# Exploring traditional Japanese herbal ointments for wound healing: A wide diversity of practices, metabolites, and effects

Manon Paul-Traversaz, Kaoru Umehara, Kenji Watanabe, Walid Rachidi, Eric Sulpice, Emmanuelle Soleilhac, Cédric Delporte, Pierre van Antwerpen, Axelle Bourez, Cécile Vanhaverbeke, et al.

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## To cite this version:

Manon Paul-Traversaz, Kaoru Umehara, Kenji Watanabe, Walid Rachidi, Eric Sulpice, et al. Exploring traditional Japanese herbal ointments for wound healing: A wide diversity of practices, metabolites, and effects. Journal of Ethnopharmacology, 2026, 355, pp.120651. 10.1016/j.jep.2025.120651. hal-05349390

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Submitted on 5 Nov 2025

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# Exploring traditional Japanese herbal ointments for wound healing: A wide diversity of practices, metabolites, and effects

Manon Paul-Traversaz<sup>a,b,c,d,*</sup> Kaoru Umehara<sup>b,c</sup>, Kenji Watanabe<sup>c</sup>, Walid Rachidi<sup>d</sup>, Eric Sulpice<sup>d</sup>, Emmanuelle Soleilhac<sup>d</sup>, Cédric Delporte<sup>f,h</sup>, Pierre Van Antwerpen<sup>f,h</sup>, Axelle Bourez<sup>f,h</sup>, Cécile Vanhaverbeke<sup>e</sup>, Michel Sève<sup>a,1</sup>, Florence Souard<sup>e,g,h,1</sup>

## ARTICLE INFO

### Keywords:

Kampo

Skin

Wound healing

Traditional medicine

Herbal ointments

Metabolomics

## ABSTRACT

**Ethnopharmacological relevance:** In Kampo Japanese traditional medicine, ointments are used to treat skin wounds and promote wound healing. These ointments are prepared by extracting herbal crude drugs in sesame oil, a technique that has been practised for centuries. Their preparation varies significantly in terms of ingredients, plant species, temperature, and extraction time, leading to a wide diversity of formulations. Unlike hydrophilic plant extracts, the phytochemical composition and wound-healing efficacy of sesame oil-based herbal extractions remain largely unexplored.

**Objective:** The objective of this study is to address this gap by focusing on lipophilic plant metabolites present in these topical formulations and to explore their potential contribution to wound healing using a keratinocyte model *in vitro*.

**Materials and methods:** A survey was conducted to document Kampo ointment-related practices, focusing on highlighting variations in extraction protocols, crude drug origins, and manufacturing practices. Sample preparation excluded solid compounds from the formulations and used different solvents for the metabolomic and biological aspects. Metabolomics profiling was performed using LC-HRMS and LC-HRMS/MS to analyse the impact of different preparation temperatures and botanical sources on extract composition. *In vitro* assays assessed keratinocyte migration and proliferation using scratch assays and EdU (5-ethynyl-2'-deoxyuridine) incorporation and data were evaluated with appropriate statistical methods.

**Results:** A diversity of practices concerning the Kampo ointments *Shiunkō*, and *Shinsen taitsukō* was identified. This mainly concerned the extraction temperature with three protocols: maceration (M), Low Heating (LH) and High Heating (HH). Significant differences in metabolite profiles were observed, particularly with regard to the heating temperature of the sesame oil and the origin of the crude drugs. These parameters markedly influenced the chemical composition through both the formation of transformed lipids and the degradation of bioactive compounds such as tocopherols and sterols. While the variety of sesame oils and *Angelica acutiloba* extracts showed no distinct biological effects on keratinocyte migration, the complete formulas *Shiunkō* and *Shinsen taitsukō* unexpectedly delayed wound closure in the model. This result is possibly due to the degradation of wound-healing metabolites and the presence of cytotoxic ones, or the inadequacy of the cell model used to evaluate these complex lipophilic preparations.

**Conclusion:** This study highlights the diversity and complexity of traditional Kampo ointments in terms of chemical composition and, to a lesser extent, biological activity. Preparation temperature and ingredient origin

<footnote><sup>a</sup> Université Grenoble Alpes, CNRS, TIMC UMR 5525, EPSP, Grenoble, 38000, France</footnote>

<footnote><sup>b</sup> Yokohama University of Pharmacy, Kampo Natural Product Chemistry Laboratory, 245-0066, Yokohama, Japan</footnote>

<footnote><sup>c</sup> Yokohama University of Pharmacy, 245-0066, Yokohama, Japan</footnote>

<footnote><sup>d</sup> Université Grenoble Alpes, CEA, Inserm, IRIG, BGE, Grenoble, 38000, France</footnote>

<footnote><sup>e</sup> Université Grenoble Alpes, CNRS, DPM UMR 5063, Grenoble, 38000, France</footnote>

<footnote><sup>f</sup> Université Libre de Bruxelles, Faculty of Pharmacy, RD3 Unit of Pharmacognosy, Bioanalysis and Drug Discovery, Campus Plaine, CP 205/07, Brussels, 1050, Belgium</footnote>

<footnote><sup>g</sup> Université Libre de Bruxelles, Faculty of Pharmacy, DPP Department - Unit of Pharmacology, Pharmacotherapy and Pharmaceutical Care, Campus Plaine, CP 205/05, Brussels, 1050, Belgium</footnote>

<footnote><sup>h</sup> Université Libre de Bruxelles, Faculty of Pharmacy, APFP Analytical Platform of the Faculty of Pharmacy, Campus Plaine, CP 205/05, Brussels, 1050, Belgium</footnote>

<footnote>* Corresponding author. Université Grenoble Alpes, LECA UMR 5553, CNRS, Grenoble, 38000, France.
E-mail address: manon.traversaz@univ-grenoble-alpes.fr (M. Paul-Traversaz).</footnote>

<footnote n="1">¹ These authors contributed equally to this work.</footnote>
markedly shaped the metabolite profiles, affecting both the formation of new lipophilic compounds and the degradation of healing-related metabolites. Higher heating temperatures and longer extraction times promoted the formation of modified lipid compounds and the loss of bioactive metabolites, such as tocopherols and sterols. Meanwhile, the botanical origin of *Angelica acutiloba* affected the quantity of glycosylated terpenoids associated with wound healing. Complete formulas were found to reduce keratinocyte migration in an *in vitro* monolayer model. Targeted metabolomics suggested a loss of vitamin E-like hydroquinone lipids in *Shiunkō* and the presence of an esterified diterpene in *Shinsen taitsukō* as possible contributors. The direct clinical relevance of these observations remains uncertain. These results emphasise the need for in-depth investigations into crude drug oily extractions, which are still underexplored. Research into traditional Kampo ointments could benefit from a more integrated approach that combines metabolomics with innovative biological models, in order to better elucidate their therapeutic applications in wound care.

Table of abbreviations

- ADGA Acyl-diacylglycerol glucoside

- Amu Atomic Mass Unit

- 2D; 3D Two; Three Dimensional

- AGE Advanced Glycation End-product

- ANOVA Analysis of Variance

- CANOPUS: Class Assignment and Ontology Prediction Using mass Spectrometry (tool in SIRIUS for predicting compound classes from MS/MS spectra)

- Ch1; Ch2 Channel 1; Channel 2 (in fluorescence imaging)

- CHH High Heating for *Chuōkō* (sesame oil)

- Click-iT EdU Imaging Kit Fluorescent labeling kit for proliferating cells based on EdU incorporation; Thermo Fisher Scientific™

- Cy3 Cyanine 3

- Da Dalton (unit of atomic mass equivalent to 1 amu)

- DAGs DiAcylGlycerides

- DMSO Dimethyl Sulfoxide

- DNA Deoxyribonucleic Acid

- EdU 5-Ethynyl-2'-deoxyuridine (proliferation marker)

- EGF Epidermal Growth Factor

- Epilife: Defined keratinocyte culture medium; Gibco™

- ESI ElectroSpray Ionisation

- FA Fatty Acid

- FDR False Discovery Rate

- FFAs Free Fatty Acids

- FGF Fibroblast Growth Factor

- GNPS Global Natural Products Social Molecular Networking

- GPCR G Protein-Coupled Receptor

- HCS High Content Screening

- HH High Heating (for sesame oil)

- HKGS Human Keratinocyte Growth Supplement; Gibco™

- HRMS High-Resolution Mass Spectrometry

- hTERT Human Telomerase

- InChI International Chemical Identifier

- IPA IsoPropyl Alcohol

- ISDB In Silico DataBase

- IkB IkappaB kinase

- JKMA Japanese Kampo Manufacture Association

- LC-MS Liquid Chromatography-Mass Spectrometry

- LC-MS/MS Liquid Chromatography-Tandem Mass Spectrometry

- LED Light Emitting Diode

- LH Low Heating (for sesame oil); LH1 and LH2 are for two different sesame oil batches

- LOTUS: natural preOducTs occUurrence database

- M Maceration (for sesame oil); M1 and M2 are for two different sesame oil batches

- m/z Mass-to-charge ratio

- MAGs MonoGlycerides

- MS Mass Spectrometry

- MSI Metabolomics Standards Initiative

- N/TERT-2G Immortalised Human Keratinocyte Cell Line

- NCBI National Center for Biotechnology Information

- NF-κB Nuclear Factor kappa-light-chain-enhancer of activated B cells

- NLRP3 NOD-like receptor family, pyrin domain containing 3

- NMR Nuclear Magnetic Resonance

- OPTI-MEM Minimum Essential Medium; Gibco™

- PBS Phosphate-Buffered Saline

- PC Phosphatidylcholine

- PCA Principal Component Analysis

- PFA ParaFormAldehyde

- PGs PhosphatidylGlycerols

- PLS-DA Partial Least Squares Discriminant Analysis

- PMDA Pharmaceuticals and Medical Devices Agency (in Japan)

- Q-TOF Quadrupole Time-of-Flight Mass Spectrometry

- QC Quality Control (metabolomics)

- RF Random Forest

- ROI Region Of Interest (cell imaging)

- ROS Reactive Oxygen Species

- Rt Retention time (chromatography)

- RWD Relative Wound Density

- RWH Relative Wound Healing

- SO1 and SO2 Sesame Oil batches

- S1M; LH; HH *A. acutiloba* extract for *Shiunkō* (Maceration; Low Heating; High Heating)

- S6HH *A. acutiloba* + *L. erythrorhizon* extract for *Shiunkō* (High Heating)

- SHH High Heating for *Shiunkō* (sesame oil)

- SVD Singular Value Decomposition

- SVR Support Vector Regression

- TAGs TriAcylGlycerides

- T1M1; T1LH1 *A. acutiloba* extract for *Shinsen taitsukō* (Maceration and Low Heating)

- TGF-β: Transforming Growth Factor Beta

- TLHO *Shinsen taitsukō* oily extract (Low Heating)

- TMO *Shinsen taitsukō* oily extract (Maceration)

- VIP Variable Important in Projection

- WFO World Flora Online

- W4M Workflow4Metabolomics

- XLogP3 Predicted lipophilicity (log P) index

- XLogP3-AA Predicted lipophilicity (log P) index with Atom Additive contributions

- ZODIAC Zodiac Optimization-based Determination of InChI confidence (algorithm improving molecular formula annotations in SIRIUS)
# 1. Introduction

Kampo represents one of the constituent elements of traditional Japanese herbal medicine. Derived from a syncretic interpretation of ancient Chinese medicinal practices and Japanese folk medicine, Kampo integrates botanical remedies aimed at restoring balance and promoting health (Yu et al., 2006). Although Kampo formulas are most commonly associated with the oral administration of decoctions and powdered extracts, they are also represented by other galenic forms, such as ointments, which are the subject of this study. Three topical Kampo remedies are found in Japan: *Shiunkō* (紫雲膏), *Chuōkō* (中黄膏), and *Shinsen taitsukō* (神仙太乙膏). These products are defined by a common preparation method wherein herbal crude drugs are extracted in sesame oil before being thickened with pasty agents such as beeswax.

These formulas are used for a variety of therapeutic indications, many of which are closely related to broader conditions such as skin dermatitis or infections, which may in some way be linked to wound healing. While herbal ointments have shown promising effects in this field, scientific evidence remains limited due to the scarcity of publications and clinical trials. Several *in vitro* and *in vivo* studies have reported the beneficial effects of Kampo on skin regeneration, with notable examples being *Shiunkō*, a well-known Kampo ointment used for treating burns, ulcers, and skin fissures (Higaki et al., 1999; Nakase et al., 2019; Okunaka et al., 2024). However, while these Kampo formulations have already been investigated, it should be noted that the existing bibliography on the natural ingredients present is difficult to extrapolate. This is mainly because studies have predominantly focused on hydrophilic herbal extracts (hydro-ethanolic or methanolic), neglecting the potential diversity of bioactive compounds found in lipophilic matrices (Al-Nemi et al., 2022). However, sesame oil matrices used for extracting crude drugs could provide new insights into the wound-healing properties of traditional herbal remedies (Paul-Traversaz et al., 2023).

A non-destructive sample preparation combined with oil-based plant extraction and mass spectrometry techniques (LC-MS and LC-MS/MS) offers a robust approach for elucidating the complex chemical composition of Kampo ointments. Researchers have increasingly been opting for a rational approach merging untargeted metabolomics and ethnopharmacology, with a particular focus on traditional natural remedies (Ahmad et al., 2022; Shi et al., 2016). These studies address the scarcity of bibliographic resources on oil-based extracts, as most research focuses on ethanol or methanol extracts, which do not accurately reflect the formulations used in traditional practice. This underscores the necessity of examining Kampo ointments from a lipophilic perspective, given that their traditional preparations involve oil-based extractions.

An inherent challenge in Kampo, as in all traditional herbal medicines, lies in the variability of ingredients and preparation methods. Factors such as botanical species selection, geographical origin, harvesting conditions, and herbal crude drug processing (e.g., boiling, specific drying, or peeling the cortical part of the root) introduce significant variability in the chemical composition of Kampo remedies. This diversity yields distinct metabolite profiles, which may modify biological efficacy.

Wound healing is a complex multi-phase process. To study it using a

cell model, it is recommended to consider both migration and proliferation. Keratinocyte migration can be assessed using scratch assays, thus simulating a breach similar to an injury (Liang et al., 2007). In addition to migration, proliferation, which plays a significant role (Pastar et al., 2008), can be measured using EdU (5-ethynyl-2'-deoxyuridine) incorporation assays (Moriyama et al., 2016). By analysing these parameters, an evaluation of the impact of Kampo ointments on keratinocyte behaviour is possible, in addition to providing insights into their wound-healing potential. Cell migration and proliferation mechanisms are closely linked, and their distribution might be imagined in the following way: migration is the predominant mechanism in this phase, accounting for approximately 60 % of the process, while proliferation contributes the remaining 40 % (Zanca et al., 2022). The function of keratinocytes in re-epithelialisation is stimulated by growth factors such as EGF, FGF, and TGF-β, making these cells an ideal model for the study of wound healing (Jiang et al., 2020). Healing can be evaluated *in vitro* or *in vivo*, with varying degrees of complexity, ranging from single-cell layer models to multicellular environments. In this study, an *in vitro* model with immortalised keratinocytes was chosen, focusing on migration and proliferation. This method facilitates screening of the effects of Kampo ointment extracts on keratinocytes to better understand their potential wound-healing benefits. Given the rise of chronic wound health issues, investigation into herbal formulations such as Kampo ointments may constitute valuable research in this field (Hegde et al., 2021; Xu et al., 2023).

The aim of this study is to deepen the understanding of how the diversity of materials and practices influences the chemical and biological properties of Kampo oil-based extracts. By examining metabolite expression in lipophilic matrices and assessing their effects on keratinocyte wound healing models, we hope to contribute to ongoing efforts to rationalise the use of traditional topical herbal remedies.

