream-based formulation with miconazole analyzed through permeation studies [110].
Several factors affecting drug diffusion are related to the solubility of miconazole in the
aqueous and the lipophilic phase, the stabilizer effect of Tween 20 and PEG 400, and
particular dynamics occurring in the formation of fluid and viscous microemulsion [47].
Thereby, droplet size and viscosity are the main physical parameters with a direct impact
on the drug release. A high level of the aqueous phase and the nanometer scale of the
dispersed droplets sustain a faster diffusion, as previously described for microemulsions
with clotrimazole [111]. Reduced droplet diameters between 119.60–188.33 nm, specific
for the ME 1–ME 4 fluid systems, were correlated to higher values of diffusion coefficients,
while in the case of ME 5–ME 8 gel-like systems, elevated sizes up to 250.20 nm slowed
down the MCZ release. It was observed that an increase in drug diffusion is linked to a
partition of the API in the two solubilizers—the oil phase and the surfactant/cosurfactant
mixture, determining the API to reside at the oil/water interface, accelerating diffusion [98].
As the oil concentration increases, the API becomes encapsulated in oily cores, implied in
strong interactions that reinforce the internal structure and increase the viscosity, promoting
a controlled drug release [49].
3.12. Statistical Analysis for the Miconazole Microemulsions Using 23 Full Factorial Design
The last step of the study comprised the application of a Quality by Design (QbD)
approach to assess microemulsions with adequate critical quality attributes. Beginning
with the projection of the 23 + 2 factorial model with 10 experimental runs generated in
Design Expert, the three formulation factors noted X1 : Oleic acid (%), X2 : Tween 20 (%), and
X3 : PEG 400 (%) were analyzed as independent variables that may influence the variation of
three main responses namely Y1 : mean droplet size—Ds (nm), Y2 : work of adhesion—Wa
(mN/m), and Y3 : diffusion coefficient—D (cm2 /min).
Response surface regression was based on a statistical interpretation of responses using
multiple linear regression combined with response surface methodology [112,113]. Using
ANOVA analysis, it was interpreted the statistical significance of independent factors and
possible interactions that may occur by analyzing the p-value for each model, together with
the lack of fit value, R2 , and adjusted R2 , by assuming p < 0.05 as desired for the significance
of the statistical model, and p > 0.05 for the lack-of-fit. Moreover, a critical interpretation
using contour plots, surface response plots, and interaction plots was proposed to visualize
formulation factors’ influence on responses [63,112,114].
The experimental design is of outstanding relevance to assess the complexity of the
microemulsions and find out a desired system that must accomplish a sum of criteria
relying on their pharmaceutical applicability for buccal application: a reduced droplet size,
an adequate work of adhesion, and a high diffusion coefficient that assumes a high release
rate of miconazole to support a sustained action on oral lesions. In this sense, a predictive
optimization was implemented to find solutions with the best desirability coefficients.
Table 10 presents the independent and dependent variables analyzed using the design
of the experiment for the ten systems generated through the full factorial plan. The
experimental values of the three responses are accompanied by predicted values proposed
over the statistical analysis.
Table 10. Independent variables presented in a relationship with the critical quality attributes
with actual and predicted responses obtained from response regression study for the analyzed
microemulsions.
Independent Variables Dependent Variables

Oleic 1Ds Ds 2W W 3 D 4 Sqrt(D) Sqrt(D)
Variable
Tween 20 PEG 400
Acid (Actual) (Predicted) (Actual) (Predicted) (Actual) (Actual) (Predicted)
(%) (%)
(%) (nm) (nm) (mN/m) (mN/m) (cm2 /s) (cm2 /s) (cm2 /s)
Code X1 X2 X3 Y1 actual Y1 predicted Y2 actual Y2 predicted Y3 actual Y3 actual Y3 predicted
ME 1 5 30 10 152.89 156.78 69.56 69.19 2.56·10−5 0.0051 0.0051
ME 2 5 40 10 128.90 131.04 81.69 83.79 2.22·10−5 0.0047 0.0047
ME 3 5 30 20 188.33 181.39 89.05 88.30 1.85·10−5 0.0043 0.0043
ME 4 5 40 20 119.60 125.34 71.98 73.70 2.11·10−5 0.0046 0.0046
ME 5 10 30 10 161.34 153.19 20.22 17.61 2.52·10−6 0.0016 0.0016
ME 6 10 40 10 202.29 209.24 16.09 16.38 5.16·10−6 0.0023 0.0023
ME 7 10 30 20 225.13 222.99 13.50 11.86 2.65·10−6 0.0016 0.0017
ME 8 10 40 20 250.20 248.72 11.84 13.10 1.00·10−5 0.0032 0.0031
ME 9 6.25 35 10 133.57 123.92 52.95 47.56 1.27·10−5 0.0036 0.0036
ME 10 8.75 30 15 144.36 154.01 11.29 16.68 3.43·10−6 0.0019 0.0018
1Ds represents the mean droplet size, 2 W—mean work of adhesion, 3 D—diffusion coefficient, and 4 Sqrt(D)—
diffusion coefficient obtained by applying a square root transformation.
3.12.1. Statistical Interpretation for Mean Droplet Size

