The leaves of Ipomoea batatas were collected from Southeast Sulawesi, Indonesia. The I. batatas plant was identified by the School of Life Sciences and Technology, Bandung Institute of Technology, West Java, Indonesia, under registration number 6726/I1.CO2.2/PL/2019. The leaves underwent a series of preparations, including sample harvesting, wet sorting, water washing, deformation by cutting into small pieces, and subsequent drying in an Air Performance Ovens Frailabo® at 50 °C to obtain dry simplicial. The simplicia powder (7000 g) was macerated with ethanol as a solvent, and the solvent was changed daily over three days. It was then concentrated using a rotary evaporator (Buchi R-100) at 50 °C into a crude extract (711.34 g) with a yield of 10.16% (w/w). A total of 260 g of the ethanolic extract underwent fractionation through successive steps: it was dissolved in hot distilled water and then fractionated with n-hexane, ethyl acetate, and butanol solvents. This process yielded an n-hexane fraction of 50 g (19.23% (w/w)), an ethyl acetate fraction of 71 g (27.31% (w/w)), a butanol fraction of 74 g (28.46% (w/w)), and aqueous fractions of 50 g (19.23% (w/w)). The ethyl acetate fraction was selected for further analysis using LC-MS/MS.

The ethyl acetate fraction (50 g) underwent fractionation using column vacuum chromatography employing silica gel 60H₂₅₄ p.a (E. Merck). Elution was carried out utilizing eluents (n-hexane – ethyl acetate) at various ratios. This chromatographic separation yielded twenty-seven subfractions, subsequently combined based on similar separation profiles observed on thin layer chromatography (TLC). Eight primary subfractions were obtained: subfraction A (comprising subfractions 1–5), B (subfractions 6–10), C (subfractions 11–12), D (subfractions 13–15), E (subfractions 16–17), F (subfractions 18–19), G (subfractions 20–21), and H (subfractions 22–27). All subfractions were selected for further LC-MS/MS analysis using a Waters Xevo G2-XS Quadrupole time-of-flight mass spectrometry equipped with an electrospray ionization interface (ESI). The ESI source operated in positive ion mode within the m/z range of 50 to 1200, with optimization parameters set as follows: acquisition time 0–17 min, high CE ramp 10–40 eV, collision energy 6 eV, cone voltage 30 V, desolvation gas flow, and temperature set at 1000 L/h and 500 °C. The column temperature was maintained at 40 °C. Solvents consisting of 0.1% formic acid in water (eluent A) and 0.1% formic acid in acetonitrile (eluent B) were utilized with a flow rate of 0.3 mL/min. The eluent composition was as follows: 0–1 min 5% eluent B, 11–14 min 100% eluent B, 17 min 5% eluent B. Samples, each with a volume of 1 µL, were injected into the column. Post-processing of the samples was conducted using Waters UNIFI® software and compared with the built-in library database from the Waters instrument (Waters Corp. Milford, MA, USA).

Additionally, the identification of flavonoid compounds from the ethyl acetate fraction of I. batatas utilized the ACQUITY UPLC BEH Shield RP18 column (100 × 2.1 mm, 1.7 µm) with eluent A (water + 0.1% formic acid) and eluent B (acetonitrile + 0.1% formic acid). The flow rate was set at 0.3 mL/minute, column temperature at 40 °C, injection volume of 3 µL, and both UV and MS detectors were employed. Post-processing of the samples was carried out using the MarkHerb database (EBM Scientific and Technology, Ltd).

The Malassezia furfur samples were obtained from Indonesia University, Jakarta. These fungi were maintained on a potato dextrose agar-olive oil (PDA-oil) medium. For three days, the fungal culture was grown in PDA oil at 35 °C. The fungal culture suspension was adjusted to a visually comparable turbidity of 0.5 MacFarland standard scales, which equated to 1.5 × 10⁹ CFU/ml. The antifungal activity of the extract and its fractions was assessed using the agar diffusion method (Kaneko et al. 2005). Briefly, the PDA oil medium was prepared and placed in sterile Petri plates. The fungal suspension was uniformly spread on the surface of the PDA oil. A 20 μL suspension of the extract and its fractions was added to a sterile well at various concentrations. Sodium CMC suspension served as a negative control, while ketoconazole suspension was used as a positive control. The plates were then incubated at 35 °C for three days to observe the fungistatic effect. Three replicate plates were used for each treatment.

