Liquid Chromatography–Mass Spectrometry-Based Molecular Profiling of Vertigoheel

TL;DR

This study identifies key chemical components in the Vertigoheel medicine using advanced mass spectrometry techniques.

Contribution

The study introduces a detailed molecular profiling protocol for Vertigoheel, revealing ingredient-specific compounds and enabling quantification of neuroactive molecules.

Findings

Untargeted analysis identified 68,622 molecular features with ingredient-specific metabolites highlighted.

Key compounds like picrotoxinin, coniine, ambrinol, and santalyl phenylacetate were quantified in drops and tablets.

The study confirms that Vertigoheel retains natural constituents and supports further pharmacological investigation.

Abstract

Vertigoheel is a multicomponent medicinal product for the treatment of vertigo and dizziness, containing Anamirta cocculus, Conium maculatum, Ambra grisea, and Petroleum rectificatum. Although clinical efficacy has been reported, the chemical composition and underlying mechanisms remain incompletely characterized. Here, we applied ultra-high-performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-ToF-MS) to profile extracts of each ingredient and the final formulations. Untargeted analysis revealed 68,622 molecular features, and multivariate statistics highlighted ingredient-specific metabolites. Representative markers included picrotoxinin and picrotin from Anamirta cocculus, coniine and N-methylconiine from Conium maculatum, ambrinol and ambroxide from Ambra grisea, and santalyl phenylacetate and mercaptostearic acid from Petroleum rectificatum. Two compounds per ingredient were further quantified by targeted UHPLC-MS/MS, confirming their presence in drops and tablets at nanogram-per-dose levels with moderate variability across batches. These findings demonstrate that Vertigoheel retains characteristic constituents from its natural sources in all tested formulations. The established protocol enabled absolute quantification of neuroactive molecules such as picrotoxinin and coniine with minimal work-up. This molecular characterization provides new insight into Vertigoheel’s composition and supports further investigation of its mechanism of action using network pharmacology approaches.

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1. Introduction

Vertigoheel is a natural over-the-counter medicinal product used to treat vertigo and dizziness, highly prevalent symptoms that significantly impact the quality of life [1]. Its production and use are approved by the German regulatory authority and comply with the standards outlined in the German Homeopathic Pharmacopeia [2], which governs the quality, manufacturing, and testing of homeopathic medicinal products. Vertigoheel is available in two oral galenic forms, drops and tablets, and contains four natural ingredients: Anamirta cocculus (Indian berry), Conium maculatum (spotted hemlock), Ambra grisea (ambergris), and Petroleum rectificatum (purified petroleum). The multi-component, multi-target approach of Vertigoheel aligns with the current trend of using network pharmacology in drug development for complex and heterogeneous pathophysiologies [3].

Vertigoheel has been shown to enhance central vestibular compensation in rat models with experimental vestibulopathy [4]. Interestingly, cognitive improvements after Vertigoheel treatment were also observed in scopolamine-lesioned rats [5], which is notable considering that patients with peripheral vestibular dysfunction often experience cognitive deficits [6]. Human studies combining data from four clinical studies support the effectiveness of Vertigoheel in treating vertigo and dizziness symptoms, along with its favorable tolerability compared to other therapies such as betahistine, Ginkgo biloba extract, and dimenhydrinate [7,8]. A recent observational study involving patients with bilateral vestibulopathy and functional dizziness demonstrated significant improvements in vertigo symptoms following treatment with Vertigoheel [9].

While clinical effects have been demonstrated, the chemical composition of Vertigoheel remains largely elusive due to the complex nature of the extracts and the relatively high dilution levels, which pose significant challenges for analysis. However, advancements in analytical separation techniques and mass spectrometric detection have greatly improved the selectivity, sensitivity, and speed of identifying and quantifying components in complex mixtures [10,11]. Liquid chromatography coupled with high-resolution mass spectrometry (LC-MS) based on time-of-flight or Orbitrap separation has emerged as the gold standard for holistic profiling of non-volatile molecules across a large abundance range in such samples [12]. The diversity of molecular structures of complex mixtures can be studied using chemical space mapping [13] and ontology-based compound class [14] classification to reveal the distribution of chemical constituents. These tools help identify which molecular families are retained or lost, and how ingredient-derived features are preserved in the final product.