Droplet size has a real impact on the pharmaceutical properties of the microemulsions,
influencing other intrinsic attributes like viscosity, adhesion, and spreadability, as well
as the release of the active ingredient at the area of treatment [63]. By appreciating the
Pharmaceutics 2024, 16,
Pharmaceutics 2024, 16, 271
x FOR PEER REVIEW 24 of 40
23 of 38
the nanometric
nanometric sizesize of the
of the microemulsion
microemulsion droplets,
droplets, it itcan
canbebeestimated
estimatedaa high
high surface area
influenced
influenced by dynamics in the molecular arrangements of surfactant and cosurfactant at
oil/waterinterface
the oil/water interfacethatthatpromotes
promotesaahighly
highlyreduced
reduced interfacial
interfacial tension
tension and
and an
an increased
increased
contact at
contact at the
thelevel
levelofofthe
thebuccal
buccalmucosa
mucosa[115].
[115].
In In
this this sense,
sense, a statistical
a statistical interpretation
interpretation for
for the
the meanmean
Ds ofDstheof projected
the projected microemulsions
microemulsions was was proposed
proposed to find
to find howhow formulation
formulation var-
variables
iables influence
influence thethe hydrodynamic
hydrodynamic diameter.The
diameter. Themathematical
mathematicalinterpretation
interpretationof of the
the Y1
initially created using the Half-Normal
response was initially Half-Normal plot plot of the standardized effect effect and
the Pareto
Pareto chart
chartthat
thatwell-defined
well-definedthe
thesignificance
significanceof ofeach term
each for for
term the the
statistical model
statistical [52],
model
beingbeing
[52], presented in Figure
presented 10—cases
in Figure (a) and
10—cases (a)(b).
and (b).
Figure
Figure 10.
10. Initiative
Initiativestep
stepininmathematical
mathematicalmodeling
modelingofof
Y1Yresponse using
1 response thethe
using Half-Normal
Half-NormalPlotPlot
of the
of
standardized effect—case (a), and Pareto Chart ranking the significant terms as a function
the standardized effect—case (a), and Pareto Chart ranking the significant terms as a function of of thethe
t-
value and Bonferroni limit—case
t-value and Bonferroni limit—case (b). (b).
According
According to tothe
theresponse
responsesurface
surface regression
regression analysis,
analysis, Ds was
Ds was significantly
significantly influ-
influenced
enced
by oleic acid (%)—X1 factor, PEG 400 (%)—X3 factor, and the X1 X2 interaction, with p <with
by oleic acid (%)—X 1 factor, PEG 400 (%)—X 3 factor, and the X 1 X2 interaction, 0.05.
pThe
< 0.05. The projected
projected model was model was significant,
significant, withofa 0.0150
with a p-value p-value of an
and 0.0150 andofan21.29.
F value F value of
There
21.29.
is onlyThere is only
a 1.50% a 1.50%
chance that anchance thatthis
F value an large
F value this occur
could large could
due tooccur
noise.dueThetopolynomial
noise. The
polynomial
equation that equation that
fitted the Y1 fitted the Yis1 presented
response response isherein
presented herein(5)):
(Equation (Equation (5)):
Y1 =Y163.92
1 = 163.92 + 29.95 X1 + 16.02 X3 + 20.45 X1X2 + 11.30 X1X3 − 7.58 X2X3
+ 29.95 X1 + 16.02 X3 + 20.45 X1 X2 + 11.30 X1 X3 − 7.58 X2 X3 (8)
(8)
The experimental assessments through DLS revealed that the mean Ds values varied
The 119.60
between experimental
nm andassessments
250.20 nm, through DLS revealed
being influenced that
by the oilthe meanPEG
phase, Ds values varied
400, and the
between 119.60 nm and 250.20 nm, being influenced by the oil phase, PEG
interaction between oleic acid and Tween 20. It can be observed that the X1 term had 400, and thea
strong positive effect in terms of increasing Ds. The same observation was valid for the Xa3
interaction between oleic acid and Tween 20. It can be observed that the X 1 term had
strongdescribed
term positive effect
by a in terms coefficient
positive of increasing
in Ds.
the The
samesame observation
manner the X1Xwas valid for the
2 interaction has
X3 term described by a positive coefficient in the same manner the X X interaction has
shown. The R22 obtained for the model was 0.9726, while the adjusted1 R222 by reference to
shown. The R obtained for the model was 0.9726, while the adjusted R by reference to
degrees of freedom was 0.9269. Table 11 exposes the terms analyzed in the ANOVA test
degrees of freedom was 0.9269. Table 11 exposes the terms analyzed in the ANOVA test as
as a function of F and p-values at a confidence level of 95%.
a function of F and p-values at a confidence level of 95%.
Similar assessments were obtained in a full factorial design to develop nanostruc-
Similar assessments were obtained in a full factorial design to develop nanostructured
tured lipid carriers with salicylic acid. The multiple linear regression was defined by sig-
lipid carriers with salicylic acid. The multiple linear regression was defined by significant
nificant factors (surfactant concentration and the ratio of the lipidic phase) implied in par-
factors (surfactant concentration and the ratio of the lipidic phase) implied in particle size
ticle size variation in an opposite manner [116].
variation in an opposite manner [116].
Response variation as a function of independent variables was graphically described
by designing contour plots of response. The representations are shown in Figure 11—cases
(a) and (b).
X2X3 459.35 1 459.35 3.37 0.1637
Residual 408.77 3 136.26
Cor Total 17,427.29 9
Response variation as a function of independent variables was graphically described
by designing contour plots of response. The representations are shown in Figure 11—cases
(a) and (b).
Table 11. Analysis of Variance results for Y1 response.
The two contour plots represented below showed how droplet size diameter changed
withSource
the increaseSumin oleic acid from 5%
of Squares df to 10% as Square
Mean a function of the Tween 20
F-Value variation
p-Value
(Figure 11—case (a)). The interaction of Tween 20 with oleic acid can be observed only in
Block 2511.59 1 2511.59
low concentrations
Model of the oil phase, where
14,506.93 5 droplet size varies between
2901.39 21.29 120–1600.0150
nm. Over
160 nm, oleic
X1 -Oleic acid acid has a dominant effect
7585.15 1 on particle size, and the
7585.15 contour lines
55.67 tend to
0.0050
become
X3 -PEGparallel
400 with2171.31
the axis of the X21term. 2171.31 25.94 0.0282
X1 X2droplet size3390.09
The 1
is differently represented 3390.09
in Figure 11—case 24.88
(b), where at0.0155
low con-
X1 X3 of oleic acid
centrations 1035.22
and PEG 400,1 Ds is concentrated
1035.22 under 7.60 0.0704
140 nm, and it tends to
X2 X3 459.35 1 459.35 3.37 0.1637
increase with the increase
Residual 408.77
in their concentration.
3
Even PEG 400 promotes a fluidizing ef-
136.26
fectCor
with increasing
Total concentration,
17,427.29 and9 it is implied in droplets growing at the maximum
oil concentration.
Figure 11.