Two-month-old local rabbits, weighing approximately 2 kg, were utilized in this study. The rabbits underwent acclimatization to laboratory conditions and received standard feed and water. The photoperiod was set at 12 hours of light and 12 hours of darkness at room temperature (25 ± 3 °C). Following protocol approval from the Halu Oleo University Ethics Committee, all treatments on the tested animals were performed to minimize the number of animals and their suffering. Three male rabbits were selected for the study, and the Tanaka method with modifications was employed (Tanaka et al. 1980).

To prepare for the application of the extract and fraction, the dorsal area hairs on the rabbits’ backs were shaved and treated with depilatory cream 24 hours before application. Six boxes, each measuring 2 × 2 cm², were created with a spacing of 2 cm between them. Each rabbit received different doses of extract suspensions (5%, 10%, 20%, and 40%, respectively), while a vehicle control (1% Sodium CMC suspension) served as the negative control, and 2% minoxidil suspension acted as the positive control. The samples were applied to the test area box with 1 ml twice daily for 21 days. Hair growth stimulating activity was assessed by observing hair growth on the rabbit’s back skin.

Hair length measurements began on the third day to evaluate the growth state of newly grown hair. Following the initiation of treatment, all rabbit hairs were selected for measurement, and the average length was calculated. The results were presented as the average hair length + SD (standard deviation) of 10 hairs. Tests were conducted to identify hair growth activity in the extract and its fractions.

The analyzed data were expressed as mean ± SD. The statistical analysis was performed using IBM SPSS Statistical software version 25. All data underwent one-way analysis of variance (ANOVA), and the significance level between treatment means was determined using the LSD range test at p < 0.05.

The Design-Expert 10 (DX-10) software was utilized to optimize the proportions of excipients in the hair tonic formulation of the ethyl acetate fraction from I. batatas leaves. A D-optimal mixture design was employed with the constraint ethanol 96% (X₁) + propylene glycol (X₂) + Tween 80 (X₃) = 30%, as depicted in Table 1. The component ranges were set as follows: 5 ≤ X₁ ≤ 15, 10 ≤ X₂ ≤ 24, and 1 ≤ X₃ ≤ 5. Design-Expert software generated 16 runs, with 11 runs being different and five runs as replicates (7 runs had no replicates, 3 runs had 2 replicates, and 1 run had 3 replicates). Through the D-optimal mixture approach, the effects of these components on the hair tonic’s physicochemical properties were assessed, and the optimal combination was determined. For optimization, the combination of factors that yielded the best responses was determined based on the effect of each factor.

The formulation of the hair tonic is outlined in Table 1. Initially, the ethyl acetate fraction is combined with ethanol and stirred until homogeneous (solution 1). Subsequently, propylene glycol is added to solution 1 (solution 2). Tween 80 is then introduced into solution 2 (solution 3). DMDM hydantoin and sodium metabisulfite are dissolved in deionized water to create solution 4. Following this, solutions 3 and 4 are mixed. Finally, deionized water is added to reach the volume limit, and the mixture is stirred until it achieves homogeneity.

The pH was measured using a pH meter calibrated by dipping the electrode into two solutions, assuming that the pH of the test solution fell between the pH values of the two solutions. Commonly used solutions for calibration are pH 4 and pH 7 (Patil et al. 2018). Viscosity was determined using the Ostwald viscometer, measuring the time required for the liquid to pass through two marks as it flowed through a vertical capillary tube (Apriani et al. 2021). Density was determined using the pycnometer method, measuring the weight of distilled water and hair tonic in the pycnometer (Febriani et al. 2016).

Fitting response values were conducted using special quartic and quadratic models (Eqs. 1–2). ANOVA at P < 0.05 was used to assess the statistical significance of the equation.

Y = λ₁X₁ + λ₂X₂ + λ₃X₃ + λ₁λ₂ X₁X₂ + λ₁λ₃ X₁X₃ + λ₂λ₃ X₂X₃ + λ₁λ₁λ₂λ₃ X₁X₁X₂X₃ + λ₁λ₂λ₂λ₃ X₁X₂X₂X₃ + λ₁λ₂λ₃λ₃ X₁X₂X₃X₃ … (special quartic) (Eq 1)

Y = λ₁X₁ + λ₂X₂ + λ₃X₃ + λ₁λ₂ X₁X₂ + λ₁λ₃ X₁X₃ + λ₂λ₃ X₂X₃..(quadratic) (Eq 2)

Y represents the predictive dependent variable (responses), such as physicochemical properties like pH, viscosity, and density. λ denotes constant coefficients for model terms, and X represents the proportions of real-components.

