INTRODUCTION:

In Indonesia with a tropical climate with high temperature and humidity, it is very supportive of fungal growth so that the risk of fungal infection is also very high¹. This is illustrated by the cases of pityriasis versicolor sufferers which are estimated to be more than 50% of the population and can occur in all groups ranging from children to parents². In addition, based on research by Rachmad Sulaiman et al. at Dr. Soetomo Surabaya General Hospital, it was found that skin infections were mostly caused by fungi from a total of 184 patients in 2020-2021³. In some cases, the prevalence of Malassezia furfur fungal infection was 2.1% higher than Candida 1.4%⁴.

The genus Malassezia comprises basidiomycetous yeasts that require lipids for growth and are naturally found on human skin as part of the normal microflora. One species of the genus, Malassezia furfur (M. furfur), has been identified as a potential causative agent of dermatological conditions such as psoriasis, atopic dermatitis, folliculitis, pityriasis versicolor, seborrheic dermatitis and dandruff^5–7.

However, the conventional use of antifungal agents requires high discipline, requires a prescription, is more expensive, and can cause resistance, in addition to the use of keratolic agents (sulfur-based and exfoliators) which are cheaper but potentially more irritating⁸. Therefore, the development of preparations as an alternative to safe and effective antifungal treatment needs to be continued, one of which is through the development of herbal medicinal raw materials into liposomal topical preparations which are expected to be more easily accepted by patients with more effective therapeutic results. In addition, drug development can also be done through in-silico approaches that can accelerate drug discovery or development in terms of cost, time, labor and cost. Currently, many new drug compounds have been successfully developed through computational methods⁹.

One of the plants that has the potential as an antifungal is the parsley plant. Parsley leaves contain compounds Apigenin-O-pentoside-O-hexoside, Kaempherol-3-O rutinoside, Kaempherol-O-deoxyhexosyl-hexoside, Isomer apigenin-O-acetyl-hexosylpentoside 1, Kaempferol-(p-coumaroyl) hexoside isomer 1¹⁰. Parsley leaves based on LCMS/MS analysis revealed (Petrocelinum crispum Mill) contains compounds Dihydroxy-2- hydroxymethylanthraquinone-3-O-β-D- xylopyranose, Stigmastan-3,6-dione, Xanthotoxin and aviprin and is predicted to have biological activity against fungi¹¹. In addition, parsley leaves are known to have other pharmacological activities, namely as atibacterial, antioxidant, anti-inflammatory, antidiabetic and antialopecia^12,13.

The purpose of this study is to assess the quality of ethanol extract of parsley leaves and its development in the form of liposome preparations so that in the future its use is easier, effective, and efficient as well as the exploration of compounds that provide the best binding to Lanosterol 14-alpha demethylase enzyme in silico.

MATERIALS AND METHODS:

Material:

Parsley leaf samples were obtained from the UPT Laboratorium Herbal Materia Medica Batu. Samples received in the form of dry simplisia which are processed directly by the UPT Laboratorium Herbal Materia Medica Batu. The chemicals used are: 100% ethanol, N-Hexan, Ethyl Asestat, Lecithin, Soy Lecithin, Chloroform, phosphate buffer pH 7.4, aqua pro injection, McFarland Standard Solution 0.5, BaCl2 1%, H2SO4 1%, Potato Dextrose Agar (PDA) media, distilled water, Malassezia furfur, NaCl, ketoconazole gel, NaOH 1N, formic acid, 750μL acetic acid, and 250 mL iPrOH. All chemicals were of analytical quality purchased from local suppliers.

The research method was conducted with the following steps:

Test Sample Preparation:

Parsley leaves were selected and cleaned, then extracted using 100% ethanol.

Fractionation of Parsley Leaf Extract:

Ethanol extract as much as 20grams was dissolved with 100% ethanol then stirred until mixed and then filtered. The filter results were put into a separatory funnel and added N-Hexan with a volume of 250ml and then stirred and allowed to stand until separation based on the level of polarity, then the layers were separated. This process is done repeatedly until the N-Hexan looks clear. Furthermore, the same treatment was carried out for ethyl acetate solvent to obtain ethyl acetate fraction.