The objective of this study was to characterize Vertigoheel, a natural multicomponent medicinal product, by LC–MS-based molecular profiling and to establish a targeted method for absolute quantification of representative ingredient-derived markers in the final formulations. Untargeted analysis and structure-based annotation were complemented by targeted UHPLC–MS/MS quantitation of two selected marker compounds per ingredient. This study provides the first LC–MS-based molecular profile of Vertigoheel and a quantitative marker panel enabling consistent cross-formulation and cross-batch comparisons. The comprehensive molecular profiles will support further mechanistic studies and quality control efforts in the future.

2. Results and Discussion

2.1. Untargeted Analysis

Untargeted analysis was performed on the individual liquid extracts of Anamirta cocculus, Conium maculatum, Ambra grisea, and Petroleum rectificatum, which are the functional ingredients of Vertigoheel, as well as on the solid and liquid Vertigoheel product samples (tablets and drops). Using ultra-high-performance liquid chromatography (UHPLC) coupled to time-of-flight mass spectrometry (UHPLC-TOF-MS/MS) in both positive and negative ionization modes with a reversed-phase stationary phase, 68,622 distinct mass spectral features were extracted, deconvoluted, and aligned, providing a comprehensive chemical profile of the ingredients and products. Multiple features, therefore, can still represent a single compound, since adducts and detection in both ionization polarities were not corrected at this level.

Principal component analysis (PCA) was conducted following logarithmic transformation and unit scaling of the dataset. The PCA score plot (Figure 1A) revealed distinct cluster separation. The first principal component (PC1), which explained 44% of the total variance, separated the complex liquid extracts of Anamirta cocculus, Conium maculatum, and Ambra grisea (positive PC1 scores) from the Vertigoheel product samples (negative PC1 scores). Petroleum rectificatum is located closer to the Vertigoheel products, reflecting its low molecular complexity in the range of features detectable by ESI, which favors polar to semipolar analytes.

The second principal component (PC2), explaining 14% of the variance, distinguished Ambra grisea from the plant-based raw materials, Anamirta cocculus, and Conium maculatum. This distinction is likely due to its unique molecular features, including lipid-like compounds, in contrast to the large set of secondary plant metabolites.

The corresponding loading plot (Figure 1B) highlights the contribution of individual features to the variance along PC1. A substantial number of features show a high positive contribution on PC1 originating from plant-based non-volatile compound classes such as procyanidins, flavone glycosides, and methylxanthines. Conversely, features near the origin of the plot, such as aldehydes, fatty acid lactones, and carbohydrates, contribute minimally to the first principal component, indicating their relatively uniform distribution across samples.

2.2. Structure-Based Chemical Space Analysis

To extend the abundance-based comparisons from PCA with a structural perspective, we constructed a t-distributed stochastic neighbor embedding (t-SNE) based chemical space map from structurally annotated metabolites exceeding an intensity threshold. Chemical spaces refer to the theoretical, multidimensional space that encompasses a set of chemical compounds, defined by their (structural) properties. Chemical compounds can be characterized by a wide range of descriptors such as their molecular formula, molecular mass, hydrophobicity, or topological polar surface area, and, on the other hand, their structure can be described by the individual atoms, their linkage by chemical bonds, and their stereochemistry. These descriptor sets form the basis for the calculation of pairwise similarities between molecules, which is subsequently visualized in general using dimensionality reduction approaches. In the resulting chemical spaces, each individual molecule is placed at coordinates corresponding to its properties, while the distance between two molecules represents their similarity.

The resulting map is based on 1620 relatively highly abundant features summarizing the combined chemical complexity of the four Vertigoheel ingredients—Anamirta cocculus, Conium maculatum, Ambra grisea, and Petroleum rectificatum (Figure 2A–D). The complete set of annotated features is available in the Supplementary Materials as individual tables for each of the four ingredients (Tables S1–S4). To further outline the distribution of distinct molecular moieties in this chemical space, mapped features were classified by means of the ClassyFire ontology. Figure 2F shows the resulting chemical space representation colored by chemical class, based on class annotations. Here, the compound landscape is segmented into major chemical categories, including carboxylic acids, fatty acyls, flavonoids, coumarins, steroids, and prenol lipids. These classes group into chemically coherent zones: for instance, prenol lipids and steroids dominate the upper-left region, while carboxylic acids and fatty acyls are enriched in the lower and central zones. Aromatic and polyphenolic compounds such as benzenes, coumarins, and flavonoids form scattered yet distinct islands in central regions.