# 2. Materials and Methods

## 2.1. Collection of manufacturing practices

An ethnopharmacological approach, involving interviews with Japanese medical doctors (n = 2), pharmacists (n = 4), and herbal medicine producers (n = 3), was taken to explore practices associated with Japanese ointments for wound healing. These investigations were conducted in the Tokyo, Kanagawa, and Nara prefectures in 2021. This investigation was complemented by several reference works on Kampo preparations (Japan Pharmaceutical Manufacturers Association, 2005, 2013). Based on this survey, three Kampo formulas were initially selected: *Shiunkō*, *Chuōkō*, and *Shinsen taitsukō*. *Shiunkō* and *Shinsen taitsukō* were the two studied here.

## 2.2. Crude drug materials

The supply of herbal crude drugs and sesame oil for the preparation of oily extracts complies with the Convention on Biological Diversity, since freely available herbal products were used. The authentication of materials, after receipt of products ordered from Japanese manufacturers, was carried out by macroscopic methods as described in the 18th

<table><caption>**Table 1**

Major abbreviations for Kampo ointments and their preparation.</caption><thead><tr><th></th><th>Shiunkō</th><th>Chuōkō</th><th>Shinsen taitsukō</th><th></th></tr></thead><tbody><tr><td>M</td><td>S<sub>n</sub>M</td><td>–</td><td>T<sub>n</sub>M<sub>n</sub></td><td>Kampo ointment crude drug extraction in sesame oil</td></tr><tr><td>LH</td><td>S<sub>n</sub>LH</td><td>–</td><td>T<sub>n</sub>LH<sub>n</sub></td><td>Maceration: at room temperature for seven days</td></tr><tr><td>HH</td><td>S<sub>n</sub>HH</td><td>CHH<sup>a</sup></td><td>–</td><td>Low Heating: at 80 °C for 30 min</td></tr><tr><td></td><td></td><td></td><td></td><td>SHH: High Heating of sesame oil before extraction: 200–230 °C for 1 h</td></tr><tr><td></td><td></td><td></td><td></td><td>CHH: High Heating before extraction: 200 °C for 10 min</td></tr><tr><td></td><td></td><td></td><td></td><td>SHH: crude drug extraction: 170 °C/140 °C for 5 min (CHH: 200 °C then stop heating)</td></tr></tbody></table>

<footnote><sup>a</sup> Chuōkō is investigated in this study only through the crude-drug-free oil control. In practice, however, it can be manufactured using the three methods M, LH and HH. The suffix “n” in the extract names is detailed in Table 2.</footnote>
<caption>**Table 2**</caption>

Crude drug details (botanical species, origin, manufacturer, batch, and harvesting date) and nomenclature of extracts related to *Shiunkō* and *Shinsen taitsukō* formulas.

<table><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Sesame oils</td><td>Controls</td><td><strong>Sesamum indicum L.</strong><br>M: Sesame oil (Room temperature – 7 days; filtration) LH: Sesame oil (80 °C–30 min; filtration) CHH: Sesame oil for Chuōkō HH (200 °C–10 min; filtration) SHH: Sesame oil for Shiunkō HH (200–230 °C – 1 h; 140 °C–5 min/170 °C–5 min; filtrations)<br>Sesame Oil (Origin: Japan; Manufacturer: Nikko®) batches: SO1: No. 061325; SO2: No. 062819 SHH, CHH, LH1, and M1 are related to batch SO1; LH2 and M2 to batch SO2.<br>Replicate notation: 'a' and 'b' → replicates using SO1 oil; 'c' and 'd' → replicates using SO2 oil</td></tr><tr><td rowspan="3">Shiunkō</td><td>Single extracts</td><td>S1M <strong>Angelica acutiloba</strong> (Siebold & Zucc.) Kitag.<br>S1LH <strong>Origin:</strong> Yamaguchi, Japan; Manufacturer: Tochimoto®<br>S1HH <strong>Batch:</strong> 008021002; Harvested: Nov–Dec 2019</td></tr><tr><td>Formula extracts</td><td>S6M S1M, S1LH or S1HH<br>S6LH + <strong>Lithospermum erythrorhizon</strong> Siebold & Zucc.<br>S6HH <strong>Origin:</strong> Inner Mongolia and Liaoning, China; Manufacturer: Uchida®<br><strong>Batch:</strong> I570215; Harvested: Mixed from 2016 to 2018</td></tr><tr><td>Shinsen taitsukō</td><td>T1M1 <strong>Angelica acutiloba</strong> (Siebold & Zucc.) Kitag.<br>T1LH1 <strong>Origin:</strong> Nara and Wakayama, Japan; Manufacturer: Uchida®<br><strong>Batch:</strong> M850334; Harvested: Autumns of 2019 and 2020. Note: use of special treatment process with blanching after six months of drying.</td></tr><tr><td rowspan="2">Formula extracts</td><td>TMO T1M1 or T1LH1</td><td></td></tr><tr><td>TLHO + <strong>Cinnamomum cassia</strong> (L.) D.Don<br><strong>Origin:</strong> Guangdong, China; Manufacturer: Uchida®; Batch N470129; Harv.: 2019–2021<br>+ <strong>Rheum tanguticum</strong> Maxim. ex Balf.<br><strong>Origin:</strong> Mie, Japan; Manufacturer: Uchida®; Batch N230326; Harv.: 2019<br>+ <strong>Paeonia lactiflora</strong> Pall.<br><strong>Origin:</strong> Nagano, Japan; Manufacturer: Takasago®; Batch 030822; Harv.: Nov 2020<br>+ <strong>Rehmannia glutinosa</strong> (Gaertn.) DC.<br><strong>Origin:</strong> Henan, China; Manufacturer: Uchida®; Batch N1L0212; Harv.: 2017<br>+ <strong>Scrophularia buergeriana</strong> Miq.<br><strong>Origin:</strong> Hunan, China; Manufacturer: Uchida®; Batch N160132; Harv.: 2017<br>+ <strong>Angelica dahurica</strong> (Hoffm.) Benth. & Hook.f. ex Franch. & Sav.<br><strong>Origin:</strong> Hebei, China; Manufacturer: Takasago®; Batch 081622; Harv.: Dec 2021</td></tr></tbody></table>

edition of the Japanese Pharmacopoeia (PMDA, 2021). The plant botanical names were confirmed using the World Flora Online database (WFO, 2025). Botanical species, origins, manufacturers, batches, and harvesting dates are given in Table 2.

## 2.3. Extract preparation

The preparation of extracts used for both metabolomic characterization and biological assays covered three Kampo formulas and three preparation methods depending on different extraction temperatures: Maceration (M), Low Heating (LH), and High Heating (HH). All the names and temperatures used for extract preparation are summarised in Table 1.

To study the effects of extraction temperature, botanical species, and geographical origin on ointment metabolites and biological effects, extracts combining, or not, crude drugs in sesame oil were prepared using different techniques. Three types of extracts were prepared: sesame oil without plants (oily controls), samples with a single crude drug extracted (single extracts), and formula-type samples with all crude drugs (formula extracts). To facilitate protocol setup, only liquid extracts were analysed, without solid components such as beeswax or lard. Table 2 associates ingredient/preparation with a nomenclature, which is utilised throughout the study. The letter “S” denotes *Shiunkō*, and “T” signifies *Shinsen taitsukō*. Subsequent letters correspond to the preparation methods employed in Japan for these ointments: HH, LH, and M, previously described in Table 1.

To ensure repeatable pharmacological activity, the manufacturing process of herbal extracts followed the protocol as shown in Fig. 1 with the amounts of crude drugs from Table 3. Upon reception, raw plant materials were milled using a grinder (Iwatani IFM-800, Co., Ltd., Japan) to achieve uniform granulometry across plant powders, ensuring comparable extraction yields. Particle size was controlled, as unequal cross-sections could influence extractability. Moreover, special attention was given to homogenisation to ensure consistency. Indeed, metabolite distribution may vary significantly depending on the plant part used, such as cortex versus root pith (Kudo et al., 2021). The powders were

then weighed in the proportions shown in Table 3, using the same analytical balance (AE240, Mettler Toledo, Columbus, OH, USA). The observed measurements showed an error margin of less than 0.05 %. The oily extracts were prepared in small batches, using 10–20 g of sesame oil and respecting the proportions in Table 3. Oil mass measurements were carried out using an analytical balance (ELT1001, Sartorius AG, Göttingen, Germany), ensuring precision to one decimal place.

Despite the intention to use the same batch of sesame oil for all extracts, two batches (SO1 and SO2) from the same manufacturer, Nikko®, were used (see Table 3). While extracts related to the *Shiunkō* formula were all made with the same SO1 batch, both batches were used for extracts related to the *Shinsen taitsukō* formula. For SHH and CHH controls and *Shiunkō*-related extracts S1HH/S6HH, S1LH/S6LH, and S1M/S6M, only batch SO1 was used. For *Shinsen taitsukō*-related extracts, both batches SO1 and SO2 were used for T1LH/TLHO and T1M1/TMO. Nevertheless, the extract preparation plan included sufficient sesame oil controls to allow for a comprehensive study.

For the M protocol, powdered herbs and oil were placed in a beaker, covered, and maintained at room temperature in the dark, and the mixtures were homogenised daily using a stainless-steel spatula. After one week, the mixtures were filtered using cotton gauze and a glass funnel. For LH and SHH, a water bath (BS600, Yamato Co., Ltd. Japan) and an oil bath (Yazawa Co., Ltd., Japan) were used for extractions, respectively. Appropriate timers and thermometers ensured consistent extraction conditions across all samples. Regular homogenisation times were kept constant.

In contrast to typical Kampo ointments, the extracts prepared for this study did not contain any solids such as beeswax or lard (Fig. 1: step 2 for HH protocols and step 4 for LH and M). Since they were liquid, they contained fine particles that tended to settle at the bottom of the polypropylene tubes, necessitating thorough homogenisation before preparing samples for metabolomic and biological analyses. Biological replicates (a,b or c,d) were prepared on different days, with the same weighing and heating procedures employed. Subsequently, the extracts were stored at 4 °C in polypropylene centrifuge tubes following the extraction and filtration process.
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Manufacturing protocols for *Shiunkō* and *Shinsen taitsukō* formulas and extraction temperatures selected.</figcaption></figure>

*: for specific steps in the traditional preparation of the *Shiunkō* formula (SHH). The formula can also be prepared following the lower-temperature protocols found for *Shinsen taitsukō*: LH and M protocols.

<caption>**Table 3**</caption>

Composition of Kampo ointments: *Shiunkō*, *Chuōkō* and *Shinsen taitsukō*.

<table><thead><tr><th rowspan="2"></th><th colspan="3">Quantities of ingredients (g)</th></tr><tr><th>Shiunkō<br>紫雲膏</th><th>Chuōkō<br>中黄膏</th><th>Shinsen taitsukō<br>神仙太乙膏</th></tr></thead><tbody><tr><td>Sesame oil</td><td>1000</td><td>1000</td><td>1000</td></tr><tr><td>Beeswax</td><td>340</td><td>380</td><td>400</td></tr><tr><td>Lard</td><td>20</td><td>—</td><td>—</td></tr><tr><td>Crude drugs</td><td>Lithospermum<br>erythrorhizon<br>root<br>Angelica acutiloba<br>root</td><td>120<br>60</td><td>Curcuma longa<br>rhizome<br>Phellodendron<br>amurense or chinense<br>bark</td><td>Angelica acutiloba and A. dahurica root<br>Cinnamomum cassia bark, Paeonia lactiflora root, Rehmannia glutinosa root, Rheum sp.<br>(R. palmatum, R. tanguticum, R. officinale) root and Scrophularia ningpoensis or<br>S. buergeriana root</td><td>32</td></tr></tbody></table>

## 2.4. Sample preparation

### 2.4.1. For metabolomics

The metabolomics samples were prepared by pipetting homogenised Kampo oil for a corresponding weight (AT250, Mettler Toledo) of 1 % mass in a 50:50 solution of acetone (LC quality, Lichrosolv, Merck KGaA, Darmstadt, Germany) and 2-propanol (LC/MS quality, Lichrosolv, Merck). Kampo oil was measured at 0.02 g for a 2 g sample, ensuring an error margin of 1.5 %. The mixtures of Kampo oil and solvents were placed in 5 ml conical tubes and then vortexed for 30 s. 800 µL of the solutions obtained were then transferred in coloured vials that were stored at -20 °C until analysis. For metabolomics analysis, quality control (QC) samples were prepared by pooling 10 µL from each vial and were injected every 15 sample injections. Additionally, blank samples, consisting of a 50:50 mixture of isopropanol and acetone, were injected at both the beginning and end of the injection sequence.

### 2.4.2. For biological assays

A key challenge in sample preparation was the highly lipophilic nature of Kampo oily extracts, which consist almost entirely of sesame oil. To address this, the Kampo oils were diluted in pure dimethyl sulfoxide (DMSO, ≥99.5 %, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) at a ratio of 15 % oil by volume. This approach ensured the formation of a homogeneous, single-phase solution. To do this, 220 µL of Kampo oil was mixed with 1500 µL of DMSO and transferred into 2 mL conical tubes. This solution served as the stock for the treatment medium, ensuring a final DMSO concentration of 1 %. Volumetric measurements were favoured over weighing owing to the low weights to be measured. All sample preparations followed the same methodology,

acknowledging potential discrepancies due to solvent density differences. The final concentration of Kampo oil in the biological experiment was 0.17 % (v/v).

The concentration of DMSO used to solubilise the oily extracts in the culture medium was determined through a cell viability assay. A 1 % concentration was selected based on a standard dilution range performed using the PrestoBlue™ assay (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA). PrestoBlue™ is a rapid cell viability indicator that relies on the reducing capacity of living cells to convert resazurin into the fluorescent compound resorufin (Lall et al., 2013). Quantitative analysis was conducted using an absorbance-based approach on a multimode plate reader (EnSight™, PerkinElmer, Waltham, MA, USA). DMSO concentrations above 1 % exhibited toxic effects on keratinocytes.

## 2.5. Evaluation of cell migration and proliferation

Wound healing is a complex process involving two distinct physiological components: migration and proliferation. Several bioassays were developed to assess them while cells were exposed to Kampo treatments. Migration was assessed using scratch assays, a highly efficient and reliable method for comparing the effects of treatments on cells grown in monolayers (Liang et al., 2007). Proliferation was evaluated by detecting DNA synthesis through the incorporation of 5-ethyl-2'-deoxyuridine (EdU), which was subsequently visualised via a histochemical reaction (Mead and Lefebvre, 2014).

### 2.5.1. Cells and culture conditions

The experiments were conducted using N/TERT-2G immortalised
keratinocytes that express human telomerase (hTERT), enabling prolonged proliferation while maintaining the characteristics of primary keratinocytes (Dickson et al., 2000). Keratinocytes were cultured in Epilife™ medium (Gibco™, Thermo Fisher Scientific, Waltham, MA, USA), supplemented with the HKGS kit (Human Keratinocyte Growth Supplement; Gibco™), 1 % penicillin-streptomycin (10,000 U/mL; Thermo Fisher Scientific), and calcium chloride (340 µM; Sigma-Aldrich). The final concentrations of the components in the HKGS-supplemented medium were as follows: bovine pituitary extract, 0.2 % v/v; recombinant human insulin-like growth factor-I, 0.01 µg/mL; hydrocortisone, 0.18 µg/mL; bovine transferrin, 5 µg/mL; and human epidermal growth factor, 0.2 ng/mL. Cells were incubated in a humidified atmosphere at 37 °C with 5 % CO₂. The culture medium was refreshed every two days, and cells were passaged at approximately 80 % confluence using TrypLE™ Express Enzyme (Thermo Fisher Scientific). For all experiments, keratinocytes at a consistent passage number or within a similar range were used to ensure uniformity in cellular response across experimental replicates.

## 2.5.2. Definition of experimental controls

To evaluate the effects of the tested extracts, experiments were conducted under different control and growth factor conditions. For the migration study and scratch assays, only a DMSO negative control was used. For the proliferation study and EdU incorporation assays, both a DMSO negative control and a positive control, consisting of a set of growth factors from the HKGS mixture, were included.