Phytosome Formulation:

Formula optimization with factorial design 2² with the use of lecithin and cholesterol. Four formulas were obtained with 3 times relication of each formula. The use of lecithin in the range of 4000-7000mg and cholesterol at 40-80mg at each level. Formula 1 (F1) with 4000mg lecithin and 80mg cholesterol, Formula 2 (F2) with 7000mg lecithin and 40mg cholesterol, Formula 3(F3) with 7000mg lecithin and 80mg cholesterol and Formula 4 (F4) with 4000mg lecithin and 40mg cholesterol.

Liposome Preparation Procedure Using Thin Layer Hydration Method:

Weighed all the ingredients according to the calculation, then put into each glass beaker. Formula 1 weighed 4g lecithin and 0.08g cholesterol, Formula 2 weighed 7g lecithin and 0.04g cholesterol, Formula 3 weighed 7g lecithin and 0.08g cholesterol and Formula 4 weighed 4 g lecithin and 0.04g cholesterol. Then dissolved using chloroform as much as 10ml for each formula and then stirred at 100rpm at 50°C until the solvent disappeared and labeled as solution 1. In a different container as much as 4.5grams of parsley leaf fraction was dissolved with ethanol and chloroform in a ratio of 1: 1 and stirred until completely dissolved and labeled as solution 2. After the solvent of solution 1 disappeared (thickened), solution 2 was added little by little while still stirring until the solution thickened. After that, 2 ml of tween 80 was added little by little and the stirring speed was reduced. The temperature was turned off on the stirrer, then 30ml of 7.4 dapar solution was added using a dropper pipette by dropping it through the beaker wall little by little. Sonication was carried out for 30 minutes at 30°C.

Liposome Quality Test:

Liposome quality testing consists of organoleptic tests carried out by visually observing the shape, color and odor of liposomes at 1hour of manufacture and after each completion of the stability test for 6 cycles. In the absorption efficiency test, the liposome preparation was centrifuged at 6000rpm for 10minutes. Then the supernatant formed was separated and measured using a UV-Vis spectrophotometer with a wavelength of 366. Liposome pH analysis was performed using a pH meter. Testing was done by dipping the electrode of the pH meter into the preparation. Measurements were taken at 1hour after manufacture and after each stability test for 6 cycles. Liposome viscosity analysis was performed using a Rion viscometer with spindle number 2. The liposome preparation was inserted into the viscometer until the bottom line of the device. Measurements were taken at 1hour after manufacture and after each stability test for 6 cycles. Determination of particle size using Particle Size Ananlyzer (PSA) tool by taking 0.3ml of the sample and then put into a cuvette, added aqua pro injection to 5ml. The cuvette was inserted into the Particle size analyzer (Malvern, Model ZEN1690 Serial number MAL1186431). Furthermore, in the spreadability test, a 0.5g sample was placed on a flat glass, then another glass was placed on it and left for 1 minute. After that, a 50g load was added and left for 1 minute and the constant diameter was measured. Measurements were made at 1 hour after manufacture and after each stability test for 6 cycles. Finally, liposome release test was conducted using Franz Diffusion Cell. The temperature used was 37±0.5oC with a stirring speed of 100rpm. Sampling was done by taking 2ml of the sample then the measurement time as follows; 0, 5, 10, 15, 30, 45, 60, 90, 120, 180. The samples taken were then measured using a UV-Vis spectrophotometer with a wavelength of 365nm.

Phytosome activity assay:

Beginning with sterilizing tools such as vials, Petri dishes, and test tubes in the oven at 180^OC for 2hours. Continue making McFarland 0.5 standard solution by pipetting 1% BaCl2 as much as 0.005ml and 1% H2SO4 solution as much as 9.95ml and put into a test tube. Mixed and vortexed and stored in the refrigerator. Furthermore, the manufacture of Potato Dextrose Agar (PDA) Media was carried out by weighing 3.9g of PDA media put into Erlenmeyer, then dissolved with 100ml of distilled water, heated and sterilized in an autoclave at a temperature of 121^oC for 15 minutes, pressure 1-2atm.