To highlight features representing individual raw materials, the set of detected metabolites is superimposed on this background map, demonstrating that the largest set of points arises from plant extracts Anamirta cocculus and Conium maculatum, which contribute broad, chemically rich sections dominated by alkaloids, coumarins, and polyphenolics. In contrast, Ambra grisea forms a more compact cluster characterized by steroid-like and terpenoid scaffolds, while Petroleum rectificatum is confined to a narrow sector dominated by non-polar hydrocarbons.

A limited number of Vertigoheel product features meeting the selection criteria (Figure 2E) were also superimposed onto this space, appearing scattered across the global chemical landscape without notable enrichment in specific regions. These product-associated features span class-defined zones, indicating that the final formulation retains a structurally and functionally diverse set of compounds from both polar and lipophilic families. This selective preservation of representative molecules from multiple raw material sources, even after concentration reduction, aligns with the PCA results and may contribute to the multi-target pharmacological potential of the product.

To complement the structural mapping, a Venn diagram analysis (Figure 3) was used to assess the compositional overlap between the three major Vertigoheel ingredients and the final product. Most product-associated compounds were also present in at least one ingredient, confirming substantial molecular transfer during formulation. A smaller subset of features was unique to the product, likely representing excipients or minor extraction artifacts. Conversely, many compounds are shared among multiple ingredients, particularly between the two plant extracts, suggesting common biosynthetic origins. This overlap analysis supports the structural findings and demonstrates that while formulation reduces overall complexity, it preserves a representative and pharmacologically relevant core.

2.3. Feature Selection and Differential Analysis

Volcano plots were generated to identify molecular features that differentiate the four ingredients (Figure 4). Of the 68,622 detected features, 24,413 were structurally annotated using spectral libraries and in silico tools. Among these, 1533 features were significantly differentially abundant—317 in Anamirta cocculus, 352 in Conium maculatum, 790 in Ambra grisea, and 74 in Petroleum rectificatum— based on a fold-change threshold of >2 and statistical significance (p < 0.01). To enable targeted quantitative analysis of metabolites as marker compounds for the individual raw materials, two features per ingredient were selected in a final filtering step. Selection criteria included the commercial availability of reference standards, the compound’s presumed physiological activity, its unique presence in a single ingredient, and sufficient abundance to allow for detection in diluted formulations without the need for concentration or enrichment steps (see Section 3.5). The selection was intended to yield representative analytical markers rather than comprehensive coverage of all constituents or structural classes. Functional information has been reported to date for only a limited number of constituents in Anamirta cocculus and Conium maculatum, leading to the selection of the respective target analytes for these ingredients. For Ambra grisea, the key limiting factor was the commercial availability of reference compounds for the identified structures, whereas selection for Petroleum rectificatum was driven by the limited detectability of the comparatively small number of candidate analytes.

In Anamirta cocculus, picrotin and picrotoxinin emerged as distinguishing features, both of which are well known for their neuropharmacological activity [15]. The analysis of Conium maculatum revealed elevated levels of coniine and N-methylconiine, two piperidine alkaloids that are primarily responsible for the plant’s pharmacological and toxicological properties [16]. For Ambra grisea, ambrinol and ambroxide were identified, terpenoid derivatives traditionally used in perfumery. The unique molecular traits also explain its distinct clustering in the PCA analysis. Finally, Petroleum rectificatum was characterized by the presence of santalyl phenylacetate and mercaptostearic acid, compounds reflecting its hydrocarbon-based chemical profile.

The differentiation of these molecular features supports the chemical distinctiveness of the individual ingredients, which likely contribute to Vertigoheel’s overall functional properties. Moreover, this chemical specificity supports the development of targeted, quantitative analyses.

