Design expert assisted formulation, characterization and optimization of microemulsion based solid lipid nanoparticles of repaglinide

Abstract

Repaglinide, a member of the meglitinide class of drugs, is a new anti-diabetic agent that is utilized as an oral hypoglycemic agent. Using glyceryl monostearate, cetyl palmitate, and tristearin as lipids and poloxamer 188 as a surfactant, repaglinide-loaded solid lipid nanoparticles were created. Solid lipid nanoparticles were prepared utilizing an o/w microemulsion technique, which included the lipids glyceryl monostearate and tristearin, as well as waxes such as cetyl palmitate and the surfactant poloxamer 188. The mean particle size of the solid lipid nanoparticles formed was around 339 nm, with an entrapment efficiency of 82.20%. In-vitro release studies continued to be conducted using the dialysis bag diffusion technique. Within 12 h, the cumulative drug release was 88.4%. The results indicate that repaglinide was released more slowly from solid lipid nanoparticles made from tristearin and glyceryl monostearate in an equal ratio. Tristearin found the controlled release and extreme entrapment from other lipid carriers like glyceryl monostearate and cetyl palmitate. Differential scanning calorimetry demonstrates that repaglinide is entangled in amorphous or molecular state within solid lipid nanoparticles. SEM microscopy revealed that the produced repaglinide solid lipid nanoparticles had a spherical shape. After one month of storage at 2–8 °C, short-term stability testing revealed no significant alteration.

Introduction

Diabetes mellitus is a widespread chronic ailment that has severe life-threatening effects on numerous organs throughout the body. The majority of diabetic patients have type I diabetes mellitus, which is not insulin-dependent. To accomplish this, a variety of oral anti-diabetic drugs are routinely used in the marketplace (Hu et al. 2000). Repaglinide (RPG), a meglitinide-class medication, is a novel oral hypoglycemic agent approved by the FDA. This one induces insulin release from pancreatic beta cells in response to a decrease in fasting glucose levels. The structure, duration of action, binding site, and mode of elimination of RPG are distinct from those of other anti-diabetic agents. RPG is nearly insoluble in water, but is rapidly and completely absorbed from the gastrointestinal tract. Drugs classified as Class II in the Biopharmaceutical Classification System (BCS II) exhibit a distinct behavior following oral administration (El-Houssieny et al. 2010). RPG has poor oral bioavailability (56%) that collectively outcomes an extensive first-pass metabolism. RPG holds an excellent plasma protein binding (> 98%), and its half-life is almost 1 h. RPG having hypoglycemia effect subsequently upon oral administration, such enabling aspect was less over the sulfonyl ureas (Maddiboyina et al. 2015). Such features projected the drug as a worthy candidate to sustain oral delivery preparations (Maddiboyina et al. 2020b). Numerous efforts have been made in this direction, including an interest in a variety of carriers, such as liquid–solid arrangements, gastro-retentive floating delivery systems, and a variety of polymeric nanoparticles (NPs) (Bummer 2004).

The oral route is the most frequently used and desired method of drug distribution, despite the fact that numerous factors such as the pH of the GI tract, residence time, absorption, and solubility can impair the bioavailability of drugs administered through this route (Maddiboyina et al. 2020a). A novel delivery approach, lymphatic delivery, has been developed to circumvent first-pass metabolism during peroral drug administration. The conventional delivery emphasis on permeation enhancers, surface modifiers, prodrug synthesis, complex development, and colloidal lipoidal carriers propagated the use of such for the distribution of drugs toward intestinal lymphatic (O’Driscoll 2002). In the current decades, solid lipid nanoparticles (SLNPs) keep fascinated by diverse considerations as nanotechnology-based drug delivery systems (Yadav Maddina et al. 2016). Their inherent advantages, such as the possibility of spatial and temporal controlled release, the sustainable combination of lipophilic and hydrophilic drugs, superior biocompatibility and low biotoxicity, the ability to avoid organic solvents during the fabrication cycle, and ease of scale-up, have been bolstered by numerous research projects, thereby increasing the likelihood of such nanostructures being used in drug delivery (Bargoni et al. 1998).