Procedure for Making Fungal Suspension:

Pure culture of Malassezia furfur was taken 1 ose and then scratched on sloping PDA media in a tube which was then incubated for 3 x 24hours at a temperature of 25^oC. The culture on PDA media was taken and put into a test tube containing NaCl solution. Antifungal activity testing using the pitting method. Taken PDA media as much as 15 and 1ml of fungal suspense and put into a test tube. Then homogenized and poured into a Petri dish, after the solid was made a hole using a 6mm diameter plug. The wells that have been made are given phytosome preparations, ketoconazole gel as a positive control and phytosome blank as a negative control. Incubated 3 x 48hours at a temperature of 25^oC. Observations were made by looking at the clear zone around the wells, then measuring the inhibition area using a caliper. Each treatment was replicated 3times. And the person correlation coefficient test was conducted between the content of biocative compounds (total phenolic fraction and total essential oil in the fraction) with antifungal activity.

Molecular Docking Simulation:

Receptor preparation:

Receptor preparation uses a homology modeling approach with the help of the Swiss-Model web server^®. Protein modeling with the homology modeling method begins with a template search with the help of the NCBI web server^® BLAST to find templates that have similarities with proteins. The next stage is the alignment of target sequences with templates and modeling using Swiss-Model^® model then evaluated using Procheck^®. Furthermore, it was identified by ramacandran plot. After that, receptor preparation was carried out using BIOVIA Discovery Studio software to separate solvent residues (water), natural ligands, and other non-standard residues from the receptor and stored in the .pdp extension. After preparation, it was reopened in AutoDock Tools software with the .pdbqt extension format.

Compound Preparation:

Metabolite compounds were obtained from literature studies, stored in the ligand.pdb file format. then downloaded 3D structures on the website https://pubchem.ncbi.nlm.nih.gov/ in .sdf format. The ligands were converted to .pdb format using Open Babel. After that, the ligand was optimized using AutoDock Tools in the form of setting the number of active torsions and then saved in the .pdbqt ligand file format.

Prediction Active side:

Prediction of the active side using the BIOVIA Discovery Studio program then the active side is determined based on the receptor cavitie after which the attributes are opened to obtain the gride box center x, y and z.

Docking Simulation:

Prepared a folder with the file name config.txt in notepad containing the receptor, original ligand and gride box center x,y and z then created a new folder that receptor and ligand in .pdbqt format. Docking of compound molecules using AutoDock Vina through promt command. After the docking process, 2 new folders appear, namely log.txt and out.pdbqt. then this file is analyzed using AutoDock Tools software to see information related to bond energy and its inhibition constata.

Interaction Analysis:

Analysis data was performed based on the bond energy resulting from molecular tethering. The bond energy value indicates the strength of the bond between the receptor and the ligand. The lower the bond energy value, the stronger the bond between the ligand compound and the receptor. The results of molecular tethering were visualized using BIOVIA Discovery Studio to see the interactions formed.

RESULTS AND DISCUSSION:

Extraction:

Making ethanol extract of Parsley (Petroselinum crispum mill) leaves using cold extraction method, namely maceration. The maceration method was chosen to extract parsley leaves because the maceration method is very suitable for leaf materials, because the nature of the material is not resistant to high temperatures and can avoid damage to the chemical content that is thermolabile if subjected to excessive heating by soaking the material to be extracted with an organic solvent for some time, so that with this soaking, the solvent or liquid will penetrate the cell wall of the extracted plant and enter the cell cavity containing the active substance, so that the active substance will dissolve. Because of the difference in concentration inside and outside the cell, the solution with the concentrated concentration will be pushed out¹⁴.

In the maceration process, soaking is done using 100% ethanol solvent where there are 2 requirements for the solvent to be used in the extraction process, namely that the solvent must be the best solvent for the material to be extracted and the solvent must be able to separate quickly after shaking. Using 100% ethanol helps avoid the addition of water which can cause degradation reactions in water-sensitive compounds, as well as reduce the risk of microorganism growth and accelerate the extraction process. 100% ethanol can break down cell walls more effectively, thereby increasing the rate of extraction of active compounds from leaf tissue. In the selection of solvents that must be considered are toxicity, availability, price, non-flammability, low critical temperature, and critical pressure to minimize operating costs and reactivity. The choice of solvent is based on the polarity of the compounds contained in the plant sample, the material and the purpose of the extracted active compounds, not only that ethanol is also a non-toxic, common, easily available solvent, and is a universal solvent that can dissolve polar compounds and non-polar compounds^15,16.