2.4. Targeted Analysis of Differentiating Molecular Features

The total set of eight analytes was used to develop a targeted quantitation method based on LC-MS/MS, aimed at enabling robust and unambiguous profiling of the raw materials. Fragmentation parameters were optimized via flow injection of the individual compounds, yielding two fragment transitions per analyte. The most intense transition under multiple reaction monitoring was selected as the quantifier trace, while the second served as a qualifier for identity confirmation. Initial testing of the combined LC-MS method demonstrated high selectivity and sensitivity. However, a notable interference in the quantifier trace of picrotin was observed upon injection of pure picrotoxinin, posing a risk for inaccurate quantitation. To resolve this, chromatographic conditions were adjusted to ensure sufficient separation of the two analytes (Figure 5). Due to the unavailability of stable isotope-labeled internal standards, quantitation was carried out using external calibration. Method validation of the eight selected compounds yielded recovery rates between 80% and 108%, with precision values (%RSD) below 18%, confirming the method’s suitability for high-throughput analysis. A notably higher recovery at 148.9% was observed for Santalyl phenylacetate, which can be attributed to the very low signal intensity in the absence of appropriate stable-isotope-labeled standards. Although the latter compound was not fulfilling the regular acceptance criteria of 80–120%, no omission was performed due to the absence of promising alternatives.

A summary of the method validation parameters for the individual analytes is provided in Table 1.

Recoveries exceeding 100% in LC–MS-based metabolomics are particularly common for very low-abundance analytes and do not indicate a more than quantitative extraction. Such recovery values typically reflect matrix-dependent signal enhancement, endogenous background relative to small spike signals, and low-end calibration model artifacts. Conceptually separating extraction recovery, matrix factor, and process efficiency can be used to diagnose the underlying cause, since values >100% can usually be traced back to a matrix factor >1 (ionization enhancement) [17]. At low concentrations, co-eluting surface-active species can disproportionately boost electrospray response, shifting adduct equilibria and droplet charging so that a post-extraction spike in matrix outperforms the same amount in neat solvent [18,19,20]. Additional low-concentration issues can result from nonlinearity near LLOQ and peak integration bias/noise picking [21]. Chemical factors can also lead to >100% recoveries at trace levels, since adsorption or degradation in solvent standards may be reduced in biological extracts, making matrix spikes appear higher. Carryover, reagent contaminants, or unresolved isobaric compounds or in-source fragments can also contribute small additional proportions to the recorded signal, resulting in a significant signal increase at low spiking levels. Besides the sufficient sensitivity and accuracy, the method also enables the application to larger sample numbers due to its high overall throughput. The overall chromatographic run time per sample, including injection, is 15 min, enabling the analysis of up to 96 samples per day. Sample preparation can be performed at equal throughput since bead-beater homogenization, equilibration, and centrifugation are highly parallelized with at least 6 samples per repetition, enabling the processing of up to 100 samples per day.

Targeted absolute quantitation (Table 2) revealed that picrotin and picrotoxinin, principal sesquiterpene lactones from Anamirta cocculus, were quantified in Vertigoheel drops at 31.37–34.01 µg/L and 86.97–90.55 µg/L, respectively, and in tablets at 0.255–0.288 µg/g and 0.524–0.589 µg/g, respectively. These compounds are known to exhibit significant neuropharmacological activity [15]. Picrotoxin—a mixture of picrotoxinin and picrotin—has recently been explored as a treatment for Menière’s disease [22,23].

Coniine and N-methylconiine, alkaloids derived from Conium maculatum, were present in Vertigoheel drops at 13.66–16.49 µg/L and 12.33–12.91 µg/L, and in tablets at 0.009–0.013 µg/g and 0.006–0.009 µg/g, respectively. They can inhibit the nervous system at high doses [24,25] and are under renewed investigation for their potential in non-addictive analgesia [16].

Ambrinol and ambroxide, characteristic markers of Ambra grisea, were detected in drops at 1.73–1.93 µg/L and 2.21–2.34 µg/L, and in tablets at 0.164–0.178 µg/g and 0.182–0.224 µg/g, respectively. While no specific pharmacological activity has been attributed to these compounds, their detection enables reliable identification and quantification of Ambra grisea in Vertigoheel products.

Santalyl phenylacetate and mercaptostearic acid, constituents of Petroleum rectificatum, were detected at very low concentrations in the finished products. In drops, santalyl phenylacetate was measured at approximately 0.0001 µg/L, while mercaptostearic acid was measured in the range of 0.0001–0.0002 µg/L. In tablets, both compounds were detected at approximately 0.0001 µg/g. These extremely low concentrations reflect the extensive dilution of this ingredient in the final product. Despite lacking defined pharmacological activity, both compounds serve as reliable markers for the identification and quantification of Petroleum rectificatum.

