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Article

Solubility Preformulation Screening of Minoxidil in Different Natural Oils Using Experimental and Computational Approaches

by
Khothatso Mapule Annah Motloung
,
Bwalya Angel Witika
and
Pedzisai Anotida Makoni
*
School of Pharmacy, Department of Pharmaceutical Science, Sefako Makgatho Health Sciences University, Pretoria 0208, South Africa
*
Author to whom correspondence should be addressed.
Processes 2025, 13(12), 4027; https://doi.org/10.3390/pr13124027
Submission received: 11 November 2025 / Revised: 4 December 2025 / Accepted: 11 December 2025 / Published: 12 December 2025
(This article belongs to the Section Pharmaceutical Processes)
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Abstract

Lipid nanocarriers present an opportunity to improve conventional drug delivery. In addition, the concomitant use of naturally occurring products with conventional medicines is garnering traction in therapeutic and cosmetic applications. Despite these advances, the rational design of lipid nanoparticles, including lipid selection, remains a challenge. We previously validated the use of Hansen solubility parameter (HSP) predictions for selecting synthetic lipids for utilization in lipid nanocarrier manufacture. Herein, we aimed to validate the use of HSP data to predict minoxidil solubility in natural and/or essential oils with known hair growth activity. We employed a dual-tiered screening strategy that integrated HSP predictions and experimental validation. Experimentally, minoxidil showed the highest solubility in shea butter, stearic acid, and rosemary oil. Further, the latter two lipids exhibited the lowest drug-lipid solubility parameter differences (ΔδT = 6.8 and 6.1 MPa1/2, respectively) and Relative Energy Difference values (1.28 and 1.61, respectively), aligning with the abovementioned laboratory experimental determinations. These findings provide a platform for the streamlined selection of natural oils which can enhance the solubility of minoxidil, in turn having implications for drug loading and/or encapsulation efficiency in formulation of lipidic carriers with potential synergistic hair growth potential. Moreover, this work adds to our understanding of reduced empirical excipient selection for potential decreased associated material costs during formulation development of lipid nanocarriers.