The two major components of preparations based on SLNPs were lipid and surfactant/stabilizer. Surfactants reduce the interfacial tension between the hydrophobic surface of the lipid core and the aqueous medium, thereby further alleviating the SLNPs association. SLNPs are also an excellent carrier for the efficient delivery of peptides (insulin) and anticancer agents (doxorubicin) through the oral intestinal route (Zhang et al. 2009). SLNPs increase the drug’s lymphatic transport, decrease its hepatic first-pass metabolism, and increase its bioavailability. Due to the fact that the intestinal lymph vessel trenches directly into the thoracic duct, auxiliary into the venous blood, and finally transient into the portal circulation (Bargoni et al. 1998). SLNPs contain a lipid core, simulating the formation of chylomicrons. This eventually transports the carrier laterally through the co-entrapped drug, ensuring the transcellular lipid absorption mechanism (Müller et al. 2000).

Materials and methods

Materials

Repaglinide received as a gift sample from Biocon Ltd., Bangalore, India; glyceryl monostearate (GMS) & cetyl palmitate (CP) from SARC Research Lab, Hyderabad; Polaxamer 188 and Tristearin (TS) as a gift sample from research labs.

Methods

Preparation of RPG solid lipid nanoparticles

SLNPs were prepared using an o/w microemulsion technique, which included the lipids glyceryl monostearate (monoglyceride) and tristearin (triglyceride), as well as waxes such as cetyl palmitate and the surfactant poloxamer 188. The microemulsion is diluted using a two-phase system composed of an inner and outer phase in this method. To begin, glyceryl monostearate was heated to its melting point, followed by the addition of tristearin and cetyl palmitate to form an internal phase mixture. In the second step, repaglinide was incrementally added to the melted mixture; an aqueous solution of poloxamer 188 was used to further achieve a perfect microemulsion at 70  °C (± 1 °C). SLNPs were prepared by dispersing the warm o/w microemulsion in cold water (2–3 °C) beneath mechanical stirring (Omwoyo et al. 2014). The above obtained was added dropwise, retained at 7 °C to ice-cold water (2–3 °C) with constant stirring (IKA-Ultra Turrax T25 USA) for 1 h to form SLNPs. The cold water assists in rapid lipid crystallization and restricts lipid accumulation. The samples were sonicated and examined for particle size. The compositions of various preparations were revealed in Table 1. Formulations were design in three sets in which variation of concentrations of lipid carrier from 1.5 to 5% at constant speed (11,000 rpm) and constant surfactants concentration (1% w/v), variation of speed from 9000–13000 rpm at constant lipid concentration (2.5% w/v).

Table 1.

Composition of repaglinide-loaded SLNPs

Formulation codes	Repaglinide (mg)	Glyceryl monostearate (%)	Cetyl palmitate (%)	Tristearin (%)	Poloxamer 188 (%)	Rotation speed (rpm)
Effect of glyceryl monostearate: tristearin ratio
F1	10	1.5	5.0	3.5	1	11,000
F2	10	2.5	5.0	2.5	1	11,000
F3	10	3.5	5.0	1.5	1	11,000
Effect of surfactant concentration
F4	10	2.5	5.0	2.5	0.5	11,000
F2	10	2.5	5.0	2.5	1	11,000
F5	10	2.5	5.0	2.5	1.5	11,000
Effect of rotation speed
F6	10	2.5	5.0	2.5	1	9000
F2	10	2.5	5.0	2.5	1	11,000
F7	10	2.5	5.0	2.5	1	13,000

Compatibility studies of repaglinide and lipids

Fourier Transform Infrared Spectrometry (FTIR): Around 300 mg of KBr was balanced and ground into a fine powder, and previously, approximately 1 mg of the sample (repaglinide, lipids, and mixture) was included and pulverized sufficiently to blend the sample through the KBr and then press that KBr blender to achieve a palate using an IR press at an 8-tons pressure (Balaji M et al. 2020).

Differential Scanning Calorimetry (DSC): The DSC examination confirmed the physical structure of the SLNPs’ inherent medicine. A typical aluminum pan was used to position and wrap the sample, and the temperature was scanned between 25 and 300 °C at a rate of 10 °C/min in a nitrogen atmosphere. A blank aluminum pan was used as a reference (Sood et al. 2020).