## 2.5.3. Keratinocyte migration

### 2.5.3.1. Experimental protocol.
N/TERT-2G keratinocytes were cultured in accordance with the previously described methodology. Incucyte® Imagelock 96-well microplates (Sartorius Ex, Essen, Göttingen, Germany) were seeded 48 h before the scratch assay at a density of 30,000 cells per well in three distinct experimental series with successive passage numbers. On the day preceding the scratch assay, the experimental treatments were conducted as previously described in section 2.4.2, utilising a 15 % oil solution in DMSO to achieve a defined DMSO concentration of maximum 1 % in Opti-MEM™ (Minimum Essential Medium; Gibco™). On the day of the scratch assay, once the cells had reached a density of greater than 90 % confluence, the culture medium was replaced with Opti-MEM™ medium. This ensured 4–6 h of nutrient deprivation, as the media lacked proteins, lipids, and growth factors. The scratch assay had to be timed carefully; if done too early, cells may not have been fully confluent, limiting wound closure assessment. If done too late, contact inhibition could have affected the response, skewing the results. The scratch assay was performed using an Incucyte® 96-Well Woundmaker tool (Sartorius). The standardised wounding procedure was followed to ensure the reproducibility of results across wells. Following the application of the wounding procedure, the media within each well was aspirated, and the wells were washed twice with culture media in order to remove dislodged cells. The quality and uniformity of the wounds were assessed under a microscope before adding the Kampo test materials at the appropriate concentrations in 100 µL of medium per well using a multichannel micropipette. The plates were then placed in the Incucyte® Zoom Live-Cell microscope (Sartorius), allowed to equilibrate for 30 min, and programmed for repeated phase-contrast imaging at 2-h intervals over a 12-h period using the “Scratch Wound” scan type (wide mode) with a Nikon 10× objective. All experiments were conducted in a humidified atmosphere at 37 °C with 5 % CO₂.

### 2.5.3.2. Scratch assay analysis.
Monitoring of wound closure, represented by the cell-free area left by the WoundMaker tool, was performed by tracking changes in the initial scratch area over time. This area gradually decreased and eventually closed completely. An image

analysis protocol was applied using the built-in image analysis software Incucyte® Zoom 2018A (Sartorius) to differentiate between regions occupied by cells and those devoid of cellular content, a crucial step in quantifying the rate of wound closure over time. Relative wound density (RWD) (Roddy et al., 2017), calculated based on an initial scratch wound mask, is determined as follows:

$$ \%RWD(t) = 100 \times \frac{(w(t) - w(0))}{(c(t) - w(0))} $$

W(t) represents the density of the wound region at time (t); c(t) corresponds to the density of the cell region at time (t). The RWD metric provides a means of quantifying the density of the wound region relative to that of the cell region, with both regions treated as variables in the calculation. However, because the automatic wound masking did not work properly due to solution opacity caused by sesame oil, the wound area had to be manually defined in Photoshop (Adobe Inc., San Jose, CA, USA; v26.0.0). This approach enabled the precise delineation of wound boundaries under conditions of suboptimal imaging. Consequently, the wound areas were calculated in millimetres squared, and relative calculations were then performed to relate the value to the initial wound area in percentage points. The relative wound healing (RWD) calculation allows wound closure evolution to be evaluated. The calculation is as follows:

$$ \%RWH(t) = 100 - \frac{(w(t) \times 100)}{w(0)} $$

The RWD data set was plotted against time, providing interpretable kinetic responses that allow conditions to be compared. To complete the data set, wound healing rates were calculated between time 0 and 6 h after the scratch test.

$$ \text{Wound healing speed} = \frac{w(0) - w(6)}{6} $$

Wound healing speed is expressed in units of surface area (mm²) per time (h). Data were plotted as box plots to compare closure responses between 0 and 6 h post-scratch.

### 2.5.3.3. Statistical analysis.
A two-way repeated measures analysis of variance (ANOVA) is typically used to assess the effect of Kampo solution on wound closure kinetics, as it accounts for both within-subject (time) and between-subject (treatment) variations (Kauanova et al., 2021; Radstake et al., 2023). However, due to occasional missing values at certain time points caused by imaging errors, a mixed model approach was applied instead, as repeated measures ANOVA cannot handle missing data. This mixed model assumes that missing values are randomly distributed and does not introduce bias into the results. A Greenhouse-Geisser correction was applied to account for any deviations from sphericity.

To assess specific group differences, Dunnett's multiple comparison test was performed, using a pooled variance estimated across all groups rather than computing individual variances for each comparison. This approach follows the standard Dunnett's test methodology, where comparisons are made between a reference group and multiple experimental groups.

In addition, a one-way ANOVA was chosen to assess the overall differences in wound healing speed across all conditions as it allows for multiple comparisons with the control group, reducing the risk of type I error (false positive) and thus increasing statistical power. Dunnett's test was then performed for pairwise comparisons. The same pooled variance estimation was applied in the relative wound healing score analysis.

The threshold for statistical significance was set at a family-wise α level of 0.05 (95 % confidence interval) for both analyses. All statistical tests were performed using GraphPad Prism (v10.3.1; GraphPad Software, Boston, Massachusetts, USA).
### 2.5.4. Keratinocyte proliferation

#### 2.5.4.1. Experimental protocol.
N/TERT-2G keratinocytes were cultured as previously described, and 96-well black Falcon® plates with a clear bottom (Falcon®, Thermo Fisher Scientific; Cat. No. 353219) were seeded with 3000 cells per well in 100 µL of HKGS-supplemented Epi-life™ medium.

On the day before the assay, test solutions were prepared by mixing Epilife™ medium (without HKGS) with DMSO-dissolved Kampo extracts. For the assay, the culture medium was carefully removed and replaced with these treatment media. After 24 h of incubation, 10 µM EdU was added to each well of non-confluent cells, followed by a 9-h pulse incubation. Then wells were washed with Phosphate-Buffered Saline (PBS++) containing calcium and magnesium ions, and the cells were fixed with 4 % paraformaldehyde in PBS++ (Electron Microscopy Sciences, Hatfield, PA, USA; Ref. 15710S).

Subsequently, double staining using the Click-iT™ EdU Imaging Kit (Thermo Fisher Scientific; Catalog No. C10269) according to the manufacturer's protocol allowed Hoechst nuclear labelling and EdU-Cy3 proliferation marking. The incorporation of EdU indicated newly produced DNA via copper-catalysed azide-alkyne cycloaddition, demonstrating proliferative activity. Cell fixation was repeated, and the plates were stored at 4 °C in PBS++ supplemented with 20 % glycerol for preservation. Thereafter, imaging was performed using the CellInsight NXT High-Content Imaging System (Thermo Fisher Scientific).

#### 2.5.4.2. EdU assay data.
Plate imaging and acquisition was enabled using HCS studio™ software (Cellomics scan v6.5.0, build 7614; Thermo Fisher Scientific), which assessed fluorescence activity associated with a biological effect. Images were acquired at 10× magnification. Channel-specific parameters were used to enhance signal detection. Channel 1 (Ch1) was used to detect nuclei and thus the Region of Interest (ROI) which allows EdU quantification with the 2nd channel on the same ROI. Ch1 used a widefield detection mode with LED excitation BGRFR_386_23 (to detect Hoechst fluorescence; 386 ± 23 nm). Channel 2 (Ch2) was used for EdU-Cy3 detection and acquired in widefield using LED excitation BGRFR_549_15 (for detection of red fluorescence; 549 ± 15 nm). Both filters used an LED system for excitation of specific wavelengths. A total of 16 fields per well were acquired to ensure a representative sampling of the cell population. Image processing involved defining ROI to enhance signal accuracy across Ch1-2. This included identification of primary objects, validation of detected objects, and refinement of selection criteria to focus on cells with EdU incorporation. Once the object of interest was identified, the system automatically determined the average cell-to-cell fluorescence intensity within each ROI, enabling accurate quantification of cell proliferation. Results for proliferative effect are presented only for the sesame oily controls (Fig. S1 in supplementary materials). This due to a lack of significant responses for the Kampo conditions tested during the experimental optimisations.

#### 2.5.4.3. Statistical analysis.
Raw data from the CellInsight NXT CX5 system were exported and subsequently processed using R and Rstudio software (v2023.06.2 + 561; R Foundation for Statistical Computing, Vienna, Austria) for a comprehensive analysis of proliferation metrics. The following parameters were used to determine the presence or absence of artifacts: valid object count, selected object count, valid field count, selected object count per valid field, mean object size on Ch1-2, mean object area for Ch1-2, mean total intensity on Ch1-2, and mean average intensity on Ch1-2. In the context of EdU detection, mean total target intensity and mean target area on Ch2 were also evaluated. R coding allowed for comprehensive data analysis, which in turn allowed for comparison of proliferative activity across experimental conditions based on the above parameters. Analysis included comparisons of EdU-positive cells between groups, fluorescence intensity per EdU-positive

cell, and the percentage of highly labelled EdU cells defined based on a threshold above background signal noise. Additional metrics, such as cell count per well, cell count per field, and cell count per condition tested, provided a robust basis for assessing differences in proliferative response between samples. Statistical analysis was performed using GraphPad Prism (v10.3.1) with a one-way repeated measures ANOVA with Greenhouse-Geisser correction followed by Tukey's multiple comparison test. Variances were estimated for each group comparison to account for heteroscedasticity. The threshold for statistical significance was defined as a family-wise α level of 0.05 (95 % confidence interval).

### 2.6. Metabolomics

#### 2.6.1. Chemicals and reagents

Formic acid, isopropanol, and LC/MS grade water, all Optima™ Fisher Chemical, were purchased from Fisher Scientific. Calibration was performed using the Agilent Low Concentration Tuning Mix (Catalog No. G1969-85000, Agilent Technologies, Santa Clara, CA, USA).

#### 2.6.2. Metabolomics analytical method

#### 2.6.2.1. LC-HRMS.
Metabolite profiling was performed using a liquid chromatograph (LC) coupled to a 6520 series electrospray ionisation (ESI) device and quadrupole time-of-flight (QTOF) high-resolution mass spectrometer (HRMS) operating in positive and negative ionisation modes (Agilent Technologies, Santa Clara, CA, USA). Chromatographic separation was performed on a ZORBAX Eclipse XDB-C18 column (4.6 × 50 mm, 1.8 µm, 600 bar) with a column temperature of 40 °C (Agilent Technologies). The mobile phases consisted of 0.1 % formic acid in pure water (solvent A) and 0.1 % formic acid in isopropanol (solvent B). The gradient was applied as follows: starting at 85 % of phase A for 0.5 min, linearly decreasing to 20 % of phase A at 10 min, held until 20 min, then decreasing to 2 % of phase A from 20.5 to 25 min, and re-equilibrating at 85 % of phase A for 32 min. LC-MS acquisition parameters, including flow rate, injection volume, Q-TOF mass spectrometer settings, ESI source conditions, calibration details, and data processing software specifications, are provided in the supplementary files. Samples were injected with five replicates and in randomised order. Injection orders were maintained consistently for both ESI modes. QC samples were injected every 15 samples and blanks were placed at the beginning and end of the injection series. Among the ions most frequently detected in ESI(−) were deprotonated molecules ([M-H]⁻), formate adducts ([M + FA-H]⁻), and chloride adducts ([M+Cl]⁻). In ESI(+), the most frequently detected ions included protonated molecules ([M+H]⁺), along with sodium ([M+Na]⁺) and ammonium ([M + NH₄]⁺) adducts.

#### 2.6.2.2. LC-HRMS/MS.
Tandem mass spectrometry was performed using the same analytical setup as for LC-HRMS (see 2.6.2.1). For both ESI modes, the acquisition method used an auto MS/MS mode over a mass-to-charge ratio (m/z) range of 25–1700, at a scan rate of 3 spectra/sec, a medium isolation width (~4 amu), and a fixed 25 eV for precursor ion fragmentation. Precursor ions were selected for fragmentation based on their abundance and predefined selection criteria. Up to three precursor ions per cycle were chosen. A minimum threshold of 25,000 counts/spectrum was applied to prioritise the most abundant ions. To optimise data acquisition, active exclusion was enabled, preventing repetitive fragmentation of the same ions. An ion was excluded after being selected three times and was released from exclusion after 0.50 min, allowing for improved coverage of less abundant metabolites. Real-time mass calibration, data acquisition, injection order, and processing followed the same protocol as described for LC-HRMS/MS. However, each sample was injected in duplicate, and a QC sample was included every 17 injections.
### 2.6.3. Metabolomics data processing

#### 2.6.3.1. LC-HRMS. ProteoWizard MSConvert (v3.0.21128-7376ae988 (developer build); ProteoWizard Project, Palo Alto, CA, USA) was used to convert Agilent raw data to mzXML files (Chambers et al., 2012). Data treatments were performed using Workflow4Metabolomics (W4M), a Galaxy web-based platform for data analysis, including preprocessing, normalisation, and QC (Giacomoni et al., 2015; Guitton et al., 2017; Galaxy Community, 2024). Detailed steps and parameters that were used for data processing are publicly available on the W4M workflow repository and main details are provided in supplementary files (Paul-Traversaz et al., 2023).

After a final format check, three files were produced by the W4M platform: a data matrix, a sample metadata, and a variable metadata. These files were then used as a basis for statistical analysis, which can also be carried out on the W4M platform. Then, initial putative annotations were made on the basis of *m/z* values explored with MassHunter Qualitative Analysis and compared with databases such as PubChem (Kim et al., 2025).

#### 2.6.3.2. LC-HRMS/MS. MS/MS data were used to construct molecular networks, enabling a comprehensive representation of all detected molecules. Data preprocessing was performed using MZmine software (v4.3.0; MZmine Development Team, Germany) (Schmid et al., 2023), followed by MetGem software (v1.5.2; Université Paris-Saclay, France) (Olivon et al., 2018). Detailed MZmine preprocessing parameters are provided in supplementary files.

### 2.6.4. Metabolomics analysis

#### 2.6.4.1. Postprocessing - LC-HRMS data.
Univariate and multivariate analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were conducted to visualise metabolite variability using W4M Galaxy platform (Galaxy Community, 2024).

For multivariate analyses, Pareto scaling was applied, as it enhances the visibility of both moderately and highly abundant features while reducing the dominance of extreme values, thus preserving meaningful biological variation without amplifying noise (Giacomoni et al., 2015; Guitton et al., 2017). Unlike unit-variance scaling, which standardises all features equally, Pareto scaling retains a degree of variance proportional to the original data, making it particularly suitable for complex metabolomic datasets (van den Berg et al., 2006). Three-dimensional PCA plots were performed using W4M and the MetaboAnalyst web platform with its statistical analysis One-Factor tool (v6.0) (Xia and Wishart, 2016). For PLS-DA was used to identify loadings (discriminant variables). The PLS-DA model was built with two components, using Pareto scaling, with the factor of interest set to the appropriate sample class. Model validation was performed using 100 permutation tests. To identify key discriminant metabolites, the Variable Importance in Projection (VIP) scores were used to select characteristic ions specific to each group. These identified loadings were subsequently annotated using complementary LC-MS/MS-based tools. Feature selection was also conducted using Biosigner, a supervised machine learning approach designed to identify statistically relevant discriminant features (Rinaudo et al., 2016). Analyses employ multiple classification techniques, including PLS-DA, Support Vector Regression (SVR), and Random Forest (RF). The Biosigner algorithm iteratively selects the most relevant variables, ensuring that the identified features are statistically significant and contribute to the classification of sample groups. Features are ranked into different confidence tiers based on their robustness across selection steps. A total of five analyses were performed using different seed numbers to assess result stability. The model was built with 50 bootstrap iterations, and statistical significance was determined using a p-value threshold of 0.05. Only ions that appeared at least five times in

the S-tier across runs were retained.

Univariate analyses were performed based on the variable of interest, namely the sample class. Since the analysed variables were qualitative, statistical comparisons were conducted using the non-parametric Kruskal-Wallis rank test, a non-parametric method suitable for multiple group comparisons. To account for multiple testing, a False Discovery Rate (FDR) correction was applied. A corrected p-value threshold of 0.05 was used to determine statistical significance.