Graphical Abstract

1. Introduction

Alopecia, a prevalent dermatological condition marked by progressive hair loss, often leads to considerable psychological distress [1]. Androgenetic alopecia is a hereditary condition driven by an increased sensitivity of hair follicles to androgens. It leads to a gradual reduction in terminal scalp hair and generally manifests after puberty [2]. Throughout the course of life, androgenetic alopecia affects approximately 50–70% of males and an estimated 40–60% of females [1,3,4,5], reflecting its considerably high prevalence. Current treatment includes conventional minoxidil (a biopharmaceutical classification system [BCS] class II drug; Figure 1 [6]) formulations, commonly delivered as sprays or foams containing ethanol or propylene glycol as solvents. However, these dosage forms frequently induce local scalp irritation, dryness, burning, and allergic contact dermatitis and deposit a greasy, hard residue on the scalp, compromising patient compliance [7]. In addition, clinical efficacy is limited by low dermal retention and suboptimal follicular permeation, necessitating routine application to preserve therapeutic effectiveness [8].
Recent research has shifted focus toward enhanced delivery platforms that can overcome the physicochemical limitations of current minoxidil treatments. To this end, advanced drug delivery carriers such as nanoparticles [9,10,11,12] and microneedle arrays [13,14] have been suggested. In these aforementioned studies, improved hair growth was achieved, in comparison to conventional minoxidil formulations, based on the delivery system via improved residence time and penetration of nano-encapsulated minoxidil.
Processes 13 04027 g001
Figure 1. Chemical structure of minoxidil; log (Kow) = 1.24 [15,16].
Figure 1. Chemical structure of minoxidil; log (Kow) = 1.24 [15,16].
Processes 13 04027 g001
It is cardinal to conduct preformulation testing to design a dosage form of the desired critical quality attributes, thereby attaining maximal therapeutic efficacy. The type of data generated during preformulation studies is reliant on the target delivery system, as well as an in-depth understanding of the formulation and production steps that will be undertaken during dosage form development [17]. In particular, lipidic nanocarrier [solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs)] formulation success relies on the solubility of the active pharmaceutical ingredient (API) in the lipidic excipients to achieve adequate loading capacity and/or encapsulation for the desired therapeutic effect [18,19]. To complement experimental screening for these determinations, computational tools such as Hansen solubility parameter (HSP) predictions have gained traction [20]. Through breaking down cohesive energy into dispersion, polar, and hydrogen bonding components, HSP analysis enables quantitative prediction of drug–lipid affinity based on molecular structure [21]. This dual approach supports rational formulation design and reduces reliance on exhaustive and expensive laboratory experimentation.
A fundamental formulation aspect for successful lipid or polymeric nanocarriers is the ability of the carrier to retain the drug payload [22]. To this end, Sun et al. predicted the loading capability of hydrophobic drugs into methoxy polyethylene glycol-poly(D,L-lactide) using solubility parameters and experimental approaches [23]. Similarly, Vijayalakshmi et al. investigated the solubility of 5-fluorouracil and curcumin in different synthetic lipids [21], while Doktorovova et al. employed the same strategy in prescreening lipids for use in formulating curcumin-loaded SLNs [24]. Moreover, we previously performed an initial validation of HSP predictions for the selection of excipients for use in lipid nanocarrier manufacture as a potential substitute for performing tedious laboratory experimental studies. However, our previous investigation, similar to those mentioned above, focused solely on API solubility in synthetic lipids that have known fixed compositions of constituent fatty acids [25], with the use of such in silico techniques for natural/essential oil selection currently limited.
The combined use of natural products with conventional medicines has been reported to enhance therapeutic efficacy in clinical applications [26,27], a strategy that is potentially applicable to cosmetic enhancements. Natural oils are widely used in cosmetic formulations for hair care owing to their multifunctional properties, including moisturizing the scalp and hair, as well as their ability to improve the mechanical properties (e.g., tensile strength, texture, and elasticity) and growth of hair [28,29]. Owing to the above-mentioned literature, we postulate that the use of natural oils with hair-growth activity in formulating NLCs for the delivery of minoxidil will potentially exhibit synergistic therapeutic activity, in addition to harnessing the ability of increasing drug concentration at hair follicles from the lipidic nanocarriers. Therefore, herein, we report the findings of a preformulation study that aimed to investigate and validate the use of HSP in predicting the natural oils (solid and liquid with known hair-growth properties) with the best ability to dissolve minoxidil for the formulation of NLCs with potential for improved hair growth. In addition, we attempted to correlate the HSP findings with Relative Energy Difference (RED) values, which are useful in quantifying the likely compatibility/miscibility between two molecules; RED ≤ 1 indicates high affinity, whereas RED > 1 represents low affinity between a solute–solvent system [30,31]. The RED parameter, derived from Hansen solubility theory, offers a predictive framework for assessing molecular affinity based on dispersive, polar, and hydrogen bonding interactions [30]. We hypothesize that HSP in silico predictions can be validated with experimental manipulations for use in reliably identifying natural oils that enhance minoxidil solubility for subsequent loading into lipidic nanocarriers.

2. Materials and Methods

2.1. Materials

Unrefined cocoa butter, cold-pressed pumpkin seed oil, rosemary essential oil, and shea butter were purchased from Essentially Natural Products (Cape Town, Western Cape, South Africa). Stearic acid and coconut oil were purchased from Sigma Aldrich Chemical Co., (Milwaukee, WI, USA). It should be noted that although the stearic acid used in these studies was synthetic, its inclusion was justified by its presence as a constituent in various natural plant and animal oils used for hair care, and its ability to induce hair growth through inhibiting 5α-reductase [32,33]. Soybean oil, flaxseed oil, and olive oil were purchased from Escentia Products Pty Limited (Benoni, Gauteng, South Africa). Minoxidil was purchased from Skyrun Industrial Co. Limited (Taizhou, China). High-pressure liquid chromatography (HPLC)-grade methanol was purchased from Fisher Scientific (Loughborough, England, UK). Nevirapine was donated by Adcock Ingram (Johannesburg, South Africa). HPLC-grade water was prepared using a Direct-Q® εt Direct-Q UV water purification system with a resistivity of 18.2 MΩ.cm at 25 °C (Merck KGaA, Darmsdadt, Germany).