Figure 11. Contour plots for
Contour plots for mean
mean droplet
droplet size
size response
response (Y
(Y11)) as a function
as a function of
of (a)
(a) X
X11:: Oleic
Oleic acid
acid (%),
(%),
X2: Tween 20 (%), and (b) X1: Oleic acid (%), X3: PEG 400 (%).
X2 : Tween 20 (%), and (b) X1 : Oleic acid (%), X3 : PEG 400 (%).
These
The twoeffects canplots
contour be better visualized
represented below using surface
showed howplots for size
droplet the mean droplet
diameter size
changed
response represented as a function of the X and X terms by variating PEG
with the increase in oleic acid from 5% to 10% as a function of the Tween 20 variation
1 2 400 concentra-
tion, as shown
(Figure 11—casein (a)).
FigureThe12.interaction
From caseof (a)Tween
to (c), it
20can beoleic
with observed thatbe
acid can variation in droplet
observed only in
size concentrations
low tends to exceedof a high
the oillimit withwhere
phase, the increase
dropletin PEG
size 400 (%).
varies between 120–160 nm. Over
160 nm, oleic acid has a dominant effect on particle size, and the contour lines tend to
become parallel with the axis of the X2 term.
The droplet size is differently represented in Figure 11—case (b), where at low con-
centrations of oleic acid and PEG 400, Ds is concentrated under 140 nm, and it tends to
increase with the increase in their concentration. Even PEG 400 promotes a fluidizing effect
with increasing concentration, and it is implied in droplets growing at the maximum oil
concentration.
These effects can be better visualized using surface plots for the mean droplet size
response represented as a function of the X1 and X2 terms by variating PEG 400 concentra-
tion, as shown in Figure 12. From case (a) to (c), it can be observed that variation in droplet
size tends to exceed a high limit with the increase in PEG 400 (%).
Figure 12. Surface plots for mean droplet size response (Y1) as a function of X1: Oleic acid, X2: Tween
Figure 12. Surface plots for mean droplet size response (Y1 ) as a function of X1 : Oleic acid, X2 : Tween
20, by fixing
Figure PEG 400
12. Surface concentration
plots from 10%—case
for mean droplet (a),(Y
size response to 15%—case (b),ofup to 20%—case (c).
20, by fixing PEG 400 concentration from 10%—case (a), to1)15%—case
as a function
(b), upX1to
: Oleic acid, X(c).
20%—case 2: Tween
20, by fixing PEG 400 concentration from 10%—case (a), to 15%—case (b), up to 20%—case (c).
The
The results
results werewere accompanied
accompanied by interaction plots
by interaction plots for
for the
the X X1XX2 term of the equation
1 2 term of the equation
and are
The presented
results in
were Figure 13.
accompanied The interactions
by interaction
and are presented in Figure 13. The interactions were graphically were
plots graphically
for the X 1X2described
term of theusing
described the
equation
using the
same principle.
and are
same presented
principle. According to
in Figure
According case
to 13. (a),
caseThe when PEG
(a),interactions
when PEG were 400 (noted as
graphically
400 (noted C) is fixed
as C) isdescribed at 10%, two
usingtwo
fixed at 10%, the
situations
situations can
can be
same principle. visualized.
be According
visualized. toWithcase
With the variation
(a),
the when PEG
variation of
of oleic
400acid
oleic from
(noted
acid from as5%C) to
5% is 10%,
to fixedthe
10%, X
X22 factor
at 10%,
the two
factor
(at
(at 30%)
30%) did
situationsdid not
can beaffect
not affect dropletWith
visualized.
droplet sizethe
size variation,
variation
variation, being constantly
of oleic
being acid from
constantly settled
5% toaround
settled 10%, the
around 150
150X2nm. If
factor
nm. If
Tween
Tween 20
(at 30%) isisfixed
20did not at
fixed at40%,
affect the
theresponse
droplet
40%, takes
takesascending
size variation,
response values
being constantly
ascending values from anan
settled
from inferior
around level
inferior 150 up up
nm.
level toIf
approximately
Tween
to 20 is fixed
approximately 200200
nm.
at The
40%,
nm. slope
the
The represented
response
slope in this
in case
takes ascending
represented with
case awith
values
this red aline
from anred crosses
inferior the black
level
line crosses up to
the
line,
black defining
approximately an
line, defining intersection
200 nm. where the
The slope represented
an intersection output remains
in this case
where the output the same
withthe
remains a redeven
lineeven
same if Tween
crosses 20 has
the black
if Tween 20
minimum
line,minimum
has definingor maximum
anmaximum
or levels.
intersection where the output remains the same even if Tween 20 has
levels.
minimum or maximum levels.
Figure
Figure 13.13. Interaction
Interactionplotsplots for
for XX11XX2 2term,
term,considering
consideringthree
threecases:
cases:(a)
(a)XX
1X1 2Xinteraction
2 interaction when
when X3Xis3
fixed at 10%;
isFigure
fixed at (b) (b)
13.10%; X1XX2 1interaction
Interaction Xplots
2 for when
interaction
X 1 X 2 X
when3 is X
term, fixed
is at 15%;
fixed
considering
3 at (c)
15%;X1X
three 2 interaction
(c) X
cases:X when
interaction
(a)
1 2 X1X 2 X 3 is fixed
when
interaction X
when
3
at
is 20%.
fixed
X3 is
fixed
at 20%.at 10%; (b) X1X2 interaction when X3 is fixed at 15%; (c) X1X2 interaction when X3 is fixed at 20%.
The reversal style of the interaction is also maintained in case (b), where the actual
factorThe
The reversal
X3 (noted
reversal style
with C)of
style was
of the interaction
thefixed at 15%.is
interaction also
also maintained
isConsequently,
maintained in
in case
it can (b),
(b), where
be seen
case that
whereat the
the actual
mini-
actual
factor
mum XX33(noted
(notedwith
factorconcentration withofC) was
the
C) wasX1fixed
and
fixed at 15%.
X2at
terms,
15%.Consequently,
the responseitis
Consequently, can be seen
itplaced
can that that
bearound
seen at150
theatnm,
minimum
thebeing
mini-
concentration
balanced up of
to the
175 X
nm and
if theX terms,
X term the
is response
considered is placed
a around
maximum
mum concentration of the X1 and X2 terms, the response is placed around 150 nm, being
1 2 1 150
point.nm,
At being
the balanced
maximum
up toof
level 175
balanced nmto
Tween
up if20,
thekept
175 X
nm 1 term
with
if theisaXconsidered
minimum a maximum
level
1 term is considered of the point.
oil phase,
a maximum Atpoint.
the output
the maximum level of
At thediminishes
maximum
Tween
through 20,
level of 100 kept with
nm, 20,
Tween a
butkept minimum
it is drastically
with a minimumlevel of
modified the oil
at the
level phase, the
of maximum output
the oil phase, diminishes
oil level. through
the output diminishes
100 nm, but
through 100itnm,
is drastically modified modified
but it is drastically at the maximum oil level. oil level.
at the maximum
In the last case (c), when PEG 400 (noted as C) was fixed at 20%, it can be observed a
large output domain from 100 nm to approximately 200 nm with the variation of X2 term

