Method for the preparation of plant materials with reduced variance

Description

[0001] The invention relates to a method for producing standardized or quantified plant materials with reduced variance in plant constituents, in particular from medicinal plants, using a mixture calculator.

[0002] Medicinal plants contain, sometimes concentrated in specific parts of the plant such as roots, leaves, flowers, or fruits, constituents with pharmacological effects and form the basis for a considerable number of medicines. Various methods exist for obtaining these constituents, most of which operate on the principle of some form of extraction, including maceration or percolation of the plants with a suitable extraction solvent or solvent. This results in a more or less selective solution and concentration of specific plant active ingredients or groups of active ingredients in the extraction solvent or extract. The extracts can be liquid, semi-solid, or solid, and in particular, a dry extract (extracta sicca), whereby, for example, the extraction residue, the resulting fluid extract (extracta fluida), or the resulting tincture (tincturae) is concentrated to dryness.Drying can be carried out by means of fluidized bed drying or by concentration to a thick or viscous extract (extracta spissa) followed by vacuum belt drying or tray drying, see also e.g. EP 0 753 306 B1 of the applicant. Graph:

[0003]

[0004] The applicant produces and distributes, for example, extracts from medicinal plants such as Bronchipret ®< , Sinupret Extract ®< , Canephron ®< , Imupret ®< , etc. as well as drug-based products (Sinupret ®< ).

[0005] The extracts in question are registered or licensed medicinal products, so-called traditional or rational herbal medicinal products (including well established medicinal use), which require adequate dosage as well as prescription in accordance with the indication, taking into account the benefit-risk ratio.

[0006] Monographs (also known as pharmacopoeia specifications (German Pharmacopoeia (DAB), European Pharmacopoeia (EuAB or Ph.Eur. for Pharmacopoea Europaea)) describe plants for medicinal use and their manufacture, particularly with regard to their active ingredients, effects, indications, contraindications, side effects, interactions, dosage, and dosage form. Plants are considered positively monographed if Commission E (the regulatory and preparation commission at the BfArM, Germany) or the Committee on Herbal Medicinal Products (HMPC) at the EMA has provided sufficient evidence of efficacy and safety based on available studies.

[0007] According to Ph.Eur. and for the purposes of this invention, extracts are preparations of liquid, semi-solid or solid consistency, which are made from, usually dried, plant drugs.

[0008] so-called standardized extractsThe concentration of active ingredients (key substances) is adjusted within permissible limits. This adjustment can be achieved by blending extract batches and / or by adding excipients.

[0009] so-called quantified extracts The extracts are adjusted to a defined range of efficacy-determining ingredients (key substances). This adjustment can be achieved by blending extract batches.

[0010] The mixing of extracts or batches can be carried out by a mixing device, whereby a mixing computer is used that sets the mixing ratio of the extracts using a computer-aided computer.

[0011] Examples of prior art for manufacturing processes of standardized extracts can be found, among others, in WO 0035467.

[0012] However, plant extracts or drug batches consist of a multitude of plant constituents. The content of these constituents can vary considerably depending on various parameters, such as growing region, weather conditions, cultivation, harvesting methods, harvest time, and many others.

[0013] Consequently, plant extracts or drug batches contain varying levels of plant constituents depending on their origin, history, harvest time, manufacturing process, and other parameters. This can lead to qualitative differences between individual samples or within a batch, as well as between batches themselves. Therefore, both plant extracts and powdered drugs exhibit the problem of phytochemical batch variability. To guarantee consistent therapeutic success in treatment with a herbal medicine, the composition of the plant constituents in an extract or herbal medicine must remain as consistent and stable as possible from batch to batch; that is, low batch variability is essential.

[0014] This is ultimately due to the fact that the plant constituents of the extracts or batches exhibit a natural, biological variance ("biological space").

[0015] Plant constituents are primarily secondary plant compounds. Secondary plant compounds, or secondary metabolites, are derived from products of anabolic and catabolic metabolism, especially carbohydrates and amino acids. For medicinal plants, secondary plant compounds are crucial for their suitability as active ingredients. Secondary plant compounds include, in particular, phenolic, isoprenoid, and alkaloid compounds such as phenols, polyphenols, flavonoids, caffeic acid derivatives, xanthones, terpenes, steroids, and other natural products. The occurrence and concentration of secondary plant compounds can vary considerably between individual plants or batches.