Characterization of the repaglinide SLNPs

Particle size analysis: the fabricated SLNPs were examined by a Nanotrac-150 particle size analyzer, and the suspension for evaluation is steadily taken in a cell (Singh et al. 2015). The combined light passes through a fiber-optic cable to a single detector, and radical electronics and software investigate the signals to compute the Doppler shifts conforming to particle size (Hu et al. 2002).

Shape and surface morphology: the RPG-loaded SLNPs were ascertained through a scanning electron microscope (SEM- Jeol, JSM-6100). Trial drip stood stacked on adhesive tape that was plunged on an aluminum stub. It was coated through gold employing a sputter coater, and photographs of the instance stood obtained for shape and surface morphology (Singh et al. 2016).

Entrapment Efficiency (EE): the EE of RPG SLNPs was studied with the bulk equilibrium reverse dialysis bag method. Concisely, a dialysis bag (cellulose membrane, molecular weight cut-off 12,000 Da) encompassing 1 mL of distilled water through 25 mg PL-188 was retained straight to 10 mL of nanosuspension (Hu et al. 2002). Subsequently, the equilibrium (12 h) dialysis bag was withdrawn after the nanosuspension. The sample composed from the dialysis bag was evaluated by UV technique at 247 nm.

In-vitro release study: Studies were accomplished employing dialysis bag diffusion practice. Dialysis membrane (molecular weight—12,000 Da) was drenched in double-distilled water for 12 h before the approach for assessment (Kotla et al. 2016). RPG nanosuspension corresponding to 5 mg (containing an equivalent to 5 mg of drug) retained in the dialysis bag encompassing 100 mL of dissolution medium at 37 ± 0.5 °C through constant magnetic stirring at 200 rpm. At fixed time intervals, the tests were withdrawn and examined spectrophotometrically at 247 nm (Ebrahimi et al. 2015).

In-vitro drug release kinetics: diverse kinetic model explicit as zero order, first order, Higuchi model and Korsmeyer-Peppas model were practical to read the drug release kinetics after the preparations. The best-fit model was categorical based on the utmost regression standards for correlation coefficient for practices (Hu et al. 2002). The release rate and procedure of drug release after organized SLNPs was examined by fitting the release data into,

Zero-order equation,

Q = K₀ t, where Q is the amount of drug released at time, t and K₀ is the release rate constant.

First-order equation

Log Q = K₁ t, where Q is the % of drug release at time, t and K₁ is the release rate constant.

Higuchi’s equation

Q = K₂ t ½, where Q is the percentage of drug released at time t and K₂ is the diffusion rate constant.

Peppa’s equation

M_t/M_∞ = Ktn, where M_t/M_∞ is the fractional release of the drug, t is the release time, K is a constant comprising structural and geometric typical of the release device, ‘n’ is the release exponent designatory of the mechanism of distribution. For non-Fickian (anomalous/zero order) release, n value remains middle amid two points 0.5–1.0; for Fickian diffusion, n < 0.5; for zero-order release, n = 1; n is predicted from linear regression of log (M_t/M_∞) Vs log t.

Permeability study: the permeability study of F2 preparation was precisely studied for an extant exploration (Rohit and Pal 2013). The release study of a drug by dialysis membrane was investigated when the membrane dipped in double-distilled water. The study was carried out after 12, 24, and 36 h with the expunged stomach/ intestinal mucosal membrane (Neelam et al. 2010).

Stability studies: the short-term stability of the SLNPs were studied beneath 2–8 °C. The SLNPs stood assessed at 2–8 °C for 4 weeks. The trials were ascertained for particle size analysis, EE, and in vitro release studies (Cavalli et al. 2002).

Ex-vivo study: the practice remains as an adaptation of the Barr and Riegelman pattern. At first, a segment of the intestine was detached from the goat and washed through Krebs–Ringer bicarbonate solution, pH 7.4. The lumen was inverted with a glass rod and a tube was inserted in one side of the intestine and tied securely with tape. The other side of the intestine was tied and 3 mL of Krebs–Ringer bicarbonate solution and 5 mL nanosuspension was poured through the hypodermic needle in the tube. The lumen of the intestine was engaged in a medium of 1.2 pH Hcl buffer for 2 h and phosphate buffer solution pH 7.4 at 37 °C. Samples have been withdrawn and examined spectrophotometrically (Barr and Riegelman 1970).