In the last case (c), when PEG 400 (noted as C) was fixed at 20%, it can be observed a
large output domain from 100 nm to approximately 200 nm with the variation of X2 term
from the low to the high level, keeping X1 as minimum. A narrowing of the output domain
from the low to the high level, keeping X1 as minimum. A narrowing of the output domain
was seen in the opposite case, at the increase in the oil level. When the oil phase and PEG
was seen in the opposite case, at the increase in the oil level. When the oil phase and PEG
400 attain the maximum level, any of the two concentrations of Tween 20 will reduce the
400 attain the maximum level, any of the two concentrations of Tween 20 will reduce the
Ds response.
Ds response.
3.12.2.
3.12.2. Statistical
Statistical Interpretation
Interpretation forfor Work
Work of of Adhesion
Adhesion
Work
Work of of adhesion
adhesion represents
represents aa superficial
superficial parameter
parameter thatthat connects
connects physico-chemical
physico-chemical
characteristics like superficial tension and contact angle with
characteristics like superficial tension and contact angle with their in-depththeir in-depth implication
implicationin
drug delivery. The work of adhesion represents the energy implied
in drug delivery. The work of adhesion represents the energy implied in the separationin the separation pro-
cess of aofliquid
process material
a liquid materialfrom a solid
from substrate
a solid substrate[117]. In In
[117]. thisthis
case, anan
case, important
important aspect
aspectis
referred to the displaying of the formulation in the area of administration,
is referred to the displaying of the formulation in the area of administration, which can which can be
obtained
be obtained with a high
with adhesion,
a high adhesion,a lower cohesive
a lower force,
cohesive andand
force, spreading
spreadingcoefficients thatthat
coefficients are
desired to tend
are desired through
to tend zero zero
through or positive valuesvalues
or positive to attain a complete
to attain wettingwetting
a complete of the formu-
of the
lation [118,119].
formulation [118,119].
Over the response regression analysis, Waa was significantly significantly influenced
influenced onlyonly by the
oleic acid—X11 factor,
factor, with p < 0.05. The The Half-Normal
Half-Normal Plot Plot of the standardized effect effect and
Pareto chart representing
representingthe theterms
termsofofpriority
prioritytotogenerate
generatethe themodel
model areare further
further exposed
exposed in
in Figure
Figure 14—cases
14—cases (a)(a) and
and (b),
(b), where
where X1Xwas
1 was codedwith
coded withA,A,XX2— with
with
2— B,
B, and
and XX 3—
3— with C for
both the isolated terms and their interactions.
Figure
Figure 14.
14. Initiative
Initiativestep
modelingofof
Y2Yresponse using the Half-Normal Plot of the
2 response using the Half-Normal Plot of
standardized effect—case (a), and Pareto Chart ranking the significant
the standardized effect—case (a), and Pareto Chart ranking the significant terms as as
terms a function of of
a function thethe
t-
value and Bonferroni limit—case (b).
t-value and Bonferroni limit—case (b).
The
The projected
projected model
model was
was significant,
significant, with
with aa p-value
p-value of
of 0.0002
0.0002 and
and an
an FF value
valueof
of115.37.
115.37.
The reducedpolynomial
The reduced polynomialequation
equationthat
thatfitted
fittedthe
the response
response is presented
is presented below,
below, andand
the the re-
results
sults of the ANOVA analysis are listed in Table 12. The model was validated
of the ANOVA analysis are listed in Table 12. The model was validated by a regression by a regres-
sion R2 ofR0.9914
coefficient
coefficient and an adjusted R2 ofR0.9825.
2 of 0.9914 and an adjusted 2 of 0.9825.
Y2 = 39.71 − 32.00 X1 − 2.26 X1X3 − 3.34 X2X3 + 3.96 X1X2X3 (9)