#### 2.6.4.2. Molecular networking on LC-HRMS/MS data.
For molecular network construction, the *m/z* tolerance was set to 0.02 Da (20 ppm), with a minimum of four matched peaks for ESI(+) and three for ESI(-). Cosine similarity scores were used to filter network edges: a threshold of 0.55 was applied for Fig. 5, and 0.3 for Fig. 7. The minimum number of matched peaks was set to 3 for Figs. 5 and 2 for Fig. 7. The maximum number of neighbours (top K) was 10 for Figs. 5 and 8 for Fig. 7, while the maximum connected component size was limited to 1,000, to control network complexity.

The databases used for molecular annotation included GNPS, MS-Dial, and ISDP libraries. For database searches, the *m/z* tolerance was set to 0.02 Da, the minimum number of matched peaks to 3, and the *m/z* parent filter tolerance to 17 Da. When exploring structural analogues, a broader *m/z* tolerance of 200 Da was applied to allow for the detection of compounds with significant structural modifications while still sharing spectral similarity. Assignments were considered reliable when MetGem similarity scores exceeded 0.7 for standards and analogues.

Additionally, preliminary putative annotation steps incorporated PubChem (NCBI, Bethesda, MD, USA) and MetFrag (Ruttkies et al., 2016).

#### 2.6.4.3. Complementary annotation on LC-HRMS/MS data.
The annotation of the MS/MS data was also performed based on pre-processed files generated using MZmine. The following software was used for annotation of specific metabolites and compound classes: Entropy Search (v2.0.1; developed by Yuanyue Li (Li and Fiehn, 2023)) and SIRIUS (v6.0.7; Leibniz Institute of Plant Biochemistry, Halle, Germany (Dührkop et al., 2019)).

For Entropy Search, the spectral.mgf file was compared with Fra-gHub libraries (Dablanc et al., 2024) corresponding to the ESI mode used and the separation technique employed. The following parameters were selected: precursors *m/z* tolerance of 0.01 Da and product ions *m/z* tolerance of 0.02 Da. The report then provides 300 hits.

In addition to Entropy Search, SIRIUS annotation software was also used. It integrates multiple tools (CSI:FingerID for structural identification, ZODIAC for improving formula ranking, and CANOPUS for compound classification beyond existing database entries), which enable putative structure annotation. Assignments were considered valid when the SIRIUS Total Explained Intensity score was above 70 %. Additionally, an *m/z* deviation tolerance of 30 ppm was applied.

The nomenclature used to describe ions detected in LC-MS follows the format M(approx. mass)T (average retention time in seconds). The corresponding *m/z* values are reported to four decimal places, while retention times (*R*<sub>T</sub>) are expressed in min. This notation is applied exclusively to results from simple LC-MS data. For tandem MS data, *m/z* values are reported to four decimal places, but *R*<sub>T</sub> correspond to those processed in MZmine. The different treatment processes between W4M and Mzmine, combined with the fact that the samples were injected over a wide time range, may explain slight discrepancies in retention times.

Metabolite identification was conducted following the Metabolomics Standards Initiative (MSI) guidelines. Compounds were classified into four confidence levels: Level 1 (confirmed by reference standards), Level 2 (putatively annotated based on spectral matching), Level 3 (assigned to a chemical class without precise structural identification), and Level 4 (unknown compounds with no reliable annotation) (Sumner et al., 2007). This investigation found that no compounds reached MSI level 1
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Analysis of the biological effects of various sesame oils on N/TERT-2G keratinocytes in a scratch assay model.</figcaption></figure>

2.a. Average wound closure kinetics in keratinocytes from 0 to 12 h post-scratch. The negative control (Opti-MEM 1 % DMSO) was compared to conditions representing different extraction temperatures for Kampo ointments. Wound closure (relative percentage of initial wound area) was determined every 2 h. Conditions and number of wells were: M (Maceration), n = 3, LH (Low Heated), n = 23, CHH (High Heated *Chuōkō*), n = 11, and SHH (High Heated *Shiunkō*), n = 10). Error bars were omitted for clarity. Statistical analysis was however performed using a mixed model with the Greenhouse-Geisser correction and Dunnett's multiple comparisons test (α = 0.05). Comparison results were: negative control vs. M (ns, *p* = 0.0994); vs. LH (*p* < 0.0001); vs. CHH (*p* = 0.0016); vs. SHH (*p* = 0.0238). 2.b. Bar graphs (mean ± SD) depicting wound closure kinetics at selected time points (2–8 h post-scratch) across different experimental conditions, compared to the negative control. These histograms illustrate the percentage of wound closure relative to the initial wound size, with all observations included for each condition. One-way ANOVAs were performed for each time point, comparing the unprocessed sesame oil condition (M) with the others. These showed no significance for all time points except at 2 h between M vs. LH (*p* = 0.0379) and M vs. SHH (*p* = 0.0210). 2.c. Representative microscopic images showing differences in biological responses between experimental groups. Images were acquired using the Incucyte® Zoom system with a 10× Nikon objective. Lighter-coloured wound masks were retrospectively applied using Adobe Photoshop (v26.0.0) for accurate wound area calculation and visualisation. Images depict wound closure at 6 h post-scratch in the negative control, compared to wells treated with Macerated sesame oil (M) and High Heated sesame oil for the *Shiunkō* formula (SHH).

due to the absence of reference standards for validation. The metabolite annotations presented there are classified as MSI Level 2 or MSI Level 3, indicating tentative structural identification or compound class categorisation based on spectrum database comparisons and in silico predictions, without direct standard validation. El Gordo from SIRIUS was utilised for lipid class annotation, employing MS/MS fragmentation patterns to categorise lipids with significant confidence. Due to the similarity of chemical formulas, this method can detect lipids, but it may not distinguish positional or configurational isomers. In some cases, this approach yielded inconclusive assignments.

When required, molecular structures were generated using Signals ChemDraw (v23.1.2.7; Revvity Signals Software, Inc.). However, neither the stereochemistry nor double bond position could be determined from MS/MS data alone.

## 3. Results

### 3.1. Defining Kampo extracts

This investigation identified the Kampo ointments used for skin wound healing. *Shiunkō* and *Chuōkō* were the first ointments identified

as two formulas developed by the Edo-era physician Hanaoka Seishū. *Shiunkō* is well recognised and even covered by Japanese healthcare insurance (JKMAUsability Research SubcommitteeEthical Kampo Products Committe, 2024). The third ointment was *Shinsen taitsukō*, a polyherbal ointment inspired by traditional Chinese medicines. This study focused on *Shiunkō* and *Shinsen taitsukō*, and investigated the crude drug use, with specific emphasis on the botanical species and their geographical origins. Several ointment manufacturing methods were explored, with both industrial-scale and small-scale extraction processes. The historical recipes of certain formulations such as *Shiunkō* and *Chuōkō* mention high extraction temperatures, which were later adopted by the pharmaceutical industry (High Heating; HH). Nevertheless, local variations exist, documented by Japanese Kampo Manufacturer Association. These other manufacturing processes involve either mild heating for sesame oil or maceration at room temperature. In this study, these processes are designated as LH (Low Heating) and M (Maceration), respectively (see Tables I and II).

The integration of these Kampo practices within the Japanese healthcare system has been investigated using A. Kleinman's framework, which divides healthcare systems into three distinct compartments (Kleinman, 1986). This ethnopharmacological study examines
medical practices within one segment of the healthcare system, defined as the "Professional sector" since the research is focused on institution- alised pharmaceutical and medical uses of Kampo ointments. The two other sectors, named "Folk" and "Popular practices within communities", were not included in this analysis, thus limiting the study's scope to professionally manufactured and administered Kampo ointments.

Through an analysis of the Japanese Pharmacopoeia (18th ed.) (PMDA, 2021) and various pharmaceutical handbooks (Japan Pharmaceutical Manufacturers Association, 2005, 2013), a comprehensive list of ingredients was compiled. Table III summarises the composition of the Kampo ointments mentioned by the professionals interviewed, detailing the quantities of each ingredient used for each formula. The formulations, *Shiunkō*, *Chuōkō*, and *Shinsen taitsukō*, share a base of sesame oil, beeswax, and additional herbal components, with variations in individual herb types and quantities for each preparation.

This research focused on specific groups of extracts, such as sesame oil controls and Kampo ointments including common ingredients such as *Angelica acutiloba* root, featured in both *Shiunkō* and *Shinsen taitsukō*. Consequently, *Chuōkō* formula is not detailed in the study and only its oily control without crude drugs was studied to explore the impact of heating on sesame oil. Extract preparation steps for Kampo ointments are outlined in Fig. 1.

In Japan, most of the preparation methods for *Shiunkō* involve pre-
heating sesame oil to a high temperature (220–230 °C) for 1 h (step
1*). During the Edo period, when it was not possible to measure the
temperature, a change in the oil’s consistency was sought by practi-
tioners (Japan Pharmaceutical Manufacturers Association, 2005). Once
the sesame oil is processed, it serves as a base for the extraction of herbal
crude drugs. Beeswax and lard are added to the mixture before crude
drug extraction: firstly, *Angelica acutiloba* root, extracted at 170 °C (step
2a*); secondly *Lithospermum* root (step 2b*) extracted at 140 °C.
Following filtration through cotton gauze (step 3), the liquid ointment is
then cooled and packed (step 5) (Japan Pharmaceutical Manufacturers
Association, 2005).

*Shinsen taitsukō* typically employs two extraction methods (step 2): Low-Heating (LH) for 30 min at 80 °C (LH), or room-temperature Maceration for seven days (M), followed by filtration through cotton gauze (step 3) and the addition of beeswax (step 4), before conditioning (step 5) (Japan Pharmaceutical Manufacturers Association, 2013). According to the practitioners involved in this ethnopharmacological investigation, these milder extraction protocols (LH) and (M) are also applicable to *Shiunkō* formulations. As a result, the following ointment preparations were possible: *Shiunkō* using the three methods (SHH), (LH) and (M); and *Shinsen taitsukō* using (LH) and (M) (Tables I and II).

3.2. Characterising the implications of heating sesame oil

The diversity of sesame oil processing methods in Kampo ointments suggests that they may significantly impact metabolite composition, both qualitatively and quantitatively, thereby influencing various bio- logical effects in the keratinocyte model. The positive effect of sesame oil on wound healing is well documented, both *in vitro* and *in vivo*, whether applied alone or added to other components (Moalla Rekik et al., 2016; Poljsak et al., 2020). For these reasons, the major ingredient in Kampo ointments – sesame oil – deserves special attention. This is important since ointment formulations involve multiple heating steps, and sesame oil serves simultaneously as an active ingredient, excipient, and extraction medium for herbal crude drugs.

A comparison of the biological activities related to wound healing for the different sesame oils used can be made on keratinocyte migration (Fig. 2) and proliferation (Fig. S1 in supplementary files). As summarised in Tables I and II, the control oils were derived from either the maceration process (M), the low heating process (LH), or the high heating process for *Shiunkō* and *Chuōkō* formulas (SHH and CHH, respectively). None of the experiments showed any pro-migratory (Fig. 2) or proliferative (Fig. S1 in supplementary files) biological effects observed in this model. Macerated sesame oil (M) appeared to favour migration; however, this trend was not statistically significant compared to the negative control. This apparent effect is likely due to variations in cell response in the assay rather than a true biological response. In addition, statistical analyses between M condition versus the others indicate that these differences in cell migration are not significant.

Although no differences were observed in biological responses in the keratinocyte model, the metabolite composition of sesame oil extracts varied. Multivariate statistical analysis on LC-MS data provided insights into the distinctive characteristics exhibited by sesame oil groups, as well as the distances between these groups. Fig. 3 illustrates the 3D PCA for both ESI(+) (Fig. 3a) and ESI(−) (Fig. 3b) analyses. 587 features were highlighted in ESI(+), and 159 in ESI(−). The high R²X values obtained (0.775 for ESI(+) and 0.776 for ESI(−)) indicate that the PCA captures a substantial proportion of variance. This likely reflects pronounced differences between heating/batch groups and should be considered when interpreting clustering patterns.

The PCA model captures 77.5 % (ESI+) and 77.6 % (ESI-) of the total variance in the dataset. Each point represents a sample injection, with colours indicating different experimental groups. High-temperature oil controls are SHH and CHH. The medium-temperature controls are LH1 and LH2, depending on the batch of sesame oil used (SO1 or SO2

<figure><img src="image_7.png" /><figcaption>Fig. 3. 3D Principal Component Analysis (PCA) of sesame oil extracts subjected to different temperatures and batches.</figcaption></figure>
respectively). Those produced by maceration at room temperature are M1 and M2, also depending on the batch of oil used. The spatial distribution reflects similarities and differences between groups based on their metabolic profiles, highlighting clustering patterns and potential sources of variation. The data were processed with Pareto scaling with 7 cross-validation segments and a Singular Value Decomposition (SVD) algorithm. Each sample was analysed 5 times, bringing the number of analyses to 60 for statistical processing, with 587 features in ESI(+) (Fig. 3a) and 159 in the case of analyses carried out in ESI(-) (Fig. 3b). Data matrices and graphical representations were created using the Galaxy Workflows for Metabolomics (W4M, v2.3.10) and MetaboAnalyst platforms (v6.0), respectively.

Metabolite group separation was primarily influenced by heating conditions and sesame oil batches. Indeed, for both ESI modes, SHH and LH2 sample clusters are clearly separated from the others. As for the temperature effect, this separation is not surprising, given the wide variation in temperature ranges; the discovery of a batch effect is more intriguing, at least at this level of intensity. This batch effect is likely due to the use of two sesame oil batches (SO1 and SO2; Table II). For SHH and CHH extracts, only batch SO1 was used, whereas for M (M1, M2) and LH (LH1, LH2), both SO1 and SO2 batches were included. While oil batch variation had no noticeable effect in the absence of heating (M), it became more pronounced at 80 °C for 30 min (LH), leading to the separation of LH2 samples on PCA plots. However, due to the use of a single sesame oil batch in the CHH and SHH conditions, its impact at higher temperatures remains uncertain.

The statistical study of the features observed following LC-MS analysis was complemented by PLS-DA to determine certain characteristic ions, or loadings. To identify them among all features, the Variable Importance in Projection (VIP) scores were used to select characteristic ions specific to each group. The selected ions were then putatively annotated using LC-MS/MS data. This can be done using several complementary tools (presented in the "Materials and methods" section). Analyses performed on SHH extracts subclass on ESI(+) revealed the presence of monoglycerides (MAGs), diacylglycerides (DAGs), and triacylglycerides (TAGs/TGs). Among the MAGs, ions M376T706 (m/z 376.3433; *R*<sub>T</sub> = 11.79 min) and M331T663 (m/z 331.2856; *R*<sub>T</sub> = 11.07 min) were respectively identified as glyceryl monostearate (C<sub>21</sub>H<sub>42</sub>O<sub>4</sub>) and glyceryl palmitate (C<sub>19</sub>H<sub>38</sub>O<sub>4</sub>). These glyceride compounds are typical products of TGs hydrolysis under elevated temperature conditions (Pop et al., 2024). As amphiphilic compounds, they may stabilise lipid bilayers and contribute to the restoration of the epidermal barrier (Nakmode et al., 2022), suggesting a beneficial role in wound healing.

The DAGs cluster included M639T1106 (m/z 638.5740 (C<sub>39</sub>H<sub>72</sub>O<sub>5</sub>; *R*<sub>T</sub> = 18.43 min)), which was annotated as 1,2-Dioleoyl-sn-Glycerol using SIRIUS in combination with the El Gordo lipid classification tool, with high confidence (classification score: 1.0). However, since lipid isomers can share the same molecular formula, this score confirms the lipid class but does not precisely distinguish the positional isomer. DAGs are intermediate molecules in the thermal degradation of TGs and act as precursors in lipid signalling pathways, highlighting the impact of high-temperature processing on lipid composition.

For TGs, ions M932T1382 (m/z 931.7792 (C<sub>59</sub>H<sub>104</sub>O<sub>6</sub>; *R*<sub>T</sub> = 23.02 min)) and M1264T1109 (m/z 1264.0652 (C<sub>85</sub>H<sub>140</sub>O<sub>5</sub>; *R*<sub>T</sub> = 18.87 min)) were reported, representing complex TGs that remained intact with heating, indicating remarkable thermal stability (Chau et al., 2021; Khor et al., 2019). Notably, no significant high molecular weight polymers, often indicative of lipid oxidation or polymerisation above 200 °C, were detected. This absence is directly attributable to the analytical and data processing parameters used, which filtered out m/z values above 1500 Da.

