1. INTRODUCTION:

Today for drug delivery systems(DDS) choosing the novel drug delivery system is the most promising tool (NDDS) where nano-technology isthe best toolfor controlling particle size, there are the two safest polymers as per FDA approval that isPLGA and PVA for the preparation of polymeric nanoparticlesthat the NPnanoparticle matrixcarrier’srole in new drug level over conventional method can provide better and more effective^1,2.

The active constituent of aloe vera is aloin, which belongs to the family named Liliaceae, the color of aloin is yellow and in the form of crystalline powder as per the USP and published inthe previous data (literature). Barbaloin is also another constituent of aloe Vera^3,4.

The use of the plant aloe vera is not known previously but it has a lot of medicinal propertiesIt can be widely used fortumors, and burnsand is a laxativeand bittering agent in the food industry, and also in many cosmetics industriesan anti-inflammatory,and a cure forthe skin^5,6. Recently itattracted researchers for showing some positiveroles in thetreatment/cure of cancer, cardiovascular diseases and diabetes, antioxidants, wound-healing activities, and the ability to promote radiation damage^7,8. The phytochemical comes under the category of anthraquinone phytochemical, it hasan anthrone glycoside activity andis found inthe form of naturally as a mixture of two diastereomers^9,10.

The work aims to investigate aloin-loaded nanoparticles (NP) to achieve and formulate a controlled release drug system (CRDS) by using the nanoprecipitation process, which was used to characterize the formulation's size, shape, drug encasing, and medication release¹¹. The study of aloin nanoparticles also examined several factors, including medication dosage, polymer content, sonication time, organic phase to aqueous phase volume ratio, and surfactant content¹². The dialysis bag method and another method of kinetic models were investigated to study drug aloinrelease parameters as calledin vitroNP release data^13,14. The goal of this experiment is to provide a scientific justification for the influence or determination of many factors that regulate the size of nanoparticles.

2. MATERIAL AND PROCEDURE OF ALOIN:

2.1 Material:

Aloin (98%)wasthe drug, Polyvinyl alcoholwas taken as a stabilizer, PLGA was taken as a Polymer, and dialysis bags (cut-off 12kDa) were applicable for the release method. All othersolvents andingredients used were of analytical grade.

2.2 Preparation of drug Aloin nanoparticles:

The nanoprecipitation process was used to preparealoin-loaded PLGA nanoparticles¹⁵. This procedure involved preparing the organic and aqueous phases separately by first dissolving the necessary quantity of drug and polymer in an appropriate organic solvent (methanol) ^16,17. Prepare an aqueous surfactant solution (PVA) of the desired concentration, add 10–20ml of the surfactant solution in a beaker, and stir overnight while the organic phase is added dropwise^18,19. After the organic solvent has evaporated, collect the nanoparticles NP using the centrifugation and lyophilization methods, then characterizethem for size and entrapment^20,21.

2.3 Characterization of nanoparticles:

2.3.1 Determination of aloin's entrapment:

The percentage of aloin drug in the produced nanoparticles NP in contrast to the original quantity of aloin drug employed to produce the nanoparticles is known as drug entrapment efficiency (E.E.) measure in percentage (%) [Error! Reference source not found.]. The following equation was used in conjunction with spectroscopy which was UV spectroscopy stated above to determine this calculation (Eq. 2). E.E % = Amount of drug in nanoparticle/Initial amount of drug*100

2.3.2 Zeta potential and particle size:

On a zeta sizer, the drug-loaded nanoparticles' zeta potential and particle size were assessed (Malvern Instruments)²³.

2.4 Invitro release profile:

Examine the in-vitro drug release of formulations including aloin-loaded nanoparticles formulation utilizing the dialysis bag diffusion technique^24,25. A beaker keeping 100ml of pH 7.4 phosphate buffer was used to store the drug-loaded aloin nanoparticles in the dialysis bag. By placing the beaker over a magnetic stirrer, the assembly's temperature was maintained/continuedat 37°C throughout the experiment. The flow rate of the experiment was at 100rpm maintained. Samples (2ml) were removed and replaced at predetermined intervals with an equivalent vol. of fresh pH 7.4 phosphate buffer^26,27. After the necessary dilutions, a UV-visible spectrophotometer was used to analyze the samples at 276nm. To define the release kinetics and interpret the in-vitro drug data, many kinetic models were used.