After completion of the macerai process all the macerates were collected and evaporated with a vacuum vaporizer until a thick extract was obtained. The extract obtained was separated from the filtrate (depleted plant material) by allowing it to drip into a holding tank through the bottom of the attached extractor, which was covered with a filter cloth. The filtrate is held above the filter cloth, and the extract is collected in the holding tank. From the holding tank, the extract is pumped into a fireworks filter to remove fine particles or colloids from the extract. The enriched extract from the percolator or extractor is fed into a rotary evaporator where it is concentrated under vacuum conditions to produce a thick concentrated extract. The concentrated extract is further fed into a vacuum chamber dryer to produce a solid mass free of solvent. The solvent obtained from the cleaned evaporator and vacuum chamber dryer is recycled back to the percolator or extractor for the next batch of plant material. The solid mass obtained is then pulverized and used directly for the desired pharmaceutical formulation or further processed for isolation of its phytoconstituents. Drying of the extract is done by a suitable method considering minimal damage to the expected active components. Drying of extracts can be done by oven method either accompanied by vacuum or not, freeze dry method, spray dry method, conveyor dryer method or other suitable methods. Temperature plays a significant role in extraction because it can increase the solubility of solutes to solvents, increase the diffusion ability of solvents to matrices/particles, reduce the viscosity of solvents, and can provide intermolecular interaction energy between components so as to allow the extraction process to occur. However, it should be noted that increasing the temperature using a moderate temperature by considering the characteristics of the ingredients and the expected active components¹⁵.

Fractionation:

After obtaining a thick extract from the extraction process, the extract is continued at the Fractionation stage which is a method of separating mixed components derived from extracted extracts to separate the components of the active compounds. Fractionation is carried out to separate the main group of ingredients from one group. The purity limit of the fraction can still be categorized as traditional medicine according to the characteristics of the plant and the traditional method of acquisition¹⁵.

According to Harborne, 1987 states that the fractionation method is generally used as a reference in estimating the nature of the polarity of a compound to be separated (target compound). Based on this, the fractionation method has advantages compared to other methods, because it can separate bioactive compounds based on the level of polarity because polar compounds dissolve in polar solvents while semi-polar compounds dissolve in semi-polar solvents and non-polar compounds dissolve in non-polar solvents¹⁷. According to Venn, 2008 the selection of solvents in fractionation depends on the nature of the analyte where the solvent and analyte must have the same properties, because the fractionation method is a separation procedure between a compound based on its level of polarity. The fractionation process in this study used ethyl acetate. This fractionation uses ethyl acetate to obtain more polar compounds such as flavonoids, aglycones, and glycosides produced. This is because aglycones contain more sugar than flavonoids such as glycosides, and glycosides contain aglycones in addition to one or more sugar groups. This fractionation process produces ethyl acetate fractions¹⁸.

The fractionation process used is the Liquid-Liquid Extraction (ECC) method. ECC is an extraction that uses two solvents that do not mix with each other and involves the extraction of analytes from the aqueous phase into an organic solvent that is nonpolar or slightly polar. Analytes that are easily extracted in organic solvents are neutral molecules that are covalently bonded to nonpolar or slightly polar substituents. Meanwhile, polar compounds and also compounds that are easily ionized will be retained in the water phase¹⁹.

Liposomes:

The design in making liposome formulas uses a factorial design. Factorial design is a design chosen to measure together the effects of several factors and the interaction between these factors. Factorial design contains several notions, namely response level factors and effects. A factor is any quantity that affects the response. Level is the value specified for each factor. Response is the observed property or result of the experiment. The measured response must be quantifiable. The effect of a factor or interaction is the average response at high levels minus the average response at low levels. Factorial design experiments combine 2 types of factors, namely (A and B) to obtain the optimal formula. If the 2 factors are at level 2, it will produce 4 formulas. Where the level of the factor is divided into two, namely low level and high level, low level is symbolized by negative (-) while high level is symbolized by positive (+)²⁰.