[0016] Plant constituents in plant materials can be characterized qualitatively and quantitatively using analytical methods (e.g., chemotaxonomy). Gas and / or liquid chromatography coupled with a mass spectrometer, such as GC-MS or LC-MS, is primarily employed. Typically, signals (peaks [m / z]) are displayed as a function of retention time (chromatogram). This also allows for the determination of concentrations (e.g., w / w or v / v) of plant constituents in the drug or extract, or in plant material, via the resulting integrated peak areas.

[0017] No techniques are described in the prior art that allow the production of plant extracts or drug batches or plant materials, thereby achieving a reduction in the variance of plant constituent content.

[0018] Therefore, it is an object of the present invention to provide plant material(s) that exhibit a reduced variance (variability) of the levels of plant constituents in a plant material, in particular a reduced batch variability.

[0019] Providing plant material with reduced variance (synonym: variability) in the content of plant constituents leads to optimized batch homogeneity. An optimized batch advantageously allows for improved batch reproducibility, thus ensuring a new quality standard. This is also of great importance for regulatory approval. For example, the FDA (US) has not yet granted approval for any plant extract or powdered drugs as a multi-component mixture.

[0020] Furthermore, it allows for a lower error tolerance in comparative studies, which in turn improves the quality of the studies and makes them comparable to synthetic drugs.

[0021] Therefore, the invention relates to a method for producing plant material with reduced variance in the levels of plant constituents, wherein at least one variance marker is used and at least two batches are mixed using a mixing calculator.

[0022] In a preferred embodiment, the problem is solved by a method according to the invention for producing plant material with reduced variance in the content of plant constituents, comprising the following steps (see also Example B ): i.) Determination of signal intensities for plant constituents in two or more batches using a detector, in particular GC-MS and / or LC-MS, ii.) Identification of at least one plant constituent that makes a contribution, preferably the largest contribution, to the variance and determination of its natural range (hereinafter referred to as variance markers), iii.) Setting one or more limit values ​​that are smaller than the respective natural range from ii.), iv.) Mixing of at least two batches, taking into account at least one limit value from iii.) using a mixing calculator, v.) Optionally, repetition of steps i.) to iv.).

[0023] As part of step ii.), a determination of the variance of the levels of plant constituents can be carried out.

[0024] The process according to the invention therefore advantageously allows the production of stable or identical, homogeneous batches, thus ensuring, in particular, the reproducibility of the batches. This is primarily due to the fact that the variance of the plant constituents is reduced by means of the identified at least one variance marker, which allows for a maximum reduction of the total variance of the plant constituents. This variance marker, identified according to the invention, contributes significantly to the dispersion and its effect on the total variability is reduced by the process according to the invention.

[0025] The term "plant constituent content" means the relative or absolute amount (mass, weight) of one or more plant constituents or their relative or absolute concentration(s) (w / w) (v / v).

[0026] In a further preferred embodiment, the inventive method for producing plant material with reduced variance in the content of plant constituents comprises the following steps (see also Example C ): i.) Determination of signal intensities for plant constituents in two or more batches using a detector, in particular GC-MS and / or LC-MS, ii.) Division of the signals into two or more sub-ranges, and summation of the signal intensities of the plant constituents within each sub-range, iii.) Identification of at least one sub-range that makes a contribution, preferably the largest contribution, to the variance, and determination of its natural range (hereinafter referred to as variance markers), iv.) Setting one or more limit values ​​that are smaller than the respective natural range from iii.), v.) Mixing of at least two batches, taking into account at least one limit value from iv.) using a mixing calculator, vi.) Optionally, repetition of steps i.) to v.).

[0027] As part of step iii.), a determination of the variance of the levels of plant constituents can be carried out.

[0028] This embodiment particularly advantageously allows the provision of plant materials from complex plant materials containing a multitude of plant constituents, in particular more than 300 plant constituents, especially secondary plant metabolites. The inventive components allow a systematic representation of the complex signal intensities by summing those signal intensities.