Experimental design

A factorial design perspective stood practical to exploit the investigational adeptness necessitating a minimum of explores to enhance the SLNPs production. The impact of the glyceryl monostearate, tristearin, and poloxamer 188 on repaglinide SLNPs was assessed by employing a 2³ factorial design consisting of 3 variables that is at 2-levels each. The dependent variables were mean particle size (Z-Ave) and entrapment efficiency (%EE). Table 2 represents the respective aspect. The lesser and greater standards were embodied by actual and coded values correspondingly. The levels were preferred on the source of the verified lesser and greater measures for the respective variable, conferring to pre-formulation modifications. The data is examined by Design Expert Software^® 12.0.1 Trial Version. The SLNPs dispersion stood subjectively formed. Analyses of statistical variance test (ANOVA) were achieved for respective reaction constraint in imperative to recognize the consequence of the properties and interfaces. A p-value < 0.1 stood deliberated statistically substantial. Design-Expert Software Version 12.0.1 was used to explore data and create contour plots and 3D response surface plots. The polynomial equation developed in the experimental design (1) was specified as

$$y0=b0+b1X1+b2X2+b3X3+b12X12+b13X13+b23X2X3+b11X2X1\dots .$$ 1

Y₀ is the dependent variable; b0 is the intercept; b1–b33 stands the regression coefficients calculated after the practical assessment measures of Y.

Table 2.

Variables in Box-Behnken design

Factor	Levels Used, Actual (Coded)
	Low (− 1)		High (1)
Independent variables
X1 = Glyceryl monosterate (%W/W)	1.5 (− 1) 1.5 (− 1) 0.5 (− 1)		3.5 (1)
X2 = Tristearin (%W/W)			3.5 (1)
X3 = Polaxamer 188			1.5 (1)
Dependent variables (%W/W)			Constraints
Y1 = Particle size (Z nm)			Minimize
Y2 = Entrapment efficiency (%)			Maximize

Results and discussion

Compatibility studies of repaglinide and lipids

FTIR: repaglinide and lipids compatibility observations recognized employing IR-200 (FTIR) to find the probable interaction in the preparations. It was identified that there was no potential interaction among the repaglinide and lipid carrier in their distinct form as shown in Fig. 1, and in combination when it was kept for three months in different conditions.

Fig. 1.

FTIR spectra for compatibility study of a repaglinide, b glyceryl monosterate, c tristearin, d cetyl palmitate, e (a) + (b) + (c) + (d)

The peaks for all functional groups such as C–H aliphatic stretching, C=O acid stretching, C=O amide/ester stretching, C=C aromatic stretching, and N–H/–N 2⁰ amide ammonium groups of repaglinide were observed at 2935 cm⁻¹, 1689 cm⁻¹, 1636 cm⁻¹, 1563 & 1494 cm⁻¹, 3308 cm⁻¹ respectively as displayed in Fig. 1a. The IR spectrum of glyceryl monostearate exhibited peaks at three positions that are 3435, 2919 and 2850 cm⁻¹. These peaks are due to –CH₂– stretching vibrations. The carboxyl group (C=O) stretching peak is observed at 1735 cm⁻¹ as represented in Fig. 1b. The IR spectrum of tristearin exhibited peaks at two positions that are 2920 and 2851 cm⁻¹. The vibrational band at 1737 cm⁻¹ indicates the presence of tristearin (C=O stretch) as exhibited in Fig. 1c. The FTIR spectrum of cetyl palmitate in the range of 1184–1475 cm⁻¹ gave many sharp peaks due to bending vibration of CH₃, CH₂ and CH bonds in the range of 1350–1470 cm⁻¹ and stretching vibration of a single C-O bond at 1210–1320 cm⁻¹ as shown in Fig. 1d. It was observed from the FTIR spectra of physical mixture as shown in Fig. 1e, that all peaks of the functional groups such as C–H aliphatic stretching, C=O acid stretching, C=O amide/ester stretching, C=C Aromatic stretching, and N–H/–N 2⁰ amide ammonium groups of repaglinide were observed. Thus results indicate that no interaction was observed between the repaglinide and the excipient in the physical mixture.