Ipomoea batatas has traditionally been used for hair care, specifically addressing issues like hair loss or alopecia. The causes of alopecia vary from genetic to environmental factors. In light of this traditional application, a phytochemical screening was conducted to identify its phytochemical compounds and assess its antifungal and hair growth stimulant activities in this study. The ethyl acetate subfractions from I. batatas were analyzed using the LC-MS/MS method (Suppl. material 1). This analytical technique was chosen for its known selectivity, sensitivity, and accuracy in rapid analysis. LC-MS/MS analysis revealed that the subfractions of I. batatas contained twenty-three compounds. Additionally, the LC-MS/MS analysis identified thirteen probable compounds such as steroids (stigmastane-3-6-dione), alkaloids (pyropheophorbide A and methyl-pyropheophorbide A), flavonoids (hyperoside, quercetin, and kaempferol), terpenoids (digiprolactone, α-onocerin, and tussilagonone), amide (moupinamide), phenolics (3-hydroxy-4-methoxy-cinnamic acid), and fatty acids (triacontanoic acid and trichosanic acid) (Table 2).

The in-vitro antifungal assay of the extract revealed varying degrees of activity against Malassezia furfur, depending on the concentration. Specifically, at a 5% ethanolic extract concentration, no discernible antifungal activity was observed. However, at a concentration of 10%, the extract exhibited a mild level of antifungal activity, which further intensified with increasing concentrations (Fig. 1A and Table 3). Similarly, the assessment of the fraction unveiled distinct antifungal properties across different fractions against M. furfur. Notably, strong antifungal activity was observed in the aqueous and ethyl acetate fractions, while the n-hexane and butanol fractions demonstrated weaker antifungal effects (Fig. 1B and Table 4).

Figure 1. 

Antifungal activity of (A) ethanolic extract and (B) fraction from I. batatas leaves against Malassezia furfur. Legend: a. 0.5% sodium CMC suspension; b. 5% ethanolic extract; c. 10% ethanolic extract; d. 20% ethanolic extract; e. 40% ethanolic extract; f. 80% ethanolic extract; g. 2% ketoconazole suspension; h. n-hexane fraction; i. ethyl acetate fraction; j. butanol fraction; k. aqueous fraction.

While providing valuable initial insights, this study is considered a preliminary test. Further investigations are imperative to delve deeper into the antifungal potential of the extract and fractions. It is essential to identify the secondary metabolite compounds responsible for the observed antifungal effects. Alkaloids, terpenoids, flavonoids, tannins, and polyphenols are among the secondary metabolites believed to contribute to antifungal activity and merit closer scrutiny in subsequent studies. The findings from this research lay the groundwork for future exploration, offering a promising avenue for deriving antifungal activity from I. batatas leaves.

The mechanism of alkaloids as antifungals is to disrupt the formation of the fungal cell membrane. Alkaloids bind to ergosterol, creating holes that lead to cell membrane leakage. This leakage results in fungal cell damage and eventual cell death (Dhamgaye et al. 2014). Similarly, tannins act by binding to the cell membrane structure of the fungus. Tannins have an affinity for ergosterol and polyphenols, and their binding to fungal membranes may contribute to antifungal action (Carvalho et al. 2018).

Polyphenols play a crucial role in plants, contributing to resistance against microorganisms, herbivores, and insects (Hammerschmidt 2005). Phenolics or polyphenols, characterized by a benzene ring with one or more hydroxyl groups, exhibit increased toxicity when a hydroxyl group is present on the benzene ring (Ruiz-García and Gómez-Plaza 2013). Various mechanisms of phenolics have been suggested to counteract pathogenic microbes, including disrupting enzymatic processes, affecting energy production and synthesis of structural components, weakening and destroying the cell membrane’s permeability barrier, altering cells’ physiological status, or affecting nucleic acid synthesis (Cutter 2000).

Flavonoid compounds may act through several targets, such as forming complexes with proteins through nonspecific bonds like hydrogen bonds and hydrophobic effects, and by forming covalent bonds. The mechanism of antimicrobial action may be associated with their ability to attach to microbes, cell envelope transport proteins, enzymes, and other targets. Lipophilic flavonoids may also interfere with microbial membranes (Cowan 1999; Mishra et al. 2009). Meanwhile, terpenoids exhibit antifungal activity through interference with cell wall permeability and inhibiting the formation of pseudohyphae and chlamydoconidia (Leite et al. 2015).