[0029] However, according to the invention, the sub-areas can be defined broadly or very narrowly (focused), i.e., each sub-area can, for example, contain only one signal of a plant constituent. Consequently, this embodiment can also include the first embodiment of the method (supra).

[0030] The invention will be explained in more detail below.

[0031] The object "Determination of signal intensities for plant constituents in two or more batches using a detector, in particular GC-MS and / or LC-MS"This preferably involves the use of liquid chromatography (LC), preferably high-performance liquid chromatography (HPLC), in conjunction with high-resolution mass spectrometry, such as time-of-flight (TOF) instruments, in particular high-resolution HPLC-TOF-MS. The aforementioned terms GC-MS and / or LC-MS are not to be understood as restrictive and encompass any embodiment of a suitable device. In particular, any detectors can be used, such as UV-VIS, thermal conductivity detector (TCD), flame ionization detector (FID), etc. It is only necessary that the plant materials used can exhibit corresponding signals (peaks [m / z]) or signal intensities as a function of retention time during the analysis using a detector, in particular GC-MS and / or LC-MS, so-called chromatograms.

[0032] Therefore, detectors that represent signal intensities as a function of retention time in a chromatogram are also included according to the invention.

[0033] If necessary, the variance markers identified by preferably GC-MS and / or LC-MS according to the invention, or represented accordingly in the sub-areas according to the invention, can be supplemented by further quality-determining analytical methods (for example: IR, NIR, Raman spectroscopy, atomic absorption spectroscopy, wet chemical assays (e.g. polyphenol determination according to Folin-Ciocalteu), fragmenting mass spectrometric techniques (MS n< )).

[0034] It is preferred that at least 100, 200, or 300 signals (or signal intensities) or more are determined in at least one batch. Furthermore, it is preferred that the signal intensities are determined in five batches, preferably different from each other, and in particular in ten or more batches.

[0035] In another preferred embodiment, the signals or signal intensities for each batch of a plant extract can be recorded in a database or memory.

[0036] The object "Determination of the variance in the levels of plant constituents" This can be carried out as follows, whereby the specific signals or signal intensities for a batch or for n batches are arranged in a matrix: A = Signal 1 Signal 2 ⋯ Signal k Charge 1 a 1 , 1 ⋯ ⋯ a 1 , k Charge 2 a 2 , 1 ⋱ ⋯ a 2 , k ⋮ ⋮ ⋯ ⋱ ⋮ Charge n a n , 1 ⋯ ⋯ a n , k

[0037] The mean values ​​can be calculated from each column. xi and the standard deviations si The mean relative standard deviation (in %) is calculated as follows: RSDX = ∑ i = 1 k s i ∑ i = 1 k x i ¯

[0038] According to the invention, the mean relative standard deviation can be mathematically determined from the aforementioned matrix and consequently used to determine the variance reduction. This determination can be performed using a computer.

[0039] The object "Identification of at least one plant constituent that makes a contribution, preferably the largest contribution, to the variance and its determination of the natural range (variance marker)" " or "Identification of at least one sub-area that makes a contribution, preferably the largest contribution, to the variance, and determination of its natural range (variance marker)"This can be carried out as follows: All mathematical methods capable of identifying variables with the greatest variance from a data matrix can be used to determine the variance markers. For example, in its simplest form, the variances of all variables can be calculated in order to then select the variables with the greatest variance. However, a principal component analysis (PCA) should preferably be used for the analysis. In PCA, a so-called "scoreplot" is created based on the obtained data ( Figure 1 ) generated, in which the group of batches used is represented in a diagram. The further the points of a group (or, as in Figure 4The values ​​of several groups (calculated within a PCA) are distributed across the coordinate system within the score plot, and the larger the confidence ellipse surrounding them, the greater the underlying variance of the group. The PCA also generates a "loading plot" (see Figure 2 ), from which it can be derived which signals contribute, preferably the largest contribution, to the total variance and, if applicable, correlate with other signals. The selection of the variance markers is implemented by calculating the loading value of each signal intensity sum of the sub-areas across, in this case, 5 principal components (see also Example D),The loading sums (explanation of > 80% total variance) are first absoluted, then summed, and finally sorted in descending order. To make the selected signals even more representative of the overall extract, a high correlation with as many other signals as possible can be used as an additional criterion. The loading sums with the highest resulting values ​​contain those subsets (and consequently signal candidates) that can be used as variance markers. Figure 3 This shows that there is usually a limited number of sub-areas (signals) that exhibit high variance.