The reported values reflect measurements from multiple batches of natural-source ingredients and are presented as concentration ranges to account for natural variability. All ingredients were produced and tested in accordance with the German and European Pharmacopeia [2,26,27], and subjected to ingredient-specific quality control prior to formulation. Nevertheless, compound levels may vary between ingredient batches due to factors such as botanical origin, harvest season, year of collection, as well as storage and extraction conditions. Importantly, these raw materials are not standardized to fixed marker concentrations before processing into the finished product. As a result, the variability observed in the final product (drops and tablets) likely mirrors the natural fluctuation in the source materials. Additionally, differences between the two oral galenic forms may also arise from differences in sample handling and preparation during HPLC analysis, which were not fully addressed in the validation process. Future studies following the identification of the set of molecules providing the targeted functionality will require a significant extension of the reported approach to overcome several limitations. The application of suitable internal standards in comparison to external calibration is expected to lead to higher method accuracy and robustness. This approach will support the quantification of ultra-low abundance levels of individual metabolites, since internal standardization mitigates several uncertainty components. However, even if stable-isotope-labeled standards (SISs) become available, isotopic impurities can seed background in MS detection; slight chromatographic offsets or differential adsorption can yield imperfect correction; and SIS transitions can be contaminated by cross-talk from abundant matrix ions or isobaric background. In the more likely case that SISs are unavailable, matrix-matched calibration or standard addition can reduce matrix effect bias; standard addition, though laborious, would offer a route to improve accuracy [28]. An additional limitation is the omission of method validation for the drop matrix. However, the drop formulation contains less than 1 mg of active ingredients per gram (e.g., 700 µg/g Anamirta cocculus extract, 200 µg/g Conium maculatum extract) in a 35% ethanolic solution. Therefore, we assessed the risk of matrix effects compared to the tablet matrix as very low. Nevertheless, as subsequent studies following the characterization of the active molecular principles in Vertigoheel will very likely include additional analytes, full validation for both delivery forms will be included.

3. Materials and Methods

3.1. Chemicals and Solvents

Briefly, 2 mL bead-beating tubes were purchased from Precellys, while other consumables were purchased from Sigma-Aldrich (Merck KGaA, Darmstadt, Germany). Ultra-high-performance liquid chromatography (UHPLC) grade solvents, mobile-phase additives, and reference standards were purchased from Sigma-Aldrich.

3.2. Samples

The finished products of the two oral galenic forms, Vertigoheel drops and Vertigoheel tablets, along with their ingredients—liquid extract preparations of Anamirta cocculus, Conium maculatum, and Ambra grisea, as well as the refined crude oil distillate Petroleum rectificatum—were received from the product manufacturer, Heel GmbH (Baden-Baden, Germany).

The liquid extracts, referred to as mother tinctures, were prepared by macerating the raw materials in a mixture of alcohol and water to extract the active compounds in accordance with the specifications of the German Homeopathic Pharmacopeia [2], which is an official part of the German Pharmacopeia [26], and the European Pharmacopeia [27]. The extracts were then filtered and stepwise diluted before the final product was prepared. Vertigoheel, in both oral galenic forms—drops and tablets—contains the following active ingredients: 700 µg/g of Anamirta cocculus extract, 200 µg/g of Conium maculatum extract, 1 µg/g of Ambra grisea extract, and 1 ng/g of Petroleum rectificatum.

Several batches of both oral galenic forms and their ingredients were provided. Specifically, two batches of Ambra grisea extracts (Batch No. 641518 and 610204), six batches of Anamirta cocculus extracts (Batch No. 575032, 584691, 632545, 693100, 652414, and 614328), two batches of Conium maculatum extracts (Batch No. 642220 and 625781), and two batches of Petroleum rectificatum extracts (Batch No. 649456 and 626780) were analyzed. Additionally, six batches of Vertigoheel drops (Batch No. 01990, 04120, 04613, 04750, 96592, and 98294) and six batches of Vertigoheel tablets (Batch No. 04873, 04875, 04876, 04877, 04884, and 98799) were also analyzed.

3.3. Sample Preparation

Solid samples (Vertigoheel tablets) were accurately weighed into 15 mL bead-beating tubes containing ceramic balls (2.8 mm diameter, CK28_15 mL, Bertin Technologies, Montigny-le-Bretonneux, France) to achieve approximately 900 mg (3 tablets). These were suspended in 5 mL of methanol/water (1:1, v/v) extraction solvent. The samples were homogenized using a bead beater (Precellys Evolution Homogenizer, Bertin Technologies, Montigny-le-Bretonneux, France) with three 20 s shaking cycles at 4000 rpm, interspersed with 30 s breaks. After homogenization, the samples were equilibrated at room temperature for 1 h. The homogenate was then centrifuged at 7600 rpm for 5 min, and the resulting supernatant was transferred to clean autosampler vials.