2.2. Solubility Studies

2.2.1. Solubility Parameter Calculations

To predict the affinity of the API (minoxidil) for the various lipidic excipients investigated herein, viz., stearic acid, shea butter, coconut oil, and cocoa butter (solid lipids) and rosemary, flaxseed, pumpkin seed, soybean, and olive oils (liquid lipids), HSP values were calculated for the API and each individual lipid computationally in situ. In particular, for minoxidil and stearic acid, the solubility parameters were determined based on molecular structures using Hiroshi Yamamoto’s molecular breaking method (Y-MB), under the DIY tab in Hansen Solubility Parameters in Practice (HSPiP) software (Hansen Solubility, Hørsholm, Denmark, version 6.0.04). The chemical structures of minoxidil and stearic acid were initially converted into their Simplified Molecular Input Line Entry System (SMILES) notations on PubChem (https://pubchem.ncbi.nlm.nih.gov/, accessed 26 October 2025). These SMILES strings were then utilized for in silico calculation of the individual dispersion (δD), polar (δP), and hydrogen bonding (δH) contributions to the total solubility parameter in situ. Similarly, for all natural oils, their individual constituents were obtained by performing a literature search [34], and the SMILES strings of the major constituents in each natural or essential oil were input adjacently into Y-MB to obtain the average δD, δP, and δH contributions, in situ, for the total solubility parameter. The computed HSP values, expressed in MPa1/2, were used to inform lipid selection based on HSP theory. In addition, the octanol water partition coefficient of each lipid constituent, including the associated structure, was obtained from the HSPiP platform and recorded. To determine RED scores for each lipid in relation to minoxidil, the abovementioned δD, δP, and δH contributions for each molecule were input into the HSPiP software to obtain the associated values.

2.2.2. Experimental Solubility Studies

Selection of Solid Lipids
An accurately weighed quantity (4 g) of each solid lipid (stearic acid, shea butter, coconut oil, and cocoa butter) was obtained using a Model RADWAG AS 220.R2 top-loading analytical balance (Radwag, Toruńska 5, 26-600 Radom, Poland), placed into a glass test tube prior to adding 15 mg of minoxidil (excess drug amount). The mixture was maintained at 80 °C [maintaining lipid melts approximately 5–10 °C above the melting point of the lipid [35] with the highest melting point (70 °C for stearic acid)] in a Series 2000 digital oven (Scientific, Johannesburg, South Africa) for 24 h to prevent selective melting of lower-melting lipids and facilitate equilibrium solubilization. Post-equilibration, the molten samples were removed from the oven and allowed to equilibrate to room temperature (22–25 °C) for approximately 1 h prior to HPLC analysis. To this end, a 10 mg sample (top layer with dissolved drug) was accurately weighed and dissolved in 10 mL of HPLC-grade methanol prior to ultrasonication for 30 min in a Biobase UC-100A ultrasonic cleaner (Jinan Biobase Medical Co., Ltd., Shandong, China) to allow complete drug dissolution. Quantification of filtered (0.45 µm HVLP Durapore® membrane filter, Millipore® Corporation, Bedford, MA, USA) samples was achieved using a SHIMADZU LC-2050C 3D Importer (Milton Keynes MK12 SRE, UK) instrument. Separation was performed on a C18 Shimadzu 5 µm 250 mm (i.d 4.6 mm) column using a methanol: water (51:49% v/v) mobile phase at an injection volume of 10 µL following method development using a structured Design of Experiments approach and validation as per International Conference on Harmonization guidelines [36]. Nevirapine was used as the internal standard, and detection occurred at the characteristic maximum absorption wavelength of the API (262 nm) [37]. All experimental studies were performed in triplicate (n = 3), and each result is reported with the associated standard deviation (SD).
Selection of Liquid Lipids
Liquid lipid candidates, including rosemary, flaxseed, pumpkin seed, soybean, and olive oils underwent an analogous solubilization process, where 15 mg of minoxidil (excess drug amount) was introduced into 4 mL of each oil and thermally equilibrated at 80 °C for 24 h in a Series 2000 digital oven (Scientific, Johannesburg, South Africa), to allow for sufficient molecular diffusion and thermodynamic equilibration of the drug within the heated-molten lipid matrix. Post-equilibration, samples were left at ambient temperature for approximately 1 h. Next, a 1 mL aliquot of the top layer (without undissolved drug) was dispersed in 9 mL of HPLC-grade methanol prior to ultrasonication and quantification of the drug using HPLC as described in Section Selection of Solid Lipids above. All experimental studies were performed in triplicate (n = 3), and each result is reported with the associated SD.
Statistical Analysis of Lipid Solubility Data
Solubility values for liquid lipids and solid lipids were presented as mean concentration values with corresponding standard deviations (SD). The dataset had identical repeating values for each lipid, rendering the variance structure unsuitable for inferential statistics.
A one-way Analysis of Variance (ANOVA) was conducted for each dataset (liquid and solid lipids), utilizing lipid type as the categorical variable. In instances where ANOVA revealed significant variations among group means, Tukey’s Honestly Significant Difference (HSD) post hoc test was utilized to determine the specific pairs of lipids that exhibited differences.
A significance level of p < 0.05 was utilized. Due to the exceptionally large effect sizes in relation to standard deviations, all comparisons satisfied the criteria for family-wise error correction.