To define the release kinetics and interpret the in-vitro drug release data, several kinetic models were used.The zero-order rate equation (2) can be used the systems in which the drug's concentration has no impact on how quickly it is released. The first order Eq. (3) describes the discharge from a system where the discharge rate is concentration-dependent. Higuchi asserts that Fickian diffusion and square root time dependence govern the process by which drugs are liberated from an insoluble matrix^28,29. Various data-treatment techniques were used to display the in vitro profileof drug aloinrelease results for each formulation and choose the best-fitted model.

2.5. SEM: Scanning electron microscopy of drug aloin was used to discover the configuration, structure, and frame of NPs using the model of Joel (JSM-7100F). Onto metal stubs, NP powder was attached using double-sided tape^30,31. The conductive carbon black was then applied/used to the stubs. The particles' morphology was then looked at.

2.6. TEM: By using TEM microscopy (Joel instrument) Transmission electron microscopy:³² The aloin-drug sample for TEM work was prepared by taking a drop of NPs dispersion onto a carbon-coated copper grid and removing out the excess aloinsample by soaking it with filter paper³³. Then the grid was allowed to air dry thoroughly, and the sample was evaluated by using microscopy and observed.

3. EXPERIMENTAL STUDY OF DRUG:

Studying the methodology of the response composite design of surface and optimizing experimental design the nanoprecipitation process was used to formulatea total of 21 preparations,³⁴ and evaluate the parameters that were taken that as size of particle and entrapment effectiveness³⁵. The differ. the ratio of the drug (A), PLGA concentration (B), and PVA concentration (C), as well as the stirring time (D), had a substantial impact on the size of aloin drug (Y1) and drug aloin entrapment efficiency value (Y2) of the formed Np. So, the study analyses these four (ABCD) variables at different varying levels low (1), medium (0), and high (+1).

Table no 1: List of both dependent and independent variables in central composite design

Independent factors variables	Levels used real value and coded value
Independent factors variables	Low value (-1)	Intermediate value (0)	High value (+1)
X₁Drug (mg)	40	60	80
X₂Polymer (mg)	50	100	150
X₃Surfactant (%)	1	2	3
X₄Stirring time (min)	10	100	150
Dependent variables	Constraints
Y₁Particle size (nm)	Size NM (80-215)
Y₂Entrapment efficiency (%)	EE % (60-100)

3.1 DOE: The experimental technique or design of a drug experiment called DOE is particularly helpful for carrying out the tests of drugsin a way that the aloin drug data of the fewest possible experimental runs can yield important data while taking interactions between the independent variables or factors into consideration ^38,39.

4. RESULT AND DISCUSSION:

In our study, the four-factor approach was used, and the overall design included 21 trial runs with five (5) central points, which are seen in Table 2. Multiple linear regression values of statistical data analysis were usedin DESIGN-EXPERT 13.0.6 trial software to study drug composition.These nanosized varied from 80 to 230 nm and 60 to 99% entrapment, respectively.

Table 2: Observed response in central composite design experimental drug design approach represents 21 tests with four variables at different concentrations.

Std	Variable 1(drug mg)	Variable 1(polymer mg)	Variable 1(surfactant %)	Variable 1(time min)	Result (Particle Size nm)	Result (EE%)
1	1	1	1	-1	205.3	63.71
2	1	1	-1	-1	215.3	63.9
3	1	-1	1	1	81.3	91.08
4	-1	1	-1	1	92.1	89.54
5	1	-1	-1	1	86.5	70.67
6	-1	-1	1	-1	156.5	86.95
7	-1	1	1	1	90.1	97.56
8	-1	-1	-1	-1	161.2	67.7
9	-1.68	0	0	0	113.3	77.73
10	1.68	0	0	0	104	87.91
11	0	-1.68	0	0	129.1	83.1
12	0	1.68	0	0	148.7	79.52
13	0	0	-1.68	0	106.8	78.2
14	0	0	1.68	0	111.2	93.19
15	0	0	0	-1.68	195	81.23
16	0	0	0	1.68	143.6	84.16
17	0	0	0	0	98.5	98.06
18	0	0	0	0	108.1	94.72
19	0	0	0	0	98.1	91.58
20	0	0	0	0	98.5	98.06
21	0	0	0	0	99.1	94.7

The lack of fit is implied to be insignificant in comparison to the pure error by the lack of fit F-value of 49.71. Lack of Fit value (F-value) has a noise probability of 10.39% of occurring. We want the model to fit, thus Non-significant lack of fit is good. Model terms suggested that all values are significant when their P-values are less than 0.0500 and that showsa Non-significant fit difference is beneficial. The shapes of 3D plots reveal the nature and amount of the interaction between different factors. The 3D RSM plots a corresponding contour plots established the relationship (b/w) between the variables that is dependent on both variables’ nature. drug and polymer interactive effect on the size of the particle (Y1)at fixed level A is demonstrated in Fig 1.