Additionally, ceramides were represented by the ion M915T1404 (m/z 914.7810; *R*<sub>T</sub> = 23.38), possibly corresponding to 2-(palmitoyl-amin)-6,7-bis(palmitoyloxy)heptanoic acid (C<sub>55</sub>H<sub>105</sub>NO<sub>7</sub>).

In terms of the documented biological effects of these compounds, TGs form a protective barrier, reduce transepidermal water loss, and

release antimicrobial and anti-inflammatory fatty acids upon hydrolysis (Madison, 2003). DAGs activate protein kinase C pathways, promoting keratinocyte proliferation and migration essential for wound closure (Denning et al., 2000). Ceramides restore the skin barrier by improving cell cohesion and preventing dehydration, thereby promoting tissue regeneration (Holleran et al., 1997). Together, these bioactive lipid classes contribute to wound healing by protecting the skin barrier, modulating inflammation, and promoting keratinocyte function, underscoring their possible beneficial role in sesame oil-based formulations.

ESI(-) mode data analysis of sesame oil revealed additional features of interest that appear specific to a particular batch of sesame oil. These include M664T1119 (m/z 663.5205; *R*<sub>T</sub> = 18.75 min), which corresponds to cycloartane triterpenoids (C<sub>40</sub>H<sub>72</sub>O<sub>7</sub>). These compounds are derived from cycloartenol, a key precursor in the biosynthesis of plant sterols such as beta-sitosterol and stigmasterol (Ohyama et al., 2009). Additionally, M652T1045 (m/z 652.4793; *R*<sub>T</sub> = 17.54 min) was attributed to a labdane diterpenoid (C<sub>35</sub>H<sub>72</sub>ClNO<sub>5</sub>S), while M662T1050 (m/z 661.5048; *R*<sub>T</sub> = 17.59 min) was annotated as open-chain polyketides (C<sub>40</sub>H<sub>72</sub>O<sub>8</sub>). Although cycloartane triterpenoids have been reported to exhibit anti-inflammatory and regenerative properties, their specific role in wound healing within this experimental context remains to be further investigated (Fang et al., 2019). In addition, open-chain polyketides may contribute to wound healing modulation. However, the variability of metabolites across different batches and manufacturing processes poses challenges to the standardisation of Kampo ointments. This variability arises from the intrinsic batch-to-batch differences in sesame oil composition, as well as its sensitivity to traditional heating protocols, which further influence its chemical profile.

The 3D PCA plot (Fig. 3) reveals a separation of the LH2 group from other oily extracts. This distinction suggests that its chemical profile differs not only by the presence of specific compounds but potentially also by the absence or reduced abundance of certain metabolites. However, this observation remains dependent on the specific molecular features identified and requires further validation. Biosigner analyses performed on LC-MS ESI(+) data identified three discriminant features M353T663 (m/z 353.2678; *R*<sub>T</sub> = 11.08 min), M381T706 (m/z 381.2990; *R*<sub>T</sub> = 11.67 min), and M382T706 (m/z 382.3019; *R*<sub>T</sub> = 11.77 min). Annotation using SIRIUS suggested that M381T706 and M382T706 correspond to possible different adducts or isotopologues of the same molecular entity, C<sub>17</sub>H<sub>29</sub>N<sub>5</sub>O<sub>2</sub>, potentially a purine alkaloid. A similar analysis on ESI(-) data highlighted the absence of certain features in the LH2 group, notably M375T705 (m/z 375.275; *R*<sub>T</sub> = 11.75 min) and M403T748 (m/z 403.3064; *R*<sub>T</sub> = 12.45 min). These were putatively annotated as fatty alcohol derivatives with molecular formulas C<sub>20</sub>H<sub>42</sub>O<sub>7</sub> (3,6,9,12,15-pentaoxapentacosane-1,17-diol) and C<sub>22</sub>H<sub>46</sub>O<sub>7</sub> (3,6,9,12,15,18-hexaoxaoctacosan-1-ol), respectively. While the identification of discriminating metabolites makes it possible to characterise the influence of the batch of oil used to make Kampo ointment, it remains essential to determine whether these compounds contribute to any biological effect on wound healing, which has not yet been established.

In addition to the visualisation of clusters made possible by PCA, univariate analyses were processed to complete the investigation with a factor of interest focused on the type of sesame oil preparation (M1, M2, LH1, LH2, CHH, and SHH). Fig. 4 depicts the variability in selected metabolite distribution across diverse sesame oil extracts (LH, M, CHH, SHH) and heating protocols.

Metabolite selection for this analysis was based on both their chemical relevance in defining sesame oil composition and their documented biological activities in wound healing. Sesame oil has a complex lipid profile, primarily composed of fatty acids, glycerides, sterols, and lignans, which contribute to its physicochemical properties and bioactivity. This univariate study focused on key representative compounds, including major fatty acids (linoleic, oleic, and palmitic acids), glycerides (dihydroxypropyl stearate, dipalmitoyl-rac-glycerol), and sterols
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Univariate analysis box-plot results for selected metabolites from sesame oil analysed using LC-MS ESI(±). Univariate analyses were performed on both ESI LC-MS data with a factor of interest focused on the type of sesame oil preparation (M1, M2, L1, L2, CHH, and SHH). This investigation selected metabolites based on their chemical relevance to sesame oil composition and their wound-healing biological activity. A Kruskal-Wallis rank test with false discovery rate (FDR) correction for multiple testing was carried out. The statistical significance threshold was set at $p \le 0.05$. Univariate analysis and associated box plots were generated using the Galaxy platform (W4M, v2.3.10).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Molecular network and clusters of specialised metabolites in sesame oil extracts under different heating conditions, using LC-ESI(+) MS/MS data.</figcaption></figure>

(sitosterol, stigmasterol). These metabolites have been associated with anti-inflammatory and skin-regenerating properties, highlighting their potential role in wound healing (Lin et al., 2017a). Sesamin and sesamolin, bioactive compounds found in sesame oil which exhibit antioxidant and anti-inflammatory activities, were also analysed (Dossou et al., 2023; Ye et al., 2020). Given that temperature and batch variability may influence chemical diversity, the impact of different heating protocols and oil batches on their distribution was investigated.

Sesame oil fatty acids have been well studied (Baydar et al., 1999; Kurt, 2018). Sesame oil is composed primarily of triglycerides containing unsaturated fatty acids such as linoleic acid (C18:2, C₁₈H₃₂O₂) and oleic acid (C18:1, C₁₈H₃₄O₂), along with lower amounts of saturated fatty acids such as palmitic acid (C16:0, C₁₆H₃₂O₂). When exposed to high temperatures, these triglycerides can undergo hydrolysis or oxidative cleavage, leading to the formation of free fatty acids (FFAs). These FFAs are more chemically reactive and may themselves undergo further transformation, including oxidation and polymerisation. In our LC-MS data, linoleic acid was detected as M279T744 (m/z 279.2328; *R*<sub>T</sub> = 12.39 min) and oleic acid as M281T768 (m/z 281.2483; *R*<sub>T</sub> = 12.80 min). Both compounds were strongly enriched in the SHH condition (high-temperature oil heating at 220–230 °C), suggesting increased liberation of FFAs. This trend is supported by prior studies showing that prolonged heating of sesame oil, especially above 200 °C, promotes triglyceride breakdown and increases FFA levels (Olaleye et al., 2019). Among these, linoleic acid is particularly susceptible to thermal oxidation due to its two double bonds, making it a contributor to chemical transformations in heated oil. Oxidation of FFAs, particularly polyunsaturated ones such as linoleic acid, can lead to the formation of secondary products such as aldehydes, ketones, and dicarboxylic acids. Paradoxically, palmitic acid (C₁₆H₃₂O₂) M239T663 (m/z 239.2375; *R*<sub>T</sub> = 11.05 min) was poorly represented in oils heated to high temperatures and showed differences in content between batches of sesame oil, this being expressed only for the LH2 condition.

Among glycerides, 2,3-dihydroxypropyl stearate (MAG; C₂₁H₄₂O₄) M381T706 (m/z 381.299; *R*<sub>T</sub> = 11.79 min) was poorly represented in both LH2 and SHH, suggesting the degradation of TGs into MAG through enzymatic and thermal processes under moderate heating (Pop et al., 2024). Similarly, 1,2-dipalmitoyl-rac-glycerol (DAG; C₃₅H₆₈O₅) M553T1078 (m/z 552.5080; *R*<sub>T</sub> = 17.96 min) showed reduced representation in LH2 and SHH, indicating that glycerides underwent

hydrolytic cleavage under heat (Kerenkan et al., 2016).

Moreover, the thermal behaviour of phytosterols, such as sitosterol (C₂₉H₅₀O) M437T881 (m/z 437.376; *R*<sub>T</sub> = 14.67 min) and stigmasterol (C₂₉H₄₈O) M435T897 (m/z 435.3606; *R*<sub>T</sub> = 14.69 min), showed distinct characteristics. High and prolonged heat drastically lowered sitosterol levels in the SHH group. Moreover, stigmasterol exhibited higher intensities in LH2 and M2 compared to other groups, showing that interbatch variability, not temperature sensitivity, determined its abundance. Since the anti-inflammatory activities of phytosterols are notable, the influence of sesame oil heating seems crucial in the understanding of Kampo ointment compounds (Sundarraj et al., 2012). Sitosterol, like other sterols, plays a restorative role in the epidermis by modulating inflammatory responses in keratinocytes and macrophages. Specifically, it has been shown to reduce the expression of NLRP3 (NOD-like receptor family) and to inhibit the activation of caspase-1 and NF-κB in macrophages (Liao et al., 2018).

For unidentified glycosylated flavonoid (C₃₆H₄₂O₂₀) M739T468 (m/z 739.2025; *R*<sub>T</sub> = 7.78 min), a thermal liability was also observed with a depletion in the CHH and SHH groups, which seems to be related to the heating duration. Lignans, including sesamin (C₂₀H₁₈O₆) M355T493 (m/z 355.1179; *R*<sub>T</sub> = 8.21 min) and sesamolin (C₂₀H₁₈O₇) M393T375 (m/z 393.0944; *R*<sub>T</sub> = 6.25 min), showed divergent profiles. These differences in compound stability illustrate the impact of thermal processing on the bioactive components of sesame oil, particularly those with pharmacological potential. Lignans, known for their wound-healing properties (Andargie et al., 2021), responded differently to heat: while sesamin levels showed moderate changes, sesamolin was unexpectedly abundant in SHH, suggesting possible stabilisation at high temperatures. Finally, antioxidants such as δ-tocopherol (C₂₇H₄₆O₂) M403T852 (m/z 403.3554; *R*<sub>T</sub> = 14.20 min) and γ-tocopherol (C₂₈H₄₈O₂) M417T845 (m/z 417.3736; *R*<sub>T</sub> = 14.08 min) were almost completely depleted in SHH, illustrating their thermal liability. Tocopherols are known for their potent antioxidant properties, which are essential for reducing levels of reactive oxygen species (ROS) during wound healing (Bopitiya and Madhujith, 2015). Tocopherols reduce oxidative stress, which speeds skin tissue regeneration and promotes angiogenesis, according to studies conducted on mice (Weber et al., 1997).

The polyphenolic compound triacetyl resveratrol (C₂₀H₂₀O₈) M377T492 (m/z 377.1002; *R*<sub>T</sub> = 8.19 min) exhibited modest reductions
in SHH, which is consistent with its known partial thermal degradation (De Cassia da Silva et al., 2017). Triacetyl resveratrol modulates glycation-related effects linked to cellular aging. Its topical application reduces glycation-induced AGEs, increases collagen expression, and improves epidermal stratification (Markiewicz et al., 2022). These results on important metabolites (fatty acids, sterols, glycerides, tocopherol, and lignans) from sesame oil emphasise the influence of heating conditions on the chemical profiles and bioactive properties, with implications for wound healing and therapeutic applications.

Molecular networking analysis using MetGem on LC-MS/MS ESI(+) completes the understanding of oil heating impact while helping with metabolite annotation (Fig. 5). Various node clusters characterise different chemical classes (glycerolipids, glycerides, lignans, and heat transformed lipophilic compounds).

One major cluster A designates glycerolipids including TGs, DAGs and phosphatidylglycerols (PGs). Cluster A represents 57 % of detected ions. This aligns with their role as the primary constituents of sesame oil, which accounts for 90–98 % of its total mass depending on the Kampo formulation (Table III). Their prevalence in the molecular network may be related to their efficient ionisation in ESI(+) (forming [M + NH₄]⁺ and [M+Na]⁺ adducts), and their high retention in the chromatographic system due to hydrophobic interactions. Among cluster A, red nodes corresponding to the SHH and CHH groups were rich in mono- and polyunsaturated fatty acid chains, with annotation of oleic acid (C18:1), linoleic acid (C18:2), and palmitoleic acid (C16:1). Several TGs contained two or three unsaturated chains, and some showed isomeric variations, such as Δ9 and Δ11 double bond positions, potentially resulting from thermal isomerisation. In contrast, the M and LH groups were associated with TGs combining saturated and monounsaturated chains, such as TG (18:1/18:1/20:0) and TG (18:0/18:1/22:0), as well as DAGs and phospholipid-derived compounds (e.g., PGs), which were absent or rare in highly heated samples. DAGs and PGs were well represented in the M and LH groups. Thus, heating the sesame oil induces a shift toward more unsaturated TGs. This might be due to the breakdown of native lipids, isomerisation of double bonds or the recombination of fatty acid chains. The low representation of polar lipid species and the increase of highly unsaturated TGs may characterise oil heating. Interestingly, some TGs and their degradation products may contribute to wound healing processes. For example, oleic acid-rich TGs have shown a favourable involvement in re-epithelialisation and in the modulation of the inflammatory response (Lin et al., 2017a).

Cluster annotation was performed using information derived from Entropy Search (v2.0.1), SIRIUS (v6.0.7), and MetGem databases (v1.5.2). Pie charts within nodes indicate the relative abundance of features across conditions, while colours represent the extraction or heating protocol where the feature was found: red (SHH: sesame oil heated for 1 h at 200–230 °C), pale red (CHH: sesame oil heated 10 min at 200 °C), orange (LH1/LH2: sesame oil subjected to mild heating), and yellow (M1/M2: unheated sesame oil).

For all sesame oil heating conditions, glycerides are gathered in cluster B with MAGs like glyceryl palmitate (C₁₉H₃₈O₄, m/z 331.2856; R<sub>T</sub> = 11.07 min) and DAGs such as DG (18:1_18:1) (C₃₉H₇₂O₅, m/z 638.5735; R<sub>T</sub> = 19.07 min). With glycerolipids, they are involved in epidermal barrier restoration and lipid signalling pathways essential for skin regeneration (Lania et al., 2019).

Cluster C is particularly interesting since it gathers nodes specific to the prolonged and high heating (SHH) group. Most of these compounds have been annotated as linear peptides, since the SHH condition likely promotes peptide and lipopeptide formation through Maillard reactions and lipid-protein interactions. The Maillard reaction, a non-enzymatic process between reducing sugars and amino compounds, leads to the production of aromatic compounds known as Maillard Reaction Products (MRPs) (Yin et al., 2021). The heating process of sesame oil has been well studied, revealing that specific reactions occur between reducing carbonyl and amino groups, leading to a cascade of condensation, dehydration, cleavage, cyclisation, and polymerisation reactions

(Hu et al., 2024). For instance, amino acids, and n-acyl amines are some of the compounds generated under intense thermal conditions. The MRPs may influence keratinocyte activity by protecting them from glycation-related damage and then preserving their functionality (Advedissian et al., 2016). Therefore, even if the biological consequences of heating on sesame oil have not yet been demonstrated, their possible beneficial effect linked to the presence of these specific metabolites should be further investigated.