[0040] It is then useful to determine the absolute content of the signals obtained from the variance markers found (using LC-MS, LC-DAD, NIR or other analytical methods) in order to obtain comparable data from several batches, preferably measured over longer periods of time.

[0041] The PCA used according to the invention is, within the meaning of this invention, a mathematical method for extracting relevant information from a very large and complex dataset and separating the statistical noise. This is a technique of so-called "data mining," which simplifies a dataset while retaining the greatest possible information content.

[0042] The result of PCA typically consists of two information blocks: a so-called "score plot" and a linked "loading plot." In the score plot, the samples are generally grouped based on their "properties" (according to the invention, these properties are the intensity values ​​of the measured (LC / MS) signals). Samples that are very similar in all their properties are grouped closely together, while those that differ more significantly are spaced further apart. The relative extent of the sample point cloud in the score plot (for example, when comparing unmixed and mixed samples) also provides information about its underlying variability. A compact point cloud has lower inherent variance than an extended point cloud.

[0043] The loading plot, on the other hand, shows which properties (or in this case, LC / MS signals) are primarily responsible for the positioning of the objects in the score plot. Signals located far from the origin in the loading plot contribute significantly to positioning (and thus have a major influence on sample variability) and are therefore a promising optimization criterion for mixture calculations.

[0044] The term "Setting one or more limit values ​​which are smaller than the respective natural range" This means setting a limit or range of levels that is smaller than the natural range of at least one variance marker. The value 0 is included here. Once the variance markers have been determined, their natural range can be calculated from the measured signal intensities.

[0045] The object "Mixing of at least two batches, taking into account at least one fixed limit value using a mixing calculator"This means that an algorithm is provided which uses a computer-aided mixing calculator to determine the ratio in which at least two or more batches must be mixed so that at least one variance marker lies within the newly selected, restricted interval or (sub-)range.

[0046] The calculation involves solving a system of linear inequalities taking into account constraints (e.g., Lay, David C. (August 22, 2005), Linear Algebra and Its Applications (3rd ed.), Addison Wesley). n Batches are available in the mixing pool and k Limit intervals are taken into account, this can be considered G * x ≥ h to be written (FN 1). G is a matrix with 2 * n lines and k Columns. Each row contains the values ​​of the measured individual parameters to be optimized for each batch, with rows 1...n a positive sign and the lines ( n + 1)...2 * n The vector h contains the limits of the mixing intervals, where the entry h 1 ... h k / 2 the lower limits and h k / 2+1 ... hk The upper limits are also included – these too must be assigned a negative sign. Solving this system of inequalities also requires simultaneously considering and solving a matrix of constraints. This matrix mandates the calculation of percentage proportions and, where applicable, the use of individual batches. It is referred to as A * x = b formulated, whereby A a matrix with m rows (with m > 1) and k The vector is in columns. b It also has m entries, which always have the value 1. The first line of AIt also only has 1 as an entry; in the further lines, it can be additionally defined whether a deliberately chosen proportion of the mixture from some batches should be taken into account, or whether individual batches should be deliberately excluded from consideration.

[0047] The mixing problem formulated in this way is solved using a suitable linear optimization algorithm, and the ratios to be mixed are given. Matrices with a value of 1 are always written in bold in the following (e.g., A ), vectors with an arrow (e.g. x ) and scalars in italics (e.g. n )).

[0048] In a further preferred embodiment, the variance from the data sets is preferably represented by means of a principal component analysis (PCA). In particular, in a further preferred embodiment, the PCA can be obtained from the signal intensity sums of the sub-regions of a mass defect plot. In a mass defect plot, the determined signals are represented as m / z plotted against the mass defect. The mass defect is calculated by dividing the decimal places of the measured mass by the total mass of the measured mass (see also Example C Plant constituents with similar mass and similar atomic composition are found close together in the plot (see Figure 11(Mass defect plot). According to the preferred embodiment already described, the sub-regions can now be represented as sub-areas. It is particularly advantageous that, in the course of the mass defect plot representation, such sub-areas can represent secondary plant metabolites such as phenols, flavonoids, etc., and the identification of a variance marker for this sub-region or sub-area can be easily carried out.