Y2 = 39.71 − 32.00 X1 − 2.26 X1 X3 − 3.34 X2 X3 + 3.96 X1 X2 X3 (9)
TableThe mathematical
12. Analysis modeling
of Variance resultsoffor
the
Y2work of adhesion was performed using the Young–
response.
Dupré equation. Thus, considering the results placed between 11.29–89.05 mN/m, it was
Source thatSum
appreciated the of Squares
oleic acid term Df Mean Square
(X1 ) negatively F-Value
affected the p-Value of
adhesion properties
Block 342.05 1 342.05
the microemulsions as a function of its concentration. The lowest values for the work of
adhesion were obtained when oleic acid was selected in a concentration over 5%.
Pharmaceutics 2024, 16, 271

Model 8925.50 4 2231.38 115.37 0.0002
27 of 38
X1-Oleic acid 8697.67 1 8697.67 449.69 < 0.0001
X1X3 41.34 1 41.34 2.14 0.2176
X2XAnalysis
Table 12. 3 89.31 results for Y 1 response. 89.31
of Variance 4.62 0.0981
2
X1X2X3 125.37 1 125.37 6.48 0.0636
Source
Residual Sum of Squares
77.37 Df4 Mean19.34
Square F-Value p-Value
Cor Total
Block 9344.92
342.05 19 342.05
Model 8925.50 4 2231.38 115.37 0.0002
X1 -Oleic acid 8697.67 1 8697.67 449.69
The mathematical modeling of the work of adhesion was performed using the <0.0001
X1 X3 41.34 1 41.34 2.14 0.2176
Young–Dupré
X2 X3
equation. Thus, considering
89.31 1
the results
89.31
placed between
4.62
11.29–89.05 mN/m,
0.0981
it was
X1 Xappreciated
2 X3
that the oleic acid term
125.37 1 (X1) negatively
125.37 affected6.48 the adhesion0.0636
properties
of the microemulsions
Residual as a function of
77.37 4 its concentration.
19.34 The lowest values for the work
of adhesion
Cor Total were obtained
9344.92 when oleic9acid was selected in a concentration over 5%.
By projecting the surface responses for Y2 as presented in Figure 15—cases (a)-(c), it
can Bybe projecting
emphasized thethe variation
surface in the for
responses work of presented
Y2 as adhesion from high15—cases
in Figure values—case (a)
(a)-(c),
itthrough
can be the lowest ones—case
emphasized (c), by
the variation inassuming
the workthat X2 and X3from
of adhesion factors didvalues—case
high not influence(a)its
variation.
through theThe surface
lowest is moved(c),
ones—case along the scale in
by assuming thattheX2same
and manner
X3 factorsasdid
the not
interaction plots
influence its
presentedThe
variation. in Figure
surface16—cases
is moved (a)–(c) show
along the theinplacement
scale of the response
the same manner values in plots
as the interaction three
main groups.
presented in Figure 16—cases (a)–(c) show the placement of the response values in three
main groups.
Figure 15. Surface plots for work of adhesion (Y2 ) as a function of X2 : Tween 20 (%), X3 : PEG 400 (%),
Figure 15. Surface plots for work of adhesion (Y2) as a function of X2: Tween 20 (%), X3: PEG 400 (%),
by
byfixing
fixingoleic
oleicacid
acidconcentration
concentrationfrom
from5%—case
5%—case(a),(a),toto7.5%—case
7.5%—case(b),
(b),up
uptoto10%—case
10%—case(c).
(c).
Firstly, it can be mentioned the domain between 60–80 mN/m from Figure 16—case
(a), where the oleic acid term (noted with A) is fixed at 5%. There is only one case where a
slight variation in the output can be observed, but just in the specified range when the X2
term and X3 term are modified. Thus, Tween 20 (40%) determined an increase in the work
of adhesion when PEG 400 is considered 10%, or in reverse, a decrease in response when
PEG 400 is considered 20%. When the oil content is increased to 7.5%—case (b), the output
range drops down to around 40 mN/m, where the significance of the X2 and X3 factors is
lowered. The most substantial effect is seen in case (c), where the lines tend to be straight
and uncrossed alongside the X2 or X3 axis, and an output concentrated under 20 mN/m.).
Figure
Figure16.16. Interaction
Interaction plots
plots emphasizing
emphasizing the
the main
main effect
effect of
of X
X11 term: (a) X
term: (a) X22XX33interaction
interactionwhen
whenXX1 1is
isfixed
fixedatat5%;
5%;(b)
(b)XX X interaction
2X23 interaction
3 when X is fixed at 7.5%; (c) lack of X X interaction
when X1 is1fixed at 7.5%; (c) lack of X2X3 2interaction
3 when X1 is Xfixed
when 1 is
fixed at 10%.
at 10%.
Firstly,
3.12.3. it canInterpretation
Statistical be mentioned for
theDiffusion
domain between
Coefficient60–80 mN/m from Figure 16—case
of Miconazole
(a), where the oleic acid term (noted with A) is fixed at 5%. There is only one case where a
The response regression analysis for the diffusion coefficient (D) followed a particu-
lar pathway compared to the previously performed designs. Thus, a square root transfor-
mation was applied to obtain a linear regression for a relevant model that can better fit the
response. Over the first initial configuration of the model with linear regression with no
transformation, the Box–Cox plot helped in diagnosing inadequacies of the statistical
slight variation in the output can be observed, but just in the specified range when the X2
term and X3 term are modified. Thus, Tween 20 (40%) determined an increase in the work
of adhesion when PEG 400 is considered 10%, or in reverse, a decrease in response when
PEG 400 is considered 20%. When the oil content is increased to 7.5%—case (b), the output
Figure drops
range 16. Interaction
down toplots emphasizing
around 40 mN/m, the where
main effect of X1 term: (a)ofXthe
the significance 2X3 interaction
X2 and X3 when
factors X1 is
fixed at 5%;
lowered. (b) most
The X2X3 interaction
substantialwhen X1 is
effect is fixed
seen at
in7.5%;
case (c)
(c),lack of X2the
where X3 interaction
lines tendwhen
to beXstraight
1 is fixed
at 10%.
and uncrossed alongside the X2 or X3 axis, and an output concentrated under 20 mN/m).
3.12.3. Statistical Interpretation for Diffusion

Diffusion Coefficient
Coefficient of Miconazole
Miconazole
response regression
The response regressionanalysis
analysisfor
forthe
thediffusion
diffusioncoefficient
coefficient(D) (D)followed
followeda particular
a particu-
lar pathway
pathway compared
compared to to
thethe previously
previously performeddesigns.
performed designs.Thus,
Thus, aa square
square root transfor-
transfor-
mation was
was applied
appliedto toobtain
obtainaalinear
linearregression
regression forfor
a relevant
a relevantmodel
modelthat cancan
that better fit the
better fit
response.
the Over
response. the the
Over firstfirst
initial configuration
initial configurationof the model
of the with
model linear
with regression
linear with
regression no
with
transformation,
no transformation, thetheBox–Cox
Box–Cox plot
plothelped
helpedinindiagnosing
diagnosinginadequacies
inadequaciesof of the statistical
model by proposing a square root root transformation.
transformation. Koliqi R. et al. used this algorithm to
obtain an improved predictive model for studying the entrapment efficiency efficiency and drug
content
content ininthe
thedevelopment
development of of
polymeric
polymeric nanoparticles
nanoparticles[120].[120].
The transformation
The transformationapproachap-
is helpful
proach in the experimental
is helpful design design
in the experimental when itwhen
is wisely appliedapplied
it is wisely to control the response
to control the re-
variance [121,122].
sponse variance [121,122].
The Half-Normal Plot of the standardized effect effect and the Pareto chart described the
significance of the terms and are are presented
presented inin Figure
Figure 17—cases
17—cases (a)(a) and
and (b).
(b).
Figure 17.
17. Initiative
Initiativestep
modelingofof
Y3Yresponse using the Half-Normal Plot of the
Figure 3 response using the Half-Normal Plot of
standardized effect—case (a), and Pareto Chart ranking the significant
the standardized effect—case (a), and Pareto Chart ranking the significant terms as as
terms a function of of
a function thethe
t-
value and Bonferroni limit—case (b).
t-value and Bonferroni limit—case (b).
Following the response surface regression analysis, the diffusion coefficient was sig-
nificantly influenced by oleic acid (%)—X1 factor, Tween 20 (%)—X2 factor, and their
interactions between the factors X1 X2 , X1 X3 , and X2 X3 , with a p < 0.05. The new model was
significant, with a p-value of 0.0004 and an F of 2362.10. There is a 0.04% chance that an F
value this large could occur due to noise. The regression equation (Equation (7)) obtained
through the square root transformation fitting the Y3 response is further presented, together
with the results of the ANOVA analysis listed in Table 13. The model was confirmed by
regression coefficients R2 of 0.9999 and an adjusted R2 of 0.9994.
Sqrt(Y3 ) = 0.0031 − 0.0012 X1 + 0.0003 X2 +0.0003 X1 X2 + 0.0002 X1 X3 + 0.0002 X2 X3 (10)
Table 13. Analysis of Variance results for Y3 response.
Source Sum of Squares Df Mean Square F-Value p-Value