3. Results and Discussion

The results of the in silico API-miscibility predictions are depicted in Table 1. Irrespective of the fatty acid constituents, the δD for the investigated lipids remained constant at approximately 16 MPa1/2 based on a linear increase in molecular volume from the change in energy, thereby resulting in the same energy density. High δP values have been reported to result in the ability of the molecule to improve the orientation of its electric charge, in turn resulting in increased solubility [34], as was the case for stearic acid (solid lipid of choice) and rosemary oil (liquid lipid of choice) (Table 1). Regarding solid lipids, cocoa butter and refined shea butter showed identical ΔδT values, implying indistinguishable miscibility of minoxidil in these natural oils. Because our previous model on the use of HSP in predicting drug-lipid solubility/miscibility recommends experimental confirmation when the difference between API and lipid total solubility parameters is >4.0 MPa1/2 [25], quantitative experimental solubility determinations were conducted, confirming the superiority of stearic acid and differences in actual solubilities of the API in the aforementioned lipids with identical ΔδT values (Figure 2).
The main fatty acid constituents in refined shea butter include palmitic, stearic, oleic, linoleic, and arachidic acids [38]. Correspondingly, cocoa butter has identical constituents [39], with likely differing percentage compositions. Those of coconut oil include lauric acid, myristic acid, and palmitic acid [40]. Stearic acid, which in its pure form showed the best API-lipid solubility prediction for the solid lipids, is postulated to be higher in refined shea butter than in cocoa butter, possibly contributing to higher drug solubility in the former as observed in the laboratory manipulations (Figure 2).
Regarding solubility in liquid lipids, rosemary oil was predicted to dissolve the most minoxidil of all investigated natural oils based on having the lowest ΔδT value of 6.1 MPa½. These findings were corroborated by laboratory experimental determinations as depicted in Figure 3.
The major constituents in flaxseed oil include palmitic and stearic acid, oleic acid, and linoleic acid [41]. Similarly, pumpkin seed oil contains palmitic acid, stearic acid, oleic acid, and linoleic acid [42]. The major constituents in olive oil are palmitic acid, palmitoleic acid, stearic acid, oleic acid, and linoleic acid [43]. Soybean oil is mainly composed of linoleic acid, oleic acid, palmitic acid, linolenic acid, and stearic acid [44]. In contrast to the rather similar fatty acid constituents and higher log (Kow) values (Table 2) in the above mentioned liquid lipids, rosemary oil is mainly composed of pinene (cyclic monoterpene; log (Kow) = 4.3), eucalyptol (cyclic oxygenated monoterpene; log (Kow) = 3.14), and camphor (cyclic oxygenated monoterpene; log (Kow) = 2.3) [45], which have relatively more similar log (Kow) values to minoxidil (1.24 [15]), which could have resulted in its superiority in dissolving minoxidil based on the “like dissolves like” principle [46]. Furthermore, in contrast to triglyceride-rich oils that are primarily of low polarity and have low hydrogen-bonding capacity, the terpenoids and phenolic compounds contained in rosemary oil [45] confer increased polarity and hydrogen-bonding capabilities, aligning more closely with minoxidil functional groups.
All liquid and solid lipids exhibited distinct solubility profiles with no overlap between mean values. ANOVA followed by Tukey HSD testing confirmed that every pair of lipids differed significantly (adjusted p < 0.001 in all comparisons). Because all groups were statistically distinct and the differences between means were orders of magnitude larger than the measurement variability, the use of graphical significance markers (e.g., asterisks or letter groupings) was considered redundant.
The calculated RED values of the investigated solid and liquid lipids in relation to minoxidil are presented in Table 3.
HSP, RED, and experimental data were aligned in terms of minoxidil solubility in the investigated solid lipids and liquid lipids. Similarly to HSP data, the lowest RED values obtained for solid and liquid lipid predictions, indicating the best API dissolving potential, were for rosemary oil and stearic acid, respectively (Figure 4 and Figure 5). Nevertheless, RED ≤ 1 is reported to indicate high affinity between a given solute–solvent system, while RED > 1 presents low affinity, with deviations observed for large molecules. Owing to this, it has been suggested that RED predictions be confirmed experimentally [31,47], as performed herein.
Put together, these findings assist in the streamlined selection of two solid lipids (stearic acid and shea butter) and one liquid lipid (rosemary oil), having hair-growth properties, with the best potential to dissolve minoxidil. This has implications for drug loading and/or encapsulation efficiency, which will ensue during formulation development and stability studies, in turn allowing for potentially better synergism of the developed NLCs from higher drug loading than what would have resulted from the eliminated lipids.