Liquid samples (Vertigoheel drops and raw extracts) were directly transferred into autosampler vials fitted with volume-reduction inserts. Before analysis, all samples were filtered through 0.45 µm regenerated cellulose syringe filters (Sartorius, Göttingen, Germany) and stored at −20 °C. Autosampler caps were replaced after the samples were removed from the LC-MS instruments.

3.4. Untargeted LC-MS Analysis

UHPLC coupled to time-of-flight mass spectrometry (UHPLC-TOF-MS/MS) analysis was performed based on established protocols [29,30] using an Exion LC UHPLC system (Sciex, Darmstadt, Germany) connected to a TripleTOF 6600 mass spectrometer (Sciex, Darmstadt, Germany) using electrospray ionization (ESI) in both positive and negative modes. The UHPLC systems consisted of two Exion LC AD pumps, an Exion LC degasser, an Exion LC AC column oven, an Exion LC AD autosampler, and an Exion LC controller. Chromatography was carried out using a RP18 stationary phase (Kinetex C18, 100 × 2 mm, particle size 1.7 μm; Phenomenex, Aschaffenburg, Germany) using a constant flow rate of 0.25 mL/min at a column temperature of 40 °C. The mobile phase consisted of (eluent A) water and (eluent B) acetonitrile, both containing 0.1% formic acid. The gradient elution started with 5% B for 2 min and increased to 100% B in 11 min, held for 2.5 min, decreased to the initial ratio of 5% B within 0.5 min, followed by 4 min of re-equilibration. The injection volume of all samples was 5 μL, and the autosampler was maintained at 10 °C. The mass spectrometer was operated in the information-dependent acquisition mode (IDA) for fragment spectral measurement. After starting with a high-resolution scan of the intact precursor ions from 50 to 1000 m/z for 250 ms, fragment ions were generated by means of collision-induced fragmentation for the eight most abundant precursor ions per cycle. The resulting fragment spectra were recorded in the high-sensitivity mode between 50 and 1000 m/z (50 ms acquisition per experiment). Ion spray voltage was set at −4500 V in negative and 5500 V in positive mode. The following source parameters were applied: curtain gas, 35 psi; gas one at 55 psi, and gas two at 65 psi at a temperature of 500 °C. Declustering potential (DP) in positive ionization mode was set to 80 V for all experiments, while the collision energy (CE) was 10 V for precursor ion scans and 35 V, including a 20 V collision energy spread for fragment ion acquisition. Similarly, a DP of −80 V and a CE of −10 V and −35 ± 20 V were applied in negative ionization mode, respectively.

For normalization to compensate for longitudinal shifts during data acquisition, a quality control (QC) sample was prepared by mixing aliquots (10 µL) of each sample. The combined QC sample was injected into the LC-MS system after every five samples, while samples were analyzed following randomization for the avoidance of batch effects.

Raw mass spectrometry data were converted to the mzML file format using Proteowizard [31] and subsequently processed using the xcms package (version 4.0.2) [32,33] within the statistical programming environment R (version 4.3.2). Peak areas of detected features in the individual samples were used for statistical analysis following normalization. To explore holistic variation and similarities among samples, principal component analysis (PCA) was applied following logarithmic transformation and unit scaling using the FactoMineR package (version 2.11) [34]. Statistical analysis for volcano plot visualization was performed using the R package limma (version 3.58.1) [35]. Identification of features in the untargeted metabolomics data was performed by comparison of fragment mass spectra and retention times to an in-house library and public spectral databases [36,37]. Features lacking a positive library hit were annotated using the SIRIUS platform [38].

For structure-based analysis of the molecular composition of individual ingredients and finished product, chemical spaces were constructed from InChIKey-annotated features exceeding a peak area of 500,000 counts per second using Klekota–Roth fingerprints [39] and Tanimoto similarity [40] with t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction [41], as described previously [42]. In detail, t-SNE was performed using the following parameters: perplexity 30, maximum iterations 1000, 2 dimensions, and no initial dimensionality reduction by PCA. After conversion to a dissimilarity matrix, two-dimensional embeddings were visualized in R using the ggplot2 package [43]. Annotated features were subsequently classified using the ClassyFire taxonomy [14] and grouped on the compound class level. Final visualization of the structural differences between sample groups was performed using ggVennDiagram (version 1.5.2) in R [44]. The petroleum fraction was excluded from the Venn due to low structural complexity.