DSC: it is a qualitative investigative tool for evaluating the interactions. The DSC thermogram of repaglinide shows an endothermic peak at 138.99 °C, which confirmed a crystalline structure. Mixture showed very small melting peak for repaglinide around 135.7 °C but which was not significant. This suggests that repaglinide sustained its endothermic peak characteristic of its sharp melting point, presenting that there was no physical interactions. The pure form and the combinations are studied after three-month storage at different conditions. It was found that the thermal peaks are identical in combination with those entire lipid carriers. The result sign-post that there is no interaction, as displayed from Fig. 2.

Fig. 2.

DSC spectra of A Repaglinide, B RPG + GMS + CP + TS

Characterization of the repaglinide SLNPs

Particle size analysis: the formulations with GMS and TS revealed an extensive distribution in particle size extending from 339 to 494 nm, as displayed in Table 3. It suggests that the size reduction was with variable speeds and size will increase with various lipid carrier concentrations.

Table 3.

Particle size and Drug Entrapment Efficiency of RPG SLNPs

Formulation codes	Particle size (nm)	Entrapment efficiency %
Effect of glyceryl monostearate: Tristearin ratio
F1	410 ± 7.51	72.00 ± 1.01
F2	339 ± 5.35	82.20 ± 0.85
F3	438 ± 3.76	78.25 ± 1.36
Effect of surfactant concentration
F4	456 ± 3.32	61.66 ± 1.24
F2	339 ± 4.15	82.20 ± 0.85
F5	494 ± 5.31	74.38 ± 1.15
Effect of rotation speed
F6	466 ± 3.26	67.60 ± 1.05
F2	339 ± 4.36	82.20 ± 0.85
F7	405 ± 3.51	74.84 ± 0.70

n = 3; data are expressed as mean ± SD

The decrease in particle size as the stirring rate increases can be explained by the intensification of micromixing (i.e., molecular mixing) between the multi-phases. Micromixing efficiency increased mass transfer and diffusion rate between the multiphase, resulting in rapid homogeneous supersaturation and hence rapid nucleation to create smaller drug particles, as illustrated in Fig. 3. Thus, a higher stirring rate is preferable in the development of smaller and more uniform drug particles (Mukherjee et al. 2009).

Fig. 3.

Particle size range of Repaglinide SLNPs of different formulations

Shape and surface morphology: this is executed to study the surface morphology of the particles, although the particles were profusely found and were spherical, as displayed in Fig. 4. Thus, the surfactant that we use produced better surface characteristics. The surface characteristics of the SLNPs acquired no longer were improved employing the type of lipid carrier, the concentration of lipid carrier, and speed.

Fig. 4.

SEM image of F2, F5 and F7 formulations

Entrapment efficiency: EE ranging from 61.66 to 82.20%, as displayed in Table 3. It has to be noticed that during the cooling process, the lipid solidifies and the drug is distributed into the shell of the particles, if the concentration of the drug in the melted lipid is well below its saturation solubility. A drug-enriched core model is formed when the drug in the melted lipid is closed to its saturation solubility. The cooling process leads to supersaturation of the drug and subsequently to drug crystallization prior to lipid crystallization. The little bit reduction in entrapment efficiencies was observed with the varying speed. The higher entrapment efficiency with tristearine is attributed to the high hydrophobicity due to the long-chain fatty acids attached to the triglyceride resulting in increased accommodation of lipophilic drugs (Kumar et al. 2007).

In-vitro drug release studies: The release studies of SLNPs were performed for 12 h as displayed in Table 4 and graphically portrayed as % CDR v/s time profile Fig. 5. The release rate of RPG is dependent on the total concentration of lipid carrier in the formulation. RPG is released more rapidly after utilizing lower lipid concentrations because of the drug-enriched shell model suggested for these particles. Due to the excellent drug loading in formulations with higher lipid concentrations, diffusion can be slightly declined and controlled the release rate. There was no predominant effect of speed over the release rate profile. Among the lipid carriers, tristearate had revealed a gentle release over GMS and CP, which can be ascribed towards the hydrophobic long-chain fatty acids of the triglyceride that retain lipophilic drugs.

Table 4.