[0049] Within the scope of this invention, a batch, in particular a plant material batch such as a drug or extract batch, is understood to be the entirety of units produced in a batch process that leads to the production of defined quantities of substances by subjecting quantities of input materials to an ordered sequence of process activities within a defined period of time, using one or more devices. The starting material is usually the plant drug from which the plant extract or the processed plant drug is obtained.

[0050] According to the invention, the mixing of two or more batches can take place in a mixer, wherein the allocation of the individual batches into a mixture is specified by the mixing computer, which in turn can be programmed by an algorithm that takes into account, in particular, the limit value for a variance marker according to the inventive method.

[0051] Plant materials (singular or plural) within the meaning of this invention comprise any plant material, such as plant parts, including leaves, stems, roots, and flowers. In particular, plant materials may be in the form of their plant drugs as well as plant extracts (supra).

[0052] The invention further comprises the obtained plant materials, in particular plant extracts, that can be obtained by the process according to the invention. The plant materials obtained according to the invention, in particular plant extracts, have at least altered contents of plant constituents; in particular, these obtained plant materials, in particular plant extracts, exhibit a reduced variance in the contents of plant constituents compared to the starting plant material. The obtained plant materials, in particular plant extracts, are specific with respect to the reduced variance, wherein at least one variance marker exhibits an altered content of plant constituent. In addition, the scatter of the obtained contents of plant constituents is reduced.

[0053] Therefore, the invention relates to plant material with reduced variance in the content of plant constituents, which is obtained or produced or is available according to the methods according to the invention.

[0054] Within the scope of this invention, "plant extract" and "plant material" are understood to mean a multi-component mixture of natural substances containing more than two natural substances, in particular more than 10 or 100 natural substances, and especially more than 200, 300, 500, or 1,000 natural substances. Plant extracts can be obtained from plant materials, for example, by extraction, percolation, or maceration. Solvents such as water, C1-C5 alcohols, ethanol, or other solvents with sufficient polarity can be used as extraction agents. A common extraction method, for example, is a mixture of water and ethanol (50:50, 70:30, 30:70).

[0055] The following genera, in particular such medicinal plants, are preferred for the plant extracts as well as plant materials according to the invention: Equiseti, Juglandis, Millefolii, Quercus, Taraxaci, Althaeae, Matricariae, Centaurium, Levisticum, Rosmarinus, Angelica, Artemisia, Astragalus, Leonurus, Salvia, Saposhnikovia, Scutellaria, Siegesbeckia, Armoracia, Capsicum, Cistus, Echinacea, Galphimia, Hedera, Melia, Olea, Pelargonium, Phytolacca, Primula, Salix, Thymus, Vitex, Vitis, Rumicis, Verbena, Sambucus, Gentiana, Cannabis, Silybum.

[0056] The following species, in particular such medicinal plants, are preferred for the plant extracts as well as plant materials according to the invention: Equiseti herba (horsetail herb), Juglandis folium (walnut leaves), Millefolii herba (yarrow herb), Quercus cortex (oak bark), Taraxaci herba (dandelion herb), Althaeae radix (marshmallow root) and Matricariae flos (or Flos chamomillae (chamomile flowers)), Centaurium erythraea (centaurium), Levisticum officinale (lovage), Rosmarinus officinalis (rosemary), Angelica dahurica (Siberian angelica, PinYin name: Baizhi), Angelica sinensis (Chinese angelica, PinYin name: Danggui), Artemisia scoparia (sweet wormwood, PinYin name: Yinchen), Astragalus membranaceus (var. Mongolicus) (tragacanth root, Chin.: Huang-Qi), Leonurus japonicus (lion's ear, Chin.: T'uei), Salvia miltiorrhiza (red root sage, Chin.: Danshen), Saposhnikovia divaricata (Siler, PinYin name: Fangfeng), Scutellaria baicalensis (Baikal skullcap, Banzhilian), Siegesbeckia pubescens (Heavenly herb, PinYin name: Xixianciao), Armoracia rusticana (horseradish), Capsicum sp. (pepper), Cistus incanus (rock rose), Echinacea angustifolia (coneflower), Echinacea purpurea (coneflower), Galphimia glauca, Hedera helix (ivy), Melia toosendan (Chinese elderberry, Chin.: Chuan Lian Zi), Olea europaea (olive), Pelargonium sp. (Pelagornia), Phytolacca americana (pokeweed), Primula veris (cowslip), Salix sp. (Willow), Thymus L. (Thyme), Vitex agnus castus (Chaste tree), Vitis vinifera (Grapeberry), Rumicis herba (Sorrel herb), Verbena officinalis (Verbena), Sambucus nigra (Elderberry), Gentiana lutea (Gentian), Cannabis sativa (Hemp), Silybum marianum (Milk thistle).