3.5. Selection of Marker Compounds for Targeted Analysis

Marker compounds for targeted LC-MS/MS analysis were selected based on the untargeted UHPLC-TOF-MS dataset. Differential abundance analysis (fold change ≥ 2, p < 0.01) was used to identify ingredient-specific features. From these, two compounds per ingredient were selected according to the following criteria: (i) commercial availability of reference standards of the identified molecules with sufficient purity, (ii) sufficient signal intensity to enable direct detection using the targeted LC-MS/MS method in the final diluted product formulations without sample concentration or enrichment, and (iii) preference for compounds with known or presumed biological relevance. The selection was intended to yield representative analytical markers rather than a comprehensive coverage of all constituents or structural classes.

3.6. Targeted LC-MS/MS Analysis

For targeted quantitation by means of UHPLC-MS/MS, a QTRAP 6500^+^ mass spectrometer (Sciex) was applied to acquire ESI fragment mass spectra in multiple reaction monitoring mode (MRM). [M+H]^+^ ions were detected in positive electrospray mode (ESI^+^) at an ion spray voltage of 5500 V, and [M-H]^−^ ions were detected at an ion spray voltage of −4500 V in negative mode (ESI^−^). The following ion source parameters were used: curtain gas (35 psi), temperature (450 °C), gas 1 (55 psi), gas 2 (65 psi). The MS/MS parameters’ declustering potential, collision energy, and cell exit potential, shown in Table S5, were tuned for each individual compound by direct flow injection (10 µL/min) of methanol/water solutions of the analytes using a syringe pump. Chromatographic separation was performed using an Exion UHPLC system (Sciex, see previous section for details), equipped with a Kinetex F5 column (100 × 2.1 mm, 100 Å, 1.7 μm, Phenomenex), and using the following solvent gradient (0.4 mL/min) of 0.1% formic acid in water (solvent A), and acetonitrile (solvent B), respectively: 2 min at 5% B, increased to 100% B within 5 min, held at 100% B for 3 min, decreased to 5% B in 1 min, and finally held at 5% B for 4 min. Analyst 1.6.3 software (Sciex) was used for data acquisition as well as instrument control, and Multiquant (version 3.0.2, Sciex) was used for quantitative data analysis. The acquisition parameters for the various analytes are given in Table S5.

Method validation was performed using spike and recovery experiments in the pill matrix. Accuracy was evaluated by calculating the ratio of the measured concentration, after subtracting the endogenous concentration, to the spiked concentration. Precision was determined as the relative standard deviation (RSD) across five replicate sample preparations. Instrument precision was similarly assessed by calculating the RSD across five replicate injections of a single sample. Linearity was defined by the coefficient of determination (R2) for each calibration curve. Sensitivity, including limits of detection (LODs) and quantification (LOQs), was established for each analyte as three and ten times the ratio of the standard deviation (calculated from five replicates at the lowest level of the calibration curve) to the slope of the calibration curve. Reproducibility was evaluated using 900 mg aliquots of solid samples and 150 µL aliquots of liquid samples for preparation and analysis. One sample per matrix was analyzed with five replicate preparations to assess both method precision and overall reproducibility.

4. Conclusions

The untargeted and targeted analyses demonstrated that the four ingredients of Vertigoheel—Anamirta cocculus, Conium maculatum, Ambra grisea, and Petroleum rectificatum—possess distinct and complementary chemical profiles. Various compounds were consistently detected in the final product across formulations and batches, indicating substantial transfer through the formulation process.

In addition to abundance patterns, the structural analysis showed that Vertigoheel preserves a broad and balanced mix of compounds from its source ingredients. Most of the detected product features could be traced back to the original extracts, while only a small number appeared unique to the formulation, likely reflecting excipients or minor processing byproducts.

These integrated findings provide a comprehensive view of the product’s molecular composition and enable future studies to examine the complex multi-target mechanism of action. Such research that requires the database generated in this study will finally contribute to an improved understanding of Vertigoheel’s therapeutic potential, particularly within the framework of network pharmacology.