In-vitro drug release study of RPG SLNPs

Cumulative drug release (CDR) (%)
F. code	Time
	2 h	4 h	6 h	8 h	10 h	12 h
F1	19.2 ± 0.3	29.8 ± 0.2	40.2 ± 1.9	55.2 ± 0.5	63.2 ± 0.6	76.4 ± 0.7
F2	24.4 ± 0.3	34.5 ± 0.9	48.9 ± 0.4	63.0 ± 0.8	76.5 ± 0.9	88.4 ± 0.3
F3	27.1 ± 0.3	30.0 ± 0.3	43.3 ± 0.6	59.5 ± 0.5	71.7 ± 0.4	82.3 ± 0.6
F4	18.1 ± 0.2	29.0 ± 0.6	39.3 ± 0.1	47.5 ± 0.6	64.7 ± 0.7	76.5 ± 0.4
F5	21.2 ± 0.9	32.6 ± 0.8	41.3 ± 0.6	51.2 ± 0.3	68.0 ± 1.4	83.1 ± 0.8
F6	21.1 ± 0.4	30.5 ± 0.6	42.3 ± 0.4	52.9 ± 0.9	66.7 ± 0.6	78.5 ± 0.9
F7	24.6 ± 0.1	33.2 ± 0.8	45.8 ± 0.3	56.9 ± 1.4	72.6 ± 0.4	85.8 ± 0.3

Mean ± SD (n = 3)

Fig. 5.

In-vitro drug release of a effect of glyceryl monostearate: tristearin ratio; b effect of surfactant concentration; c effect of rotation speed

The graphical depictions spectacle that release of drugs from the formulations is biphasic. The burst release was reasonable at the initial 1–2 h (all formulations) and released nearly 20–25% of the drug from the SLNPs. Subsequently, a prolonged-release was acquired and released 5–8% of the drug from the SLNPs at each hour. The enormous surface of nanoparticles and particular drug adsorbed on the surface of nanoparticles or precipitated in the lipid matrix surface. The dissolution profile of the SLNPs revealed an increase in drug release in the preliminary stage. By the advanced stage, drug release was constant and slow, representing that the drug release rate was determined by diffusion of the drug from the rigid matrix structure.

In-vitro drug release kinetics: RPG’s release rate depends on the total concentration of lipid carrier in the formulation, as shown in Table 5. RPG is further released after using lower lipid concentrations because the drug-enriched shell model projected for these particles is Fickian diffusion. Due to the extensive drug loading in formulations with developed lipid concentrations, diffusion can decline and control the release rate. There was no principal effect of rapidity over the release rate profile. Tristearine and glyceryl monostearate had revealed good release that can be ascribed towards the triglyceride’s hydrophobic long-chain fatty acids, which retain lipophilic drugs.

Table 5.

Kinetic data of various models for release study

Code	Zero-order		First-order		Matrix		Peppas			Hixon Crowell	Best fitting model
	R	k	R	k	R	k	R	k	n	R
F1	0.7066	0.0084	0.7067	− 0.0001	0.9677	0.0255	0.9958	0.0399	0.2855	0.7067	Peppas
F2	0.7336	0.0081	0.7338	− 0.0001	0.9740	0.0248	0.9955	0.0373	0.3000	0.7337	Peppas
F3	0.6770	0.0077	0.6771	− 0.0001	0.9586	0.0236	0.9828	0.0390	0.2550	0.6771	Peppas
F4	0.6705	0.0077	0.6706	− 0.0001	0.9575	0.0234	0.9866	0.0388	0.2533	0.6706	Peppas
F5	0.7090	0.0086	0.7091	− 0.0001	0.9674	0.0263	0.9908	0.0414	0.2783	0.7091	Peppas
F6	0.7205	0.0092	0.7207	− 0.0001	0.9715	0.0280	0.9981	0.0426	0.2950	0.7206	Peppas
F7	0.7358	0.0088	0.7359	− 0.0001	0.9707	0.0267	0.9749	0.0414	0.2850	0.7259	Peppas

Where R represents correlation coefficient and k represents release constant in Zero Order

R represents correlation coefficient and k represents release constant in First Order

R represents correlation coefficient and k represents release constant in matrix

R represents correlation coefficient, k represents release constant, and n is the diffusional exponent in Peppas

R represents Correlation coefficient in Hixon Crowell

Permeability study: the release study of a drug by dialysis membrane was explored when the membrane dipped in double-distilled water. It was carried out after 12, 24, 36 h and goat intestine. The result shows that the membrane after 12 h 77.81%, after 24 h 77.11%, after 36 h 80.09% and from ex-vivo using the goat intestine, is 71.84%. From the result, it is evident that after 12 h and 24 h are given the almost identical effect, and subsequently, 36 h, the release design of the drug marginally increases. When the same study is performed using the goat intestine, the release of the drug decreases slightly.