Similarly to cluster C, cluster P was also found to be highly specific to the SHH group. This cluster contains two subgroups. The first one contains, according to MetGem, specific TGs, such as TG (18:1/18:2/22:0), observed as [M + NH₄]<sup>+</sup> at m/z 816.6709 (R<sub>T</sub> = 20.03 min), 790.6562 (R<sub>T</sub> = 19.42 min) and 790.6557 (R<sub>T</sub> = 19.33 min). Characterised by a combination of oleic (18:1), linoleic (18:2), and behenic (22:0) acids, these TGs may be derived from thermal rearrangement of sesame oil lipids. Indeed, heating processes can lead to the generation or concentration of longer-chain saturated fatty acids such as behenic acid, which are typically low in raw sesame oil (Hu et al., 2024). These specific TGs may have implications for wound healing, as certain long-chain fatty acids have been associated with modulation of inflammation, and keratinocyte behaviour (Lin et al., 2017b). In addition, cluster P gathers some other lipid-derived compounds, such as open-chain polyketides, lipopeptides, and prenol lipids, based on SIRIUS putative annotations. While these assignments are not confirmed, their presence is consistent with expected products from Maillard-like reactions, lipid rearrangements, and terpene degradation under high temperatures. Thus, the thermal generation of specific TGs or these lipid-derived compounds may contribute to the biological activity of heated sesame oil-based ointments and merits subsequent investigations.

Furthermore, lignans from cluster H, including sesamin (C₂₀H₁₈O₆; m/z 355.1179; R<sub>T</sub> = 8.23 min), were identified across all heating conditions, although with some variability. Their antioxidant and anti-inflammatory properties, combined with their structural resilience, support their role as stabilisers in sesame oil (Andargie et al., 2021; Dossou et al., 2023). Since sesamin has been associated as a wound healing agent (Sharif et al., 2013; Correia Alves et al., 2023), and heating does not alter its structure, it is possible that its healing effect is maintained in all Kampo ointments, regardless of the extraction temperature.

In addition to these observations, several lipid classes detected across clusters represent likely contributors to wound healing. Triglycerides and their hydrolysis products (DAGs and FFAs) can support epidermal barrier restoration and modulate keratinocyte signalling through protein kinase C pathways (Lania et al., 2019). Ceramides and related sphingolipids, although less abundant, are critical for barrier repair and hydration, while phytosterols such as β-sitosterol and stigmasterol exhibit well-documented anti-inflammatory and pro-regenerative effects. Sesame lignans, notably sesamolin and related compounds, provide additional antioxidant protection. The relative stability or loss of these metabolites with increasing temperature may therefore influence the potential healing properties of sesame oil-based ointments.

This diversity of bioactive lipids provides a clear context for the molecular-networking data, which show how heating reshapes the metabolomic profile of sesame oil by balancing native compounds and heat-induced metabolites. This underscores the importance of considering thermal processing in modulating the chemical and therapeutic properties of Kampo ointments.

### 3.3. Impact and influences of *A. acutiloba* root in Kampo ointments

In addition to sesame oil metabolites, it is necessary to examine those from extracted crude drugs. Among them, Angelica acutiloba root, as a key ingredient in Shiunkō and Shinsen taitsuko, is particularly relevant due to its documented wound-healing properties (Mo et al., 2023). To study metabolites from *A. acutiloba* root, single extracts involving this ingredient in sesame oil were required. As shown in Table II, Shiunkō is
related to three extraction methods: S1M, S1LH, and S1HH, while *Shinsen taitsukō* involves two: T1M1 and T1LH1. No HH extract was made for the latter, as high-temperature extraction is not traditionally used for this formula. Regarding oil batch variability, S1M, S1LH, and S1HH were produced with SO1 sesame oil, while T1M1 and T1LH1 were primarily made with SO2 sesame oil. Additionally, the two formulas differ in *A. acutiloba* root content per 100 g of oil: 6 g in *Shiunkō* vs. 3.2 g in *Shinsen taitsukō* (Table III). To reflect the diversity of Kampo ointments made in Japan, *A. acutiloba* was sourced from different suppliers: Tochimoto® (Yamaguchi, Japan) for *Shiunkō* and Uchida® (Nara and Wakayama, Japan) for *Shinsen taitsukō* (Table II).

Keratinocytes exposed to *A. acutiloba* oily extracts showed no difference in effect compared to oil controls, regardless of extraction method, root origin, or concentration. Fig. 6 illustrates this homogeneous response in wound closure speeds (Fig. 6a) and microscopic images (Fig. 6b). At 6 h post-scratch, cells exposed to extracts with contrasting extraction techniques (S1HH vs. T1M1) showed no significant difference in migration, suggesting that geographical origin, concentration, or extraction method had no measurable impact compared to sesame oil alone (M, LH, SHH; Fig. 6a). However, this does not rule out differences in their metabolite composition.

The molecular profile of *A. acutiloba* root extracts in highly heated sesame oil (S1HH) revealed a low representation of oxidised lipids, as shown by the ESI(−) MS/MS molecular network (Fig. 7). The network highlights specific features to SHH extracts (deep red in pie charts), while their lack in S1HH suggests a possible inhibition by metabolites from *A. acutiloba*.

Among oxidised lipids, PC 18:1_18:1; 2O (Phosphatidylcholine; m/z 973.7326; R<sub>T</sub> = 17.17 min) with its oxidised fatty acyl chains, indicates a lipid peroxidation. These oxidised compounds are key markers of heat stress and oxidative lipid degradation (Niki, 2015). In addition to phosphatidylcholine, HBMP 16:1_16:2_18:2 was found (hexanoyl-bis (monoacylglycerol)-phosphate; m/z 1019.7019; R<sub>T</sub> = 13.82 min). It contains linoleic acid which is likely formed under high oxidative stress.

Similarly, ADGGA 18:2_16:0_18:1 (acyl-diacylglyceryl glucoside; m/z 979.6910; R<sub>T</sub> = 16.59 min), combines saturated and unsaturated fatty acids. This lipid class is rare in sesame oil, suggesting oxidative lipid remodelling at high temperatures. Additionally, PE (phosphatidylethanolamine 18:1/18:1; C<sub>41</sub>H<sub>77</sub>NO<sub>8</sub>P; m/z 789.624; R<sub>T</sub> = 20.20 min) with two monounsaturated fatty acids may also result from oxidative modifications. These compounds, characteristic of SHH extracts, form two distinct lipid subclusters (a1 and a2) in Fig. 7, illustrating the correlation between high-temperature heating and lipid oxidation. This suggests that *A. acutiloba* metabolites, particularly furanocoumarins and polyphenols, may act as antioxidant protectors against heat-induced lipid oxidation (Piao et al., 2004). Metabolomics data analysis underscores the possible ability of these metabolites to stabilise lipid-based preparations against oxidative degradation. Indeed, the high-temperature heating process (200–230 °C) generates reactive oxidising species and initiates lipid peroxidation, leading to the formation of unstable derivatives. The addition of *A. acutiloba* in sesame oil at 170 °C may neutralise free radicals or inhibit further oxidative degradation (Ayala et al., 2014). Subsequent analyses, incorporating targeted assays for oxidation markers (e.g., malondialdehyde, 4-hydroxynonenal) and lipidomic profiling, could offer insights into these interactions and define the antioxidant potential of *A. acutiloba* in highly heated sesame oils (Zheng et al., 2014).

In addition, other metabolites are specifically associated with *Angelica-containing* groups, including a detected ion at *m/z* 387.1134 (*R*<sub>T</sub> = 1.01 min), initially annotated as a disaccharide derivative (Fig. 7). However, this annotation is unlikely since disaccharides have a low log P value (predicted XLogP3 value of −3.7 for sucrose), indicating strong hydrophilicity and poor extractability in a lipophilic oily matrix. XLogP3, a computational model that estimates log P values based on fragment contributions and empirical corrections, is widely used to predict molecular lipophilicity. While the presence of disaccharides has been documented in *Angelica* species (Han et al., 2022), their actual extraction and stability in this lipidic environment remain uncertain.

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Analysis of the biological effects of various Kampo oily extracts containing *Angelica acutiloba* root on N/TERT-2G keratinocytes in a scratch assay model. 6.a. Diagram (mean ± SD) showing wound closure speeds (mm²/h) under different experimental conditions using sesame oil preparations (M, LH, and SHH) and Kampo extracts containing *A. acutiloba* root. Extracts were prepared following protocols for *Shiunkō* (S1M, S1LH, S1HH) and *Shinsen taitsukō* (T1M1, T1LH1). Values are based on multiple wells: S1M (n = 3), S1LH (n = 8), S1HH (n = 8), T1M1 (n = 5), and T1LH1 (n = 11). S1 and T1 extracts differ in their origin and *A. acutiloba* content. Wound closure speeds were calculated from surface reduction between 0 and 6 h post-scratch. A one-way ANOVA with Dunnett's correction was performed for multiple comparisons. No condition showed a statistically significant effect (p < 0.05) between S1 and T1 extracts or compared to the oily controls.</figcaption></figure>

6.b. Representative microscopic images illustrating differences in biological responses between experimental conditions. Images were captured using the Incucyte® Zoom system with a Nikon 10× objective. Lighter-coloured wound masks were retrospectively applied using Adobe Photoshop (v26.0.0) for accurate wound area calculation. The wound closure at 0, 6, and 8 h is compared between two Kampo extracts: *A. acutiloba* extract prepared using the Maceration protocol (T1M) for *Shinsen taitsukō* and the High Heated protocol (S1HH) for *Shiunkō*.
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Molecular network of metabolites in *Angelica acutiloba* oily extracts and their respective controls under different heating conditions, using LC-ESI(−) MS/MS data. Cluster annotation was performed using Entropy Search (v2.0.1), SIRIUS (v6.0.7), and MetGem databases (v1.5.2). Transformed lipid clusters are presented in subgroups a1 and a2. Pie charts within nodes indicate the relative abundance of features across conditions, while colours represent the extraction or heating protocol where the feature was found: red (SHH: sesame oil heated for 1 h at 200–230 °C), orange (LH1/LH2: sesame oil subjected to mild heating), and yellow (M1/M2: unheated sesame oil extracts). The green gradient is for *A. acutiloba* root extracts (S1HH, S1LH, S1M, T1M1, and T1LH1). For the *Shinsen taitsukō* formula, two batches of sesame oil (SO1 and SO2) were used, leading to a diversity of extracts presented. The oil used is specified in the legend. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

Furthermore, no direct correlation can be made between their potential presence and wound healing activity.

The metabolomic study highlights that heating is the most discriminant factor in the diversity of metabolites observed among the extracted oily Kampo herbs (Fig. 8a). The S1HH group is indeed separated from other sample groups by the first component (t1 or p1), equivalent to 42 % of the variance. The separation into two groups observed in the PCA (Fig. 8a) occurs particularly because of temperature and not according to *A. acutiloba* root origin. Thus, it is possible that the p1 component, which accounts for over 40 % of the variance, is correlated with changes in features due to sesame oil heating.

To compare the effect of *A. acutiloba* origins on extract metabolites, it was essential that sesame oil batch and extraction temperature were controlled parameters. Therefore, extracts prepared using both the same batch and extraction method (here, LH) were retained for further analysis: S1LHa and T1LHa (the letter refers to the type of replicate: a and b for samples using SO1 oil; see Table II). Consequently, the only remaining experimental variables when comparing S1LHa and T1LHa were the geographical origin and year of harvest of *A. acutiloba* and its concentration in the extract. Thus, the only a priori differences concern the origin of *A. acutiloba* and its concentration.

PLS-DA analysis revealed a separation into distinct groups for S1LHa and T1LHa, using data from both ESI modes (Fig. 8b). Regarding Biosigners (Fig. 8c), ESI(+) mode defines linoleic acid (C₁₈H₃₂O₂) M281T700 (m/z 281.2479; *R*<sub>T</sub> = 11.66) as the only biosigner. However, ESI(−) mode is more informative, likely due to better predictive performance (Q²Y = 0.642 > 0.5). Two ions were isolated (at least three times out of the five Biosigner analyses with an S-level score): M279T743 (m/z 279.2328; *R*<sub>T</sub> = 12.38 min) and M621T786 (m/z 621.4368; *R*<sub>T</sub> = 13.11 min). While M279T743 is also associated with linoleic acid, M621T786 is clearly specific for the extracted *A. acutiloba*. SIRIUS putative annotation suggested a glycosylated triterpenoid previously documented for its anti-inflammatory and wound-healing

properties, as evidenced by studies on *Centella asiatica* (Thong-on et al., 2024). *Centella asiatica* is widely used for its wound-healing properties due to its active compounds, such as triterpenoids, which promote collagen synthesis and fibroblast proliferation. Its traditional and pharmacological applications in skin regeneration have been extensively documented (Somboonwong et al., 2012). According to SIRIUS, two possible annotations could be proposed for this compound of interest (Fig. 9) depending on the sugar position (Fig. 9a1 and b1). SIRIUS proposed a candidate with an exact mass of 622.4445 Da (neutral form). The observed m/z 621.4368 corresponds to the [M−H]⁻ ion, with a theoretical m/z of 621.4372. The delta between observed and theoretical ion m/z is - 0.0004 Da, or - 0.6 ppm.

These two possible molecules differ in the position of the hydroxyl group on the steroid nucleus: at C3 in molecule b1 and at C12 in molecule a1. They also differ in the stereochemistry of the side chain (2S in a1 vs. 2R in b1) and at C13 (13R in a1 vs. 13S in b1). It should be noted, however, that these assumptions about stereochemistry are uncertain, since only NMR analyses could be informative in this respect. These structures have XLogP3-AA values (a computational prediction model based on atom additive contributions to estimate lipophilicity) of 4.3 for a1 and 5.9 for b1 (Kim et al., 2025). This lipophilic character facilitates extraction in an oil matrix despite the presence of a hydrophilic glycosyl group.

Regarding the distribution of this glycosylated triterpenoid among samples, sesame oil extracts prepared with *A. acutiloba* root from Yamaguchi Prefecture (S1LHa/b and T1LH1a) exhibited a higher abundance, as shown in Fig. 9b with the univariate analysis box plots. However, it is worth noting that the T1LH1a extract, associated with the *Shinsen taitsukō* formula, was prepared with half the standard amount of *A. acutiloba* root, in accordance with traditional formulations (Table III). Even when accounting for this difference, the intensity of this glycosylated terpenoid in T1LH1a remained lower than in S1LHa/b samples, reinforcing the impact of *A. acutiloba* origin on the final phytochemical
<figure><img src="image_12.png" /><figcaption>b. PLS-DA ESI(+) and ESI(-) LC-MS: S1LHa and T1LH1a</figcaption></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>c. Biosigners</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 8.** Set of multivariate statistical analyses (PCA, PLS-DA, and Biosigners) performed on LC-MS ESI(±) data related to Kampo oily samples involving *Angelica acutiloba* root.</figcaption></figure>

8.a. Principal Component Analysis (PCA) performed on ESI(+) mode data, using Pareto scaling. The two principal components (PC1 and PC2) are represented on the axes, with their respective R²X values. The associated loading plot highlights key compounds for the S1HH group. A variance explained plot is also provided.

8.b. PLS-DA performed on the basis of the ESI(+) and (-) mode; scores are displayed along with R²X, R²Y, Q²Y, RMSEE and pre. The scaling used is Pareto and a number of 100 permutation tests was chosen with seven cross-validations. PLS-DA scores show the formation of distinct groups for S1LHa and T1LHa (using the same sesame oil batch).

8.c. Summary of Biosigner analyses (molecular signature discovery from omics data) is presented. It assigns tiers (S, in green, to E, in red) to features based on their significance in selection steps. A total of 5 analyses were carried out with different seed numbers. The number of bootstraps was 50, and the p-value threshold was 0.05. The ion is presented if it appears at least five times with an S tier among each independent analysis. All Figures were created using the Galaxy Workflows for Metabolomics platform (W4M v2.3.10). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

profile. This finding is relevant here given the known wound-healing properties of glycosylated terpenoids (Thong-on et al., 2024), suggesting that the origin of *A. acutiloba*, along with other factors, may influence the wound healing activity of the final formulation.