Fig 1: Shows that 3D response plots of Y1 response of Y2 response

4.2.1.Factor affects the aloin composition:

4.2.2.Study the effect of sonication time: In this method, the sonication period was changed from 1 to 5 minutes to explain how it affected the size of the nanoparticles.The applied energy may be demonstrated with the aid of a graph if the stirring time rises from (1 to 5 min) and this causes a drop in the size of nanoparticles (from 215 to 80 nm).

4.2.3. Study the PLGA content effect: This workinfluenced polymer content in formulation,theresult observed that the size of the nanoparticle increases with increasing the amount of PLGA in the organic phase, and at a point, it decreases ⁴³. So as per reports consider the PLGA is important factor.

4.2.4. Studying the impact of drug surfactant content: The amount of PVA effect on Aloin nanoformulation formation was explained as PVA concentration is raised, nanoparticle diameter progressively grows after initially decreasing. The results are displayed in a 3D graph ⁴⁴. PVA molecules stabilise them and stop them from aggregating with one another when they are presentThe increased viscosity of the aqueous phase causes the size of the particles generated by this approach to the first drop and then grow when the concentration of PVA is raised; the decreased net shear stress ⁴⁵.The size of the particles produced by this method changes as the concentration of PVA rises because the aqueous phase's viscosity increases, which decreases the net shear stress that can cause droplet breakdown, Therefore, the size is reduced as a result of improved interfacial stabilization while it is raised as a result of increased aqueous phase viscosity. In this approach, the surfactant concentration is crucial.However, additional surfactant concentration increased the size of the particles as a result of PVA adhering to the NP surface, forming a thick coating ⁴⁶.

4.3 In-vitro release profile:

4.3.1 Particle size of formulation: The optimized formula of the drug Aloin nanoformulation size is shown in belowFigure 98.5nm

Fig. 5 represents the optimized Aloin formulation

Fig.6 Zeta potential of Aloin formulation

Pic.1 SEM images of Aloin Np

Pic.2. Transmission electron microscopic Aloin NP pictures of the experimental formulation; large size AL-NP of the drug (A) and small size AL-NP of the drug (B).

5. OPTIMIZATION:

With the help of software establishing the polynomial equations of formulation which describe the relationship b/w the factors and the responses, the process was carried out. Response, magnitude, and entrapment efficacy were observed as the result of the formulation of aloin. Therefore, a drug concentration of 60mg was the optimum level of formulation observed thus suggesting that aloin formulation is trustworthy and the model suggests that the predication value is fit for the model.

6. CONCLUSIONS:

A nanoprecipitation method of aloin-loaded PLGA NP has been successfully employed in this study with desirable size and high entrapment efficiency. With the aimof evaluating the impact of four formulation factors on the particle size(nm), EE (%), and zeta potential of AL(Aloin)-nanoparticles. In this sense, a Central Composite experimental design was constructed to study the belongings of the variables and to optimize the manufacturing process conditions.According to the results, data indicated the PVA concentration and PLGA concentration with high speed of string were the optimal conditions for the preparation of AL-NP, and in conclusion, a Central Composite response design was successfully used with optimized characteristics.

7. ACKNOWLEDGEMENTS:

We would like to acknowledge M.M.U deemed to be University Mullana, Ambala for enabling us to use its laboratory instruments.

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Received on 08.01.2024 Revised on 07.05.2024

Accepted on 15.07.2024 Published on 20.01.2025

Available online from January 27, 2025

Research J. Pharmacy and Technology. 2025;18(1):266-272.

DOI: 10.52711/0974-360X.2025.00041

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Y1					Y2
Model	R²	Std. Dev.	Adjusted R²	Predicted R²	R² value	Std. Dev. value	Adjusted R²	Predicted R²
Linear Model	0.5638	29.49	0.4547	0.2390	0.3844	6.77	0.2305	0.2290
2FI Model	0.7177	30.01	0.4354	1.4771	0.7673	5.26	0.5347	3.4018
Quadratic Model	0.9915	6.74	0.9715	0.4204	0.9305	3.71	0.7684	0.723	Suggested model
Cubic model	0.9972	4.69	0.9862		0.9668	3.14	0.8341		Aliased