Stability studies: the selected formulations were stored at 2–8 °C for a month, as exhibited in Table 6. There was no substantial variation of particle size, EE, and In-vitro drug release. Graphical representation of a release profile with model fitting data analysis shows that the system follows the Peppas model after one month of stability study in formulation F2. The excellent stability indicated a decline from the slow transition of lipid in SLNPs and small particle size.

Table 6.

Stability studies of F2 formulation

Code	Particle size (nm)		Entrapment efficiency (%)		Cumulative drug release (%)
	Before storage	After storage	Before storage	After storage	Before storage	After storage
F2	339 ± 3.45	342 ± 2.50	82.20 ± 0.65	81.12 ± 0.90	88.4 ± 0.4	87.2 ± 0.7

Mean ± SD (n = 3)

Ex-vivo study: the optimized formulation and free drug solution were indicated for ex-vivo research. Results show that the free drug solution of RPG diffuses fast from the intestinal membrane contrast with SLNP (F2), as displayed in Tables 7 and 8. This is because, in a free drug solution, the drug is merely accessible for diffusion through the membrane, whereas in optimizing SLNP (F2), the drug is entrapped in a lipid matrix.

Table 7.

Ex-vivo drug release profile of free drug solution

Time (h)	Abs	Conc. (mcg/mL)	Drug release (mg)	CLA (mg)	Cum drug release (mg)	Cum drug release (%)	CDR retained (%)
1	0.218	9.2173	1.8434	0.00	1.8434	36.869	63.130
2	0.401	17.1739	3.4347	0.034	3.4691	69.382	30.617
3	0.536	23.0434	4.6086	0.046	4.6547	93.095	6.904

Table 8.

Ex-vivo drug release profile of F2

Cumulative drug release (CDR) (%)
F. Code	Time
	2 h	4 h	6 h	8 h	10 h	12 h
F2	20.902	36.711	44.264	51.993	57.262	64.815

Model fitting to data: the 2³ full factorial designs are practiced for statistically enhance the processing constraints and utilized to probe the quadratic response surfaces and for fabricating second-order polynomial model using Design-Expert software (Version 12.0.1, Stat-Ease Inc., Minneapolis, MN). A total of 8 runs were generated. The variety of independent variables beneath study and responses was represented in Table 9 laterally with their small and significant levels.

Table 9.

Experimental runs and observed values for 2³ full factorial design

Formulation code	Actual values			Measured responses
	X1	X2	X3	Y1	Y2
NF1	3.5	1.5	1.5	362	78
NF2	1.5	3.5	1.5	354	80
NF3	1.5	3.5	0.5	430	70
NF4	1.5	1.5	1.5	380	74
NF5	3.5	1.5	0.5	460	65
NF6	3.5	3.5	1.5	339	82
NF7	3.5	3.5	0.5	426	71
NF8	1.5	1.5	0.5	470	61

The statistical rationality of the polynomials in the Design-Expert program was developed based on the variance analysis (ANOVA) provision. The significance level was considered to be < 0.05 at > F. The best-fitting mathematical model is preferred over numerous statistical constraints, comprising the coefficient of variation (CV). The multiple correlation coefficient (R²), the adjusted multiple correlation coefficient (adjusted R²), and the software-provided residual sum of squares (PRESS). PRESS signposts just how fine the model fits the data and intended for the preferred model. It ought to be insignificant effective into the added model beneath the examination.

The 3D response surface graphs and the 2-D contour plot were similarly produced via the Design Expert^® software.

Effect on the particle size of formulation: the subsequent equivalence (2) was proposed through the model for the particle size of (Y1) formulation

$$Y1=314.53-5.44A-9.44B-21.00C+1.12AB-1.19AC+1.56BC$$ 2

Y1 is the particle size preparation. Among the independent factors, a combination of X1, X2, and X2, X3 has an advanced positive outcome on the particle size and was apparent from the authentic great positive assessment on its coefficient. The negative coefficient for independent factors revealed that the particle size of the preparations was amplified at greater concentrations. The interaction among the independent factors is similarly instituted to be significant. Inclusive, the model are significant (F-value = 2622.33; p < 0.0149). Figure 6 the contour plots and its 3D response plots such spectacle the influence of diverse independent factors on the formulation.