### 3.4. Comparative exploration of complete Kampo formulas compared to *A. acutiloba* extracts

While studying the biological effects induced by complex Kampo formulas remains possible, as with single crude drug extracts, the main challenge lies in metabolomic analysis. Chemical diversity and overlapping signals in large data matrices make compound annotation more difficult, especially as herbal metabolites (and particularly lipophilic compounds) are still underrepresented in databases. The impacts of *Shinsen taitsukō* and *Shiunkō* extracts on cell migration were assessed while comparing them with sesame oil and *A. acutiloba* extracts (T1 and S1). Fig. 10 presents kinetic curves, wound closure rate graphs (mm²/h), and representative microscopic images resulting from this investigation. The bioassay showed delayed wound closure for the two complete formulations: TMO (*Shinsen taitsukō*, maceration) and S6HH (*Shiunkō*, high-heat extraction).

Statistical analysis of wound closure curves (Fig. 10a) revealed a significant delay for formula exposures: S6HH (1) and TMO (2)

compared with the corresponding single extracts (respectively S1HH and T1M1). This delay is also illustrated by microscopic images (Fig. 10c), showing representative wells 8 h after scratch. For the SHH oily control, the wound is completely closed, whereas in the S6HH formulation, it remains open with an approximate width of 500 µm. However, examination of wound healing speed data nuances this analysis (Fig. 10b). ANOVA analysis comparing T1M1 vs. TMO and S1HH vs. S6HH conditions was performed and no significance was observed in wound healing speeds. Therefore, although the graphical representation of the wound healing speeds seems informative, this shows the importance of studying closure kinetics as a whole rather than between restrictive time points.

#### 3.4.1. Study of *Shiunkō* formula extracts (S6HH)

The comparison between S6HH and S1HH responses aims to understand why the S6HH group, representative of the full *Shiunkō* formula, showed a reduced cell migration on the experimental model as shown in Fig. 10. For this, Biosigner analyses have been used to compare the S1HH sample data with S6HH only, in order to find specific compounds.

A series of five analyses on the LC-MS ESI(+) data revealed two ions that are specific to S1HH but less represented in the S6HH sample group (Fig. 11b). Both of these ions were annotated using SIRIUS as quinone/
**a. Glycosylated triterpenoids from A. acutiloba**
annotation for ion on ESI(-) M621T786
*m/z 621.4368; R<sub>t</sub> = 13.11 min

<figure><img src="image_16.png" /></figure>

a1.

<figure><img src="image_17.png" /></figure>

b1.

**b. Univariate analysis, M621T786 (fdr=0.0046)**

<figure><img src="image_18.png" /><figcaption>**Fig. 9.** Putative annotations of ion M621T786 as a glycosylated triterpenoid and its associated univariate analysis showing its representation among *A. acutiloba* extract groups (T1LH1a and S1LHa).</figcaption></figure>

**9.a.** Putative annotations were determined using SIRIUS (v6.0.7) based on ESI(-) LC-MS/MS data, with molecular structures generated using Signals ChemDraw (v23.1.2.7).

a1 is (2R,3R,4S,5S,6R)-2-[[[(3S,5R,8R,9R,10R,12R,13R,14R,17S)-3-hydroxy-17-[(2S)-2-hydroxy-6-methylhept-5-en-2-yl]-4,4,8,10,14-pentamethyl-2,3,5,6,7,9,11, 12,13,15,16,17-dodecahydro-1H-cyclopenta [a]phenanthen-12-yl]oxy]-6-(hydroxymethyl)oxane-3,4,5-triol.

b1 is (2R,3R,4S,5S,6R)-2-[[[(3S,5R,8R,9R,10R,12R,13S,14R,17S)-12-hydroxy-17-[(2R)-2-hydroxy-6-methylhept-5-en-2-yl]-4,4,8,10,14-pentamethyl-2,3,5,6,7,9,11, 12,13,15,16,17-dodecahydro-1H-cyclopenta [a]phenanthen-3-yl]oxy]-6-(hydroxymethyl)oxane-3,4,5-triol.

For a1 and b1: *m/z* 621.4368 ([M-H]⁻), Δ - 0.6 ppm.

**9.b.** Univariate analysis was performed for the corresponding ion using ESI(-) LC-MS data, applying a Kruskal-Wallis rank test with false discovery rate (fdr) correction for multiple testing. The statistical significance threshold was set at *p* ≤ 0.05. Univariate analysis and associated box plots were generated using the Galaxy platform (W4M, v2.3.10).

hydroquinone lipids which are structurally close to the vitamin E and tocopherols. The first is M415T773 (*m/z* 415.3576; *R*<sub>T</sub> = 12.88 min), annotated as C<sub>28</sub>H<sub>46</sub>O<sub>2</sub> (2,7,8-trimethyl-2-[[(E)-4,8,12-trimethyltridec-3-enyl]-3,4-dihydrochromen-6-ol); the second is M431T786 (*m/z* 431.3527; *R*<sub>T</sub> = 13.1 min), annotated as C<sub>28</sub>H<sub>46</sub>O<sub>3</sub> ((2E,5E,7E)-(1R,3R)-2-ethylidene-19-nor-9,10-seco-5,7-cholestadien-1,3,25-triol). M415T773 and M431T786, presented in Fig. 11a, share structural similarities with tocopherols, including a chromene/hydroquinone core and a long lipophilic side chain.

For compound a1, the observed *m/z* 415.3576 corresponds to the [M+H]⁺ ion (theoretical *m/z* 415.3571), yielding a mass deviation of +0.0005 Da (+1.2 ppm). For compound b1, the observed *m/z* 431.3527 corresponds to the [M+H]⁺ ion (theoretical *m/z* 431.3520), with a mass deviation of +0.0007 Da (+1.6 ppm). These features suggest a potential beneficial role in keratinocyte migration, consistent with previous studies on tocopherol-related compounds (Horikoshi et al., 2018).

The delayed migration observed when keratinocytes were exposed to *Shiunkō* complete formula S6HH compared with *A. acutiloba* extract S1HH alone could be the result of several phenomena. The reduced quinone/hydroquinone content observed in S6HH compared to S1HH (Fig. 11), despite identical amounts of *A. acutiloba*, may be attributed to increased oxidative degradation. Among the major characteristics of the S6HH group is the additional presence of *Lithospermum* sp. root which is traditionally associated with pro-healing uses (Barkizatova et al., 2024). However, studies have reported that it may induce oxidative stress and cytotoxicity, potentially delaying keratinocyte migration and skin regeneration (Sun et al., 2022). Consequently, characteristic compounds from this crude drug may contribute to an oxidative environment, accelerating quinone/hydroquinone reduced abundance and thus partly causing a delay in keratinocyte migration induced by *Shiunkō* extracts.

### 3.4.2. Study of Shinsen taitsukō (TMO) formula extracts

In order to investigate the delayed cell migration observed with *Shinsen taitsukō* (TMO) compared to the *A. acutiloba* single extract (T1M1), Biosigner analyses were performed on both ESI mode data.

On ESI(+), five Biosigner analyses were conducted to identify metabolites specific to the TMO sample group on three independent tests.

Two major ions were significantly more present in the TMO group: M377T648 (*m/z* 377.2673, *R*<sub>T</sub> = 10.80 min) and M929T1368 (*m/z* 928.7604, *R*<sub>T</sub> = 22.8 min). M377T648 was putatively annotated using Entropy Search as C<sub>23</sub>H<sub>36</sub>O<sub>4</sub>, an esterified diterpene with an associated XLogP3-AA of 5.9 which predicts a molecular lipophilicity (Kim et al., 2025). Fig. 12 shows the putative structure (12a). However, the structure remains uncertain, since only NMR analyses could be informative in this respect. The observed *m/z* 377.2673 corresponds to the [M+H]⁺ ion (theoretical *m/z* 377.2687), yielding a mass deviation of - 0.0014 Da (-3.7 ppm). Next to the putative structure, the box plot of the Biosigner test performed is presented (12b). It illustrates the signal differences observed between T1M1 and TMO samples groups for this ion.

Several similar terpenoids have been reported to influence cell migration by interacting with membrane receptors such as G protein-coupled receptors (GPCRs) and integrins (Merecz-Sadowska et al., 2021). These interactions can alter the organisation of the cytoskeleton, and signalling pathways involved in keratinocyte motility (Pedro et al., 2020). Furthermore, certain sesquiterpenes and triterpenes have been found to affect migration processes (Paduch et al., 2007). More specifically, the presence of diterpenes in crude drugs such as *Cinnamomum* bark suggests that these herbal compounds could contribute to the observed inhibition of cell migration (Wang et al., 2020). The esterified specification for diterpenes refers to compounds that occur naturally as esters or that undergo esterification reactions due to thermal processing. For the second ion detected in the Biosigner analyses, M929T1368, it has been putatively annotated as a specific ester compound C<sub>53</sub>H<sub>100</sub>O<sub>8</sub>, known as pentaerythrityl tetra laurate. This compound is an ester of pentaerythritol and lauric acid, mainly used as an emollient and thickener in cosmetic formulations (Becker et al., 2015). It therefore seems unlikely to be adversely involved in the delayed closure speed observed when keratinocytes are exposed to TMO extract. However, it is important to note that these analyses cannot implicate any specific metabolite as being responsible for the entirety of the biological effect shown in the model, due to the extensive molecular profile examined.

Furthermore, other repeated Biosigner analyses on ESI(-) data isolated two discriminating ions between T1M1 and TMO: M341T61 (*m/z* 341.1090; *R*<sub>T</sub> = 1.02 min) and M387T61 (*m/z* 387.1143; *R*<sub>T</sub> = 1.02 min).
<figure><img src="image_19.png" /><figcaption>Fig. 10. Analyses of the biological effects of Kampo oily extracts from *Shiunkō* and *Shinsen taitsukō* formulas on N/TERT-2G keratinocytes in a scratch assay model.</figcaption></figure>

10.a. (1) and (2).

Average wound closure kinetics in keratinocytes from 0 to 12 h post-scratch. Sesame oil controls were compared to two conditions per graph: M vs. T1M1 and TMO (1); HH vs. S1HH and S6HH (2).

Wound closure (relative percentage of initial wound area) was determined every 2 h.

Conditions and number of wells are: (1) M (n = 3), T1M1 (n = 5), and TMO (n = 7); (2) HH (n = 10), S1HH (n = 8), and S6HH (n = 7). Error bars were omitted for clarity. Statistical analysis was performed using a mixed model with the Greenhouse-Geisser correction and Dunnett's multiple comparisons test (α = 0.05). Statistical comparisons were carried out with Prism (v10.4.0), using mixed effect analysis. Results are the following:

(1) M vs. T1M1: *p* = 0.8801; M vs. TMO: *p* = 0.1023; T1M1 vs. TMO: *p* = 0.0056.

(2) HH vs. S1HH: *p* = 0.8066; HH vs. S6HH: *p* < 0.0001; S1HH vs. S6HH: *p* = 0.0027.

10.b. Diagram (mean ± SD) showing wound closure rates (mm²/hour) under different experimental conditions using sesame oil preparations (M, SHH) and Kampo extracts. The extracts were prepared following protocols for *Shiunkō* (S1HH, S6HH) and *Shinsen taitsukō* (T1M1, TMO). Values employed are from a screening of multiple wells: S1HH (n = 10), S6HH (n = 7), T1M1 (n = 5), and TMO (n = 7). S1 and T1 extracts differ in origin and concentration of angelica in the oily extract. S6HH and TMO extracts reflect the composition of the two ointments, *Shiunkō* and *Shinsen taitsukō*. Wound closure speeds (mm²/h) were calculated based on the wound surface reduction from time 0–6 h post-scratch. One-way ANOVA analysis yielded the following results: S1HH vs. S6HH: ns (*p* = 0.1885) and T1M1 vs. TMO: ns (*p* = 0.0579).

10.c. Representative microscopic images illustrating differences in biological responses between experimental conditions. Images were captured using the Essen Incucyte® Zoom system with a Nikon 10× objective. Lighter-coloured wound masks were retrospectively applied using Adobe Photoshop (v26.0.0) for accurate wound area calculation. The wound closure at 0 and 8 h is compared between two extracts: High Heated sesame oil control (HH) and its corresponding equivalent for *Shiunkō* (S6HH).
a. Quinone/hydroquinone lipids from *A. acutiloba* (S1HH) annotation for ions on ESI(+) M415T773 and M431T786

a1. C₂₈H₄₆O₂
M415T773
m/z 415.3576 ; R$_{T}$ = 12.88 min

a2. C₂₈H₄₆O₃
M431T786
m/z 431.3527 ; R$_{T}$ = 13.1 min

b. Biosigners 'S' signature, M415T773 (PLSDA, RF)

<figure><img src="image_20.png" /></figure>

Biosigners 'S' signature, M431T786 (PLSDA)

<figure><img src="image_21.png" /><figcaption>Fig. 11. Putative annotations for specific metabolites related to the S1HH group and possibly involved in keratinocyte migration.</figcaption></figure>

11.a1 and a2. Quinone/hydroquinone lipids annotations were determined using the SIRIUS (v6.0.7) based on ESI(+) LC-MS/MS data, with molecular structures generated using Signals ChemDraw (v23.1.2.7).

a1 is (2,7,8-trimethyl-2-[(E)-4,8,12-trimethyltridec-3-enyl]-3,4-dihydrochromen-6-ol
m/z 415.3576 ([M+H]⁺, Δ + 1.2 ppm.

b1 is (2E,5E,7E)-(1R,3R)-2-ethylidene-19-nor-9,10-seco-5,7-cholestadien-1,3,25-triol.
m/z 431.3527 ([M+H]⁺, Δ + 1.6 ppm.

11.b. Biosigners were identified through comparative analysis of S1HH and S6HH using ESI(+) LC-MS data. 'S' significance ions are highlighted, representing features consistently detected across at least three out of five independent analyses conducted with different seed numbers. Bootstrapping was performed with 50 iterations, and statistical significance was set at a p-value threshold of 0.05. Box plots were generated using the Galaxy Workflows for Metabolomics platform (W4M v2.3.10).

min). Entropy Search and SIRIUS suggested a concordant putative annotation as sucrose (C₁₂H₂₂O₁₁) for both ions. However, this identification remains uncertain due to the challenge of distinguishing disaccharide spectra. Additionally, sucrose's negative XLogP3 (−3.7) suggests it is unlikely to be extracted in a lipophilic matrix. Its detection may instead indicate the presence of disaccharide fatty esters, which could be extracted from sesame oil due to their amphiphilic nature (Teng et al., 2021). However, SIRIUS does not allow their exact structure to be determined when analysing fragments and might wrongly annotate them as simple disaccharides. As such, while the Biosigner analyses show a statistically significant differentiation among T1M1 and TMO groups, the possible annotation for sucrose seems neither reliable nor relevant, as sucrose is unrelated to keratinocyte migration.

The presence of seven crude drugs in *Shinsen taitsukō* (TMO) introduces a wide range of metabolic variables beyond *A. acutiloba*. This metabolite richness was observed using Entropy Search, which detected the presence of compounds related to other crude drugs. Among them, the monoterpene glycosides from *Paeonia lactiflora* with albiflorin (ESI(+) M503T100 m/z 503,1517; R<sub>T</sub> = 1.68 min) and paeoniflorin (ESI(−) M525T119, m/z 525,1608; R<sub>T</sub> = 1.99 min) were detected. Moreover, specific anthraquinones from *Rheum* sp. were also reported, including emodin (ESI(−) M269T592, m/z 269,0446; R<sub>T</sub> = 9.86 min), chrysophanol (ESI(−) M253T371, m/z 253,05; R<sub>T</sub> = 6.18 min), and rhein (ESI(−) M283T526, m/z 283,0212; R<sub>T</sub> = 8.77 min). However, these Entropy search-based annotations cannot be considered reliable, as it was

impossible to group the different ions associated with anthraquinones in the same cluster of the molecular network. Since these compounds have very similar molecular structures, it is unlikely that they would not be a part of the same cluster among the molecular network.