Table 1. Phytosome Optimization with Factorial Design 2² using expert design application In vitro Antifungal Assay

Std	Run	Factor 1 A: lecithine mg	Factor 2 B: Cholesterol mg
3	1	4000.00	80.00
2	2	7000.00	40.00
4	3	7000.00	80.00
1	4	4000.00	40.00

This factorial design has the advantage of being economical and the possibility to identify the effect of each factor, as well as the interaction effect between factors. This method is quickly practical because it can avoid determining the formula by trial and error²¹.

Organoleptic Test Results:

The results of the organoleptic test of the liposome of the ethyl acetate fraction of parsley leaves (15%) based on the shape, odor and color based on the cycling test can be seen in table 2 with the results that the liposome of the ethyl acetate fraction of parsley leaves (15%) has not changed and is stable at 1 hour of storage and cycling test.

Table 2. Organoleptic test results of 1 hour storage and cycling test

Check	Formula	Organoleptic Observation
Check	Formula	I hour Storage	After Cycling Test
Aroma	F1	Typical Parsley Fraction	Typical Parsley Fraction
	F2	Typical Parsley Fraction	Typical Parsley Fraction
	F3	Typical Parsley Fraction	Typical Parsley Fraction
	F4	Typical Parsley Fraction	Typical Parsley Fraction
Color	F1	Brownish Yellow	Brownish Yellow
	F2	Brownish Yellow	Brownish Yellow
	F3	Brownish Yellow	Brownish Yellow
	F4	Brownish Yellow	Brownish Yellow
Shape	F1	Somewhat Thick	Somewhat Thick
	F2	Somewhat Thick	Somewhat Thick
	F3	Somewhat Thick	Somewhat Thick
	F4	Somewhat Thick	Somewhat Thick

Description:

F1: Liposome formula with ethyl acetate fraction (15%), low lecithin concentration and high cholesterol.

F2: Liposome formula with ethyl acetate fraction (15%), low lecithin concentration and high cholesterol.

F3: Liposome formula with ethyl acetate fraction (15%), low lecithin concentration and high cholesterol.

F4: Liposome formula with ethyl acetate fraction (15%), low lecithin concentration and high cholesterol.

Sorption Efficiency:

The absorption efficiency is determined by separating the free drug from the formula using the centrifugation technique. Centrifugation dispersion aims to separate the unabsorbed drug. The amount of free drug (F) is called supernatant, the supernatant obtained is derivatized to measure its absorbance using UV-Vis spectrophotometer at 365 nm wavelength. Determination of sorption efficiency is used to determine the amount of drug that is successfully trapped/absorbed in the solvent. The greater the absorption efficiency value, the faster the penetration of active substances through the skin, which is caused by the greater concentration gradient that encourages passive diffusion in penetration²². Percent absorption efficiency is one of the characterizations of phytosomes which states the amount of active natural ingredients that are successfully absorbed in the vesicle system formed. The percent absorption efficiency value is expected to be higher until it approaches 100%, which is between 80-100%. The absorption efficiency values of the formulas can be seen in table 3.

Table 3. Sorption Efficiency Test

Formula	Absorbance	Entrapment Efficiency (% EE)
F1	0,231	94,106 ± 2,61
F2	0,274	92,609 ± 2,04
F3	0,302	91,611 ± 7,17
F4	0,505	84,567 ± 3,25

The results of the calculation of the percent of absorption efficiency show that the phytosomes of the ethyl acetate fraction of parsley leaves (Petroselinum crispum Mill) have the ability to absorb good phytoconstituents with the lowest percent absorption efficiency ranging from 84.567% in formula 4 and the highest in formula 1 which is 94.106%. Based on morphological observations and calculation of the percent of absorption efficiency, it can be seen that formula 1 has the best results of the four formulas.

The four formulas that have been made are also tested for pH, spreadability and viscosity. The following are the results of the 1-hour storage test and the average value of the 6-cycle cycling test.

In the 3 test parameters above, none of the formulas has a percent shift value of more than 10%, so it can be said that the four formulas have good quality.