Fig. 6.

A the effects of X2 and X3 on particle size at the mid-level of X1; counter-plot B the effects of X1 and X3 on entrapment efficiency at the mid-level of X2; counter-plot

Effect on entrapment efficiency of formulation: the ensuing equivalence (3) was anticipated by the model for entrapment efficiency of (Y2) preparation

$$Y2=84.59-0.0625A+9.375B+2.31C+0.6250AB+0.06251.19AC-0.3125BC$$ 3

Among the independent factors, X2 and X3 were practical to necessitate a vividly greater positive effect for the EE of preparations and was apparent after the precise great positive assessment on its coefficient. The negative coefficient on the concentration of X1 indicated that the EE of preparations declined at greater concentrations. The interaction among the independent factors is also instituted to be significant. Inclusive, the model are significant (F-value = 501.00; p < 0.0342). The R-squared assessment was in an adequate range. Figure 6 signifies the contour plots and their 3D response plots, showing the impact of diverse independent factors on EE.

Evaluation and validation of the optimized formulation: a numerical optimization practice with the desirability technique was used to combine all of the above responses with various goals. The optimal preparation was achieved by imposing constraints on variable dependent responses and independent variables. The suggested concentrations of the independent variables were designed from the above plots by the Design Expert Program, which has desirability close to 0.1. Subsequently, the practice was enhanced when causing the model polynomial equations to concern the dependent and independent variables. By this model, the ultimate optimal investigational constraints are determined those are glyceryl monostearate (X1) 3.12%, tristearin (X2) 3.40%, poloxamer 188 (X3) 0.8%, which makes the compromise between different responses and explorations for a permutation of factor rates such together optimize a collection of reactions by meeting the necessities for the respective answer in the group.

Conclusion

In this study, we demonstrated that the microemulsion approach can be utilized to successfully produce repaglinide-loaded solid lipid nanoparticles, which can be used to increase the effectiveness of the treatment while reducing the side effects associated with the dose. A variety of variables, including surfactant concentrations, lipid carrier concentrations, and the speed at which the particles were formed, were used to create these solid lipid nanoparticles. A decrease in the concentration of lipid carriers is associated with an increase in the size of the particles. The reduction in size is achieved by increasing the pace at which the stirring is performed. Following the in vitro release and stability information, it was discovered that the drug release is closely controlled and that solid lipid nanoparticles have a high degree of physical stability in their natural environments. Other lipid carriers such as glyceryl monostearate and cetyl palmitate, among others, were found to have a regulated release and excessive trapping of tristearin, which was proven to have both properties. To a certain extent, this can be linked to the presence of hydrophobic long-chain fatty acids in the triglycerides, which aid in the retention of lipophilic drugs while also prolonging their duration of presence in the body. When compared to nanoparticle formulations, the free drug solution of repaglinide diffuses rapidly from the intestinal barrier, according to the results of the ex-vivo evaluation. This study investigated the in vitro release of a lipophilic drug by employing a factorial design to determine the effect of preparation limitations such as surfactant concentration, lipid carrier concentration, and process parameters such as stirring speed, on the efficacy of encapsulation and the in vitro release.

Abbreviations

Repaglinide

RPG

SLNPs

Solid lipid nanoparticles

DSC

Differential scanning calorimetry

FTIR

Fourier transform infrared spectroscopy

CP

Cetyl palmitate

BCS

Biopharmaceutical classification system

TS

Tristearin

NP

Nanoparticles

ANOVA

Analysis of variance

PVA

Polyvinyl alcohol

PRESS

Provided residual sum of squares

CDR

Cumulative drug release

SESD

Solvent emulsification solvent diffusion

CV

Coefficient of variation

R²

Correlation coefficient

EE

Entrapment efficiency

GMS

Glyceryl monostearate

HPLC

High-performance liquid chromatography

UV

UV–visible spectroscopy

PDI

Polydispersity index

SEM

Scanning electron microscopy

ICH

International Council on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use

Funding

The authors did not receive support from any organization for the submitted work.

Declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.