[0057] Mixtures of the aforementioned genera and / or species are also included according to the invention. Examples and illustrations:

[0058] The following examples serve to further illustrate the invention, without, however, limiting the invention to these examples.

[0059] Figures 1-12 are explained above and below.

[0060] Figure 13 This section summarizes the methods for determining the variance markers listed in Examples B, C, and D. These descriptions are not exhaustive. Example A Examples of successful mixing Primula veris

[0061] For the drug Primula veris, 30 unmixed batches were initially analyzed using HPLC / ToF-MS. Their natural variance (i.e., the RSDX (supra)) was determined, and the variance markers were then calculated using the procedure described above. Using this information, a mixture calculation was performed for several formulations, which were then prepared in the laboratory and analyzed again using HPLC / ToF-MS. For the mixture calculation and subsequent analysis, five variance markers with the highest absolute summed loading values ​​were used. A comparison of the resulting RSDX values ​​is presented in [reference missing]. Figure 5 shown. For the example of Primula veris, a PCA was also calculated with data from mixed and unmixed samples, and a confidence ellipse was placed around each sample group ( Figure 4The smaller the area of ​​this ellipse, the lower the variance, which, as expected, is lowest in the mixed samples. This visualizes the same phenomenon in an alternative way to RSDX (supra). Rumex crispus

[0062] For the drug Rumex crispus Initially, 40 unmixed batches were measured, and then the procedure was the same as for Primula veris. A comparison of the RSDX values ​​obtained is shown in the Figure 6 depicted. Sambuccus nigra

[0063] For the drug Sambuccus nigra Initially, 40 unmixed batches were measured, and then, as for Primula veris procedure. A comparison of the values ​​achieved for the RSDX is in the Figure 7 depicted. Verbena officinalis

[0064] For the drug Verbena officinalis, 30 unmixed batches were initially measured, and then the procedure was the same as for Primula veris. A comparison of the RSDX values ​​obtained is presented in the Figure 8 depicted. Example B Example of the procedure for variance reduction of plant materials by mixture calculation in Rumex crispus

[0065] Excerpts of the original data sets are shown, which sufficiently convey the inventive procedure. i) Determination of plant constituent signals using LC / MS on multiple batches

[0066] The result is typically a table like the following - the numbers are the measured intensity values, for example from an LC / MS measurement. ii) Determination of the total variance / standard deviation of the 5 samples

[0067] iii) Identification of the signals that contribute most to the variance / standard deviation

[0068] Calculation of the table loadings from step i) using a principal component analysis (PCA) (e.g. 5 principal components):

[0069] Sum of the absolute values ​​for all signals:

[0070] Sorting from largest to smallest value - identification of the 5 most important signals for later optimization (highlighted in orange here):

[0071] Determination of the natural ranges of the 5 identified signals: iv) Determination of the content of the plant constituents underlying the variance markers

[0072] This step can be performed optionally, allowing specific concentrations (e.g., in mg / L) to be assigned to the intensities. Appropriate quantification methods are available for various analytical techniques. However, this step can also be skipped (as in this example), since it only represents a linear transformation of the intensities. v) Setting a new, reduced span

[0073] The permitted minimum is raised, the permitted maximum is lowered, thus reducing the permitted range of the mixture(s) (see Figure 14 ): vi) Performing a mixture calculation

[0074] The calculation results in the following instructions for the batches to be used for mixing:

[0075] Expected result for this mixture example:

[0076] All expected signal intensities are within the desired reduced range. vii) This calculation can be repeated accordingly, for example when mixing different batches. Example C Example of the procedure for variance reduction of plant raw materials by mixture calculation using Rumex crispus

[0077] Excerpts of the original data sets are shown, which sufficiently convey the inventive procedure. i) Determination of plant constituent signals using LC / MS on multiple batches

[0078] The result is typically a table like the following - the numbers are the measured intensity values ​​from, for example, an LC / MS measurement. ii) Division of the signals into several sub-areas and summation of these sub-areas

[0079] 1.1. Various strategies are available for dividing the signals into several sub-ranges. For example, one could sort by retention time of the LC / MS signals or by mass and then group them accordingly. 1.2. Summing the signals from the sub-areas, then proceeding with step iii) 2.1. Another possibility arises from the use of the mass defect plot, which allows the signals to be categorized according to their chemical class. The mass defect of each signal is calculated as follows: MD = mz − floor mz mz ∗ 10 6 MD = Mass defect mz m / z ratio to 4 decimal places floor() = Function that rounds a decimal number down to the nearest integer.

[0080] Example calculation for some m / z ratios mz (measured) floor ( mz ) mz − floor mz mz MD (Mass defect) 183,1748 183 0,00095428 954,2797372 187,0981 187 0,000524324 524,3238707 357,0568 357 0,000159078 159,0783315 585,1575 585 0,000269158 269,1583035 839,1849 839 0,000220333 220,3328492

[0081] The result is a graphic like in Figure 11 , in which each signal can be plotted on a coordinate system where the X-axis represents the m / z ratio and the Y-axis the corresponding mass defect. The position of the signal is generally characteristic of the substance group (e.g., flavonoids, terpenoids, etc.) to which the signal belongs. 2.2. Subsequently, for example, the sum of the intensities of the signals contained in each cell can be calculated for each batch using the overlaid grid (shown here in gray). 2.3. The number of signals per cell (a cell can contain multiple signals of varying intensities) can be displayed in a heatmap for overview purposes (see Figure 122.4. When summing the signal intensities in the individual cells, the following excerpt is obtained, for example (cells whose sum is 0, since there is no signal there, have been excluded. The labeling always follows the scheme "Cell (coordinate on X-axis | coordinate on Y-axis)").

[0082] This table can then be used analogously from point iii) onwards; the further procedure is identical. iii) Identification of the sub-areas that contribute most to the variance / standard deviation

[0083] Calculation of the table loadings from step 1.2 (or 2.4) using a principal component analysis (here, for example, 4 principal components; more principal components can be selected at any time):

[0084] Sum of the absolute amounts for all areas:

[0085] Sorting from largest to smallest value - identification of the 2 most important areas for later optimization (underlined here; more areas can be selected for later optimization): iv) Determination of the content of the plant constituents underlying the variance markers

[0086] This step can be performed optionally, allowing specific concentrations (e.g., in mg / L) to be assigned to the intensities. Appropriate quantification methods are available for various analytical techniques. However, this step can also be skipped (as in this example), since it only represents a linear transformation of the intensities. v) Determination of the natural range of the selected areas

[0087] vi) Determination of a reduced span

[0088] The permitted minimum is raised, the permitted maximum is lowered, thus reducing the permitted range of the mixture(s) (see Figure 15 ): vii) Performing a mixture calculation

[0089] The calculation results in, for example, the following instructions for the batches to be used in the mixture:

[0090] Expected result for this mixture example:

[0091] All expected signal intensities are within the desired reduced range. viii) This calculation can be repeated accordingly, for example when mixing different batches. Example D Examples of how LC / MS signals can be identified during mixture calculations and used for optimization.

[0092] The following are two possible examples: a) By evaluating the principal component analysis (PCA) of an LC / MS measurement of a sample of crude drugs b) By evaluating the standard deviations of measured LC / MS signals of a sample of crude drugs

[0093] For reasons of space, only excerpts of the original data sets are shown here, which are intended to convey the principle.