Block 7.987·10−7 1 7.987·10−7
Model 0.0000 6 2655·10−6 2361.10 0.0004
X1 -Oleic acid 0.0000 1 0.0000 11,725.47 <0.0001
X2 -Tween 20 6.020·10−7 1 6.020·10−7 535.41 0.0019
X1 X2 6.471·10−7 1 6.471·10−7 575.58 0.0017
X1 X3 4.076·10−7 1 4.076·10−7 362.55 0.0027
X2 X3 2.777·10−7 1 2.777·10−7 247.01 0.0040
Residual 2.249·10−9 2 1.124·10−9
Cor Total 0.0000 9
Considering the experimental results, drug diffusion was characterized by diffusion

coefficients between 0.252·10−5 cm2 /s and 2.56·10−5 cm2 /s, values that are closely related
to the main effects described in the regression analysis. It was observed in this case that the
oleic acid (%)—X1 term had a negative effect on drug diffusion, while the X2 term and the
three double interactions were significant for a positive effect on drug release.
A graphical interpretation for the statistical analysis and significant factors affecting
drug diffusion (Y3 ) response was implemented using surface plots, as can be seen in
Figure 18—cases (a)–(e). In the first case (a), where the X3 term was fixed at a minimum
level, the MCZ diffusion coefficient reached a maximum point as long as the oil phase
(%)—X1 term was fixed at 5%. A decline can be well observed when the X1 term tended
to vary through 10%, without any interference from the X2 term. The contour lines were
oriented in a perpendicular manner on the X1 axis.
The situation was changed in the case (b). After setting the X3 term at 20%, the
profile of the response surface was changed, and a slight decrease in drug diffusion and
a dependence of the response upon the two factors can be noticed. Thus, in this case, the
contour lines are oriented through both the X1 and X2 axis. At the minimum concentration
of the oil phase and the maximum level of Tween 20, diffusion coefficients are magnified,
determining a rapid release. Intermediary results were obtained when variating the oil
content through middle values and keeping surfactant content at the maximum level. In
contrast, an opposite effect is achieved when a higher oil phase is stabilized with a lower
concentration of the stabilizer.
Furthermore, in Figure 18—cases (c)–(e), the variation of the diffusion coefficient was
observed from a different perspective, taking into consideration the primary axis for X2
and X3 terms dynamics under the effect of progressive modification of the oil phase (%)
from 5% to 10%. The profiles preserved a similar allure as previously presented over the
statistical analysis for the Y2 response.
For final appreciations, interaction plots were represented in Figure 19—cases (a)–(c)
to emphasize the particular effects of the formulation factors with an accent on the X1 X2
effect on drug diffusion when X3 was selected on the 10–20% interval. At any variation of
PEG 400, drug diffusion was delayed when oleic acid (%) increased to 10%. A favorable
dynamic was seen when PEG 400 was fixed at 20%. Tween 20 40% may interfere in drug
diffusion, obtaining intermediary values of D (cm2 /s), even at higher levels of the oil phase.
Figure 18. Surface plots for diffusion coefficient (Y3 ) as a function of X1 : Oleic acid (%), X2 : Tween 20
Pharmaceutics 2024, 16, x FOR PEER Figure
REVIEW18. Surface plots for diffusion coefficient (Y3) as a function of X1: Oleic acid (%), X2: Tween
32 of
2040
(%) terms, by fixing PEG 400 concentration at 10%—case (a), 20%—case (b); and as a function of X2 :
(%) terms,
Tween by X
20 (%), fixing PEG 400 concentration at 10%—case (a), 20%—case (b); and as a function of X2:
3 : PEG 400 (%), by fixing oleic acid concentration at 5%—case (c), 7.5%—case (d), and
Tween 20 (%), X3: PEG 400 (%), by fixing oleic acid concentration at 5%—case (c), 7.5%—case (d),
10%—case (e).
and 10%—case (e).
Furthermore, in Figure 18—cases (c)–(e), the variation of the diffusion coefficient was
observed from a different perspective, taking into consideration the primary axis for X2
and X3 terms dynamics under the effect of progressive modification of the oil phase (%)
from 5% to 10%. The profiles preserved a similar allure as previously presented over the
statistical analysis for the Y2 response.
For final appreciations, interaction plots were represented in Figure 19—cases (a)–(c)
to emphasize the particular effects of the formulation factors with an accent on the X1X2
effect on drug diffusion when X3 was selected on the 10–20% interval. At any variation of
PEG 400, drug diffusion was delayed when oleic acid (%) increased to 10%. A favorable
dynamic was seen when PEG 400 was fixed at 20%. Tween 20 40% may interfere in drug
diffusion, obtaining intermediary values of D (cm2/s), even at higher levels of the oil phase.
Figure 19. Interaction plots emphasizing X1 X2 term when X3 term is fixed at 10%—case (a), at
15%—case (b), up to 20% specific for lack of interaction—case (c).
Figure 19. Interaction plots emphasizing X1X2 term when X3 term is fixed at 10%—case (a), at 15%—
caseIt(b),
canupbe
to stated
20% specific for lack
that the of interaction—case
fluid-like ME 1–ME 4(c).
microemulsions prepared with oleic
acid 5% exhibited a higher drug release with maximum diffusion coefficients (1.85·10−5 –
2.56·10It−can
5 cmbe stated
2 /s) that the fluid-like
and adequate adhesionME 1–ME 4 microemulsions
(56.07–89.05 mN/m). Takingprepared with oleic
into consideration
acid
the 5% exhibited
presence of somea higher drugphenomena
instability release withinmaximum diffusion
the case of ME 1, ME coefficients
3, and ME (1.85·10
9 (as
−5–
2.56·10out
pointed
−5 cmin/s)
2
theand adequate adhesion
organoleptic analysis), (56.07–89.05 mN/m).
stability studies Taking
will be into consideration
proposed in future studies the
presence of some instability phenomena in the case of ME 1, ME 3, and
to evidentiate the thermodynamic stability of microemulsions. In the present case, it ME 9 (as pointed
outvalued
was in the organoleptic
the contribution analysis),
of ME stability
2 and MEstudies will be
4 as model proposed
systems that in
canfuture studies
solubilize andto
evidentiate the thermodynamic stability of microemulsions. In the present
enhance miconazole release. Similarly, the two microemulsions are characterized by nano- case, it was
valued
sized the contribution
droplets under 200 nm,of ME 2 andinfluencing
directly ME 4 as model systems
the kinetic that can and
mechanism solubilize and en-
the adhesion
hance miconazole
properties. To promote release. Similarly,
a controlled the two
release andmicroemulsions are characterized
a higher MCZ liberation, the gel-likebyMEnano-
8
sized droplets under 200 nm, directly influencing the kinetic mechanism and the adhesion
properties. To promote a controlled release and a higher MCZ liberation, the gel-like ME
8 microemulsion can satisfy these conditions due to its maximum concentration of PEG
400 20% and Tween 20 40%. Even if the two stabilizers impacted the droplet size by in-
creasing it up to 250 nm, creating gel-like structures became a promising approach for
microemulsion can satisfy these conditions due to its maximum concentration of PEG 400
20% and Tween 20 40%. Even if the two stabilizers impacted the droplet size by increasing
it up to 250 nm, creating gel-like structures became a promising approach for further
research of mucoadhesive systems with tailored physico-chemical and biopharmaceutical
attributes. ME 10 has distinguished itself from the group as a gel-like microemulsion with
a superior consistency index and an average droplet size of 144.36 nm but with a reduced
drug diffusion (3.43·10−6 cm2 /s) and lower adhesion (11.29 mN/m).
Implementation of new factorial designs inspired by the need to develop new plat-
forms for drug delivery by using old molecules with improved therapeutic profiles, like
antifungals. A 23 full factorial design was proposed to optimize PLGA nanoparticles with
itraconazole by analyzing the impact of PLGA, benzyl benzoate, and the drug as formu-
lation factors influencing particle size [123]. The combination of formulation design with
mathematical modeling creates an excellent opportunity to rigorously characterize and
describe the intimate contribution of the formulation factors for pharmaceutical-relevant
attributes like droplet size, work of adhesion, and drug diffusion.
3.12.4. Optimization of Miconazole-Based Microemulsions