These difficulties emphasise the chemical intricacy of complex herbal formulations like *Shinsen taitsukō* (TMO) and the difficulty in defining its possible biological effects. Through Biosigner analysis, significant metabolites were found, including an esterified diterpene (M377T648, Fig. 12) that may influence the observed delay in wound closure. However, obstacles were encountered in annotating metabolites from specific families, such as anthraquinones. Subsequent research should focus on the isolation and functional validation of essential metabolites to confirm their chemical plausibility and biological relevance to keratinocyte migration and wound healing.

## 4. Discussion

### 4.1. *Variability in crude drug processing*

Post-harvest handling of herbal medicines is a key determinant of their chemical profile, and variations in drying or blanching steps can markedly change the metabolite composition of herbal crude drugs. In the case of *Angelica acutiloba* root, such processing diversity represents an important source of variability.

To our knowledge, both *Angelica* roots used for the S1LHa/b and
a. Esterified diterpene from *Shinsen taitsukō* (TMO)
putative annotation for ion on ESI(+)
M377T648 ; m/z 377.2673 ; R<sub>T</sub> = 10.80 min

<figure><img src="image_22.png" /><figcaption>**Fig. 12.** Putative annotations for esterified diterpene from *Shinsen taitsuko* (TMO) and its relative distribution among sample groups.</figcaption></figure>

12.a. Probable annotations of esterified diterpene were determined using the SIRIUS (v6.0.7) based on ESI(−) LC-MS/MS data. Possible molecular formula could be methyl (Z)-5-[(1R,4aR,8aR)-5,5,8a-trimethyl-2-methylidene-3,4,4a,6,7,8-hexahydro-1H-naphthalen-1-yl]-3-(acetyloxymethyl)pent-2-enoate molecular. m/z 377.2673 ([M+H]⁺), Δ - 3.7 ppm. Molecular structure generated using Signals ChemDraw (v23.1.2.7).

12.b. Biosigners were identified through comparative analysis of T1M1 and TMO using ESI(+) LC-MS data. 'S' significance ions are highlighted, representing features consistently detected across at least 3 out of 5 independent analyses conducted with different seed numbers. Bootstrapping was performed with 50 iterations, and statistical significance was set at a p-value threshold of 0.05. Box plots were generated using the Galaxy Workflows for Metabolomics platform (W4M, v2.3.10).

T1LH1a extracts underwent a blanching process. The *A. acutiloba* root used in the *Shinsen taitsukō* formula (batch M850334, provided by Uchida®) was subjected to an extended process involving an initial drying phase lasting several months, followed by blanching in hot water and a second drying stage. However, specific parameters such as blanching duration and temperature were not disclosed. Initially, the treatment conditions for the *A. acutiloba* root used in the *Shiunkō*-related S1LH extract (batch 19202, supplied by Tochimoto®) remained unclear, raising the possibility that the absence of blanching might explain differences in glycosylated terpenoid content. Thanks to the supplier's traceability system, we were later able to confirm that the S1LH extract made using *Angelica* from Tochimoto® also underwent blanching. In contrast to the Uchida® batch, this process seemed to involve a relatively short exposure to warm water at approximately 60 °C. These findings suggest considerable variability in blanching conditions (particularly temperature and duration) between suppliers. Such heterogeneity in post-harvest processing likely plays a decisive role in shaping the metabolite composition of extracts, potentially influencing their pharmacological activity. This supports the idea that both biotic/abiotic factors influencing the plant growth period and the post-harvest processing steps can impact the biological activity of traditional formulations. Ultimately, this variability presents a significant challenge to the rationalisation of traditional medicine practices. At the same time, however, it reflects the diversity and contextual richness of traditional knowledge and processing techniques.

While it is difficult to hypothesise the biological effects that a
particular metabolite might have, it is more challenging still to char-
acterise the effect of a formulation, given that it contains several hun-
dred metabolites. Moreover, the oily nature of Kampo topical
formulations presents additional analytical complexity, particularly
regarding inter-batch variability in sesame oil composition and its
chemical stability over time. In addition to these difficulties, investiga-
tion of metabolites is further complicated because the sesame-oil back-
ground cannot be subtracted, as would be possible with a pure analytical
solvent. These factors highlight the intricate nature of exploring Kampo
ointments and the need for advanced analytical strategies to fully cap-
ture their chemical and pharmacological diversity.

b. Biosigners 'S' signature
M377T648 (PLSDA)

<figure><img src="image_23.png" /></figure>

4.2. Analytical challenges of lipophilic herbal matrices

Traditional medicine research and rationalisation face several chal-
lenges, notably due to the existing variety of preparation protocols,
crude drugs, and variables that affect preparation quality and effec-
tiveness. Kampo ointments with lipophilic sesame oil matrices require
particular analytical methods for metabolomic and biological
assessment.

This study employed a C18 reversed-phase column, optimised for nonpolar and moderately polar compounds. However, alternative chromatographic strategies could further refine metabolite detection. The use of C8 columns, which provide enhanced retention for medium-polarity compounds, could have been beneficial (Cajka and Fiehn, 2014). Given the strong hydrophobic nature of sesame oil extracts, future investigations could benefit from combining C18 and C8 approaches to ensure a broader metabolite coverage, particularly for semipolar compounds. Furthermore, collision energy optimization constitutes another factor in improving the analytical method. This study employed standard LC-MS/MS settings, but stepped or ramped collision energy could enhance fragmentation patterns, facilitating the structural annotation of complex lipophilic compounds. This approach is particularly relevant for the annotation of natural compounds such as diterpenes and flavonoids, which often present challenging fragmentation due to multiple isomers (Guan et al., 2021).

Moreover, another challenge in the metabolomic study of Kampo
ointments is the limited availability of dedicated databases for lipophilic
plant metabolites. While triterpenes and certain lipid-soluble com-
pounds have been extensively studied and well-documented (particu-
larly in extracts obtained with ethyl acetate or hexane) (Kellogg and
Cech, 2020), most metabolomic resources still prioritise hydrophilic
metabolites due to the prevalence of ethanol-, methanol-, or
aqueous-based extractions in plant studies. Although lipidomics data-
bases have grown significantly, they primarily cover endogenous lipids
rather than the diverse range of lipophilic secondary metabolites found
in herbal medicine (Conroy et al., 2024). This gap underscores the need
for specialised resources focusing on lipid-soluble constituents extracted
using nonpolar solvents such as sesame oil, which play an important role
in herbal ointment formulations.

Finally, the physicochemical characteristics of Kampo ointments
presented considerable difficulties in both metabolomics and biological evaluations. These compositions typically come as semi-solids containing waxes or animal fats such as lard, making their direct injection into LC-MS systems problematic. This study exclusively investigated filtered oily fractions to avert interference inside the column and ion source thereby ensuring consistency and accuracy. This approach provided interesting insights, but the exclusion of paste-like components resulted in the omission of several metabolites, including wax-derived compounds. In biological assays, handling lipophilic and viscous extracts proved to be highly difficult.

### 4.3. Polyherbal formulations and challenges in annotation

The variability in herbal crude drug processing and the analytical complexity of lipophilic matrices already present obstacles to the study of Kampo ointments. Indeed, when several herbal ingredients are extracted in a single preparation, these challenges are amplified, making metabolite annotation and the interpretation of biological effects more demanding. Nevertheless, targeted Biosigner analyses revealed key metabolic differences that may explain the reduced keratinocyte migration observed with both complete formulas *Shiunkō* (S6HH) and *Shinsen taitsukō* (TMO) compared with their respective *Angelica acutiloba* single extracts.

For *Shiunkō*, two ions specific to S1HH but under-represented in S6HH were annotated as quinone/hydroquinone lipids structurally related to tocopherols (m/z 415.3576 and m/z 431.3527). Their lower abundance in the complete formula suggests enhanced oxidative degradation or antagonistic interactions with *Lithospermum* constituents, potentially contributing to delayed cell migration.

In *Shinsen taitsukō*, several discriminant metabolites were also detected, including a putative esterified diterpene (m/z 377.2673) that may affect cytoskeletal signalling pathways involved in motility. Other detected ions, such as anthraquinones from *Rheum* and monoterpene glycosides from *Paeonia*, emphasise the formulation's chemical diversity, but their biological roles and structural identification remain unresolved.

These findings emphasise both the chemical complexity of polyherbal ointments and the inherent difficulty of confidently linking individual metabolites to biological effects. Isolation and functional validation of candidate compounds will be required to confirm their contribution to the observed modulation of keratinocyte migration.

### 4.4. Experimental model considerations

Despite the rigorous nature of the experimental model utilised, the direct exposure of Kampo ointments to 2D keratinocyte cultures may not have been the most suitable approach to fully capture their biological effects. A major limitation was the challenge of solubilising lipophilic constituents in an aqueous cell culture medium, which likely restricted their bioavailability and cellular interaction. The use of DMSO as a solvent further illustrates this issue, as the lipophilic constituents of the ointments are not fully soluble at concentrations compatible with cell viability. Furthermore, DMSO does not necessarily extract the full range of metabolites present in the original preparation. For example, to improve the miscibility of the oily extract with the cells, a specific nanoemulsification technique could be employed (Pereira Oliveira et al., 2023).

Alternative models, such as three-dimensional skin equivalents or explants, could provide a more physiologically relevant environment for evaluating the wound-healing efficacy of these formulations (Hofmann et al., 2023). These models would allow the application of whole Kampo ointments while better preserving their original matrix and circumventing solubility constraints inherent to monolayer cultures. They would also overcome the difficulties of maintaining close contact between the oily matrix and the cells, help prevent evaporation of the preparation, and offer a protective trophic microenvironment that more

closely mimics *in vivo* conditions.

Although the keratinocyte scratch and EdU assays provided valuable information on proliferation and remodelling, they did not directly address the inflammatory phase of wound healing. Classical markers of inflammation, such as cytokine release or immune cell recruitment, were not measured, so potential anti-inflammatory activities of *Shiunkō* and *Shinsen taitsukō* could not be fully evaluated. In light of the current state of Kampo practices in Japan, this may be seen as a limitation, given that field investigations among Japanese practitioners emphasise the importance of inflammatory processes. *Shinsen taitsukō* is also a more complex formulation that combines a greater variety of medicinal plants than *Shiunkō*. This broader botanical composition plausibly expands its therapeutic applications, including those related to inflammation. Some practitioners reported a preference for *Shinsen taitsukō*, which is considered to have stronger anti-inflammatory properties than *Shiunkō*. To our knowledge, this aspect has not yet been explored in clinical studies, either through case reports or more extensive research.

### 4.5. General discussion and future perspectives

This study conducted on Kampo ointments combined ethnopharmacology, metabolomics, and biological investigations to advance the understanding of these lipophilic herbal formulations and their role in wound healing. Given the complexity and variability of these preparations, a multidisciplinary approach was essential to characterise their diverse phytochemical profiles and potential medicinal properties. While this work enabled the putative annotation of interesting metabolites for cell migration (such as tocopherols or glycosylated terpenoids), it also underscored the challenges posed by the heterogeneity of herbal preparations and the inherent difficulty of complex oil matrices.

Future research should focus on isolating and structurally characterising Kampo ointment metabolites, ensuring their detection in extracts remains both feasible and biologically relevant. However, based on the current models studied, no definitive biological activity can be attributed to these preparations. While some traditional uses suggest potential therapeutic benefits, the lack of direct clinical validation prevents firm conclusions regarding their efficacy. In addition, the intrinsic variability of Kampo ointments, influenced by processing methods, herbal crude-drug sourcing, and formulation practices, must be systematically evaluated. Expanding metabolic databases to better represent lipophilic plant-derived compounds, refining analytical methodologies, and developing advanced skin models will be key for further elucidating their therapeutic potential.

A deeper understanding of these factors would enhance the scientific validation of Kampo traditional ointments and facilitate their potential integration into wound care strategies, particularly for chronic wounds such as ulcers.

## 5. Conclusion

While the effects of keratinocyte migration and proliferation are challenging to explore due to the lipophilicity of Kampo ointments, the diversity of metabolites associated with the variety of preparation protocols was demonstrated.

The extraction temperature of the crude drugs in sesame oil and the origin of the oil were sources of modifications involving the formation of new metabolites and, conversely, the degradation of certain others. The higher the temperature and heating time, the more significant the modifications, with the production of transformed lipid compounds and the destruction of compounds such as tocopherols or sterols recognised as healing agents. Nevertheless, in the cellular model used, the different sesame oil preparations showed no significant differences in their effects on keratinocyte migration or proliferation. Similarly, extracts containing only *A. acutiloba* exhibited no distinct biological activity compared to their corresponding sesame oil controls. However, the plant origin had an important effect on known healing metabolites such as
glycosylated terpenoids. Conversely, the complete Kampo formulas *Shiunkō* and *Shinsen taitsukō* showed a detrimental effect on migration compared to extracts containing only *A. acutiloba*. For *Shiunkō*, this slowdown could be due to the degradation of hydroquinone lipids similar to healing vitamin E; for *Shinsen taitsukō*, the observed slowdown could be due to the presence of esterified diterpenes. However, these ointments are empirically known for their healing efficacy, and these results in the monolayer model remain difficult to interpret.

Further metabolomics and biological studies are now needed to better characterise the therapeutic effect of Kampo ointments. This should establish more relevant correlations between lipophilic herbal metabolites and biological efficacy for wound healing.

### CRediT authorship contribution statement

**Manon Paul-Traversaz:** Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization. **Kaoru Umehara:** Writing – review & editing, Project administration. **Kenji Watanabe:** Writing – review & editing, Project administration. **Walid Rachidi:** Resources, Methodology. **Eric Sulpice:** Resources. **Emma-nuelle Soleilhac:** Resources. **Cédéry Delporte:** Writing – review & editing, Resources. **Pierre Van Antwerpen:** Writing – review & editing, Resources. **Axelle Bourez:** Writing – review & editing, Resources. **Cécile Vanhaverbeke:** Writing – review & editing, Formal analysis. **Michel Sève:** Writing – review & editing, Supervision, Project administration, Methodology, Formal analysis. **Florence Soudar:** Writing – review & editing, Supervision, Project administration, Methodology, Formal analysis.

### Funding

This research was supported by the French National Research Agency in the framework of the "Investissements d'avenir" programmes ANR-15-IDEX-02, CBH-EUR-GS, and ANR-17-EURE-0003. Université Grenoble Alpes (France), IRIG-BGE CEA (France), and Yokohama University of Pharmacy (Japan) supported this research.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

The authors would like to express their gratitude to all the collaborators and institutions involved in the academic collaboration between Yokohama University of Pharmacy (Japan) and Université Grenoble Alpes (France). They would also like to acknowledge the partner laboratories that contributed to this project: the research was conducted in collaboration with the TIMC-IMAG EPSP UMR 5525 (Université Grenoble Alpes, France) and the Biometrics team from the IRIG-BGE laboratory (CEA, France), the Analytical Platform of the Faculty of Pharmacy (Université Libre de Bruxelles, Belgium), and the Laboratory E31 Kampo Natural Products Chemistry (Yokohama University of Pharmacy, Japan). This research was supported by the French National Research Agency in the framework of the "Investissements d'avenir" programs ANR-15-IDEX-02 and CBH-EUR-GS, ANR-17-EURE-0003. Univ. Grenoble Alpes (France). Some of this work was also performed at the CMBA platform from IRIG-BGE, CEA-Grenoble (a member of GIS-IBISA and ChemBioFrance infrastructure) supported by GRAL, the Grenoble Alliance for Integrated Structural & Cell Biology, a programme of the Chemistry Biology Health Graduate School of Université Grenoble Alpes (ANR-17-EURE-0003). For metabolomics, the Galaxy server used for some calculations is maintained by the Institut Français de Bioinformatique (IFB), funded by the Programme d'Investissements

d'Avenir (PIA), grant Agence Nationale de la Recherche, number ANR-11-INBS-0013. Additionally, the authors would like to express their gratitude to the Japanese Kampo health professionals who provided invaluable insights into the practical applications of these therapeutics.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jep.2025.120651.

### Data availability

Data will be made available on request.

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