Particle Size and Polydispersity Index:

The particle size for liposomes usually ranges from 50 nm to 1000nm. However, the most commonly used liposomes in research and medical applications are around 100nm to 300nm in size. This size is important as it affects the biodistribution, absorption, and delivery efficiency of the drug in the body. The appearance of liposomes from preparations under a microscope can be seen in figure 1.

Figure 1. Liposomes under a microscope at 1000x magnification.

The four formulas were subjected to vesicle size and distribution determination using Particle Size Analyzer with the results shown in Table 4.

Table 4. Particle Size and Polydispersity Index of Parsley (Petroselinum crispum Mill) leaf ethyl acetate fraction liposomes

Formula	Particle Size (nm)	Polydispersity Index (PDI)
F1	251,6 ± 18,7	0,319 ± 0,05
F2	287,96 ± 5,27	0,359 ± 0,02
F3	297,23 ± 4,45	0,324 ± 0,03
F4	306,96 ± 2,27	0,301 ± 0,04

The measurement results show that Formula 1 has the smallest average particle size, which is 251.6nm and has a small polydispersity index, which is 0.319. Vesicle size is one of the factors that influence the effectiveness of vesicles in improving transdermal drug penetration. One of the transdermal delivery routes is the paracellular route through the intercellular cleft.²². The small size of phytosomal vesicles can facilitate the penetration of active substances into the skin barrier through the paracellular pathway to deliver drugs at the inflammatory site. The polydispersity index measurement aims to determine the homogeneity of the vesicle size with a standard value of <0.5 for a polydisperse system. The average particle size and polydispersity index of Formula 1 with the best value among other formulas can be due to the ratio of phytoconstituents to phospholipids used at the optimal amount. Liposomes with an optimal ratio of constituent components can produce the smallest vesicle size and polydispersity index²³.

After obtaining data from 2 levels, it is then entered into the table of running formula results and re-optimized to determine the interaction in the formula. The existence of interactions can be seen from the graph of the relationship between response and factor level. If the curve results show parallel lines, it can be said that there is no interaction between excipients in determining the response. If the curve shows a non-parallel line, it can be said that there is interaction between excipients in determining the response²⁰. The plot graph of the 2 levels can be seen in Figure 2.

(a)

(b)

Figure 2. Normal Plot of Particle Size (a); Sorption efficiency (b)

Liposome release test using Franz Diffusion Cell:

Franz diffusion cells are used to evaluate the permeability and stability of formulations such as topical (gels and creams) and transdermal (lipid nanoparticle formulations). Diffusion cell is a direct assay that can measure in-vitro and ex-vivo drug release from topical preparations such as creams, ointments, liposome formulations and gels.

The suitability of transdermal systems is generally demonstrated using permeation studies. Franz diffusion cells are widely used to determine the permeation of drugs through the skin. Skin permeation studies on human skin dermatome explants are considered the standard for assessing drug delivery from transdermal systems. However, ethical and economic reasons pose major problems to the availability and use of human skin. Due to long-term storage, skin lacks viability and enzymatic activity, resulting in variations in skin permeation^7,12,13,24. Isolated skins from domesticated animals such as pigs; primates; rodents (guinea pigs, rats, and mice); rabbits; and snake skins are considered as alternatives to human skin, as they can be obtained easily, can be cut fresh before skin permeation studies with viability and enzymatic activity, and show less variability^25,26. In this test, researchers used a membrane derived from mice skin.

The penetration test of the ethyl acetate fraction of parsley leaves (Petroselinum crispum Mill) in the liposome system was carried out by calculating the cumulative amount of the ethyl acetate fraction of parsley leaves (Petroselinum crispum Mill) that penetrated and the penetration rate expressed in units of micrograms per unit area cm² per minute (flux). The cumulative amount of active substance that penetrated the four formulas for 180 minutes showed that F3 had the highest amount of active substance that penetrated then followed by F1, F4 and F2.