4. Conclusions

With the growing application of nanotechnology and the use of natural oils in topical applications, rational design of dosage forms that deliver the desired critical quality attributes is essential. The findings of this study add to our understanding of the use of in silico techniques in streamlining the selection of lipidic excipients for use in the development of lipidic nanocarriers, in turn paving the way for the elimination of the need for empirical excipient selection and potentially minimizing material use associated with repetitive trial-and-error laboratory operations. Tedious laboratory experimentation can be eliminated by performing a simple literature search and applying the “like dissolves like” principle based on the log (Kow) values of a particular API and those of the main constituents of natural/essential oils using HSPiP and other available in silico software. Nonetheless, the generalizability of our findings is limited by the investigation of a single BCS class molecule, requiring the use of other model APIs to fully validate the utility of the proposed strategy for lipid screening during the manufacture of lipidic nanocarriers. Furthermore, the computational model is limited by using averaging assumptions for multicomponent oils based on their major individual constituents, which could be a source of error. Future validation studies initially using other BCS class II molecules, and eventually other BCS class drugs, are required to determine whether the proposed model for screening natural lipids for the manufacture of nanocarriers is scalable for potential industrial application. Moreover, the development of machine-learning models built from partial least squares regression, artificial neural networks, principal component analysis, and K-nearest neighbors algorithms based on HSP data would be beneficial for improving the accuracy and success rate of the model, thereby enabling its use in formulation pipelines.

Author Contributions

Conceptualization, P.A.M. and B.A.W.; methodology, K.M.A.M.; software, P.A.M. and B.A.W.; validation, P.A.M., B.A.W. and K.M.A.M.; formal analysis, P.A.M., B.A.W. and K.M.A.M.; investigation, K.M.A.M.; resources, P.A.M. and B.A.W.; data curation, P.A.M., B.A.W. and K.M.A.M.; writing—original draft preparation, K.M.A.M. and P.A.M.; writing—review and editing, P.A.M. and B.A.W.; visualization, K.M.A.M.; supervision, P.A.M. and B.A.W.; project administration, P.A.M. and B.A.W.; funding acquisition, K.M.A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation (Grant number: PMDS240626233713).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
HSPHansen Solubility Parameter
BCSBiopharmaceutical classification system
APIActive pharmaceutical ingredient
Log (Kow)Octanol-water partition coefficient
NLCsNanostructured lipid carriers
REDRelative Energy Difference
HSPiPHansen Solubility Parameters in Practice
SLNsSolid lipid nanocarriers
SMILESSimplified Molecular Input Line Entry System
HPLCHigh-performance liquid chromatography