The concentration of the ethanol extract in stimulating hair growth yielded different results at all concentrations compared to 2% minoxidil. Similar outcomes were also observed with the I. batatas fraction (Figs 2, 3). The research results indicate a significant increase in hair growth after the administration of 2% minoxidil, the ethanol extract, and the fraction of I. batatas leaves (Tables 5, 6). However, at 5% and 10% ethanol extract concentrations, the butanol and water fractions exhibited a less effective hair growth profile compared to 2% minoxidil. Meanwhile, ethanol extract concentrations of 20% and 40%, as well as the n-hexane and ethyl acetate fractions, demonstrated effectiveness equivalent to 2% minoxidil, as they showed no significant differences based on statistical test results (Tables 5, 6). Based on these findings, it is evident that the ethanol extract of I. batatas leaves at concentrations of 20% and 40%, as well as the n-hexane fraction and ethyl acetate fraction, present greater opportunities as an alternative in treating hair loss.

Figure 2. 

The hair growth profile of male rabbits (A) administered varying concentrations of ethanol extract and (B) fractions from I. batatas leaves.

Figure 3. 

Molecular interaction of (A) hyperoside (1), (B) pyropheophorbide A (2), (C) methyl-pyropheophorbide A (3), and (D) quercetin (4) with AR based on docking result.

The potential stimulation of hair growth is attributed to the secondary metabolite content of this plant. Phytochemical compounds, whether individually or in a mixture form, are often reported as non-medical treatments to alleviate symptoms of various dermatological hair conditions (Daniels et al. 2019). Alkaloids, such as caffeine, are known to play a role in modulating hair growth (Daniels et al. 2019). The presence of polyphenols offers opportunities for hair growth, as they are potent antioxidant and anti-inflammatory agents. Additionally, terpenoids and tannins also have the potential to act as hair growth stimulants through their action as 5α-reductase inhibitors (Kaushik et al. 2011; Jahan et al. 2020). Meanwhile, flavonoid compounds stimulate nerves and the production of IGF-1 (Harada et al. 2007).

Molecular docking analyses were utilized to predict the preferred binding modes of compounds from I. batatas leaves identified through LC-MS/MS results with the androgen receptor (AR). This involved binding affinity predictions and the identification of residual amino acid interactions. The objective of this analysis was to estimate the plant’s potential to stimulate hair growth by inhibiting androgen receptors. It aimed to predict the binding energy, indicating the plant’s inhibitory effect on androgen receptors and its ability to promote hair growth. Lower binding energy suggests higher binding efficiency and enhanced inhibition (Citra et al. 2023). Our focus included key amino acid residues like Tyr857, Gln858, Lys861, Glu793, Trp796, and Leu797 in the androgen receptor, crucial for ligand interaction (Hsu et al. 2014). The molecular docking process follows the procedure outlined by Henny et al. on the same receptor. This procedure yields an RMSD value of 1.64 Å, classified as valid, aiming to reproduce the minoxidil conformation on AR (Kasmawati et al. 2022a, 2022b).

Table 5 illustrates that the docking scores of 10 bioactive compounds in I. batatas leaves were lower than those of minoxidil and other compounds, ranging from -4.9 to -7.6 kcal/mol. Interestingly, the groups of flavonoid compounds (hyperoside and quercetin) as well as alkaloids (pyropheophorbide A and methyl-pyropheophorbide A) exhibit highly promising binding affinities to AR compared to minoxidil. These four compounds, have binding energies of -7.6, -7.5, -7.2, and -6.5 kcal/mol for hyperoside (1), pyropheophorbide A (2), methyl-pyropheophorbide A (3), and quercetin (4), respectively. Meanwhile, compounds 11 and minoxidil showed identical affinity energy. In contrast, compounds 12 and 13 demonstrated a more positive binding energy compared to the other compounds, with values of -4.5 kcal/mol and -3.8 kcal/mol, respectively.

Based on the energy analysis, we focused on examining the interaction patterns of the four best compounds (Fig. 3). Compound 1 forms four hydrogen bonds with Arg786, Glu793, Asp864, and Leu862 residues. Additionally, hydrophobic interactions with the AR binding pocket’s Lys861 and Asp864 residues were identified. Compounds 2 and 3 exhibit two hydrogen bonds with residues Arg786 and Ser865, and Glu793 and Leu862, respectively. Furthermore, these two compounds engage in hydrophobic interactions with Trp796, His789, Lys861, Leu797, and Pro868. Notably, in compound 4, only one hydrogen bond and hydrophobic interaction with Ser865 and Arg786 residues were observed. This analysis reveals that the four best compounds can interact with key residues of the AR while showcasing their ability to bind to this receptor.