Figure 3. Cumulative penetration rate graph of liposomes from ethyl acetate fraction of parsley leaves (Petroselinum crispum Mill)

Figure 4. Average flux values of formula1, formula 2, formula 3 and formula 4

The results of the formula flux calculations presented in figure 4, show that in steady state conditions give high flux values in F3 7.385±0.063 and F1 5.778±0.030 while F4 and F3 are in the following order with flux values of 3.670±0.057 and 2.776±0.034 (mcg/cm² /min) respectively. This data explains that the penetration rate of F3 and F1 is faster than that of F2 and F4 and the statistical analysis shows F1 and F3 there is an insignificant difference while F3 against F2 and F4 shows a significant difference. A good flux value can be interpreted as having a composition of lecithin and cholesterol that affects the diffusion speed of an active substance with an increased ratio in the liposome system that acts as a penetration enhancer so that it can carry the active substance effectively penetrated through the skin membrane of mice.

The factor that affects the diffusion rate in the liposome formula is the particle size. The smaller the particle size, the greater the diffusion rate. Liposomes that have a small size can also more easily penetrate the skin layer or biological membrane, thereby increasing diffusion efficiency. - Large liposomes are slower in releasing active substances due to thicker bilayers and relatively smaller surface areas compared to their total volume. The type of lipids used in liposomes, such as phospholipids and cholesterol, affects the fluidity and permeability of the liposome membrane. Phospholipids with more saturated and longer fatty acid chains will produce a more stable and less permeable bilayer, thereby reducing the diffusion rate. In contrast, phospholipids with unsaturated fatty acid chains make the membrane more fluid and increase permeability, accelerating the release of active substances. Cholesterol serves to stabilize the lipid bilayer, reduce leakage of active substances, and slow down diffusion. The higher the cholesterol content, the more stable the liposomes, but the diffusion rate usually decreases. Hydrophobic nature of the active substance tends to get trapped in the lipid bilayer, so the release is slower because it has to pass through the lipid layer²⁷.

Liposome Antifungal activity assay:

The antifungal activity test of the four formulas made against Malassezia furfur showed that only formula 1 was able to inhibit the growth of the fungus with an inhibition zone diameter ±SD of 7.22mm±1.17, including in the moderate category. This ability is related to the active substance content in the form of ethyl acetate fraction of parsley leaves. Based on the results of LCMS analysis, it is known that the ethyl acetate fraction of parsley leaves positively contains a class of furanocoumarins compounds, namely xanthotoxin and Aviprin. The activity of furanocoumarins specifically against Malassezia furfur has not been widely reported, but the activity of furanocoumarins as antifungals has been reported by Ramírez-Pelayo et al., 2019 against Colletotrichum sp., through the mechanism of Mycelial growth inhibition and Inhibition of spore germination²⁸. In addition, furanocumarin, which is a derivative of coumarin, is known to have very complex activities, but is generally divided into two processes, namely light-dependent and light-independent. Photosensitizer compounds when exposed to radiation can cause toxic reactions.

In a different study, Petroselinum crispum was reported to be able to inhibit the growth of Candida albicans both in the form of extracts and fractions^29–32. the fraction with a concentration of 0.15 g/ml was able to inhibit the growth of Malassezia furfur with an inhibition zone diameter of 6.44mm±1.3¹¹. In this study, the liposome preparation (F1) has increased activity against malassezia furur when compared to the research of Hasanuddin et al., 2022 which only tested in the form of ethyl acetate fraction of parsley leaves¹¹. Thus, the development of liposome dosage forms in this study can increase the activity of the ethyl acetate fraction of parsley leaves. This is predicted because formula 1 has a smaller viscosity value, the smallest particle size, and the greatest percent absorption efficiency compared to other formulas. This is in accordance with the theory which states that liposomes have several advantages including increasing the stability of active substances through encapsulation, solubility of lipophilic and amphiphilic drugs, controlled drug release and increasing the penetration of active substances into tissues³³.

Molecular Docking Simulation:

The receptor preparation carried out by homology modeling approach obtained binding coordinates on the axes x:4.416, y:160.635 and z:9490 with grid box sizes x:48, y:28 and z:24. Simulation of aviprin and xanthotoxin into the active side of Lanosterol 4α demethylase is presented in figure 6(a). Aviprin and Xanthotoxin compounds do not build hydrogen bonds with amino acid residues in the ketoconazole binding site and only form van der waals interactions and hydrophobic interactions.