[0094] Regarding a): i) Determination of plant constituent signals using LC / MS on multiple batches

[0095] The result is typically a table like the following – the numbers are the measured intensity values ​​from, for example, an LC / MS measurement. (In the following example, 40 batches were measured and 363 signals were determined for each; the following table is an excerpt.) ii) Performing a Principal Component Analysis (PCA) Display of scores & loadings

[0096] Specifically, six principal components are calculated and displayed for the loadings – in this example, this corresponds to a total variance of approximately 80% and therefore describes the dataset sufficiently well. However, it is always possible to include further principal components to increase the explained total variance.

[0097] As described in Example A, the absolute value of the obtained loading values ​​is calculated, summed over the selected principal components, and then sorted in descending order of size. Figure 10The five signals contributing most to the variance were selected and marked in red in the loading plot graphs (which display two of the six principal components per plot). (Additional signals can be selected at any time.) The signals marked in red are indeed always the same signals, represented only by different principal components. They cover the entire range of signals and adequately represent the samples.

[0098] The signals selected in this way can then be used for the mixture calculation as described in Example A.

[0099] Regarding b): An alternative signal selection is possible by simply considering the standard deviation of the signal intensities from batches measured by LC / MS. For example, the 5 signals with the highest standard deviation can be identified and also used for the mixture calculation. i) Measurement of multiple batches using LC / MS (here: excerpt from obtained signal table) and calculation of the standard deviations for each signal

[0100] ii) Sorting / identification of the signals with the highest standard deviation

[0101] In this example, 5 signals were chosen (more are always possible) and underlined marked.

[0102] These selected signals can also be used to perform a mixture calculation, as already explained elsewhere. This second method identifies largely the same signals as the principal component analysis (PCA) method.

[0103] For example, if 20 signals are selected using the two methods instead of just 5, the selection in this example still shows a match of 17 signals (see Figure 9 ).

Claims

1. A method for producing plant material having reduced variance in plant-based ingredients content, said method having the following steps: i.) determining signal intensities for plant-based ingredients in two or more batches by means of a detector, in particular GC-MS and / or LC-MS, ii.) (a) identifying at least one plant-based ingredient which makes a contribution, preferably the greatest contribution, to the variance and identifying its determination of the natural span, or (b) dividing the signals into two or more sub-ranges, and summing the signal intensities of the plant-based ingredients within each sub-range, identifying at least one sub-range which makes a contribution, preferably the greatest contribution, to the variance and identifying its determination of the natural span, iii.) setting one or more limit values which is / are smaller than the natural span from ii.), iv.) mixing at least two batches, wherein at least one limit value from iii.) is taken into consideration by means of a mixture calculator, v.) optionally, repeating steps i.) to iv.).

2. The method for producing plant material having reduced variance in plant-based ingredients content according to claim 1, characterised in that at least one detector is selected from the group of liquid chromatography (LC), in particular HPLC, preferably in conjunction with high-resolution mass spectrometry, such as time-of-flight (TOF) devices, in particular high-resolution HPLC-TOF-MS, UV-VIS, heat conductivity detector, flame ionisation detector, or detectors that display signal intensities in a chromatogram in dependence on the retention time.

3. The method for producing plant material having reduced variance in plant-based ingredients content according to one of the preceding claims, characterised in that at least 100, 200 or 300 signal intensities or more are determined in a batch.

4. A method for producing plant material having reduced variance in plant-based ingredients content according to one of the preceding claims, characterised in that the signal intensities are determined in 5 or 10 batches.

5. The method for producing plant material having reduced variance in plant-based ingredients content according to one of the preceding claims, characterised in that the plant material is selected from the group of Equiseti, Juglandis, Millefolii, Quercus, Taraxaci, Althaeae, Matricariae, Centaurium, Levisticum, Rosmarinus, Angelica, Artemisia, Astragalus, Leonurus, Salvia, Saposhnikovia, Scutellaria, Siegesbeckia, Armoracia, Capsicum, Cistus, Echinacea, Galphimia, Hedera, Melia, Olea, Pelargonium, Phytolacca, Primula, Salix, Thymus, Vitex, Vitis, Rumicis, Verbena, Sambucus, Gentiana, Cannabis, Silybum.

6. The method for producing plant material having reduced variance in plant-based ingredients content according to one of the preceding claims, characterised in that the plant material is a plant extract.

7. The method for producing plant material having reduced variance in plant-based ingredients content according to one of the preceding claims, characterised in that a principal component analysis (PCA) is used.