The application of mixture design in pharmaceutical product development is an efficient method for optimizing formulation composition and gaining fundamental insights into the underlying relationship between independent and observed (dependent) variables (Basalious et al. 2010). In mixture design, the sum of all independent variables is kept constant, and only the relative proportions of each component are examined for their impact on the overall properties of the mixture (Martinello et al. 2006). While there are several types of mixture designs, such as simplex-centroid and simplex-lattice designs, D-optimal designs were chosen in this study for optimizing excipients when there are unusual restrictions on experimental settings. The criteria depend on minimizing the overall variance of the predicted regression coefficient by maximizing the value of the determinant of the information matrix (Karoui et al. 2023).

The best model was selected based on low standard deviation, low predicted sum of squares, and high R-squared p-values. The p-values of the acceptable models were lower than 0.05, and the p-values of lack of fit were higher than 0.05 (Amini Sarteshnizi et al. 2015). Following these response criteria, a special quartic model was the most suitable for pH results, while quadratic models were fitting for viscosity and density parameters.

Three independent variables (96% ethanol, propylene glycol, and tween 80 concentrations) were chosen, along with three response variables comprising hair tonic pH, viscosity, and density, to optimize physicochemical properties. Hair tonic is widely used to promote hair growth and strengthen hair follicles. Using excipients that can dissolve and enhance the penetration of active ingredients while maintaining good physicochemical properties is a crucial factor in hair tonic formulations. Ethanol, propylene glycol, and tween 80 play a role as solvents and penetration enhancers.

Sixteen experimental runs were conducted using Design Expert, and each product’s pH, viscosity, and density were determined as responses. The results of the experiments are shown in Table 9, where the formulation variables include: X₁: 96% ethanol; X₂: propylene glycol; X₃: tween 80, and the dependent variables include: Y₁: pH, Y₂: viscosity, Y₃: density.

Tables of ANOVA can be applied to assess how well the model and each parameter fit the data by analyzing the mean least square error estimates to the mean pure experimental error and ensuring that the errors are normally distributed. Then, the F-test can be used to evaluate the significance of the fit for both the model and the individual parameters (Jeirani et al. 2012). The F-test can determine whether parameters are significant or non-significant. This check applies a 95% confidence interval to assess the significance of the parameter. Therefore, a probability value (P-value) of 5% would be a significant level in the F-test for interpreting results (Pertiwi et al. 2023). This study utilized ANOVA to identify which factors significantly affect pH, viscosity, and density.

The statistical parameters applied in selecting and evaluating the best fitted model are regression data (p value and F value), lack-of-fit, coefficient of determination (R²), adjusted coefficient of determination (adjusted R²), and prediction coefficient of determination (prediction R²). Statistical analysis also builds the most suitable model equation (Table 10)

As shown from Table 10, for the pH model it is statistically significant (P_0.05); The overall model F-value of 8.53 and p-value of <0.05 illustrate that the model is considerable model terms. The p-value of the quartic model is 0.0098 (<0.05), which means that the results are significant. The lack of fit for pH is 0.5061 (>0.05). This value indicates good because there is no pH variation for each replication in the optimization process (Pertiwi et al. 2023). A negative predicted R² implies that overall means a better predictor of this response than the current model (Anderson et al. 2017).

Contour plots (Fig. 6) for each response were meticulously crafted utilizing the advanced features of Design-Expert software version 10 to achieve the utmost desirability in the optimization process. In-depth statistical scrutiny, detailed in Table 12, entailed a comprehensive one-sample test analysis to discern the optimal hair tonic based on prediction values generated by the software. The predictive value emanating from the solution exhibiting the highest desirability was carefully selected, attaining a specific balance with an exemplary desirability score of 0.881. This desirability was meticulously determined, considering the intricate interplay of ethanol, propylene glycol, and tween 80 (Fig. 6). The formulation of the hair tonic was intricately devised, aligning with the optimal levels dictated by the software’s sophisticated algorithms. Upon practical implementation, the observed response remarkably mirrors the predictive value derived from the meticulously optimized process, as briefly presented in Table 13. Impressively, the observed results fall seamlessly within the anticipated prediction index range, affirming the robustness and reliability of the optimization strategy employed.

Figure 6. 

Contour plot of pH, viscosity, and density.