In tethering the ligand molecule, ketoconazole forms 10 interactions with amino acids, the absence of hydrogen interactions and has many hydrophobic interactions. When viewed from the resulting affinity, ketoconazole shows the best affinity value, this is possible because of hydrophobic interactions on many amino acids and thus contributes greatly to the affinity produced by ketoconazole.

(a)

(b)

(c)

Interaction of Aviprin (a); Xanthotoxin (b); Ketoconazole (c) on Lanosterol 14α demethylase receptor binding site

In the docking simulation, the researchers used ketoconazole as a comparison ligand used to treat Malassezia furfur. Ketoconazole is the longest azole class antifungal on the market and is often given in oral treatment for systemic infections. Ketoconazole actively fights fungi, dimorphic fungi and dermatophytes. The basis of the antifungal activity of ketoconazole and other azoles is to inhibit the conversion of lanosterol to ergosterol, which is important for maintaining cell membrane integrity, by inhibiting cytochrome P-450 lanosterol 14-a-demethylase (CYP51), an enzyme responsible for the oxidation of the C-14 methyl group on lanosterol. Disruption of ergosterol biosynthesis can cause cell membrane damage by increasing permeability and causing cell lysis and ultimately cell death³⁴.

From the simulation results, it is found that the affinity value of xanthotoxin is smaller than aviprin, while aviprin and xanthotoin, which are compounds obtained from LCMS analysis, show smaller binding than ketoconazole. The greater the affinity value formed by the ligand, the stronger the interaction with the amino acid.

CONCLUSION:

The ethyl acetate fraction of parsley leaves (Petrocelinum crispum Mill) was prepared in 4 liposome formulas that were stable in preparation and met the quality test parameter values. Formula 1 has antifungal activity against Malassezia furfur, this activity is also supported by the prediction of compound activity carried out in silico, where previously reported compounds provide binding activity to receptors with aviprin affinity values of -8.2 kcal/mol and -7.4 kcal/mol.

CONFLICT OF INTEREST:

Nil.

ACKNOWLEDGMENTS:

The researcher would like to thank the Directorate of Research and Community Service, Ministry of Education, Culture, Research and Technology for the research, indonesia funding support provided. The researcher would like to thank the Mandala Waluya University Pharmacy Laboratory, Ngudi Waluyo University Pharmacy Laboratory, Halu Oleo University Medical Laboratory and all those who have helped this research.

REFERENCES:

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Received on 24.12.2024 Revised on 16.04.2025

Accepted on 08.07.2025 Published on 01.12.2025

Available online from December 06, 2025

Research J. Pharmacy and Technology. 2025;18(12):6091-6100.

DOI: 10.52711/0974-360X.2025.00881

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

Check	Check	Observation Results		Average	Shift Value (%)
Check	Check	1 hour Storage	After Cycling Test	Average	Shift Value (%)
pH	F1 F2 F3 F4	4,99	5,11	5,05	9,64
		5,21	5,07	5,14	6,61
		5,40	5,34	5,37	5,79
		5,32	5,15	5,24	0,75
Spreadability	F1	12,81	11,67	12,24	9,77
	F2	9,61	10,45	10,03	8,72
	F3	10,42	11,52	10,97	9,52
	F4	10,53	10,74	10,64	1,96
Viscosity	F1	80,11	87,84	83,97	9,64
	F2	81,44	86,83	84,14	6,61
	F3	83,33	88,16	85,75	5,79
	F4	84,44	83,81	84,13	0,75

Receptors	Ligand	Affinity (kcal/mol)	Amino Acids involved
Lanosterol 14α demethylase	Aviprin	-8,2	HIS:415, ILE:418, GLY:419, CYS:417, ALA:261, LYS:101, LEU:97
	Xanthotoxin	-7.4	LEU:307, ALA:314, LEU:425, ALA:384
	Ketoconazole (Comparator Ligand)	-11.1	VAL:75, LEU:45, LEU:46, LEU457, PHE:188, TYR:22, ILE:331, LYS:48, LEU:79, PHE:458