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Processes 13 04027 g002
Figure 2. Solubility of minoxidil in 4.0 g of different solid lipids at 80 °C (n = 3; SD).
Figure 2. Solubility of minoxidil in 4.0 g of different solid lipids at 80 °C (n = 3; SD).
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Processes 13 04027 g003
Figure 3. Solubility of minoxidil in 4.0 mL of different liquid lipids at 80 °C (n = 3; SD).
Figure 3. Solubility of minoxidil in 4.0 mL of different liquid lipids at 80 °C (n = 3; SD).
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Processes 13 04027 g004
Figure 4. Scatter plot depicting the relationship between drug solubility (concentration) in liquid lipids and RED values. The legend in the upper right-hand box depicts the ranges in ΔδT values.
Figure 4. Scatter plot depicting the relationship between drug solubility (concentration) in liquid lipids and RED values. The legend in the upper right-hand box depicts the ranges in ΔδT values.
Processes 13 04027 g004
Processes 13 04027 g005
Figure 5. Scatter plot depicting the relationship between drug solubility (concentration) in solid lipids and RED values. The legend in the upper right-hand box depicts the ranges in ΔδT values.
Figure 5. Scatter plot depicting the relationship between drug solubility (concentration) in solid lipids and RED values. The legend in the upper right-hand box depicts the ranges in ΔδT values.
Processes 13 04027 g005
Table 1. Solubility parameters of minoxidil and lipidic excipients.
Table 1. Solubility parameters of minoxidil and lipidic excipients.
API-Lipid SystemδD (MPa½)δP (MPa½)δH (MPa½)δT (MPa½)ΔδT (MPa½)
Minoxidil18.77.813.224.2
Solid lipids
Cocoa butter16.51.22.116.67.6
Shea butter (refined)16.51.22.116.67.6
Stearic acid16.22.95.717.46.8 *
Coconut oil16.42.44.117.07.2
Liquid lipids
Flaxseed oil16.51.72.716.87.4
Olive oil16.51.42.316.77.5
Pumpkin seed oil16.51.72.716.87.4
Rosemary essential oil17.82.81.518.16.1 *
Soybean oil16.61.42.316.87.4
* Solid and liquid lipids predicted to have the best potential to dissolve minoxidil.
Table 2. Chemical structures and log (Kow) values of investigated lipid constituents.
Table 2. Chemical structures and log (Kow) values of investigated lipid constituents.
Compound or IUPAC NameChemical StructureLog Kow
Palmitic acidProcesses 13 04027 i0016.8
Stearic acidProcesses 13 04027 i0027.9
Oleic acidProcesses 13 04027 i0037.7
Linoleic acidProcesses 13 04027 i0047.2
Palmitoleic acidProcesses 13 04027 i0056.6
Linolenic acidProcesses 13 04027 i0066.5
Lauric acidProcesses 13 04027 i0074.8
Myristic acidProcesses 13 04027 i0085.8
Arachidic acidProcesses 13 04027 i0098.8
PineneProcesses 13 04027 i0104.3
EucalyptolProcesses 13 04027 i0113.1
CamphorProcesses 13 04027 i0122.3
Table 3. RED values of the different lipids based on HSP data.
Table 3. RED values of the different lipids based on HSP data.
DrugδD (MPa½)δP (MPa½)δH (MPa½)RED
Minoxidil18.77.813.20.00
Solid Lipids
Stearic acid16.22.95.71.28 *
Cocoa butter16.51.22.11.71
Shea butter (refined)16.51,22.11.71
Coconut oil16.42.44.11.44
Liquid Lipids
Pumpkin seed oil16.51.72.71.61 *
Rosemary essential oil17.82.81.51.61 *
Flaxseed oil16.51.72.71.61 *
Soybean oil16.61.42.31.66
Olive oil16.51.42.31.67
* Solid and liquid lipids with the highest predicted potential to dissolve minoxidil.
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Motloung, K.M.A.; Witika, B.A.; Makoni, P.A. Solubility Preformulation Screening of Minoxidil in Different Natural Oils Using Experimental and Computational Approaches. Processes 2025, 13, 4027. https://doi.org/10.3390/pr13124027

AMA Style

Motloung KMA, Witika BA, Makoni PA. Solubility Preformulation Screening of Minoxidil in Different Natural Oils Using Experimental and Computational Approaches. Processes. 2025; 13(12):4027. https://doi.org/10.3390/pr13124027

Chicago/Turabian Style

Motloung, Khothatso Mapule Annah, Bwalya Angel Witika, and Pedzisai Anotida Makoni. 2025. "Solubility Preformulation Screening of Minoxidil in Different Natural Oils Using Experimental and Computational Approaches" Processes 13, no. 12: 4027. https://doi.org/10.3390/pr13124027

APA Style

Motloung, K. M. A., Witika, B. A., & Makoni, P. A. (2025). Solubility Preformulation Screening of Minoxidil in Different Natural Oils Using Experimental and Computational Approaches. Processes, 13(12), 4027. https://doi.org/10.3390/pr13124027

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