Droplet size was a critical parameter influenced by the oil phase (%), PEG 400 (%), and
the interaction between the oil and Tween 20. Minimizing the oil level represents a primary
condition for optimizing the formation of microemulsions with reduced droplet size, rapid
diffusion, and a higher work of adhesion. ME 4 properties were found to agree with these
requirements, being considered a model system.
On the other side, an increase in the oil phase carries itself a better wettability, a good
spreading determined by smaller contact angles, a high stability promoted by the creation
of strong networks specific for gel-like systems, but in reverse, a poor adhesion. The higher
droplet growth delayed the drug diffusion. Response transformation using the square root
algorithm offered consistent information revealing the contribution of the oleic acid for
drug release and the particular interaction between the three excipients.
Starting from the leading-edge systems (ME 4, ME 8, and ME 10) characterized
over the study, a numerical optimization was implemented to find microemulsions with
reduced droplet size, moderate adhesion, and high diffusion coefficient. In this final stage,
the parameters were fixed using constraints to achieve solutions with high desirability
coefficients [124], as seen in Table 14. The solutions obtained using the predictive tool of
the software are presented in Figure 20.
Table 14. Setting parameters according to the findings of the factorial design to implement predictive
optimization.
Trial 1 Trial 2
No. Variables
Goal Limits Goal Limits
1 Oleic acid 7% 5.0–10% in range 5–10%
2 Tween 20 maximize 30–40% in range 30–40%
3 PEG 400 in range 10–20% in range 10–20%
4 R1 (nm) minimize 119.6–250 nm minimize 119.6–250 nm
5 R2 (mN/m) 60 mN/m 11.29–89.05 mN/m 60 mN/m 11.29–89.05 mN/m
6 R3 (cm2 /s) in range 10−5 –2.56·10−5 cm2 /s in range 10−5 –2.56·10−5 cm2 /s
In the first case, for a microemulsion containing oleic acid 6.99%, Tween 20 40%, and
PEG 400 10%, the predicted responses proposed a mean droplet size of 147.53 nm, a work
of adhesion of 49.90 mN/m, and a drug diffusion coefficient of 1.19·10−5 cm2 /s, with a
good desirability of 0.8870.
The second solution obtained over the analysis described a potential microemulsion in
which miconazole can be solubilized using oleic acid 5.55%, with droplets stabilized using
Tween 20 40% and PEG 400 20%. The system is characterized by predictive responses that
Table 14. Setting parameters according to the findings of the factorial design to implement predic-
tive optimization.
Trial 1 Trial 2
Pharmaceutics 2024, 16, 271 No. Variables 32 of 38
Goal Limits Goal Limits
1 Oleic acid 7% 5.0–10% in range 5–10%
2 Tween 20 maximize 30–40% in range 30–40%
assume a mean droplet size of 124.26 nm, a work of adhesion of 60 mN/m, and a maximum
3 PEG
diffusion 400 in of
coefficient range
1.719·10−5 cm10–20%
2 /s, with the bestin range
desirability 10–20%
coefficient of 0.9820.
4 The R1predictive
(nm) minimize 119.6–250 nm minimize 119.6–250
optimization results offered a promising perspective on developing nm
5 R2 (mN/m)platforms
nanostructured 60 mN/mfor drug
11.29–89.05
deliverymN/m 60further
that can be mN/m designed 11.29–89.05 mN/m
to promote anti-
6 Ractivity
fungal 3 (cm2/s) in range
in oral 10−5 – 2.56·10−5 cm2/s
candidiasis. in range 10−5 – 2.56·10−5 cm2/s
Figure 20. Predictive optimization to depict two optimized solutions for microemulsions with
Figure 20. Predictive optimization to depict two optimized solutions for microemulsions with ade-
adequate
quate critical
critical attributes.
attributes.
4. Conclusions
In the first case, for a microemulsion containing oleic acid 6.99%, Tween 20 40%, and
The10%,
PEG 400 present
the study revealed
predicted an open
responses pathway
proposed for the
a mean development
droplet process
size of 147.53 nm, aofwork
new
microemulsions for local therapy in oral candidiasis. In this context, miconazole base
of adhesion of 49.90 mN/m, and a drug diffusion coefficient of 1.19·10 cm /s, with a good
−5 2
was the antifungal model drug studied to improve its solubility and bioavailability using
desirability of 0.8870.
nano-sized microemulsions composed of oleic acid 5–10%, Tween 20 30–40%, and PEG
The second solution obtained over the analysis described a potential microemulsion
400 10–20%. The formulation study was combined with a 23 full factorial design with
in which miconazole can be solubilized using oleic acid 5.55%, with droplets stabilized
two lack of fit points. Designed as fluid and gel-like systems, the microemulsions were
using Tween 20 40% and PEG 400 20%. The system is characterized by predictive re-
characterized from physical and biopharmaceutical perspectives and further researched for
sponses that assume a mean droplet size of 124.26 nm, a work of adhesion of 60 mN/m,
pharmaceutical applications in buccal delivery.
The characterization process was accompanied by statistical analysis, succeeded by
response surface methodology to optimize microemulsions with adequate critical quality
attributes.
The connections found between physical and biopharmaceutical attributes conceived
a promising algorithm to optimize microemulsions that can exhibit a reduced droplet size,
a moderate adhesion up to 60 mN/m, and superior diffusion, being inspired by three
systems resulting from the factorial plan.
A microemulsion containing oleic acid 6.99%, Tween 20 40%, and PEG 400 10%, with
predicted responses quantified as mean droplet size of 147.53 nm, a work of adhesion of
49.90 mN/m, and a drug diffusion coefficient of 1.19·10−5 cm2 /s was a first solution of the
predictive optimization for a model microemulsion.
The second solution obtained over the analysis described a potential fluid microemul-
sion in which miconazole can be solubilized using oleic acid 5.55%, with droplets stabilized
using Tween 20 40% and PEG 400 20%. The system was characterized by predictive re-
sponses that assume a mean droplet size of 124.26 nm, a work of adhesion of 60 mN/m,
and a maximum diffusion coefficient of 1.719·10−5 cm2 /s. The last solution resembled
the model microemulsion described in the research, but in this case, a slight increase in
the oil content can modulate the response parameters to the desired constraints, result-
ing in promising performant systems that may impose their quality attributes in buccal
drug delivery.
Author Contributions: Conceptualization, M.-T.T., L.P. and M.V.G.; methodology, M.-T.T., L.P.,
M.V.G., C.-E.D.-P. and V.A.; software, M.-T.T., L.P. and M.V.G.; validation, M.-T.T., L.P. and M.V.G.;
formal analysis, M.-T.T., L.P., M.V.G. and R.M.P.; investigation, M.-T.T., L.P., M.V.G. and V.A.; re-
sources, L.P., M.V.G., V.A. and C.-E.D.-P.; data curation, M.-T.T., M.V.G. and L.P.; writing—M.-T.T.;
writing—review and editing, M.-T.T., M.V.G. and L.P.; visualization, L.P., M.V.G., V.A., C.-E.D.-P. and
R.M.P.; supervision, L.P. and M.V.G.; project administration, C.-E.D.-P.; funding acquisition, C.-E.D.-P.
All authors have read and agreed to the published version of the manuscript.
Funding: The materials for the development of the experimental studies were supported by the
“Carol Davila” University of Medicine and Pharmacy Bucharest through Contract No. CNFIS-FDI-
2023-F-0708.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: The data presented in this study are available in the article.
Acknowledgments: The authors acknowledge the financial support offered by the “Carol Davila”
University of Medicine and Pharmacy Bucharest through Contract No. CNFIS-FDI-2023-F-0708.
Conflicts of Interest: The authors declare no conflicts of interest. The funder had no role in the design
of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript,
and in the decision to publish the results.
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pharmaceutics

Academic Editors: Hassan Almoazen

Received: 5 January 2024

Pharmaceutics 2024, 16, 271. https://doi.org/10.3390/pharmaceutics16020271 https://www.mdpi.com/journal/pharmaceutics

Year Pharmaceutical Formulation API Content Excipients Observation Ref.

8 Labrasol and PG, used as surfactants and cosurfactants in

2. Materials and Methods

2.2. Solubility Studies for Miconazole

2.3. Screening Study to Design Pseudo-Ternary Phase Diagrams

Oleic Acid (%) Tween 20 (%) 1 PEG 400 (%)

2.5. Organoleptic Analysis

of 1:100. Accurate measurements were performed in triplicate using a VascoKin particle

dHapp = kB T/3πηDapp , (1)

where dHapp represents the hydrodynamic diameter, kB —Boltzmann constant, T—the

2.10. Zeta Potential Analysis

2.11. Rheological Evaluation

2.12. Superficial Analysis

γSG = γSL + γLG cos θ, (3)

Wa = γLG (1 + cos θ), (4)

2.13. In Vitro Release Studies

experimental design consisted of the preparation of 10 formulations equivalent to 10 runs

3. Results and Discussion

Components Excipient Solubility (mg/mL)

3.2. Screening Study to Design Pseudo-Ternary Phase Diagrams

It was concluded that microemulsions could be adequately obtained if the Tween

Table 6. Physico-chemical parameters of the O/W microemulsions determined at 24 ± 0.5 ◦ C (n = 3).

Conductivity Refractive Zaverage D10% D50% D90% Zeta

3.5. Conductivity Determination

3.6. Refractive Index Determination

3.7. Rheological Evaluation

with isopropyl myristate 5.50%, Labrasol 19.80–29.70%, cosurfactant 19.80–29.70% and

Notably, the structural attributes in microemulsions depend upon the composition

3.8. Dynamic Light Scattering Analysis

In the case of the ME 9 and ME 10, intermediate values were obtained.

3.9. Zeta Potential Analysis

3.10. Superficial Analysis

Table 8. Mean values of superficial parameters of the microemulsions determined at 24 ± 0.5 ◦ C.

Parameters Tested through Goniometric Technique

3.11. In Vitro Drug Release

Independent Variables Dependent Variables

3.12.1. Statistical Interpretation for Mean Droplet Size

Pharmaceutics 2024, 16, 271 26 of 38

Y2 = 39.71 − 32.00 X1 − 2.26 X1X3 − 3.34 X2X3 + 3.96 X1X2X3 (9)

Pharmaceutics 2024, 16, 271

Pharmaceutics 2024, 16, x FOR PEER REVIEW 29 of 40

3.12.3. Statistical Interpretation for Diffusion

Sqrt(Y3 ) = 0.0031 − 0.0012 X1 + 0.0003 X2 +0.0003 X1 X2 + 0.0002 X1 X3 + 0.0002 X2 X3 (10)

Table 13. Analysis of Variance results for Y3 response.

Source Sum of Squares Df Mean Square F-Value p-Value

Considering the experimental results, drug diffusion was characterized by diffusion

3.12.4. Optimization of Miconazole-Based Microemulsions

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